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👉 Biotech startups rarely fail all at once. They usually fail while everyone is still working hard.   Experiments continue. Meetings happen. Progress is reported. Yet over time, alignment fades, and decisions start to feel disconnected. 👉 Many founders ask why biotech startups fail  not after a collapse, but when the company starts to feel harder to run without a clear reason . Nothing is obviously broken, but clarity is missing. Priorities blur. Execution slows. This is not a motivation problem. It is a structural one.  When complexity grows faster than strategy, biotech companies begin to fall apart quietly. 👉 This article examines why biotech startups fail when strategy is absent , and how that outcome can be changed. The difference between surviving and failing in biotech is rarely science. It is whether clarity exists when complexity grows. When complexity grows faster than clarity 👉 Early-stage biotech is complex by default. Science evolves in parallel with regulatory funding pressure and team growth.   Each of these dimensions introduces uncertainty. On their own, none of them is fatal. The problem begins when they expand faster than the company’s ability to make clear decisions. Most founders do not notice this shift immediately. Work continues. Experiments multiply. New ideas are added on top of existing ones. Complexity feels like progress.   👉 In reality, it often signals that priorities are no longer explicit. This is one of the earliest reasons why biotech startups fail . Not because the science stops working, but because the organization stops knowing what matters most right now . When everything feels important, nothing truly is. Without strategic filtering, teams accumulate parallel efforts. Scientists explore promising side questions. Leadership avoids saying no because every option feels valuable. Over time, focus dissolves. 👉 The company becomes busy instead of deliberate. At this stage, failure does not look like failure. It looks like motion. But motion without clarity slowly erodes confidence, execution, and trust.  Decisions become harder. Tradeoffs are postponed. The cost of complexity compounds quietly. 👉 The danger is not that biotech is hard. The danger is letting complexity grow without a structure that contains it.  When clarity does not scale with ambition, disorder fills the gap. The illusion of progress and the slow loss of direction One of the most dangerous phases in a biotech startup is when everything appears to be moving forward . Experiments are running. Data is being generated. Timelines are discussed with confidence. From the outside, progress looks real. 👉 Inside the company, however, direction often becomes unclear. Activity replaces alignment.  Teams execute tasks without a shared understanding of which decisions those tasks are meant to inform. Milestones are reached, but no meaningful choices follow from them. This is a central reason why biotech startups fail . Progress becomes performative rather than strategic. Work is measured by output instead of insight. The organization stays busy, but learning slows down. 👉 This pattern usually shows up in a very specific way: 1️⃣ Experiments are added faster than old ones are stopped 2️⃣ Milestones exist, but they do not unlock real decisions 3️⃣ Data accumulates without changing direction 4️⃣ Roadmaps grow longer instead of sharper 5️⃣ Everyone is working hard, yet priorities feel unstable Each item on its own looks reasonable. Together, they create drift. More experiments feel safer than fewer deliberate ones.  Additional data feels like risk reduction, even when it does not change the strategic picture. Over time, confidence erodes. Teams are no longer sure what success looks like. 👉 No single decision breaks the company, but the absence of decisive moments weakens it. The startup does not fail because it stops doing things. It fails because it stops knowing why it is doing them.  When progress is no longer tied to clear strategic questions, direction quietly dissolves. Clarity before chaos is what prevents biotech startups from slowly falling apart. Strategy as a system for making fewer better decisions The turning point for many biotech startups comes when strategy stops being a document and starts functioning as a decision system. ✅ The role of strategy is not to predict the future, but to reduce unnecessary complexity in the present. Teams that regain control do not suddenly become more confident about biology. They become clearer about what matters now and what does not.  Strategy creates a shared filter that connects science execution and business reality. ✅ At its core, this is what a functional strategy provides: 1️⃣ Clear priorities that limit parallel work 2️⃣ Explicit decision points tied to experiments and data 3️⃣ Agreed criteria for stopping as well as continuing 4️⃣ A common language for tradeoffs across science and leadership This is where the pattern of why biotech startups fail  begins to reverse. Instead of adding more work to feel safer, teams start designing fewer experiments with sharper intent. Data is no longer collected because it might be useful someday. ✅ It is generated to answer a specific question that unlocks a decision. Importantly, strategy does not slow teams down. It removes hidden friction.  When priorities are explicit, execution accelerates because teams no longer need constant alignment checks. Scientists understand why an experiment matters. Leadership understands what outcome would trigger a change in direction. ✅ The goal is not certainty. The goal is coherence.  When decisions follow a visible structure, complexity becomes manageable rather than overwhelming. Designing order before chaos becomes expensive What separates resilient biotech startups from those that slowly fall apart is not confidence or optimism. ✅ It is the presence of someone actively designing order while uncertainty is still manageable.   Order does not emerge naturally in biotech. It has to be built deliberately and revisited continuously. Founders who succeed understand that strategy is not a one-time exercise. It is a repeated act of clarification.   They pause regularly to ask which assumptions are still valid, which decisions are being avoided, and which activities no longer justify their cost in time, focus, or capital. This is where many teams escape the typical pattern of why biotech startups fail . Instead of letting chaos accumulate quietly, they surface it early. They make uncertainty explicit rather than hiding it behind optimism or additional experiments. ✅ They accept that clarity is a moving target and treat it as such. Designing order also changes how teams experience pressure. When priorities are visible and decisions are intentional, stress becomes directional rather than paralyzing. People know what they are optimizing for.   Tradeoffs feel purposeful instead of arbitrary. Most importantly, order creates trust. Scientists trust leadership because decisions are grounded in logic rather than mood. Leadership trusts execution because work is clearly tied to strategic intent. ✅ The company stops reacting and starts responding. Biotech does not become easy when an order is designed. It becomes survivable.  And in a field defined by uncertainty, that shift makes all the difference. Strategic Takeaway - Why biotech startups fail Biotech startups rarely fail because of a single mistake. They fail when complexity grows without structure and clarity.  Over time, this creates drift that no amount of effort can correct. ✅ Strategy is what holds a biotech company together under pressure.   It makes priorities explicit, decisions visible, and tradeoffs intentional. Without it, even strong science slowly loses direction. 👉 Understanding why biotech startups fail  is not about predicting collapse. It is about designing clarity early enough that chaos never takes over. Ready to Break Your Bottlenecks? If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

👉 Why has intellectual property become a first-order fundraising signal? Biotech fundraising has undergone a subtle yet significant shift. Capital still exists, but investors are making decisions earlier and filtering more carefully . As a result, intellectual property is no longer something that comes up late in the process. 👉 It has become an early signal of whether a biotech company is fundable at all. This shift does not mean founders need more patents or heavier legal work. What investors are really assessing is how clearly intellectual property supports the business being built . A strong IP position is not defined by volume. It is defined by alignment, control, and credibility . 👉 Many biotech fundraising efforts stall even with solid science and data. The issue is rarely the absence of intellectual property.  More often, the IP does not clearly map to the commercial story the company is telling. That disconnect creates uncertainty, and uncertainty is enough to stop momentum early. From an investor's perspective, biotech fundraising is a process of risk reduction. ✅ Intellectual property now acts as a proxy for how well a founding team understands and manages its core risks. ✅ When the IP story is coherent, trust builds quickly. When it is fragmented, even promising science struggles to move the round forward. Clear intellectual property builds confidence on both sides of the biotech fundraising table Why biotech fundraising breaks before diligence even starts 👉 How does intellectual property become an early filter? Biotech fundraising rarely fails in the diligence phase itself. It fails earlier, often after the first or second meeting.  At that point, investors are not validating documents. They are pattern matching. 👉 What they are really asking is simple. Does this team understand how value will be protected if the science works? 👉 This is where intellectual property gaps start to matter. Not because something is legally wrong, but because the IP story does not answer investor questions clearly enough . Founders often present patents as proof of strength, while investors read them as indicators of risk management. 👉 An early red flag appears when intellectual property is treated as a static asset. Slides list filings and dates, but do not explain how those rights support the fundraising narrative . Investors then have to fill in the gaps themselves, and that rarely works in the founder's favor. In modern biotech fundraising, intellectual property functions as a shortcut. It signals whether the company has thought through scale, competition, and control.  When that signal is weak or confusing, investors slow down. When momentum slows, deals quietly disappear. 👉 Biotech fundraising does not stop because investors find a fatal flaw.   It stops because the IP creates unanswered questions that feel too expensive to explore further. The 3 IP gaps investors notice first 👉 How small misalignments quietly derail biotech fundraising? In early-stage biotech fundraising, investors do not analyze intellectual property in isolation. They evaluate how IP behaves as part of a larger system.  When that system shows cracks, confidence erodes quickly. The most common intellectual property gaps fall into three categories. None of them are legal mistake. All of them are strategic mismatches. 1️⃣ Intellectual property that is disconnected from the business being built This is the most frequent issue investors encounter. The science is strong, the patents exist, yet the IP does not clearly protect the company's commercial direction . Common signals include: 👉 Patents focused on one indication, while the business story targets another 👉 Claims that protect research use but not real-world commercialization 👉 Intellectual property that explains the science well, but not the value capture From an investor's perspective, this creates confusion rather than confidence.   If the IP does not map cleanly to how the company plans to generate returns, the fundraising narrative starts to feel unstable. The issue is not patent quality. It is the lack of alignment between intellectual property and business intent. 2️⃣ Ownership and control that feel unclear or constrained This gap appears most often in academic spinouts, but it is not limited to them. Even experienced founders underestimate how sensitive investors are to ownership details. Typical problem areas include: 👉 University licenses with complex or restrictive terms 👉 Unclear rights to future improvements or new filings 👉 Founders assume that investors cannot verify When ownership is ambiguous, investors struggle to understand who truly benefits if the company succeeds.   That uncertainty increases perceived risk, even if the underlying science is compelling. Importantly, this is rarely about bad decisions by founders. It is a system-level issue that becomes a fundraising issue when it is not clearly framed and explained. 3️⃣ No clear Freedom to Operate narrative Many biotech teams assume Freedom to Operate is a legal exercise that comes later. Investors see it differently. They are not asking for formal reports at the pitch stage. They are looking for evidence of strategic awareness. Red flags emerge when: 👉 Founders cannot articulate who might block market entry 👉 Competitive patents are acknowledged but not contextualized 👉 There is no discussion of workarounds or design choices The absence of a Freedom to Operate narrative signals unexamined risk.   Even if the risk is manageable, not addressing it makes the fundraising process harder. What matters most is not certainty. It is demonstrated thinking.  Investors want to see that the team understands the landscape it is entering. 👉 Why do these gaps matter so early? Individually, each of these issues might seem manageable. Together, they form a pattern. A pattern that suggests the company has not fully connected science, IP, and business into a coherent whole. In biotech fundraising, coherence builds trust. And trust is often what determines whether a conversation moves forward or quietly ends. From insight to funding, clarity turns analysis into investor confidence How IP Expectations Are Evolving Toward 2026 👉 What does this mean for biotech fundraising? Expectations around intellectual property in biotech fundraising are becoming more structured. This shift is not about stricter rules, but about earlier and clearer risk assessment . Investors want to understand sooner how intellectual property supports the business they are being asked to fund. What is changing is the focus. Less attention is placed on legal volume, and more on strategic coherence.  Intellectual property is increasingly evaluated together with development plans and commercial intent, not as a separate legal topic. Several patterns are already emerging: 👉 IP discussions happen earlier in fundraising conversations 👉 Investors look for alignment rather than legal perfection 👉 Founders are expected to show awareness of constraints and options ✅ Looking toward 2026, this trend is likely to continue. Biotech fundraising will favor teams that use intellectual property as a tool for clarity rather than protection alone.  When science, IP, and business reinforce each other, fundraising conversations move faster and with less friction. ✅ For founders, this creates an advantage. Clear intellectual property thinking reduces uncertainty and builds trust early.  In a selective capital environment, that clarity can be as valuable as new data. Strategic Takeaway - Founder Clarity 👉 Biotech fundraising rarely fails because intellectual property is missing. It fails when intellectual property does not clearly support the business being built.  This is not a legal issue, but a clarity issue. 👉 Founders who raise successfully treat intellectual property as part of their core narrative. They can explain what the IP protects, where its limits are, and how that fits the company's direction.  This makes investor decisions easier and faster. The key insight is simple. Fundraising rewards coherence.  When science, intellectual property, and business intent reinforce each other, trust builds early and momentum follows. ✅ For biotech founders, the takeaway is clear. Intellectual property is not a checkbox. It is a signal of how well you understand your own company. Ready to Break Your Bottlenecks? If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

Founders love the idea that a new year, or a new quarter, will reset the company. But here’s the uncomfortable truth: 👉 Your biotech is already running on an operating cadence you didn’t consciously design. And that cadence is shaping everything: timelines, decisions, investor calls, BD traction, internal focus. Most CEOs think they’re steering the strategy. 👉 In reality, their operating cadence is steering them. And until you see it, you can’t change it. Operating cadence is the quiet force behind biotech momentum, the rhythm that turns intention into real progress. The Pattern: Your “Accidental Cadence” 👉 Every biotech has a cadence. The question is whether it’s intentional. I call the default version Accidental Cadence , the rhythm the company falls into when urgency, science, and founder bandwidth collide. 👉 What it usually looks like: Weekly priorities shift based on whoever raised the loudest concern. Investor updates happen when guilt spikes, not when alignment demands it. BD conversations move optimistically but without a structured readiness signal behind them. Timeline slips are absorbed as “part of biology” rather than examined as managerial signals. The CEO oscillates between fire-fighting and “let’s step back and think,” depending on emotional energy. 👉 None of this looks dysfunctional from the outside. That’s the danger. 👉 From the inside, however, it creates a quiet drift: lots of activity, little compounding progress. Why This Is Dangerous A weak operating cadence doesn’t cause a crisis. It causes erosion . 👉 Investor confidence softens  because the story feels reactive. 👉 BD partners disengage  because timing and readiness seem inconsistent. 👉 Teams hedge their work  instead of committing to clear priorities. 👉 The CEO becomes the unofficial project manager , even if they believe they’re “empowering the team.” Many early-stage biotechs show the same pattern: strong scientific progress paired with an operating cadence built on reactivity, heroic sprints, long silences, last-minute preparation, and shifting assumptions about BD timing. In these situations, the science is rarely the real bottleneck. The cadence is. When teams adopt structured decision cycles, consistent narrative checkpoints, and predictable timeline discipline, the organization typically shifts from “busy but slightly lost” to “focused, aligned, and gaining traction.” Same people. Same science. A different cadence and a different trajectory. Strategy sets direction. Cadence creates movement. Progress is the result. The Three Components of an Effective Operating Cadence 👉 If you’re a biotech founder, your operating cadence is your real operating system. Here’s the framework I use when diagnosing and rebuilding it. 1️⃣ Cadence of Decisions: When You Decide, Not Just What You Decide Founders often obsess over the content of decisions. But what kills momentum is timing inconsistency . 👉 Without a defined decision cadence: Choices get deferred until you “have all the data.” Teams start planning around delays BD and investor messaging drifts Timelines become aspirational rather than operational ✅ What “good” looks like: Predictable decision points (monthly, biweekly, quarterly) aligned with science cycles, not emotions. Decisions are made once and communicated clearly. 2️⃣ Cadence of Communication: The Rhythm That Builds (or Erodes) Trust Communication is not a byproduct of progress; it is a mechanism  of progress. 👉 Weak cadence leads to: sporadic investor updates BD conversations that lack mutual timing expectations internal teams working with partial context narrative inconsistency across stakeholders ✅ What “good” looks like: A clean, repeatable communication rhythm: internal alignment weekly cross-functional integration biweekly investor check-ins monthly or per milestone BD narrative calibration monthly This doesn’t create overhead. It creates clarity . 3️⃣ Cadence of Execution: The Drumbeat That Keeps Timelines Honest Most biotechs believe they have execution problems. In reality, they have cadence problems  masquerading as execution issues. 👉 When your cadence is unstable: Timelines slip quietly Dependencies surface late Teams optimize for activity, not momentum Scientific surprises hit harder because the system has no buffer ✅ What “good” looks like: Short, predictable, cross-functional execution cycles that keep reality visible early: What moved? What didn’t? What must change? What deserves escalation? ✅ A stable cadence doesn’t eliminate surprise. It eliminates blindness . Strategic Takeaway Your biotech is already running on an operating cadence. The question is whether you  chose it. 👉 Founders don’t drift because they lack discipline. They drift because their cadence isn’t designed. The shift is simple: 👉 From reactivity → to rhythmic leadership 👉 From heroic effort → to structured momentum 👉 From “we hope next year is better” → to “our cadence ensures it will be.” ✅ If you want a stronger 2025, start by shaping the one thing that shapes everything else: your operating cadence. Ready to Break Your Bottlenecks? If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

As scientists, we know curves don’t equal clarity. As 2025 comes to a close, this final edition of Weekly News focuses on how GPCR binding affinity experiments  are interpreted—and how those interpretations quietly shape SAR, lead selection, and development timelines long before anyone notices. The goal isn’t more data. It’s cleaner interpretation. And that’s exactly what carries strong discovery programs into 2026. Premium Sneak Peak:  Human Substance P–NK1R interactions observed by NMR; Endocrine Metabolic GPCRs 2026 and Biophysical Society Meeting previews; positive ACCESS data for Structure Therapeutics’ oral GLP-1 agonist aleniglipron, Principal Scientist—In Vitro Pharmacology. Terry’s Corner: GPCR Binding Affinity Experiments That Hold Up Orthosteric GPCR binding affinity experiments  sit at the core of drug discovery. They inform SAR, rank compounds, and influence which molecules move forward. Yet small design choices—often treated as technical details—quietly reshape the affinity you think you’re measuring. Tracer concentration, receptor density, equilibrium assumptions, and ligand kinetics all influence whether Ki and IC₅₀ values reflect molecular reality or experimental convenience. When these factors aren’t understood, binding data can appear precise while quietly misleading decision-making. In this Terry’s Corner lesson, Dr. Terry Kenakin reframes GPCR binding affinity experiments  as context-dependent measurements , not fixed molecular constants. By walking through saturation curves, displacement assays, stoichiometry pitfalls, and kinetic traps, the lesson equips scientists with a diagnostic lens: how to tell when affinity data are trustworthy—and when they are not. This week’s lesson helps you: Diagnose when equilibrium, tracer behavior, or stoichiometry distort affinity estimates , leading to false confidence in Ki values derived from GPCR binding affinity experiments. Interpret multi-phase binding curves correctly , recognizing G-protein coupling and kinetic effects rather than invoking multiple receptor populations. Design GPCR binding affinity experiments that tell the truth , ensuring affinity data support—rather than undermine—lead selection and development timelines. Already available in Terry’s Corner: 30 in-depth lessons and 3 live AMAs  spanning binding, kinetics, efficacy, mechanism, and experimental design—built for repeat use, not one-off viewing. More new courses arrive weekly in 2026, expanding coverage across GPCR pharmacology fundamentals. Premium Membership pricing increases in 2026, and the 67% Terry’s Corner discount is going away. Join before year-end to secure both. 👉   Join Terry's Corner Before Dec 31st, 2025 🎧 Dr. GPCR Podcast: When GPCR Tools Scale Even the most elegant tools only matter when they scale beyond a single lab. Academic innovation moves discovery forward only when assays are validated, distributed, and adopted across organizations. In Episode 3 of 3  of our series with Celtarys Research , leaders from academia and biotech unpack what effective collaboration really looks like when developing and scaling GPCR tools. Maria Majellaro, Johannes Broichhagen, and David Hodson discuss GLP-1 receptor probes, fluorescence-based assays, and why availability can matter as much as discovery itself. 🎧 Listen to Part 1 with Dr. Hudson 🎧 Catch up on Part 2 with Dr. Broichhagen You’ll hear: What it really takes to translate GPCR tools from academia into industry workflows How collaboration improves rigor, reproducibility, and screening impact Why scalable access amplifies the value of GPCR binding affinity experiments Listen to the final episode of the series ➤ Dr. GPCR Year in Review: Carrying Better Experiments Into 2026 As 2025 closes, the Dr. GPCR offers a clear signal of where the field is heading. This year brought 20+ podcast episodes, 40% audience growth, and an 811% increase in new listeners. This growth reflects something deeper than metrics: a shared demand for clearer interpretation, stronger experimental design, and better decision-making across GPCR pharmacology. A quick rating on Spotify or Apple Podcasts — and a YouTube subscribe — helps us reach more scientists: Spotify: https://open.spotify.com/show/1KQHbC2qhkRIrdgBDtiQVF Apple Podcasts: https://podcasts.apple.com/us/podcast/dr-gpcr-podcast/id1514231064 YouTube: https://www.youtube.com/@DrGPCR Looking ahead to 2026: Live and on-demand University courses Co-creation pathways for academic contributors Launch of The Foundry , supporting strategy and CRO alignment In-person community moments, including GPCR Happy Hours Why Dr. GPCR Premium Membership Gives You an Edge Premium Membership is designed for scientists and teams who need signal, not noise . Each week, Premium delivers curated GPCR intelligence: expert-led lessons, classified industry updates, priority event alerts, and career-relevant opportunities—organized to support real decisions in discovery and development. Whether you’re refining GPCR binding affinity experiments , evaluating leads, or aligning teams around translational strategy, Premium provides context that helps you act with confidence. Beyond access, Premium sustains the nonprofit mission behind Dr. GPCR—supporting open resources while giving members deeper insight and earlier visibility. Already a Premium Member? 👉 Access this week’s full Premium Edition here ➤ Voice of the Community “It’s like being at a GPCR conference again—getting new ideas and hearing real scientific rigor.” The strongest discovery programs aren’t built on instrumentation alone—they’re built on interpretation that holds up. Happy holidays—and here’s to a 2026 of cleaner data and faster discovery. 👉 Become a Dr. GPCR Premium Member And Lock in The Current Rate➤ https://www.ecosystem.drgpcr.com/gpcr-university-pricing

The Quiet Drift You Don’t Feel Until It’s Too Late 👉 Every early-stage biotech reaches a moment where the science finally starts clicking… and the company quietly stops doing anything else. BD conversations stay warm but motionless. Investor updates become thinner. Internal meetings slowly morph into scientific colloquia instead of decision-making forums. 👉 The uncomfortable truth: your company is doing a lot of science and very little building. No drama. No blow-ups.Just a gradual slide into a world where the internal noise is high, but the external signal is near zero. 👉 The real issue isn’t the science. It's the isolation . Clear structure gives your science the visibility and consistency that traction is built on. The Pattern: Scientific Isolation Scientific Isolation  = when a biotech’s internal activity becomes so dominated by experiments, data nuance, and scientific discourse that the organization loses strategic visibility, external traction, and narrative coherence. 👉 What it looks like in real life: Meetings end with explanations, not decisions BD calls “went great,” but nothing moves Timelines are built around assay availability, not strategic inflection points The phrase “after the next dataset…” becomes a company mantra Investor communication feels reactive and apologetic Key documents live in the CEO’s brain or slide decks that haven’t been touched in months 👉 From the inside, it feels like diligence. From the outside, it looks like ambiguity. Why Scientific Isolation Is Dangerous and Expensive This pattern rarely causes a single catastrophic failure. It causes a slow erosion of everything that matters. 1️⃣ External Trust Collapses Quietly Investors and potential partners don’t need more data; they need clarity. When the story isn’t evolving, they assume the company isn’t either. 2️⃣ BD Momentum Flatlines Pharma teams don’t chase raw science. They chase direction, intent, and timing. If you can’t articulate a trajectory, they categorize you as “interesting but unready.” 3️⃣ Internal Alignment Frays When “the science” drives everything, different teams start drifting at their own pace. This is how a 6-month slip appears without a single dramatic mistake. 4️⃣ Fundraising Windows Shrink Isolated science creates the illusion of runway control. In practice, founders overestimate how much time they have, usually by several months. The danger is subtle: 👉Scientific isolation feels busy, intelligent, respectable. But strategically, it’s suffocating. Alignment, structure, traction, visibility: these are the forces that pull a biotech out of scientific isolation and back into momentum. The Three-Layer Anti-Isolation Framework Breaking scientific isolation isn’t about adding more process. It’s about restoring the connection  between science, strategy, and the outside world. 👉 Here’s a structure that consistently keeps companies out of isolation. 1️⃣ External Readiness (Are you visible and comprehensible to the outside world?) Most early biotechs think their problem is a lack of data. It’s usually a lack of narrative discipline. 👉 What this layer requires: A crisp BD trajectory that evolves every quarter A 1–2 page positioning document (“Why This Matters Now”) Predictable touchpoints with partners and investors, even without new results Good looks like: You can articulate your scientific rationale, commercial logic, and strategic sequence in five minutes, without slides . ✅ If you can’t do that, you’re isolated. 2️⃣ Internal Decision Systems (Do decisions only happen when new data appears?) In isolated companies, decisions are tethered to experimental data → no decisions. 👉 A functioning decision system introduces: clear thresholds default actions go/no-go moments ownership of decisions locked vs flexible timeline components Good looks like: The team knows what happens next, even when experiments slip . ✅ This alone removes much of the latent anxiety in early-stage teams. 3️⃣ Strategic Communication Cadence (Does everyone share the same story?) Most communication problems aren’t information gaps. They’re cadence gaps. 👉 A strong cadence is simple: Monthly CEO strategic alignment note Quarterly “state of the trajectory” session Bi-weekly refresh of BD and investor narratives A single, living source of truth for timelines and decisions It looks good: No one, from intern to CSO, board member, or pharma partner, gives a different story about what the company is doing and why. ✅ Coherence is momentum. Strategic Takeaway - Why Traction Slips Scientific isolation doesn’t feel like a mistake. It feels like focus. That’s why it’s so dangerous. 👉 The founder shift is this: from scientific immersion → strategic connection from data-dependency → decision-readiness from internal comfort → external relevance ✅ Your science is the engine. Your ability to connect it to investors, BD, the market, and your own team is the company. Ready to Break Your Bottlenecks? If you're feeling the friction, indecision, misalignment, or slow momentum, it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck, fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

Binding affinity appears straightforward: add ligand, measure signal, fit a curve. Yet discovery teams routinely lose time and misallocate resources because the underlying biology behaves nothing like the idealized systems we learned in textbooks. GPCRs couple, decouple, isomerize, deplete tracers, and shift apparent affinity depending on stoichiometry and time. The result is a recurring pattern across programs—clean data that is not actually telling the truth. Orthosteric binding experiments remain a cornerstone of pharmacology, but they demand rigor. Terry Kenakin’s session examines not just how  binding works, but why  so many datasets mislead even seasoned scientists. In this session, you’ll gain: Why saturation and displacement assays fail when protein stoichiometry shifts How two-stage GPCR binding creates “high” and “low” affinity states What temporal kinetics quietly change about the affinity you think you measured Understanding Orthosteric Binding Foundations Orthosteric binding experiments rely on a measurable event: a tracer binds a receptor, and anything that displaces it alters that signal. But as Dr. Kenakin stresses, the apparent simplicity collapses once biological reality intrudes. Tracers bind not only to receptors but also to surfaces and unwanted proteins; non-specific binding must be corrected, not assumed. Running total and protected curves simultaneously is essential to reveal true receptor binding. Even the familiar saturation experiment hides traps. Linear-scale plots appear to plateau early, encouraging premature calls of B max. But plotting on a logarithmic axis exposes how far the system may actually be from true saturation. The midpoint and maximum—core parameters for downstream modeling—are only meaningful when the assay fully explores the system’s capacity. Unexpected outcomes typically trace back to a single issue: transferring assumptions from idealized models into messy GPCR systems. Take home message: interpret orthosteric binding data only after verifying that the biology behaved as expected. Displacement Curves and the Illusion of Potency Displacement assays measure affinity when no traceable analog exists. But potency in these curves is not intrinsic affinity—it shifts with tracer concentration. A displacer appears weaker at high tracer occupancy because more ligand must be displaced. In practice: The IC 50 you measure is a function of tracer levels. Changing tracer concentration moves the displacement curve. True affinity requires correcting for occupancy state. Earlier decades relied on Scatchard, Hanes, and other linear transforms to “clean” nonlinear data. Dr. Kenakin is unequivocal: do not use them. Modern computation handles raw nonlinear data precisely, whereas transforms distort error, compress dynamic range, and violate regression assumptions. When potency is mistaken for affinity, program decisions drift. Proper orthosteric binding design prevents those errors before they propagate into SAR narratives. Complex Two-Stage Biology Behind Orthosteric Binding GPCR pharmacology rarely follows the neat Langmuir adsorption isotherm. Proteins are not inert surfaces, and orthosteric binding reactions often continue beyond the first encounter. After a ligand binds, the receptor may transition further—often via G protein coupling. That second step stabilizes a higher-affinity configuration, explaining classical “high” and “low” affinity states. Mechanistically: Ligand binding (A + R → AR) is only step one. AR can become AR* or ARG, raising apparent affinity. The measured affinity becomes an operational composite. Removing coupling partners (e.g., GTPγS) collapses the system to a single low-affinity curve. This is not receptor heterogeneity—it is a collapsed two-stage system. Understanding these transitions is essential for interpreting orthosteric binding data accurately. Stoichiometry: The Quiet Driver of Curve Shape Two-stage systems expose how easily stoichiometry distorts outcomes. When G proteins are abundant, curves look clean because every receptor–ligand complex can couple. When receptor levels rise or G proteins become limiting, the system becomes stoichiometrically constrained. The high-affinity state is undersupported, creating biphasic curves. This frequently masquerades as: Two binding sites Multiple receptor subtypes Allosteric modulation But often the explanation is simpler: depletion of a required binding partner. Overexpression systems are particularly vulnerable. High receptor levels also deplete free tracer when tracer concentrations are low, breaking the assumption that added concentration equals free concentration. Across CRO-generated binding panels, this remains one of the most common sources of erroneous affinity estimates. Distinguishing Multiple Sites from Two-Stage Orthosteric Binding Genuine multiple-site binding has its own diagnostic signature. When a tracer binds two sites with different affinities, curve shape reflects the ratio of affinities and abundance. Small differences produce subtle curvature; large differences produce biphasic behavior. Clues that point to true multiple-site binding rather than two-stage GPCR biology: Disrupting G protein coupling does not  collapse the biphasic curve. Changing receptor expression or G protein levels does not  remove curve heterogeneity. Curve shifts track site properties, not system stoichiometry. Dr. Kenakin's message is practical: never assign “two sites” before ruling out two-stage orthosteric binding and stoichiometric imbalance. Experimental Conditions That Make or Break Orthosteric Binding Data Dr. Kenakin outlines a pragmatic checklist for producing reliable orthosteric binding measurements: Cell type and receptor expression:  Overexpression can distort stoichiometry and drive artifacts. Protein concentration:  Too much receptor depletes tracer and invalidates mass-action assumptions. Non-specific binding control:  Adsorption to surfaces changes free ligand concentration. Equilibration time:  Many assays stop before the system reaches equilibrium, especially with slow competitors. Clear curves are not evidence of equilibrium. Dr. Kenakin demonstrates how premature stopping mis-ranks compounds, particularly when tracer and displacer bind at different rates. Ensuring equilibrium is non-negotiable. Temporal Kinetics and the Hidden Bias in Affinity Kinetic imbalance is one of the most common—and least recognized—artifacts. If the tracer binds faster than the displacer, early time points exaggerate tracer occupancy and underestimate competitor potency. If the displacer binds faster, the opposite occurs. Many programs unknowingly compare compounds measured under different kinetic biases. You may see: Early stopping → potency distortions Different stopping times → incomparable datasets Curve shape → hints about missing equilibrium Real-time binding systems remove the guesswork. By observing the full onset and offset kinetics, scientists obtain affinity, kinetic rates, and equilibrium confirmation in one experiment—ideal for GPCR-focused discovery teams. Why Terry’s Corner Weekly pharmacology sessions with Dr. Terry Kenakin give scientists an uncommon advantage: the ability to interrogate foundational assumptions before they distort program decisions. Through deep-dive lectures, monthly AMAs, and a growing on-demand library, the Corner helps discovery teams refine binding strategy, troubleshoot complex GPCR systems, and understand when orthosteric binding data is lying—and why. Built for pharmacologists strengthening fundamentals, program teams navigating bottlenecks, and leaders who need credible guidance fast, the Corner brings clarity to the complexities shaping modern GPCR innovation. Those who invest now shape the breakthroughs that follow. This orthosteric binding lesson closes out the year—marking 30 courses released  and 3 live AMAs hosted  since launch. As we prepare for the next wave of content in 2026, Premium members receive 67% off Terry’s Corner throughout 2025 , unlocking full access to every session already available and all new weekly releases next year. As a member, you get: ✅ Full access to every course  — All 30 lessons released this year, plus new ones launching after the year-end break. ✅ AMA replays + priority Q&A  — Rewatch all 3 live AMAs and move your questions to the front of the line. ✅ Deep-dive learning paths  — Structured progression from foundational concepts to emerging and expert-level decision making. ✅ Member-only pricing  — Preferred rates across Terry’s Corner and the broader Ecosystem Premium. 40 years of expertise at your fingertips : Explore the full library ➤

Every scientist has stood in a crowded conference room rehearsing a question they’re too nervous to ask. The expert they admire is right there, but the fear of sounding unprepared wins. Yet one well-timed question can unlock clarity, accelerate a stalled project, or even spark a collaboration. In this episode, JB pulls the curtain back on the mindset and tactics he’s used for years—including the exact line that makes intimidating conversations surprisingly easy. It’s a masterclass in asking better questions in science, not as a skill you’re born with, but one you can intentionally build. Curiosity as the Engine Behind Asking Better Questions in Science JB’s story makes one thing clear: asking better questions in science starts long before the conference hallway. It starts by noticing what grabs your attention, what sparks those quiet “aha” moments during a lecture or when reading a paper, and what you can’t stop thinking about afterward. Throughout his training, he treated curiosity not as a trait but as a deliberate method. When an idea clicked, he paused, dissected it, and followed the thread. That discipline—paying attention to the internal spark—became the foundation of his scientific communication style and the steady confidence he brings into every discussion. Following curiosity is the first step toward asking better questions in science. When curiosity becomes intentional, questions become easier, sharper, and more useful. The One-Line Icebreaker That Makes Asking Better Questions in Science Easy At conferences, most early-career scientists freeze. JB short-circuits that anxiety with a single line he’s used for years: “Hi, I’m JB. I’m running into a problem and I know you work on something similar — can I pick your brain for one minute?” This opener works because it’s honest, specific, and respectful. You’re stating your purpose without hedging, signaling awareness of the other person’s expertise, and framing the conversation as short and attainable. And once the ice breaks? One minute becomes ten. Ten becomes an idea, a solution, or a collaborator. This is the practical side of asking better questions in science: not the wording, but the willingness to start. That first sentence is often the only barrier between you and the insight you need. Overcoming the Fear That Blocks Asking Better Questions in Science The biggest obstacle isn’t lack of knowledge—it’s fear. Fear of sounding inexperienced. Fear of asking something “basic”. Fear of wasting someone’s time. JB dismantles that fear with a refreshing truth: not knowing something isn’t a flaw—it’s common ground. Entire rooms full of scientists wonder the same things you do. Asking better questions in science requires accepting that uncertainty is part of the process, not a personal failing. When you articulate a question out loud, the assumptions become visible. The problem sharpens. The path forward emerges. Silence, by contrast, protects your ego but slows your research. As JB puts it, the cost of not asking is much higher than the cost of momentary discomfort. Why Asking Better Questions in Science Improves the Work Itself For JB, questions fuel the way he designs chemical probes, collaborates with biologists, and navigates technical barriers. His tools exist because he constantly asks: What limitation is chemistry solving here? What limitation is biology solving? What are we missing because we aren’t looking at the system correctly? This cross-functional back-and-forth is exactly how breakthroughs happen. In his collaboration with David Hodson, every major leap—from early ligand design to GPCR visualization tools—started with someone asking a question neither side could answer alone. Asking better questions in science isn’t a soft skill. It’s an R&D accelerant. It shortens feedback loops. It reveals flaws early. It stops wasted experiments. And it transforms collaborators into co-problem-solvers. Better questions lead to better data, faster decisions, and fewer wrong turns. Building a Scientific Career Through Asking Better Questions in Science Late in the conversation, JB offers advice that should be printed on the badge of every first-time conference attendee: Be curious. Ask questions. Engage with people regardless of their seniority. This mindset shapes careers far more than publications alone. Throughout his own journey—from organic chemistry to chemical biology to GPCR imaging—every pivotal step was rooted in conversations he initiated by asking better questions in science. Your next collaboration, job, or insight might already be within reach. It may just require walking across the room and asking one thoughtful question. The scientist who asks the best questions builds the strongest network—and the most resilient expertise. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . For more insight and nuance, listen to the full episode with JB.  🎧 Listen to the full episode https://www.ecosystem.drgpcr.com/dr-gpcr-podcast/chemical-probes-for-gpcr-imaging-and-internalization If JB's story resonates 🎧 Listen to part 1 of this series with Dr. David Hodson

Some breakthroughs don’t start with a hypothesis. They start with a sentence that freezes the room. I can image the whole islet. Not a single cell, not a cropped region, not a patch of fluorescence — the entire pancreatic islet , 100–200 microns across, lighting up in real time. That moment didn’t just validate a probe. It opened a new window into GPCR imaging in native tissue, and reshaped what this collaboration between a chemist and a biologist could make possible. The Moment GPCR Imaging Became a Turning Point Before the islet lit up, the collaboration wasn’t even aimed at imaging. Johannes “JB” Broichhagen trained as a synthetic chemist — someone who trusted carbon–carbon bonds far more than live-cell behavior. Yet curiosity and chemistry pulled him into the world of GLP-1R, pancreatic β-cells, and the biological questions David Hodson had been exploring for years. The call from David — the glowing islet — created a pivot the team couldn’t ignore. A fluorescent peptide probe binding with clarity and specificity was exciting enough. Seeing that probe expose receptor distribution across an entire native islet changed what they believed was possible. This was more than data. It was ignition. A single successful GPCR imaging experiment can transform a project’s trajectory. From that moment, imaging wasn’t an add-on. It became the center of gravity. How a Chemical Design Sparked a GPCR Imaging Breakthrough The concept was elegant: antibodies weren’t delivering reliable GLP-1R visualization. A ligand-based peptide probe could, offer the consistency and surface selectivity GPCR imaging demands. One issue: JB had never made peptides Solution: collaborate. Working with a peptide specialist at the Max Planck Institute, the team moved quickly from concept to synthesis. What emerged was more than a ligand — it was a tool that enabled reproducible, stable, and high-contrast GPCR imaging across cells and tissue. Once the first images came in, the scientific questions multiplied: Could the probe support super-resolution GPCR imaging? Could they map receptor heterogeneity across the islet? Could they quantify plasma membrane vs. intracellular receptor pools? Could these tools scale to multiple GPCRs? The design didn't just work — it revealed. Interdisciplinary design isn’t optional in GPCR imaging. It’s the catalyst. The breakthrough didn’t happen because the chemistry was perfect. It happened because the chemistry and the biology met in the right way. The Human Reaction Behind a GPCR Imaging Milestone Scientists rarely talk about the emotional side of discovery — the instant where the experiment stops being data and starts being meaning. JB describes early experiences vividly: Seeing calcium waves flicker in cells. Realizing tissue is alive, unpredictable, and full of hidden structure. Feeling the urge to take phone pictures of super-resolution data and send them to collaborators because he couldn’t keep the excitement to himself. That same emotional imprint hit with the whole-islet image. It wasn’t just successful GPCR imaging — it was proof that receptors could be seen as they truly exist in native tissue, not simplified models. GPCR imaging doesn’t just visualize receptors. It gives scientists a way to feel the biology. This emotional spark carried the team through the next steps — validation, iteration, and expanding the scope of what these probes could do. Why Chemical Probes Shift the GPCR Imaging Landscape Chemical probes don’t replace antibodies outright — but they excel where antibodies struggle. For GPCR imaging, their strengths are practical and decisive: Consistency from batch to batch Long-term stability Compatibility with live cells and intact tissue Surface-receptor specificity A compact footprint that fits sub-10 nm resolution techniques These attributes enable experiments that previously required compromise. And the most striking validation came from in vivo GPCR imaging. Two-photon microscopy revealed a glowing islet in a living mouse — a moment JB calls the “Holy Grail” of chemical biology. Better GPCR imaging doesn’t just capture biology — it expands the biological questions the field can ask. The tools didn’t simply visualize receptors. They unlocked pharmacologically relevant insights that were previously inaccessible. The Collaboration Model That Makes GPCR Imaging Possible Behind every technical advance in this story sits something less tangible but equally decisive: a collaboration grounded in trust and fun. That’s how JB describes it — and it’s exactly why the work moved quickly. He learned tissue complexity from David; David picked up the quirks of acetonitrile. They exchanged instincts as much as data, and built a shared rhythm of problem-solving. Strong GPCR imaging tools come from strong interdisciplinary relationships. Good collaborations share protocols. Great collaborations share momentum. The trust between chemistry and biology drove the project forward faster than either discipline could have moved alone. Where GPCR Imaging Goes Next Once the breakthrough happened, the horizon widened dramatically. JB’s team now moves GPCR imaging toward: New fluorophores engineered for deep-tissue clarity Multi-color strategies for parallel receptor mapping Super-resolution imaging of receptor nanodomains AI-assisted probe design Multi-receptor visualization in complex tissue The dream is ambitious and increasingly feasible: A catalog where scientists choose a receptor, choose a color, and visualize biology exactly as it exists — in cells, in tissue, in living organisms. Not one receptor at a time. Not one color. Not one imaging depth. The islet lighting up wasn’t the pinnacle. It was the proof of concept. GPCR imaging is evolving from a specialized technique into a foundational method for receptor biology. And this breakthrough became one of the stepping-stones. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . If this behind-the-scenes story resonated, you’ll love the full conversation.  🎧 Listen to the full episode https://www.ecosystem.drgpcr.com/dr-gpcr-podcast/chemical-probes-for-gpcr-imaging-and-internalization If JB's story resonates 🎧 Listen to part 1 of this series with Dr. David Hodson

The Gaps They Already See 👉 As a biotech founder, it’s easy to mistake volume for readiness . A solid preclinical package, promising safety data, and a consistent in vivo proof-of-concept, it feels like you’re ready for that pre-IND meeting. And yet, many founders walk out of their first FDA conversation with a quiet sense of confusion . 👉 No dramatic rejection. No loud red flags.Just a series of subtle but firm questions pointing to what’s missing . While you’re focused on showcasing what you’ve done, the FDA is already scanning for: What’s not there? Where does this break later? ✅ FDA approval is not a documentation checkpoint. It’s a strategic filter. Clarity, not complexity, is what makes approval possible, and biotech sustainable. The Illusion of Readiness 👉 Many biotech founders walk into a pre-IND meeting with quiet confidence . They believe their data package is strong, their models are validated, and they’re ready to move forward. But what looks solid internally often fails to signal true readiness from the FDA’s perspective . 👉 The real mistake is not scientific; it’s strategic . Founders often assume that regulatory readiness is just a matter of scientific progress . In reality, it’s about anticipating how the FDA will stress test your assumptions . 👉 While your team highlights compelling results , the FDA is already thinking differently. They are looking for what’s missing , not what’s impressive. Where’s your dose justification?   How consistent is your manufacturing?   What’s the rationale behind your patient stratification?   Is there a missing comparator?   How does this translate into a clear safety margin? These aren’t just documentation issues. They are signs of strategic incompleteness . Founders who prepare to present  often miss the deeper expectation: the FDA is not just listening, it is probing for structural weak points . And if your development plan was designed to persuade , not to withstand pressure , then you are not ready . 👉 Scientific strength does not guarantee regulatory clarity.  That is the illusion. What the FDA Is Actually Optimizing For 👉 Biotech teams often treat the FDA like an evaluator. In reality, the FDA acts more like a systems-level risk assessor . They are not there to confirm what works; they are trained to identify what might break later . While founders try to demonstrate confidence, the FDA is systematically looking for structural weak points  across your development plan. And they are remarkably consistent in where they look. 👉 Here’s what the FDA is actually optimizing for, and where most early-stage teams fall short: 1️⃣ Predictable safety margins: Can your preclinical data meaningfully forecast patient safety in a first-in-human setting? 2️⃣ Dose selection logic: Is there a clear, mechanistic, and empirical rationale for how you plan to dose in trials? 3️⃣ Manufacturing consistency: Can you show that your process will be scalable, repeatable, and GMP-compliant from the start? 4️⃣ Comparative value: Have you positioned your therapeutic against the right standard or competitor, not just scientifically, but clinically? 5️⃣ Patient targeting rationale: Are you selecting the right patient subgroup with a clear justification for the benefit-risk balance? 6️⃣ Long-term viability signals: Does your data hint at durability, repeatability, or translatability, or are you building on single-use findings? Each of these areas points to the same thing: strategic foresight . The FDA is not asking for perfection; they are asking whether your plan shows signs of future collapse . 👉 When they spot a mismatch between your data and your development logic, it triggers doubt. And doubt slows down approval. ✅ The sooner you understand what they’re optimizing for, the faster you can align your strategy to de-risk not just your data, but your decisions . Reverse Engineering Your Path to FDA Approval Most biotech development plans are built forward : start with the science, generate data, reach milestones, and eventually think about approval. However, that sequence conceals a significant flaw; it treats FDA approval as the  final checkpoint , rather  than the initial filter . 👉 The most successful biotech teams flip this logic. They start by asking: What would FDA approval actually require from us, and what decisions need to reflect that now? This is the principle of strategic reverse engineering . 👉 It means designing your development path backwards , beginning with the criteria that the FDA uses to approve. Then you work upstream to define what your preclinical and early clinical data must demonstrate. That shift changes everything. Instead of collecting interesting data, you start collecting strategic evidence . Here’s how that mindset plays out in practice: You align trial endpoints with future label claims , not just scientific curiosity You design preclinical studies that support your dose justification , not just efficacy You plan manufacturing processes with scale-up and CMC documentation  in mind You assess safety signals based on their translatability to human risk mitigation This is not overengineering. It’s clarity. ✅ Reverse engineering from FDA approval is not about slowing down. It’s about building forward with fewer surprises, fewer iterations, and fewer expensive course corrections. FDA approval is not the end of your roadmap. It's the test of how well you built it. Building FDA-Ready Thinking into Your Strategy Regulatory success is not a function. It’s a mindset. The most resilient biotech startups are not the ones with the most experienced RA consultants; they are the ones where regulatory awareness is built into every key decision . This doesn’t mean turning your CEO into a regulatory affairs expert. It means shifting how your leadership team evaluates options. ✅ Every strategic decision, from indication selection to study design, should be filtered through the question: “What would this look like in an approval discussion?” 👉 FDA-ready thinking is not about documentation. It’s about decision architecture . Here’s how that shows up in strong biotech teams: 1️⃣ The CEO treats FDA clarity  as a strategic metric, not just a compliance task 2️⃣ The CSO aligns data generation with approval-relevant endpoints , not academic ones 3️⃣ The clinical lead evaluates protocols for regulatory traction , not just feasibility 4️⃣ The team knows that missing a question now means delaying approval later 👉 You don’t need a full regulatory department on day one. But you do need a framework for identifying and addressing regulatory gaps early . Because the truth is simple: 👉 The FDA already sees your weak points. If you can’t see them too, you’re not building strategy, you’re building surprises. Strategic Takeaway 👉 Biotech founders often view regulatory engagement as a milestone, a sign that things are moving forward. But approval is not a checkpoint. ✅ It’s a strategic mirror. It reflects every shortcut, every missed signal, every assumption you didn’t stress test. If you wait for the FDA to point out what’s missing, you’re already behind. Because by the time they do, your timelines will stretch, your budgets will strain, and your confidence will erode. The smart move is not to react faster. It’s to design smarter , from the very beginning. When you build with regulatory logic from day one, approval becomes a process of confirmation, not correction. ✅ That’s how real biotech strategy works. Ready to Break Your Bottlenecks? If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

The cannabinoid receptor type 2 (CB2R) has emerged as a compelling target across inflammation, immune modulation, and pain research. Despite its therapeutic potential, CB2 pharmacology remains difficult to interrogate with confidence. Traditional assays—particularly membrane-based radioligand binding—often provide high-throughput measurements, yet they can struggle to capture receptor behavior in its full physiological context. Subcellular membrane mixtures, altered receptor conformations, and non-specific interactions introduce noise at precisely the stage where medicinal chemistry teams need clarity. Live-cell high-content screening (HCS) offers an increasingly valuable alternative. By quantifying ligand–receptor interactions directly in intact cells, HCS allows researchers to observe binding events under near-physiological conditions while simultaneously generating image-based evidence to support numerical affinity estimates. For targets such as CB2, where nuanced shifts in receptor conformation affect signaling outcomes, a whole-cell environment can strengthen early-stage decision-making. In a recent collaborative study, 16 synthetic cannabinoid receptor agonists (SCRAs) were evaluated using a CB2 live-cell HCS assay incorporating the fluorescent tracer CELT-331. SCRAs—although often known for their undesired toxicological profile—offer a chemically diverse set of scaffolds that can help elucidate CB2 binding determinants and biased agonism mechanisms. This dataset highlights how HCS can be used both to triage compound series and to extract quantitative structure–affinity relationships. In this article, you’ll learn:   How live-cell HCS provides physiologically relevant affinity measurements for CB2 ligands  What the screening results reveal about 16 SCRAs tested at 1 µM and in concentration–response formats  Why image-based confirmation (and transparent Ki reporting) can strengthen medicinal chemistry decisions Why Live-cell High-Content Screening Matters for CB2 Ligand Profiling CB2 is a GPCR whose signaling behavior is sensitive to cellular context. Receptor localization, membrane composition, and intracellular trafficking states influence ligand binding in subtle but meaningful ways. Traditional membrane-based assays isolate receptors from this environment, which simplifies quantification but can introduce artefacts. Membrane preparations also contain non-target organelles—endoplasmic reticulum, Golgi, mitochondria—that may bind lipophilic probes and obscure true affinity. In contrast, high-content screening retains the full cellular architecture. Using HEK-293 cells stably expressing CB2R, fluorescent tracers such as CELT-331 can report on ligand competition events directly at the cell surface. Because imaging is captured across thousands of intact cells, each measurement incorporates receptor conformation, local membrane effects, and dynamic trafficking states that radioligand panels typically cannot resolve. For cannabinoid chemistry programs—where small structural shifts can significantly alter receptor preference or signaling bias—access to live-cell binding information can sharpen structure–activity relationships early in the optimization cycle. Furthermore, image-based data provide an additional check against off-target cytotoxicity or morphological changes, reducing the risk of misinterpreting affinity due to lost cell viability. Primary Screening at 1 µM: Identifying CB2 Competitors The study began with a one-point displacement screen, assessing how each of the 16 SCRAs competed with CELT-331 at 1 µM. Specific binding was defined as the difference between total fluorescence and GW405833-defined non-specific binding. Nuclear staining with Hoechst ensured that displacement values were not confounded by cell loss or compromised morphology. The results showed a clear division between strong, intermediate, and weak competitors: Strong displacement (>80%) :  AAN396 (93.08%), AAN397 (92.94%), AAN405 (88.59%), SON86 (81.56%), AV13 (81.37%), AV07 (78.31%), AV06 (76.13%) Moderate displacement (50–70%) :  AV18A (68.83%), AV11 (60.78%) Low displacement (<50%) :  Compounds including AAN488, AAN584, AAN705, AV19, AV31, AV61, AV64 The ~80–93% displacement range observed in several SCRAs at 1 µM strongly suggested high affinity and warranted full concentration–response profiling. Notably, no compound displayed toxicity or morphological changes at this concentration, supporting the interpretation that reductions in tracer signal reflected genuine competitive binding. These initial rankings provided a rapid, physiologically grounded triage of ligand candidates—exactly the type of early clarity medicinal chemistry teams need before committing to deeper profiling. Concentration–Response Profiling: Extracting IC₅₀ and Ki Values Nine compounds exceeded the 50% displacement threshold and progressed to seven-point concentration–response assays (10⁻¹⁰ to 10⁻⁶ M). Fluorescence intensity was quantified across all wells, and 4-parameter logistic (4PL) curves were fitted to derive IC₅₀ values. Ki values were then calculated using the Cheng–Prusoff equation, with CELT-331 parameters fully reported (Kd ≈ 160 nM; [L] = 80 nM). Figure 1. Table reporting the % of displacement measured at 1 µ M and the corresponding Ki for those showing a % higher than 50%. This transparent context is essential: two Ki values derived from different tracer–Kd conditions are not directly comparable without these details. Providing full tracer information ensures that researchers can recalculate or align affinity values across platforms. The resulting potencies spanned the low-nanomolar to submicromolar range: Most potent ligands :  AAN396 (Ki = 7.79 nM), AAN397 (15.1 nM), AAN405 (32.58 nM) Intermediate affinity :  AV11 (65.07 nM), AV07 (75.87 nM), SON86 (93.86 nM), AV06 (100.2 nM), AV18A (101.4 nM) Lower affinity : AV13 (698.7 nM) The data show a strong correlation between one-point displacement and full concentration–response performance—an important validation of the primary screen. The top three ligands consistently demonstrated robust, concentration-dependent competition, with IC₅₀ values well aligned with expectations for high-affinity CB2 agonists. Image-Based Confirmation: Visualizing Competition in Live Cells Using High-Content Screening One distinguishing strength of HCS is the ability to visually validate competitive binding. Representative images demonstrated progressive loss of CELT-331 fluorescence (red channel) as concentrations of AAN396, AAN397, and AAN405 increased. Importantly, Hoechst-stained nuclei (blue) remained consistent across all concentrations, indicating that reduced tracer signal was due to receptor occupancy rather than cytotoxicity or reduced cell count. Figure 2. Displacement of CELT331 binding by 9 compounds test in HEK-293T CB₂ cells. (a) Representative concentration–response curve for AAN396, AAN397, AAN405, SON86, AV06, AV07, AV11, AV1 and AV18A showing specific displacement of CELT331 (80 nM) with fitted IC₅₀ values (mean ± SEM, n = 2). (b) Representative HCS images illustrating CELT331 binding (red) and Hoechst-stained nuclei (blue) across increasing concentrations of AAN396, AAN397 and AAN405 (10⁻¹⁰ to 10⁻⁶ M). A progressive reduction in tracer signal is observed at higher concentrations, consistent with competitive displacement  These visual layers act as built-in quality controls. When medicinal chemists evaluate affinity jumps between analogues, image data can help resolve questions such as: Is the signal change due to true receptor competition? Are we observing partial displacement or plateauing? Does any compound induce morphological alterations at active concentrations? For GPCR programs—where trafficking, receptor reserve, and internalization are common confounders—access to these images supports more confident cross-series comparisons. What This Means for CB2 Drug Discovery Programs Taken together, the dataset offers several insights into how live-cell HCS can support CB2 ligand discovery: 1. Physiological context strengthens data reliability.   By profiling binding directly in intact HEK-293 cells, the assay reduces artefacts common in membrane-based platforms, particularly for lipophilic cannabinoid scaffolds. 2. Early triage becomes more precise.   The clear separation between strong, moderate, and weak binders at 1 µM allowed rapid prioritization without sacrificing mechanistic transparency. 3. Quantitative affinity estimates are transparent and reproducible.   Reporting tracer concentration and Kd enables recalculation of Ki values—essential for medicinal chemistry benchmarking. 4. Image-based validation adds interpretive power.   Visual displacement provides an additional confidence layer that traditional homogeneous binding assays cannot match. For teams optimizing CB2 modulators —or exploring biased agonism, polypharmacology, or downstream signaling—live-cell HCS provides a rigorous platform that shortens uncertainty during the hit-to-lead and lead optimization phases. Conclusion This case study highlights how live-cell high-content screening can transform early-stage CB2 ligand characterization. By combining quantitative affinity measurements with image-based validation in intact cells, the approach provides a richer picture of ligand–receptor interactions than traditional radioligand binding alone. Among 16 SCRAs evaluated, three compounds—AAN396, AAN397, and AAN405—emerged as nanomolar binders with consistent competitive displacement profiles and no detectable cytotoxicity. For researchers working in cannabinoid pharmacology, inflammation, or GPCR-mediated analgesia, these findings reinforce the value of physiologically relevant binding assays. As the field moves toward more nuanced understandings of CB2 signaling, tools that preserve cellular context will be increasingly important for designing ligands with both potency and functional precision. References Brogi, S., Corelli, F., Di Marzo, V., Ligresti, A., Mugnaini, C., Pasquini, S., & Tafi, A. (2011). Three-dimensional quantitative structure–selectivity relationships analysis guided rational design of a highly selective ligand for the cannabinoid receptor 2. European Journal of Medicinal Chemistry, 46(2), 547–555.

Some breakthroughs don’t start with a grant or a roadmap — they start with a question no one expects to matter. For JB, that moment was a cold email from a biologist he’d never met, asking if he could synthesize a molecule “when you’re back in Munich.”  That simple ask pulled a young chemist out of the fume hood and into the messy, electrifying world of live-cell biology. What followed — a trip to London, confocal imaging marathons, and a partnership built on trust and curiosity — reshaped both careers and helped unlock a new generation of GPCR imaging tools. This is the story of how collaboration quietly rewires a field. This collaboration would become the foundation of a GPCR imaging breakthrough that neither of them anticipated. How a Collaboration Led to a GPCR Imaging Breakthrough JB didn’t set out to contribute to a GPCR imaging breakthrough, but a simple molecule request set the entire trajectory in motion. He was a PhD student studying ion channels — living in a world defined by reaction mechanisms, synthetic routes, and the reassuring logic of chemistry. Then the unexpected request arrived. David Hodson needed molecules that were only one synthetic step beyond what JB was already making. The ask was simple; the impact wasn’t. That brief exchange connected two people who had never met but were equally driven by curiosity. When David later shared early data — including a moment where he realized he could image an entire islet — it became clear that this wasn’t just a small contribution. It was the start of a scientific partnership with the potential to shift how GPCRs could be visualized in their native environments. How Chemistry and Islet Biology Converged to Enable a GPCR Imaging Breakthrough The collaboration deepened when JB traveled to London, a trip that unexpectedly accelerated what would become a GPCR imaging breakthrough. What he expected to be a technical visit became a complete reframing of how he thought about biological systems. Instead of round-bottom flasks, he was looking at living cells under a confocal microscope. Freshly isolated pancreatic islets. Real-time calcium activity. Signaling waves pulsing across clusters of beta cells. Seeing those images, he realized just how different biological reality is from chemical idealization. Molecules weren’t abstract entities anymore — they were tools that could illuminate dynamic, excitable tissues and reveal mechanisms driving hormone secretion.That shift in perspective became foundational. It would later shape how he designed fluorescent probes, how he evaluated biological constraints, and how he approached GPCR imaging as both a chemical problem and a physiological one. How Chemical Probes Transformed GPCR Imaging and Outperformed Antibodies As JB continued exploring the biology, a major obstacle emerged: validated antibodies for GPCRs, including GLP-1R, were inconsistent and incompatible with high-resolution imaging. For a field that depends on understanding where receptors actually are — and how many are available at the cell surface — this was a major limitation. The shift toward chemical probes became a defining moment in achieving a true GPCR imaging breakthrough. Chemical probes offered a solution. They could be engineered to target surface-exposed receptors, remain stable across batches, support live-cell imaging, and tolerate super-resolution workflows. There was one challenge: JB had never synthesized peptides. The project required designing peptide–fluorophore conjugates that would bind GLP-1R with high specificity. Instead of stopping, he teamed up with a peptide specialist at the Max Planck Institute. Together, they built the first generation of GLP-1R fluorescent ligands — probes precise enough to visualize the receptor across islets, tissue slices, and ultimately living animals. Early images showed clean, bright labeling across whole pancreatic islets. That breakthrough launched the first wave of GLP-1R visualization studies and opened the door to deeper questions about receptor distribution, density, and trafficking. Designing Reliable GPCR Imaging Tools for Real Biological Systems Success brought new challenges. Chemical probes may be elegant, but biology isn’t. Tissue is messy. Cells behave differently day to day. Receptors internalize, traffic, recycle, and degrade. To build tools that performed consistently, JB and collaborators shifted toward a more rigorous parallelized screening approach. Instead of testing one compound at a time, they evaluated multiple probes in the same experimental conditions — same transfection, same cells, same humidity, same everything. This strategy accelerated discovery and reduced noise, helping them understand how each design change influenced labeling, specificity, and photophysical behavior. It also gave them confidence in how the probes would perform once shipped to external labs. The payoff was substantial. These optimizations enabled dual-color labeling strategies, surface-selective imaging, and ultimately in vivo visualization. These parallelized experiments were critical for turning early ideas into a reproducible GPCR imaging breakthrough. Two-photon microscopy experiments showed GLP-1R signaling in intact animals — a milestone that demonstrated just how powerful well-engineered chemical tools can be when paired with the right biology. Collaboration as the Driver Behind Today’s GPCR Imaging Breakthroughs Behind the technical success lies a partnership shaped by trust, shared energy, and a willingness to learn each other’s language. JB brought chemical intuition and a love for toolmaking. David brought deep experience in islet biology, calcium imaging, and tissue physiology. Over the years, they learned from each other in ways that shifted both careers. JB gained a grounded understanding of tissue heterogeneity, signal variability, and the biology that makes GPCR research challenging. David picked up unexpected chemistry insights — including a well-loved lesson involving acetonitrile in conjugation reactions. What made the collaboration durable wasn’t simply aligned expertise. It was a shared sense of fun, the kind of scientific joy that makes late-night imaging sessions feel lighter and big failures feel solvable. That chemistry — human chemistry — is what allowed the science to move as quickly as it did. Curiosity also played a central role. JB emphasizes how much of their progress came from staying open, asking questions freely, and engaging people at conferences regardless of title or reputation. Many of the connections that shaped the probes’ development came from simple conversations that began with genuine scientific interest. Their trust-driven collaboration is ultimately what allowed the GPCR imaging breakthrough to take shape. The Future of GPCR Imaging Breakthroughs: AI, Multiplex Tools, and In Vivo Discovery Today, JB leads an interdisciplinary group at the FMP in Berlin — chemists, theorists, biochemists, toxicologists, and cell biologists — all working toward the same goal: building better tools for visualizing cell-surface proteins, especially GPCRs. The work now stretches far beyond a single receptor. His team is exploring AI-enabled probe design, multiplex fluorescent strategies that allow visualization of multiple GPCRs at once, and approaches capable of mapping receptor crosstalk at nanometer scale. They’re also performing increasingly complex imaging experiments that capture receptor dynamics in intact tissue and live animals, expanding what’s possible in both basic research and translational settings. What started as one molecule request is now a platform vision — a future where any GPCR could be illuminated with high precision, in any tissue, across multiple colors, with tools designed as much by computation as by human intuition. And it all began with a simple moment of collaboration. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . If this behind-the-scenes story resonated, you’ll love the full conversation. 🎧 Listen to the full episode https://www.ecosystem.drgpcr.com/dr-gpcr-podcast/chemical-probes-for-gpcr-imaging-and-internalization If JB's story resonates 🎧 Listen to part 1 of this series with Dr. David Hodson

Most GPCR programs don’t fail because of weak molecules—they fail because biology behaves differently than the assay implied. This week’s feature goes straight to the foundation: how system-level GPCR thinking  protects discovery teams from the costly misinterpretations that derail programs. If your work touches GPCR pharmacology, these insights aren’t optional—they’re essential. Breakthroughs this week: Eli Lilly cuts Zepbound prices; GNAI1 missense mutation study; rapid Gαs endosomal translocation. 🔍 This Week in Premium: Sneak Peek Industry insights:  Lilly cuts Zepbound prices; Lilly hits $1T valuation; Novo advances amycretin. Upcoming events:  Adhesion GPCR Workshop; GRC—Transporters, Ion Channels & GPCRs; MPGPCR Joint Satellite Meeting. Career opportunities:  Senior/Principal Scientist—GPCR Pharmacology; Principal Scientist—In Vitro Pharmacology; Research Associate—Biologics Discovery. Must-read publications:  Gαi1 neurodevelopmental mutation; Gαs endosomal signaling; primary cilia as transduction hubs. Terry’s Corner: GPCR Pharmacology Insights That Prevent Real Drug Discovery Failures Discovery collapses when teams assume stable, linear, receptor-to-response relationships. Dr. Kenakin’s AMA made the central point unmistakable: GPCR systems constantly reshape ligand behavior through coupling efficiency, receptor density, local signaling architecture, and physiological feedback loops. This is where system-level GPCR thinking  becomes a competitive advantage—long before a molecule reaches animals or patients. When you see the distortions baked into the system, you interpret your data differently and protect your program from preventable failures. What You’ll Gain Spot false confidence early  → Sensitivity differences can turn full agonists into partials or even antagonists depending on system load. Avoid misleading mechanistic labels  → NAMs, PAMs, and biased agonists behave in system-dependent ways that single assays cannot reveal. Translate potency and efficacy realistically  → Recognize when deviations reflect biology rather than compound failure. Premium Members get 67% discount when they join Terry’s Corner in 2025 Sharpen your interpretation skills ➤ Dr. GPCR Podcast: Chemical Probes for GPCR Imaging with Dr. Johannes Broichhagen Reliable imaging tools change how researchers see receptor behavior. In this episode, Dr. Johannes Broichhagen explains how next-generation fluorescent probes—designed with precise synthetic logic—enable deeper insight into GPCR internalization, trafficking, and surface organization. His work shows why chemical design can outperform antibodies and how rigorous assay validation bridges chemistry and biology effectively. What You’ll Learn Why peptide–fluorophore probes succeed where antibodies fail How parallel synthesis& testing accelerates probe optimization How surface-exposed receptor pools reshape interpretations of trafficking Listen to the episode ➤ High-Content Screening for GPCR Programs: Overcoming Assay Limitations with Fluorescent Ligands High-content screening (HCS) is now indispensable for GPCR workflows—especially when spatial context, trafficking behavior, and live-cell kinetics matter. But HCS only works when assays are built with rigor and powered by the right fluorescent ligands. This feature from Celtarys Research outlines how to structure an HCS workflow that avoids batch effects, imaging artifacts, and variability while delivering reliable, mechanistic data. What You’ll Learn Why traditional radioligand assays miss critical spatial and kinetic signals Five phases of a robust, reproducible HCS pipeline How fluorescent ligands strengthen specificity, relevance, and assay confidence Read the full HCS feature ➤ Why System-Level GPCR Thinking Changes Data Interpretation And How Dr. GPCR Premium Membership Gives You an Edge Premium gives GPCR scientists and biotech teams a single, trusted source of weekly insight that cuts through noise. Members access deep-dive lectures, expert frameworks, curated jobs, upcoming events, and classified more. It’s a system-aware resource built for researchers who need clarity fast—reinforcing system-level GPCR thinking  every week so your interpretations stay sharp and aligned with real biology. FAQ 🔹 What’s included? Weekly research, careers, and industry intelligence; GPCR University; 200+ expert talks; networking; and member-only discounts. 🔹 Who is it for? Researchers, pharmacologists, biotech teams, and decision-makers who rely on accurate, efficient, interpretation-first information. 🔹 Why now? GPCR innovation is accelerating—and misinterpretation compounds quickly. Staying informed today prevents the delays others won’t see coming. Don’t Fall Behind—Access the Edge You Need Already a Premium Member? 👉 Access this week’s full Premium Edition here ➤ What Members Say "I am a convert! I will keep Dr. GPCR and the offered resources in my work sphere." Help us reach more scientists by providing quick rating on Spotify or Apple Podcasts — and a YouTube subscribe. Spotify: https://open.spotify.com/show/1KQHbC2qhkRIrdgBDtiQVF Apple Podcasts: https://podcasts.apple.com/us/podcast/dr-gpcr-podcast/id1514231064 YouTube: https://www.youtube.com/@DrGPCR Want to support Dr. GPCR? 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The Cost of Confusion Let’s be honest. Most biotech pitches don’t fail because the science is weak. They fail because the story is unclear. 👉 A confusing pitch doesn’t just slow down progress. It silently shuts down opportunity. You might still get the meeting. You might still get a few questions. But behind the polite nods, your audience is checking out. Here’s the uncomfortable truth: 👉 People make up their minds in the first few seconds. If your pitch doesn’t immediately tell them who it’s for, why it matters, and what makes it different, then they start mentally moving on, even if you’re still speaking. The result? You walk out of the meeting thinking it went well. They walk out already forgetting what you said. 👉 And that gap between delivery and perception is where momentum dies. For biotech founders, this is more than a presentation problem. It’s a strategic vulnerability. Because if you can’t explain your value clearly, your audience assumes there is none. A clear biotech pitch answers three key questions immediately. If your audience has to guess, you’ve already lost the room. The Most Common Mistakes in Biotech Pitches Even the most brilliant science can get lost in a poor pitch. And most of the time, the issue isn’t style. Its structure, sequencing, and focus. 👉 Here are the most common gaps we see in early-stage biotech pitches, even from smart, well-prepared teams: 1️⃣ Starting with the science Founders often begin with detailed technical information, pathways, targets, and models. But your audience isn’t evaluating you as a researcher. They’re trying to understand the opportunity. 👉 Opening with mechanisms forces the listener to do all the work. They have to guess why it matters, what the application is, and whether it fits. ✅ Start with relevance, not results. 2️⃣ Using buzzwords instead of clarity Words like “platform”, “breakthrough”, or “transformative” feel powerful. But without concrete context, they’re empty. Your listener doesn’t want to be impressed. They want to understand. 👉 Replace vague claims with focused positioning: What does your solution actually do ? Who specifically is it valuable for? Why now? 3️⃣ No clear strategic angle You might explain what your technology is. But do you explain why it fits your audience’s world? ✅ Strategic fit is not assumed. It has to be demonstrated. If your pitch doesn’t address timing, portfolio alignment, or internal traction, the audience won’t do that thinking for you. They’ll smile. Nod. Then pass. 4️⃣ Forgetting to frame the next steps One of the most common gaps? No clear “what now”. You finish the pitch ... and wait. If your listener doesn’t know what to do next or who should be involved, the conversation stalls. ✅ A strong pitch ends with direction, not silence. These aren’t “presentation mistakes.” They’re symptoms of an unclear strategy. And the good news is, they can be fixed. Strong biotech pitches don’t just inform, they align. Every sentence should move the conversation forward. How to Fix the Gaps 👉 Fixing your biotech pitch doesn’t require a rebrand. It requires a realignment. The strongest pitches follow a clear, strategic logic, not just a narrative arc. 👉 Here’s a four-part structure that helps founders move from scattered storytelling to focused positioning: 1️⃣ Who it’s for ✅ Begin by clearly defining your audience or market. Avoid vague generalizations. When the listener knows exactly who your solution targets, they can immediately place it in their mental map. ✅ This clarity signals strategic focus and shows that you're not casting a wide net. It shows you’ve made deliberate choices about application, indication, or customer. 2️⃣ Why it matters ✅ This is about urgency and relevance. Instead of leading with technology, lead with the problem it addresses. ✅ Frame the situation in terms of what’s at stake, whether that’s patient outcomes, time delays, unmet needs, or inefficiencies. This immediately shifts the conversation from academic interest to practical significance. 3️⃣ Why it’s different ✅ Differentiation must be more than a claim. It has to be obvious, credible, and valuable. Make it easy for the listener to understand what sets your approach apart from existing solutions or current standards and why that difference matters. Without this, you blend into the noise. 4️⃣ Why it fits ✅ Your pitch should always reflect an understanding of your listener’s world. Consider their priorities, constraints, and objectives. If your message doesn’t show alignment with their strategy or timeline, they won’t engage, no matter how strong your science is. A great pitch makes it easy for the other side to connect the dots and move forward with confidence. This framework is not about simplification. It’s about strategic clarity. ✅ When your pitch follows this logic, it respects the listener’s time, builds trust fast, and moves the conversation toward real decisions. What Changes When Your Pitch Works When your biotech pitch lands, the difference is immediate and powerful. You stop pushing. People start leaning in. You stop explaining. People start connecting the dots for you. 👉 This is what clarity creates. A clear, strategic pitch doesn’t just share information. It communicates that you know who you’re building for, why now is the right time, and how your solution fits into something bigger than your own science. ✅ It shifts perception. From: “That’s interesting” To: “This is worth moving forward.” When that shift happens, follow-ups come faster. Stakeholders engage earlier. And opportunities become more structured, not just more numerous. Because a well-positioned pitch is not just about communication, it’s about leadership. 👉 You’re showing that you think in context. That you understand the system you're entering. That you’re ready to operate at the next level. And in the early stages of a biotech company, that’s often what separates promising science from real traction. So if your meetings keep ending with polite nods and no momentum, it might not be your data. It might be your framing. Reworking your pitch is not polishing. It’s focusing. And when you focus on what your audience actually needs to hear, you don’t just earn attention, you earn action. Strategic Takeaway: Clarity Wins. Fast. 👉 Biotech founders don’t lose opportunities because their ideas are weak. They lose them because their positioning is unclear. A strong biotech pitch isn’t about saying more. It’s about making your value obvious, fast. 👉 The goal is not to simplify your science. It’s to clarify its strategic relevance, in seconds, not slides. If your pitch keeps stalling, stop editing your deck. Start refining your message. Ready to Break Your Bottlenecks? If you're feeling the friction, indecision, misalignment, or slow momentum, it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck, fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

Discovery programs rarely fail because a molecule “did nothing.” They fail because a molecule behaved exactly as the underlying system allowed—amplified, buffered, redirected, or reshaped by layers of receptor biology that weren’t accounted for. The October 30th AMA with Dr. Kenakin  highlighted a fundamental truth: GPCR systems do not offer stable, proportional input–output relationships. Receptor density, constitutive activity, coupling efficiency, local signaling architecture, and physiological feedback loops continuously rewrite the connection between ligand engagement and measurable response. Teams equipped with deep GPCR pharmacology insights make different decisions. They design assays differently. They interpret deviations differently. And they avoid the costly surprises that appear when in vitro conclusions collide with human physiology. In this session, you’ll gain: How system sensitivity transforms potency, efficacy, and agonist classification. Why allosteric modulators require a fundamentally different strategic lens. How enzyme behavior introduces nonlinear risk even in receptor-driven programs. The sections below synthesize the key topics addressed during the AMA and highlight the GPCR pharmacology insights that emerged from Dr. Kenakin’s answers. Physiological Feedback Reshapes Pharmacology The dobutamine example resurfaced for a reason. Its clinical utility emerged from the interplay between β₁-mediated inotropy and α-mediated vascular effects that buffered reflex tachycardia. This wasn’t predictable from a one-pathway model—and as Dr. Kenakin  noted, it wasn’t designed. It was revealed only when the drug encountered the full complexity of the cardiovascular regulatory network. This is a core GPCR pharmacology insight:ligand → receptor → G protein is never the entire story. Physiological reflexes instantly counteract, amplify, or redirect receptor-level effects. Multi-receptor involvement—intentional or not—often dictates the phenotype. Biased agonism introduces additional layers where one pathway may mimic “reflex-like” counterbalancing of another. Dr. Kenakin revealed  practical ways to anticipate these system-level interactions before they appear as clinical liabilities. Allosteric Modulators: System-Conscious Control Orthosteric ligands displace native signaling and impose their own control. Allosteric modulators interact with the system already in motion, shaping the receptor’s behavior without overriding endogenous tone. Dr. Kenakin  emphasized that the key advantage is not subtlety for its own sake—it’s bounded pharmacology. Orthosteric dose increases drive continuously stronger responses; NAMs and PAMs have structural ceilings. For complex GPCR systems, this boundary is a strategic advantage: NAMs can only shift an agonist curve so far—dose escalation won’t produce runaway suppression. PAMs permit enhancement without replicating the liabilities of orthosteric agonists. Endogenous ligands remain part of the signaling equation, preserving physiological patterning. These are not “gentler” mechanisms—they are more system-aware  mechanisms, a crucial distinction in modern GPCR pharmacology insights. In this AMA session, Dr. Kenakin talked about  the specific allosteric properties orthosteric drugs cannot offer. Receptor Density: The Distortion Engine One of the AMA’s recurring themes was the impossibility of interpreting efficacy without system context. Efficacy is not a molecule-only attribute—it's a joint property of ligand and system. High-coupled systems inflate apparent efficacy; low-coupled systems expose its limits. Dr. Kenakin  showed how the same agonist can behave as near-full, partial, or even silent depending on receptor expression and coupling efficiency. This isn’t experimental noise—it’s biology. Dual-assay strategies (high and low sensitivity) are essential, not optional. Benchmarks anchor efficacy expectations to clinically relevant responses. Constitutive activity governs whether inverse agonism is observable or physiologically meaningful. These GPCR pharmacology insights become critical when translating in vitro behavior to tissue environments with radically different receptor density—and therefore different operational efficacy. Assay Volume Control: Classification Through Contrast Sensitivity doesn’t merely change the size of the response—it changes the apparent identity  of the ligand. An agonist in one system becomes an antagonist in another. A partial agonist appears neutral until expression or coupling is increased. Dr. Kenakin  highlighted historical β-adrenergic cases where tachycardia appeared only once compounds reached more sensitive human systems. This is why experts never classify ligands from a single system: The same molecule can occupy different mechanistic categories across assay contexts. Without contrast (low vs. high expression), misclassification is nearly guaranteed. Translation requires understanding where the ligand sits on the operational curve—not just where it sits in one assay. These are core GPCR pharmacology insights for preventing interpretive drift as programs move toward in vivo work. NAMs, PAMs, and Subtle Mechanistic Traps Modulators are frequently labeled correctly but characterized incompletely. Dr. Kenakin  stressed that low-alpha NAMs can resemble competitive antagonists unless deeper kinetic or concentration-range testing is performed. Common mechanistic traps: Alpha-driven effects misinterpreted as beta-driven, or vice versa. PAMs assumed therapeutically viable without verifying whether they amplify affinity or efficacy. Concentration ceilings misunderstood—leading teams to misjudge modulatory reach. For teams seeking fine-grained control over receptor output, these GPCR pharmacology insights determine whether a series advances or stalls. Enzyme Behavior: The Nonlinear Gatekeeper In GPCR programs, CYP interactions often appear late—usually too late. Dr. Kenakin  emphasized that CYP enzymes are inherently allosteric, meaning inhibitory behavior is probe-dependent, substrate-dependent, and often counterintuitive. These nonlinearities matter: Competitive inhibition decreases as substrate increases. Uncompetitive inhibition strengthens as substrate increases—opposite of intuition. A compound may appear benign with one substrate and problematic with another. Time-dependent inhibition adds another nonlinear dimension: once the enzyme is trapped, recovery depends on synthesis, not on clearance. These GPCR pharmacology insights ensure receptor-focused teams don’t underestimate the metabolic landscape their molecule must navigate. In this AMA session, Dr. Kenakin reveals  the substrate strategy needed for credible DDI assessment. Irreversible and Pseudo-Irreversible Binding: Mechanism Dictates Risk Irreversibility is not a single category. Dr. Kenakin  drew a sharp contrast between chemically reactive irreversible inhibitors and pseudo-irreversible tight-binding compounds. One carries broad off-target risk; the other behaves more like a high-affinity ligand with slow dissociation. Strategic considerations: CYP time-dependent inactivation is mechanistically distinct from GPCR irreversibility. Extremely strong binders can fail in structured tissues because they saturate the periphery and never penetrate the core. Lower-affinity alternatives may produce deeper, more therapeutically relevant coverage. These GPCR pharmacology insights refine potency-driven thinking into distribution-driven thinking—especially for oncology or compartmentalized tissues. In the full AMA session, Dr. Kenakin reveals  how teams choose between slow-off and true irreversible strategies. Ranking Partial Agonists Without Losing Meaning Chemists want a single number. Biology rarely gives one. EC₅₀ and Emax uncouple affinity and efficacy, making cross-agonist comparison unreliable. Dr. Kenakin  emphasized that only operational-model–derived ratios anchored to a benchmark partial agonist provide interpretable comparisons. Practical takeaways: Use a clinically relevant partial agonist as the anchor. Interrogate agonists across multiple receptor-expression states. Ratios—not absolutes—capture the true structure–activity shifts. These GPCR pharmacology insights are essential for directing chemistry toward the property that actually matters in vivo. Dr. Kenakin revealed  the decision workflow for ranking agonists with translational intent. Why Terry’s Corner Give You The GPCR Pharmacology Insights You Need Terry’s Corner gives discovery scientists direct access to weekly masterclasses from Dr. Kenakin , monthly AMAs, and a continuously expanding on-demand library focused on sharpening interpretation—not creating noise. It equips pharmacologists, discovery teams, and biotech leaders to see around mechanistic corners, recognize the nonlinear behaviors that define GPCR systems, and protect programs from subtle but fatal interpretive errors. GPCR innovation is accelerating, and those who invest in deeper GPCR pharmacology insights today will shape tomorrow’s breakthroughs. 40 years of expertise at your fingertips: Explore the full library ➤

Watch Episode #177 Some scientific breakthroughs don’t start with a grant or a perfectly architected project plan. They start with a chance email, an unexpected visitor at the door, or the moment a team realizes the question in front of them is simply too big for one mind. In research, including the GPCR world collaboration isn’t a luxury. It’s survival. The future of discovery will belong to scientists who know how to build the right partnerships and stay humble enough to let others’ strengths unlock their own. The GPCR Collaboration Mindset Behind Breakthrough Science Most researchers have a story about the moment they realized they couldn’t push their science any further alone. For Hodson, that moment came early. His career moved through veterinary school, immunology, neuroendocrinology, and finally into islet biology — each step revealing a simple truth: Complex problems require multiple minds. By the time his lab began dissecting the GLP-1 and GIP receptor landscape in islets and brain, the signal became undeniable. GPCR signaling wasn’t linear. It wasn’t clean. And it certainly wasn’t something a single lab could unpick with isolated tools. To understand how incretin receptors behave in intact tissue, Hodson needed people who saw problems differently — chemists, structural biologists, cryo-EM experts, genetics teams, and collaborators who could challenge his assumptions without ego. That mindset shaped his partnership with JB, the chemist who would eventually help his lab visualize receptors in living systems with far more precision than antibodies ever allowed. Their collaboration didn’t start as a big strategic play. It started with curiosity, openness, and the humility to admit that better answers required better tools — and those tools lived in someone else’s expertise. How GPCR Collaboration Bridges Chemistry and Physiology Great collaborations often begin where frustrations peak. For years, the GPCR community wrestled with unreliable antibodies. Some worked in one tissue but failed in another. Some detected off-targets. And some simply misled entire research programs. Hodson’s group felt the impact directly: imaging incretin receptors in intact islets and brain slices was nearly impossible. That changed when JB’s team walked in with a different lens. Chemists don’t look at receptors the way physiologists do. They think in functional groups, fluorophores, linkers, and binding pockets. And that perspective unlocked something powerful. Instead of forcing antibodies to do what they weren’t built for, JB’s group engineered fluorescent ligands based on known GLP-1 and GIP pharmacology. The result was a set of chemical probes that finally allowed researchers to visualize where receptors exist, how drugs access them, and what cell types respond. These tools didn’t appear because someone wrote “visualize GPCRs better” in a grant. They appeared because one lab’s bottleneck became another lab’s engineering challenge — and together, they solved something neither could crack alone. This collaboration reshaped the way Hodson’s lab studies receptor biology. It didn’t replace physiology with chemistry. It fused them, creating a hybrid view of receptor signaling that has now been adopted by labs worldwide. When GPCR Collaboration Makes the Data Finally Click Every long collaboration earns a breakthrough moment — often after months or years of confusion. For Hodson, that moment came with a protein he’d been tracking for a decade: vitamin D binding protein, a glucagon-related secretion from alpha cells. For years, the data made no sense. The signaling didn’t line up. The knockout behaved differently than expected. And interactions with GLP-1 pathways were inconsistent. Most scientists would have shelved the project. Hodson nearly did. The turning point came when the cryo-EM data arrived — a structure solved through the same collaborative network that had built the fluorescent tools. Suddenly, the anomalies aligned. The protein was interacting with GPCRs in a way that no single technique could reveal. Chemistry, imaging, physiology, and structure finally intersected. This is the power of collaboration in GPCR research: insights emerge when one group’s “weird data” becomes another group’s missing puzzle piece. And when those pieces come together, the field jumps forward faster than any lab could push it alone. Why GPCR Collaboration Is Essential for Modern Science Hodson makes the point bluntly: modern GPCR science requires specialists. You need genetics teams for variant interpretation, metabolic phenotyping facilities for in vivo work, structural experts for cryo-EM, chemists for tool development, and data scientists who can integrate everything. No one person can be excellent at all of it — and pretending otherwise slows discovery. The shift toward team science isn’t cultural. It’s technical. The questions are larger, the stakes higher, and the datasets more complex. Collaboration is not “nice to have.” It is the only path to meaningful discovery. And it’s not just about capability. It’s about trust — the kind of trust built when collaborators confirm your data, replicate your results, and call out your blind spots before reviewers do. Hodson and JB’s collaboration works not because their skills align but because their thinking styles differ. One pushes chemistry further. The other pushes physiology deeper. Together, they push GPCR science faster. The Future of GPCR Collaboration in Metabolic Research The next decade of metabolic research won’t hinge on a single target. It will hinge on the teams who can map GPCR signaling with precision and design therapies that fit real biology — not idealized models. From GLP-1 and GIP dual agonists to the growing field of GPCR-based delivery systems, collaboration will control the pace of innovation. Here’s where the biggest opportunities will emerge: Building receptor-specific delivery systems for gene or peptide therapeutics Mapping cell-type–specific GPCR signaling in metabolic tissues Using genetics to understand responder vs. non-responder profiles Developing muscle-sparing metabolic therapies by combining GPCR pathways Creating chemical tools that finally show how drugs reach their targets These aren’t solo-lab problems. They’re team problems — the kind that require chemistry, physiology, pharmacology, structural biology, computational modeling, and clinical insight working as one system. The labs that collaborate boldly will discover faster, validate better, and translate more effectively. This is where GPCR science is heading: toward deeper integration, shared tools, and partnerships that amplify what each discipline does best. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . If this story resonates with your work or curiosity, go deeper. 🎧 Listen to the full conversation with Dr. David Hodson

High-content screening (HCS) has become a cornerstone in GPCR and phenotypic drug discovery, enabling researchers to quantify cellular responses with spatial, temporal, and mechanistic depth. For GPCR-focused programs, the ability to visualize receptor localization, internalization kinetics, and ligand interactions in intact cells offers advantages that extend far beyond traditional biochemical or radioligand assays. Yet, despite remarkable progress, HCS workflows remain vulnerable to several performance-limiting factors: variable cell behavior, imaging artifacts, batch effects, and incomplete assay optimization. These challenges can obscure real biological signals and complicate the identification of robust hits. Overcoming them requires careful assay design and strategic use of the right fluorescent probes. In this blog, you’ll learn:  How HCS works and why it is increasingly central to GPCR-based drug discovery  The key phases of designing a reproducible HCS workflow  How fluorescent ligands strengthen assay robustness and biological relevance What Is High-Content Screening and Why It Matters for GPCR Programs High-content screening integrates automated microscopy, multiplexed imaging, and computational analysis to evaluate cellular responses under chemical or genetic perturbations. Unlike biochemical assays, which reduce biology to a single readout, HCS captures whole-cell phenotypes and single-cell heterogeneity. Modern HCS instruments combine robotics, high-speed imaging, environmental control, and image-analysis pipelines capable of extracting hundreds of features per cell. The resulting multiparametric datasets are well-suited for GPCR research, where receptor trafficking, spatial dynamics, and context-dependent signaling significantly influence pharmacology. For GPCR assay developers, HCS supports:  Quantitative visualization of receptor internalization and trafficking  • Live-cell kinetic measurements  unavailable to endpoint assays  Multiplexed assessment of pathway activation   Improved confidence in hit prioritization through phenotypic fingerprints HCS is also becoming critical in toxicity screening, mechanistic target validation, and ligand profiling—making it an essential tool across the GPCR drug discovery pipeline. Why Traditional Radioligand Methods Fall Short for Modern Screening Needs Radioligand binding assays have historically been the standard for GPCR pharmacology. However, their limitations become increasingly important as drug discovery moves toward high-information, high-throughput formats. Key limitations of radioligand assays include:  • No spatial information  — signals are measured in bulk, masking subcellular dynamics  • Low temporal resolution  — difficult to use in kinetic or live-cell experiments  • Regulatory and safety constraints  that complicate workflows  • High waste-disposal requirements  • Reduced compatibility with phenotypic screening frameworks By contrast, HCS-based ligand binding assays—especially those enabled by next-generation fluorescent ligands—support:  Repeated imaging for equilibrium measurements  High-resolution spatial localization  Multiparametric phenotypic profiling  Full compatibility with automated screening infrastructure  Safer and more sustainable workflows For GPCR researchers aiming to reduce ambiguity in early hit-finding, the shift from radioligands to fluorescent HCS assays offers substantial scientific and operational benefits. The Phases of a Reliable HCS Workflow Designing a robust HCS assay requires a structured, iterative approach. The following phases minimize batch effects, reduce imaging artifacts, and strengthen reproducibility. 1. Assay Design and Pilot Optimization Successful HCS begins with a clearly defined biological question and the careful selection of a physiologically relevant cell model. Pilot experiments are essential to optimize:  Cell density  Fluorescent probe concentration  Exposure times and illumination settings  Imaging channel configurations The goal is to achieve a high Z′ factor , reflecting assay robustness and dynamic range. Early optimization prevents later variability and sets the foundation for scalable screening. 2. Plate Layout and Sample Handling Automated liquid handlers and randomized plate layouts are used to minimize positional effects and edge-related artifacts. Incorporating internal controls, including known agonists or antagonists, allows normalization and facilitates detection of plate-level drift. Probe panels—such as lysosomal dyes or cytoskeletal markers—can be integrated to support multiplexed readouts and mechanistic interpretation. 3. Imaging Calibration and Acquisition These steps ensure that quantitative signals reflect biology, not instrument variation. Imaging instruments must be calibrated for:  Focus stability  Light-path alignment  Illumination homogeneity  Spectral separation Environmental control (CO₂, humidity, temperature) prevents drift during long acquisition runs. 4. Image Processing and Feature Extraction Once images are acquired, segmentation algorithms convert them into quantifiable data. Increasingly, deep-learning-based segmentation  is becoming the standard for capturing single-cell features such as morphology, intensity, and localization. Retaining single-cell data preserves heterogeneity and enables mechanistic analyses, particularly important for GPCR signaling where subpopulations often drive distinct responses. 5. Data Analysis, Normalization, and Hit Identification Dimensionality reduction, batch correction, and standardized normalization methods prepare data for hit selection. Multivariate scoring allows integration of multiple phenotypic features, improving the robustness of hit identification relative to single-endpoint measures. When executed as a unified pipeline, these phases ensure an HCS assay capable of supporting both exploratory phenotypic screens and targeted GPCR binding studies. Figure 1. Standard HCI experimental pipeline. (A) After experimental design, wet lab work is performed to acquire high-content cell images, which then require several canonical image analysis steps. Cell segmentation is optional, but it will allow single-cell profiling downstream. (B) After image featurization,  image-based profiling steps are performed to prepare data for downstream analyses. (C) This full pipeline is orchestrated by reproducible software tools to ensure data provenance and to enable benchmarking. Source: Way GP, Sailem H, Shave S, Kasprowicz R, Carragher NO. Evolution and impact of high content imaging. SLAS Discov. 2023 Oct;28(7):292-305.  How Fluorescent Ligands Strengthen HCS Assays: The Case of CELT-331 Fluorescent ligands are now considered the gold standard for image-based GPCR assays. Their ability to visualize ligand–receptor interactions directly in living cells produces data that are both more physiologically relevant and more reproducible than traditional methods. Key scientific advantages include: Physiological Relevance Fluorescent ligand binding occurs in intact cells, preserving receptor conformation, trafficking, and native membrane context—key variables for GPCR pharmacology. Cleaner Signal and Higher Specificity Modern fluorophores minimize background, enabling precise quantification of binding and displacement curves. Non-Radioactive Workflow By removing isotopes, researchers gain safer, more scalable, and more environmentally responsible workflows. Visual + Quantitative Data Fluorescent ligand assays generate both numerical values (IC₅₀, Kᵢ) and spatial information that clarifies receptor behavior under different ligand conditions. Case Study: CELT-331 in CB2 High-Content Binding Assays In CB2-expressing HEK cells, the fluorescent ligand CELT-331  produces precise membrane-localized binding signals. When combined with a competitor such as the CB2-selective partial agonist GW40583, displacement curves can be visualized and quantified directly through HCS microscopy. This approach improves readout clarity, strengthens data reproducibility, and enables kinetic or equilibrium measurements impossible in endpoint radioligand assays. Figure 2. CB 2  cannabinoid high-content competition binding screening experiments with CELT-331. CB 2 -expressing HEK cell lines are labeled with CELT-331 at 80 nM (right), while competition with the CB 2 -selective partial agonist GW40583 is studied (left) to measure competitor binding affinity.  For cannabinoid researchers, this capability supports:  Accurate CB2 affinity determination  Visualization of ligand binding dynamics  Scalable, reproducible high-throughput assays  A smoother transition from screening to mechanistic studies At Celtarys, these capabilities are provided as a complete CB2 HCS service—allowing teams to integrate fluorescent ligand technologies without needing internal imaging infrastructure or specialized assay development expertise. Conclusion High-content screening continues to reshape GPCR drug discovery, offering richer biological context, improved assay sensitivity, and more confident identification of lead candidates. But fully leveraging HCS requires rigorous assay design, careful imaging calibration, and the strategic use of high-performance fluorescent ligands. As shown through the CELT-331 case study, fluorescent ligand–enabled HCS workflows provide physiologically relevant, reproducible, and multiparametric insights that traditional methods cannot match. For teams working in GPCR pharmacology or cannabinoid research, these tools accelerate hit validation, reduce ambiguity, and support more data-driven decision-making across early discovery. Looking ahead, combining HCS with advanced probe design, scalable analytics, and expert scientific support will further strengthen its role across the drug discovery ecosystem. At Celtarys, we remain committed to enabling this transition and supporting researchers as they design and optimize their next generation of cell-based assays. 👉 Learn more about CELT-311 References Booij TH, Price LS, Danen EHJ. 3D Cell-Based Assays for Drug Screens: Challenges in Imaging, Image Analysis, and High-Content Analysis. SLAS Discov.  2019.  Lin S, Schorpp K, Rothenaigner I, Hadian K. Image-based high-content screening in drug discovery. Drug Discov Today. 2020.  Way GP et al. Evolution and impact of high content imaging. SLAS Discov.  2023.

Burning Cash Isn’t the Problem. Burning Alignment Is. Every biotech founder fears the day the cash runs out. You track the burn rate. You watch the runway shrink. You delay hires. You negotiate term sheets from a place of panic. But here’s what most founders miss. 👉 Cash isn’t your biggest problem. Misalignment is. Not the obvious kind either. We’re not talking about personality clashes or investor drama. 👉 We’re talking about the type of quiet misalignment that appears to be progress but feels like confusion . The team is moving. The calendar is full. The experiments are running. But when you zoom out, you’re not actually getting closer to your next strategic inflection point . That’s what we call the hidden burn . 👉 This post breaks down how biotech misalignment happens, what it costs you, and how to fix it before your runway disappears without a clear outcome to show for it. Scientific progress doesn’t guarantee startup success; strategic clarity does. Where Biotech Misalignment Starts 👉 Most misalignment doesn’t start with conflict. It starts with silence. You assume your CSO knows where you’re headed. You assume the board is aligned with milestones. You assume your cofounder sees the same finish line you do. They don’t. 👉 Biotech misalignment usually begins when scientific logic and business logic quietly diverge . At first, it’s just different vocabulary. Later, it becomes different roadmaps. And by the time you catch it, your burn rate is up and your traction is down. Here are the three most common sources of internal drift in early biotech teams: 1️⃣ Scientific versus commercial vision 👉 Your science team optimizes for validation. Your business team optimizes for traction. If no one owns the connection between the two, they pull in opposite directions . Example: You validate a biomarker for a broad indication. Your BD person starts framing it for a niche diagnostic use. The board expects an IND package. No one’s wrong, but no one’s aligned. 2️⃣ Founder-team decision asymmetry 👉 The founders make strategic calls in 1:1s or ad hoc Slack threads. The team only finds out when timelines shift. This breeds passive execution, second-guessing, and a lack of ownership . People stop thinking ahead because they don’t know what’s coming. 3️⃣ Silent conflict inside your SAB or board 👉 Scientific advisors disagree with your go-to-market direction. Investors push for speed. No one wants to say it out loud. You end up running two strategies in parallel . One in your deck. One in your team’s head. How Misalignment Drains Your Runway 👉 Misalignment doesn’t show up as chaos. It shows up as wasted momentum. Your team is working. Your lab is busy. Your timelines look full. But the wrong things are moving. Or the right things are moving in the wrong order. 👉 That’s how biotech teams burn through capital without hitting real inflection points . Here’s how it happens: 1️⃣ Duplicated effort 👉 Two teams think they’re building toward the same milestone. In reality, they’re solving different problems. You pay for both. You benefit from neither. Example: Your platform team is building a modular assay framework. Your clinical lead is already assuming a fixed diagnostic protocol. By the time it surfaces, you’ve lost two months of budget and alignment. 2️⃣ Milestone redefinition spiral 👉 The milestone was “complete preclinical package by Q3.”Then it became “optimize lead series.”Then “refine bioavailability model.”Then “add a secondary endpoint.” The date never changed. But the scope moved. And now your next raise is behind schedule. 3️⃣ Strategic dilution 👉 You keep adding just one more use case. Just one more backup program. Just one more exploratory study. Your story gets fuzzy. Your team gets stretched. Your capital gets fragmented. Investors don’t fund complexity. They fund momentum. And misalignment kills momentum in slow, silent, irreversible ways. Real biotech traction starts when decisions are driven by shared strategy, not disconnected deliverables. Fixing the Alignment Problem Before It Kills Your Strategy Biotech misalignment does not fix itself. It does not go away with more meetings, louder all-hands sessions, or rewritten pitch decks. 👉 It only gets resolved when you rebuild how decisions are made and what truly matters inside your company. 1️⃣ The first shift is reframing what you call a milestone. A milestone is not a scientific phase. A milestone is a decision point that moves your company in a strategic direction. If nothing changes after it, it was just a lab update. Not progress. 👉 If your roadmap is full of scientific deliverables but empty of decision triggers, you’re burning runway without building value. 2️⃣ The second shift is clarifying roles, not titles. Most biotech founders don’t suffer from having the wrong people. They suffer because everyone has a different idea of what their role actually is.   Your CSO is not your COO. Your SAB is not your operating committee. Your cofounder is not your board. When these lines blur, so do accountability and execution. 3️⃣ The third and hardest shift is restoring shared context. Not by overexplaining. Not by trying to align on every single choice. But by making the decision framework visible across the team.  People don’t need to vote on everything. They just need to understand what game they’re playing. Here’s the truth biotech founders miss. Alignment is not a culture topic. It’s a leverage tool. ✅ When you fix alignment, you free up speed, clarity, and execution, without adding headcount or budget. Realignment as a Growth Lever, Not Just a Fix 👉 Most founders treat alignment like a hygiene issue. Something to clean up when it gets bad enough. A background process. A soft skill. ✅ But in biotech, alignment is a multiplier. When your team is aligned, you move faster without more funding. You adapt quicker without losing direction. You communicate with investors without rewriting your story every month. ✅ Science doesn’t just advance. It connects to business outcomes. Some of the most promising biotech teams aren’t failing; they’re just stuck. They have strong early data and an even stronger burn rate. Everyone’s busy. No one’s clear. But the moment they shift from disconnected workstreams to a shared, milestone-driven roadmap tied to strategic decisions, not just scientific deliverables, momentum changes. ✅ Realignment unlocks clarity. Clarity attracts capital. And suddenly, it becomes obvious what to kill and what to scale. ✅ That’s the power of strategic realignment. It’s not just damage control. It’s how biotech companies move from drift to direction. Conclusion: Don’t Let Misalignment Drain Your Future Misalignment rarely announces itself. It doesn’t crash your system. It just slowly redirects energy, delays clarity, and erodes momentum. 👉 You don’t notice it until you’re out of time, out of cash, and out of direction. But if you catch it early and fix it decisively, alignment becomes one of your strongest strategic assets. 👉 Not because it makes everyone agree. But because it ensures everyone is solving the same problem. ✅ If your biotech startup feels like it’s moving but not advancing, the issue might not be speed. It might be a direction. Ready to Break Your Bottlenecks? If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

Watch Episode #177 The Experiment That Was Never Meant to Succeed When David Hodson’s lab teamed up with chemist Johannes Broichhagen aka JB, the goal was bold and elegant: Create a photo-switchable ligand to remotely control GPCR signaling with light. This was the moment when photopharmacology felt like the future. The literature was buzzing. Labs were competing. The idea was simple — turn signaling on or off with a flash of light. Except: Nothing behaved. Receptor access was unpredictable. Tissue responses defied the model. They had a tool that did bind the GPCR… but not in the light-controlled way they wanted. Most labs would have stopped there — archived the data, moved on, written it off as a failed bet. They didn’t. Sometimes the things you think are going to end up on the cutting-room floor become the best work. Instead of abandoning the compound, the team did something different: they looked at what it could do, not what it failed to do. And that shift changed everything. The Moment a Failed Tool Became a GPCR Imaging Breakthrough What the compound did reliably do was label and bind receptors in living tissue — in a way that made receptor location and accessibility visible. This solved a long-standing problem in GPCR biology: You can't understand signaling if you can’t see where the receptor actually is. For decades, GPCR localization relied on: Antibodies of inconsistent specificity Fixed tissue sections Indirect signaling readouts Researchers in the field know this frustration intimately: an antibody works in one context and fails entirely in another. Knockouts don’t behave as expected. Live-tissue dynamics become guesswork. This accidental tool changed that. It enabled: Live-tissue visualization Cell-type-specific receptor mapping Validation in both the periphery and brain Being able to see receptor distribution is not just aesthetic — it shifts interpretation. For metabolic GPCRs (like GLP-1 and GIP receptors): Drug efficacy depends on which cells express the receptor Side effects are tied to where agonists bind Weight-loss and appetite effects often originate in precise brain regions, not just the pancreas This tool helped clarify: Which neurons respond Which cell populations drive therapeutic benefit Where not  to target to avoid adverse effects Why GPCR Imaging Tools Matter More Than Ever This tool could not have emerged from a single lab. It happened because Hodson and JB thought differently — and allowed the clash of disciplines to be productive. Hodson: physiology, disease context, and imaging logic JB: chemistry, ligand engineering, mechanistic boldness Their collaboration worked not because they were aligned — but because they were complementary. And importantly, they liked working together. We’re not here long enough to spend 30 years collaborating with people we don’t enjoy. This is the part labs often underplay: scientific culture shapes scientific possibility. Collaboration, Chemistry, and the Pivot That Changed the Project Goal:  Develop a photo-switchable GPCR ligand Result:  The switching didn’t work Observation:  Binding + localization were unexpectedly robust Reframing:  Use the compound as a visualization tool Impact:  Shared widely → now used globally to map GPCR activity in live systems The success wasn’t in the discovery. It was in recognizing that the failure was useful. The Larger Lesson for Scientists and Innovators This story isn’t just about a GPCR imaging tool. It’s about how translation happens. Experiments fail for reasons that contain information. “Negative data” isn’t negative — it’s directional. The most valuable outputs often come from the “wrong” projects. For Early-Career Scientists Don’t optimize your trajectory for papers. Optimize it for questions that won’t leave you alone. Scientific progress is rarely linear. But depth compounds. What Changed After This Data This imaging tool is now being used to: Re-evaluate where GLP-1 and GIP receptors matter most Clarify brain vs. peripheral contributions to metabolic therapy Guide how next-generation incretin drugs are designed Support cell-targeted conjugate therapeutic strategies It didn’t just solve a problem. It opened a new category of problems to solve more efficiently. Which is the real definition of impactful science. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . If this story resonates with your work or curiosity, go deeper. 🎧 Listen to the full conversation with Dr. David Hodson

Watch Episode #177 The Career That Was Never Planned To Focus on GLP-1 Hodson didn’t begin with the identity of “scientist” — and that’s the point. He grew up working on farms and initially aspired to operate heavy machinery  simply because it looked satisfying. Later, exposure to veterinarians working in agricultural settings inspired him to train in veterinary medicine, where he was introduced to physiology and pharmacology  for the first time. Those courses didn’t feel like career-defining moments at the time — just requirements to pass. But seeds planted early often germinate later. The real pivot came during clinical rotations. Surgery electives meant long nights, constant patient responsibilities, and unpredictable call schedules. Meanwhile, researchers in the building across the way turned off the lights at 6 PM. Hodson made what seemed like a purely tactical decision: choose a research elective to focus on exams. That choice led him to immunology research on pigs — and, eventually, to a PhD. Early decisions don’t need to be perfect. They need to keep you moving. Curiosity compounds. And paths reveal themselves while walking. Following the Data Into GPCR, Metabolic Disease and GLP-1 After his PhD, Hodson entered neuroendocrinology — the “interface” between brain and body. The work introduced him to hormonal signaling, appetite regulation, and cellular communication systems. But something was missing. Growth hormone disorders, while scientifically rich, were relatively rare. Hodson wanted to contribute to a disease that affects millions. That brought him to type 2 diabetes  — a condition affecting nearly every family, marked by social and economic disparities in care. Studying the pancreatic islet — specifically the beta cells  that release insulin — offered a unique model: Rich in GPCR signaling pathways Experimentally accessible Deeply relevant to metabolic disease and obesity This shift also aligned Hodson’s work with a major scientific wave: The rise of incretin-based therapies, especially GLP-1 receptor agonists, now used in diabetes and obesity management. You always need a scientific anchor — but you also need the courage to follow data where it leads. GPCRs Re-Enter the Story — Not as Theory, but as Tools GPCRs have always been powerful drug targets — yet challenging to drug. Receptor localization, ligand access, and intracellular signaling can look different in actual tissues vs. cell lines. For real translational understanding, you need to see  the receptor in context. Enter a long-term collaboration with chemist Dr. Johannes Broichhagen - aka JB  — which, amusingly, began when Hodson opened the door wearing cleaning gloves mid home renovation. That partnership eventually produced fluorescent GPCR tools  that allow researchers to visualize GPCR engagement in live tissues , including: Mapping where  GLP-1 and GIP receptors are expressed Observing which cell types  respond to different therapies Understanding why similar drugs perform differently in different patients These tools have now been shared with hundreds of labs , accelerating research in obesity, hypertension, platelet biology, and more. Collaborations don’t start with strategy decks. They start with people you actually like working with . Skills + respect + shared curiosity = long-term impact. The “Aha” Moment — Ten Years in the Making Many discoveries unfold slowly — dozens of experiments that don’t make sense yet. For Hodson, one sustained curiosity thread involved a protein released by alpha cells in the pancreas: Vitamin D Binding Protein (GC-globulin). It affected hormone signaling between alpha and beta cells, but the mechanism was unclear. The breakthrough finally came when imaging and structural studies revealed that this protein was interacting with GPCRs involved in metabolic signaling — explaining confusing data that had accumulated for years. Suddenly, the puzzle snapped into place. A long-running side project became a central insight. GPCR–islet signaling links extended beyond classical ligand models.Collaboration and long-term persistence proved essential to discovery. Sometimes the experiments you almost quit are the ones that matter most. The Future of GPCR Therapeutics in Metabolic Disease Even with GLP-1 and GIP agonists reshaping diabetes and obesity care, the biggest questions — and opportunities — are still ahead. Key next questions: Why do some patients respond better than others? Why it matters: Personalizing care depends on understanding biological variability. Emerging direction: Genetics + receptor-distribution mapping. How do we prevent lean muscle loss during weight loss? Why it matters: Muscle mass shapes longevity, resilience, and overall metabolic health. Emerging direction: Multi-target GPCR + myostatin-pathway combinations. Should patients stay on incretin therapies for life? Why it matters: Cost, tolerance, and long-term side effects will define real-world adoption. Emerging direction: Treatment sequencing + guided de-escalation. Can GPCRs act as “delivery ZIP codes” for targeted therapies? Why it matters: Cell-specific delivery reduces off-target effects and boosts efficacy. Emerging direction: Peptide–drug conjugates for precision targeting. The next breakthroughs will come not from new receptors , but new ways of engaging and combining the ones we already know. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . If this story resonates with your work or curiosity, go deeper. 🎧 Listen to the full conversation with Dr. David Hodson

Drug discovery often assumes receptor inhibition follows simple rules—agonist binds, antagonist blocks, and data fit neatly into predictable curves. Yet, any pharmacologist who’s pushed beyond textbook theory knows: biology rarely plays fair. Schild analysis remains one of the few conceptual anchors that can tell us when “simple” truly is simple—and when deeper receptor dynamics are at play. In this session, you’ll gain: A clear conceptual map of Schild analysis and its origins. Insights into applying it to partial, inverse, and hemi-equilibrium systems. Ways Schild plots reveal hidden complexities like allosterism and receptor heterogeneity. The Legacy of Schild Analysis Schild analysis was born from an elegantly simple equation published by Sir John Gaddum in 1937. That paper quantified how one drug could block another’s access to a receptor—a framework that Heinz Schild later formalized into the tool we use today. At its heart, Schild analysis isn’t about math—it’s about validation. It asks whether the antagonism observed is truly competitive  or if unseen factors distort the result. It quantifies the degree of agonist blockade using dose ratios . It infers the affinity (Kᵦ)  of an antagonist by linking concentration shifts to receptor occupancy. Its strength lies not in complexity, but in the rigor of its simplicity. In the full lecture, Dr. Kenakin reveals how Schild’s equation became the first real “receptor test”—a filter that separates mechanistic truth from experimental illusion. Simple Competitive Antagonism A truly competitive antagonist obeys four essential rules—each serving as a gatekeeper to validity. Criteria for genuine competition: Parallel rightward shift  of the agonist concentration–response curve. No reduction  in the maximal response (the receptor can still be fully activated). Linearity  of the Schild regression (log(DR–1) vs. antagonist concentration). Slope ≈ 1 , indicating equilibrium binding. In practice, these criteria test the purity of interaction. Deviations—like reduced maximal responses or non-linear regressions—signal confounding kinetics, receptor mixing, or non-equilibrium conditions. As Dr. Kenakin emphasizes, “Schild plots don’t just confirm competition—they expose when competition is an illusion.” When Rules Bend: Non-Ideal Systems Not every system plays by Schild’s rules. Some responses—especially in calcium assays or complex tissue systems—depress the maximum agonist effect. Others show subtle curvature or slope deviations. Here, the Schild plot becomes a diagnostic tool  rather than a checkbox test: Slopes >1  often mean incomplete equilibration; lower antagonist concentrations haven’t had enough time to bind. Slopes <1  may reveal allosteric modulation , where the antagonist binds at a secondary site. Curvature  can signify heterogeneous receptors  or mixed response mechanisms. These deviations aren’t failures—they’re clues. Schild analysis turns receptor pharmacology into detective work, spotlighting mechanistic fingerprints buried in dose–response data. Extending Schild to Partial Agonists Partial agonists complicate things—they activate receptors but less effectively than full agonists. When these same molecules act as antagonists, their curves shift not only in position but also in shape. In the full lecture, you will learn how to extract affinity even in this dual-behavior scenario: Focus on the parallel portions  of the curves. Derive dose ratios  from those sections only. Fit those points to the Schild equation for an accurate estimate of Kᵦ. Partial agonists don’t break Schild analysis—they refine it. The model’s flexibility accommodates both agonism and antagonism as long as the analysis targets equilibrium regions. Inverse Agonists and Hemi-Equilibria In systems with constitutive receptor activity , inverse agonists reduce baseline signaling. Schild analysis still applies—if used carefully. The depression of basal response  is accounted for by focusing on curve segments where inhibition behaves competitively. Even when maximal responses are altered, affinity constants remain extractable  from correctly chosen data regions. Calcium flux assays, though tricky, yield valid Schild plots when analysis excludes non-equilibrium maxima. These adaptations underscore Schild’s resilience—it remains one of the few analytical frameworks flexible enough to handle inverse agonists, partial agonists, and non-ideal systems without collapsing conceptually. The Quick Glance: pA₂ as Shortcut Sometimes, discovery projects don’t have the luxury of full curve analyses. That’s where pA₂ values  come in—a quick estimate of antagonist affinity from a single concentration. Defined as the negative log  of the antagonist concentration that causes a twofold agonist EC₅₀ shift. Under ideal equilibrium, pA₂ ≈ pKᵦ . Caveat: Without multiple concentrations, competitive nature remains an assumption. pA₂ is an elegant glimpse—but not the whole picture. It ’s a screening tool, not a substitute for rigorous Schild validation. Schild as a Window into Mechanism Beyond affinity estimation, Schild analysis acts as a window into receptor behavior . It can uncover what’s really  happening inside a complex system. Common revelations include: Mixtures of receptor subtypes  producing hybrid response patterns. Allosteric vs. orthosteric inhibition , distinguishable by the plateauing of effect. Incomplete equilibration , where kinetics distort linearity. In practice, a deviation in slope or curvature isn’t noise—it’s the receptor speaking. Schild analysis translates that language. Each non-linearity is a coded message about the true mechanism. Learn how to reframe Schild analysis not as a relic of linear regression, but as an early machine-learning algorithm in human form: trained to detect outliers that matter. From Equilibrium to Exploration Schild’s greatest value today isn’t computational—it’s conceptual. Modern pharmacology has powerful modeling software, yet Schild analysis remains the litmus test for mechanism . Its purpose extends far beyond its 1930s origin: It teaches equilibrium thinking—recognizing when binding truly stabilizes. It sharpens interpretation—distinguishing real affinity  from apparent effect. It encourages skepticism—forcing researchers to prove competition before quantifying it. In Kenakin's words, “Every slope, every curvature, every failure to fit—those are the whispers of the receptor.” Schild analysis remains the simplest, most revealing conversation we can have with biology. Watch the course trailer 👇 Why Terry’s Corner Weekly pharmacology lectures by Dr. Terry Kenakin, monthly AMAs, and a growing on-demand library help scientists sharpen fundamentals, challenge assumptions, and strengthen pipelines. Built for pharmacologists refining tools, discovery teams solving bottlenecks, and leaders seeking credible insight fast. GPCR innovation is accelerating—those who act now will define tomorrow’s breakthroughs. Explore the full library ➤

Welcome back GPCR Fans, Clean data can still mislead if the underlying assumptions aren’t tested. Schild analysis is one of the few tools that tells you whether your “competitive antagonist” is actually behaving competitively. This week, we help you tighten your interpretations and strengthen your decisions at the bench and in discovery. Breakthroughs this week: McGPCR multimodal model; Endocrine Metabolic GPCRs 2026; Pfizer–Metsera acquisition. This Week in Premium: Sneak Peek Industry insights:  Domain CMO; Pfizer–Metsera; Novo Nordisk strategy shifts. Upcoming events:  GPCR-TDD Europe; Pharmacology 2026. Career opportunities:  GPCR Biology; Protein Science. Must-read publications:  OX2R dynamics; GPR68 nociception. Terry’s Corner: Schild Analysis — Why It Matters Most assays show a clean rightward shift and we assume “competitive antagonism.” But if the underlying criteria aren’t tested, that assumption can quietly erode the reliability of your conclusions. In this week’s lesson, Dr. Kenakin breaks down why Schild analysis remains the gold standard for verifying true competition — and why misclassification propagates error across affinity estimates, mechanism claims, and downstream modeling. Watch the trailer 👇 What you’ll gain Validate the model behind the data.  Use the four canonical criteria to distinguish genuine orthosteric antagonism from apparent shifts that mask allosterism or non-equilibrium. Quantify affinity you can defend.  Apply dose-ratios and Schild regressions to derive Kᴮ or pA₂ values that won’t collapse under scrutiny. Catch subtle mechanistic drift.  Diagnose hidden effects like mixed receptor systems or slope deviations before they distort your interpretation. Premium Members get 50%+ discount  when they join Terry’s Corner. Access this week’s key insight ➤ Dr. GPCR Podcast: Visualizing GLP-1 & GIP Receptors in Islets and Brain Understanding incretin biology depends on more than ligand potency — it hinges on where receptors actually are, how they internalize, and how tissues interpret signals in real time. In this conversation, Prof. David Hodson walks through how his team uses fluorescence tools and chemically engineered ligands to map receptor distribution, internalization, and engagement across pancreatic islets and brain circuits. The result is a clearer view of how incretin-based therapies act in complex metabolic environments. Why this matters Receptor distribution shapes incretin hormone drug effects across islets and neural circuits. Visualization tools redefine our understanding of signaling in intact metabolic tissues. Fluorescent ligand engineering clarifies receptor behavior that cell lines can’t reveal. Who should listen Researchers navigating complex datasets, balancing innovation with assay rigor, or working across chemistry–pharmacology–physiology interfaces will find this episode particularly relevant. This conversation is part of a three episode series produced in collaboration with our partners at Celtarys Research . Listen to the episode ➤ Quick Links Assess GPCR Biased Signaling of Agonist How GPCR Collaboration Built an Innovation Engine From Pipettes to Platforms: The Evolution of GPCR Research How GPCR Spatial Signaling Sparked a Scientific Journey Molecular Creativity in Drug Discovery Why Dr. GPCR Premium Membership Gives You an Edge Premium delivers a clear, noise-free stream of GPCR intelligence every week: deeper analysis, classified industry updates, expert frameworks, curated job listings, on-demand lectures, and priority event alerts. It helps you stay informed without overwhelm, move faster with context, and make stronger decisions with fewer blind spots. With live GPCR University courses returning next year and platform capabilities expanding, Premium pricing will increase soon. Anyone who joins before the change is fully grandfathered  — your rate stays locked, and your whole team benefits as the platform grows. Dr. GPCR is a nonprofit organization , and Premium Membership directly supports our mission to make reliable GPCR education and community infrastructure accessible to scientists worldwide. For those who prefer to contribute outside of membership, one-time or recurring donations  also ensure these resources remain available and continue to expand. You also gain access to member-only discounts, full GPCR University content, and an integrated view of publications, events, insights, and opportunities designed to support your career, your lab, or your organization. FAQ 🔹 What’s included? The complete Weekly News digest, curated jobs and events, classified GPCR publications, industry intelligence, expert lecture archives, and member-only discounts. 🔹 Who is it for? GPCR scientists, translational pharmacologists, biotech discovery teams, and decision-makers who need concise, credible, high-value intelligence to stay ahead. 🔹 Why now? GPCR innovation is accelerating. Acting on the right signals today shapes tomorrow’s breakthroughs — and prevents delays others won’t see coming. 👉 Access all the complementary news ➤ Already a Premium Member?  👉 Access this week’s full Premium Edition here ➤ What Members Say “Dr. Kenakin is a leading expert in the field. Aside from his vast experience in drug development, not to mention his extensive publication record, Dr. Kenakin is a masterful teacher and communicator.” Stay informed, stay competitive, and elevate your GPCR decisions — become a Premium Member today. See you in the Ecosystem, Dr. GPCR Team

Your scientific thinking built the foundation, but leadership is what scales it. The Invisible Obstacle   👉 Brilliant science. Stalled progress. It’s a pattern we see far too often in early-stage biotech operations and startups. The experiments work. The data looks promising. But decisions lag, the team spins, and investors get nervous. Science isn’t the problem; scientific thinking is. What makes you excel in the lab can quietly sabotage your leadership in the boardroom. 👉 Scientific thinking  rewards depth, rigor, and precision. But in a startup, those same instincts to analyze deeply, minimize error, and delay action until “enough” data is in can kill momentum . 👉 Most founders don’t even realize they’re still running their company like an academic research group. They explain instead of deciding. They analyze instead of acting.   ✅  This post explores how scientific thinking can become a leadership liability and what mindset shifts are needed to evolve from research reflexes to CEO decisions.   ✅  You don’t need to abandon your scientific instincts. But you do need to adapt them if you want your startup to scale .     The 3 Golden Rules of Scientific Thinking — and Why They Break Down in Biotech Operations Leadership   👉 Scientific thinking trains you to be precise, methodical, and skeptical. These instincts are critical in the lab, but they often undermine leadership when blindly applied in a startup . Let’s unpack the three core “rules” most scientific founders unconsciously carry into their companies:   1️⃣ “Only act when the data is solid.” In research, acting on incomplete or shaky data can destroy your credibility. In startups, waiting too long for certainty can destroy your momentum . 👉 Biotech founders often delay critical business moves, hiring, BD outreach, or funding decisions, because the data isn't “mature enough.” But in business, decisions must be made under uncertainty . Clarity doesn’t precede action; it follows it.   2️⃣ “Eliminate error at all costs” Labs are built around error reduction. You control variables. You minimize noise. But startups are inherently noisy. Trying to eliminate all risk leads to overengineering and stagnation . 👉 Instead of shipping early and iterating, many scientific founders keep refining decks, processes, and team structures until they feel bulletproof. But by then, the window of opportunity has often closed.   3️⃣ “Deep analysis leads to better answers” Scientific training favors deep thinking. More analysis = better outcomes. But in leadership, depth without speed equals paralysis . 👉 Startups don’t reward depth alone; they reward direction and decisiveness . Over-analysis becomes a form of avoidance. And while you're analyzing, someone else is executing.   👉 Bottom line:  Scientific thinking is invaluable, but only when it’s reframed for the role you’re actually in. ✅ You’re not optimizing experiments anymore. You’re steering a company.     Startups Play by Different Rules — and Most Scientific Founders Miss That   A research lab is designed for precision. A startup is designed for progress. And that difference changes everything. 👉 Scientific thinking values thoroughness, error reduction, and complete data before action.  But in the startup environment, these instincts can quickly become liabilities. You rarely have perfect data. 👉 You can’t eliminate every variable. And waiting too long often means missing the moment. Startups demand something different: ✅ Clarity of direction even when the picture is incomplete. The ability to decide when no option is risk-free. The discipline to align a team without all the answers. Founders who keep operating like researchers often create internal confusion. They revise instead of committing. They analyze instead of align. They aim for perfect clarity, and in doing so, they delay momentum, erode trust, and weaken execution. ✅ Scientific thinking can make you cautious when your company needs decisiveness. Unless you update the way you lead, your startup will struggle to translate insight into impact.   Leadership isn’t in your lab notebook; it’s in how you decide     How to Know If You're Still Leading Like a Scientist   👉 Leadership isn’t a title. It’s a way of thinking. And if your thinking is still shaped by academic norms, your startup will keep running like a lab, not a company. 👉Scientific thinking is precise. Leadership thinking is directional. The transition between the two isn’t automatic, even for the most capable founders. It requires conscious shifts in how you process uncertainty, how you frame decisions, and how you lead people through ambiguity. Here are three questions to help you check in with yourself: 1️⃣ Do you delay or dilute decisions while waiting for more clarity? Real leadership often means choosing without all the answers. If you find yourself looping decisions or delegating them upward, you might be leaning on scientific habits to avoid risk.   2️⃣ Do you overvalue internal logic over external action? It’s tempting to refine the deck, rework the roadmap, or re-analyze the market. But leadership is outward-facing. It’s about choosing direction, enabling others to move, and owning tradeoffs with imperfect inputs.   3️⃣ Do you explain more than you align? Explaining a model is not the same as rallying a team. When your communication centers on logic and detail instead of clarity and momentum, your team stays in wait mode, and execution stalls.   ✅ The shift from scientist to CEO  is not about abandoning your expertise. It’s about realizing that your value now lies in decisions, not just in depth.     Strategic Takeaway   👉 Scientific thinking will always be your strength, but it must be reshaped to serve your new role. 👉 As a biotech founder, your impact no longer comes from precision alone, but from your ability to lead through uncertainty, prioritize progress over perfection, and turn insight into execution. ✅  Leadership is not the opposite of science. It’s what gives it direction.   Ready to Break Your Bottlenecks?          If you're feeling the friction — indecision, misalignment, slow momentum — it's not just operational. It's strategic. Attila runs focused strategy consultations for biotech founders  who are ready to lead with clarity, not just react to pressure. Whether you're refining your narrative, making tough tradeoffs, or simply feeling stuck, this session will get you unstuck — fast. 👉 Book a 1:1 consult and start building the mindset your company actually needs.

When you walk into a typical academic lab, the boundaries are obvious: this PI’s corner, that group’s benches, their grants, their silos. But in Melbourne, a quiet experiment challenged that model — and it worked. It wasn’t about whose lab it was. It was about what we could build together, recalls Michelle Halls. What emerged wasn’t just another pharmacology group — it became a collaborative engine for GPCR discovery . Turning Silos into Systems The early 2000s were not kind to exploratory pharmacology. Funding models rewarded independence, not shared resources. Most labs operated like small, competing startups. But at Monash Institute of Pharmaceutical Sciences, a different idea took root: what if collaboration wasn’t a last resort — but the operating system ? Michelle was part of a cohort that joined as postgrads in shared facilities, pooling reagents, ideas, and failures. Rather than carving out turf, they grew by designing around collective capacity . No one had their own lab. That meant no one could build a fiefdom — and everyone had to talk. Why This Matters GPCR research demands integration — pharmacology, structural biology, signaling pathways, high-content imaging. A shared environment lowers friction between these specialties, making innovation structurally inevitable rather than aspirational. This wasn’t just a clever idea on paper — it changed how science happened, day to day. Engineering Collaboration: The Monash Lab Model Traditional academic labs mirror feudal structures. The Monash model flipped that logic: Shared infrastructure, not duplicated equipment Centralized core facilities for receptor assays PhD students trained across multiple techniques, not just one pipeline Senior scientists embedded as “connectors” between programs This design had a strategic effect : talent density increased, but so did interdisciplinary surface area  — the number of conversations where breakthroughs could happen. What Happens When Labs Stop Competing Michelle credits this environment for catalyzing the GPCR signaling projects that shaped her early career. For Early-Career Scientists Don’t just pick a project — choose an ecosystem. The structure you train in often matters more than the experiment you start with. The Power of GPCR Collaboration: Funding as a Force Multiplier Collaboration sounds warm and fuzzy. But at Monash, it was also ruthlessly pragmatic . At Monash, GPCR collaboration wasn’t just a cultural value — it was a deliberate strategy to build capacity, infrastructure, and momentum. Instead of competing for multiple small grants, groups strategically pooled resources to build critical infrastructure once . That gave them capabilities others couldn’t match — from receptor biosensor platforms to live-cell imaging cores. Shared funding gave us leverage. Suddenly, we could do experiments that individual labs just couldn’t afford. This changed not just what got funded, but what was possible . The lab went from project-level thinking to platform-level strategy  — a critical shift for GPCR biology, where complex systems need integrated tools. What Changed After This Data The pooled funding model turned the lab into a magnet: postdocs, visiting scientists, and industry partners wanted access to infrastructure they didn’t have. This wasn’t luck. It was designed. Culture as Infrastructure: How Trust Was Built Collaboration at this scale doesn’t happen by chance. It’s engineered . The Monash team built rotational PhD cohorts  — students cycled through multiple groups in their first year, gaining technical fluency and social trust. Senior postdocs acted as bridges, not gatekeepers. Weekly seminars were mandatory but lightweight, designed to connect  rather than perform. Michelle notes that the absence of individual ownership over physical space or specialized equipment removed the incentives to hoard knowledge . Most GPCR discoveries aren’t blocked by science — they’re blocked by structures that make sharing hard. Monash turned structure itself into a collaboration tool. Beyond the Bench: Leadership, Luck, and Leverage Michelle is quick to note that none of this was perfectly planned. “There was a bit of luck,” she admits. Timing, leadership alignment, and a critical mass of motivated scientists converged. But luck alone doesn’t sustain an ecosystem. What mattered was how leadership leveraged luck into durable structure  — through funding strategies, talent pipelines, and open lab architecture. This ecosystem outlasted individual PIs  and became a GPCR innovation hub  with global reach. Mini Timeline: How the Model Evolved Year 0  — Shared lab space established; PI buy-in secured Year 2  — Core imaging and signaling platforms launched Year 4  — Pooled grants fund expansion and training programs Year 7  — International collaborations and industry partnerships take root Today  — Model continues to produce high-impact GPCR science What the Rest of the Field Can Learn The Monash model isn’t an Australian story. It’s a blueprint . Biotech teams, CRO alliances, and academic consortia face the same challenge: how to align incentives to make collaboration not optional but structural . GPCR science thrives in complexity — meaning no single lab or company can do it alone. This ecosystem design blueprint applies to biotech teams and academic consortia alike. Michelle’s story shows what happens when an ecosystem is built deliberately, not accidentally. And for those shaping the next generation of GPCR discovery — from AI integration to next-gen biosensors — the lesson is clear : build together or build smaller. Collaboration isn’t a feel-good story. It’s a competitive advantage. 🎧 Listen to the full episode:   Leadership, Luck, and GPCR Signaling 🔓 Join   Dr. GPCR Premium  for deep dives, strategic tools, and behind-the-scenes conversations shaping the GPCR field.

Watch Episode 176 The first time Michelle ran a cyclic AMP assay, she did it with a single-channel pipette, trays of melting ice, and the kind of focus that only comes from knowing one mistake could waste weeks of work. We’d spend hours sitting there with trays of ice, transferring one by one with samples to a 384-well plate. No robots. Just her, radioactive ligands, and steady hands. That’s not a story about nostalgia — it’s a snapshot of how GPCR research was built, on technique at a time . And it’s a reminder that the way we do science today wasn’t inevitable — it was engineered, learned, and fought for by people who believed the field could be better. The Era of Cold Fingers and Patience Back then, GPCR signaling experiments weren’t elegant; they were endurance tests. Michelle describes spending hours in the lab measuring cyclic AMP levels — without multi-channel pipettes or high-throughput plate readers. Assay samples were layered on trays of ice. These weren’t quaint inconveniences. They shaped how questions were asked. These painstaking manual workflows laid the foundation for what would become the evolution of GPCR research — a transformation that reshaped how scientists design experiments and interpret signaling. We were doing assays on ice, pipetting one sample at a time. Every step felt like it could make or break the result, The pace and precision of GPCR research today — from high-throughput ligand screens to real-time signaling readouts — are built on the discipline forged in those early manual workflows. When Technology Became a Force Multiplier The introduction of multi-channel pipettes, automation, and standardized readouts wasn’t just an upgrade — it was a turning point. Suddenly, questions that were too risky or expensive to ask in the “manual era” became accessible. Instead of a dozen wells, you could test hundreds. Michelle notes that the real shift wasn’t just speed. It was confidence. When technology reduced the cost of failure, scientists could push boundaries faster. Reading those first papers on GPCR signaling organization absolutely fascinated me — the idea that receptors could cluster and control specificity blew my mind. What Changed After This : High-throughput capabilities meant researchers could map GPCR signaling more comprehensively. The field moved from single readouts to integrated signaling landscapes, accelerating drug discovery timelines and expanding targetable receptor families. This leap mirrors what’s happening now in other parts of the field: platforms replacing isolated tools, enabling both reproducibility and creativity. Those early cyclic AMP assays weren’t just cold and slow — they were part of a bigger puzzle. The Mindset Shift: From Technique to Strategy Michelle reflects on how early-career researchers once prided themselves on “perfect hands.” Today, success depends less on manual precision and more on experimental design, data interpretation, and strategic collaboration . In other words: the craft moved up the value chain. Where once a well-run assay was the pinnacle, now it’s the foundation — a starting point for more ambitious questions about GPCR networks, biased signaling, and functional selectivity. For Early-Career Scientists: Don’t over-invest in proving your pipetting skills. The real leverage comes from mastering the strategy behind the experiment, not just the execution. As technology absorbs the “how,” human expertise shifts to the “why” and “what if.” The GPCR field needs thinkers who can direct platforms, not just operate them. Leadership, Luck, and the Lab The episode isn’t just about technology — it’s also about how careers are shaped in science. Michelle’s path wasn’t linear. Like many, it was a mix of opportunity, timing, and the courage to say yes before everything was figured out. Her career didn’t follow a straight line — it zigzagged through long nights, and calculated leaps. Technical mastery opened doors, but what kept her moving was knowing when to say yes before everything was figured out — and how to grow from great pipettor to PI. Mini Timeline: Manual assay years — technical rigor as foundation Technology boom — scaling curiosity Strategic shift — experiments as decisions Leadership leap — from pipette to PI The Evolution of GPCR Research Every generation of GPCR scientists inherits the tools of the last and builds the next. What started as hand-built assays on ice has become integrated platforms for drug discovery, systems pharmacology, and real-time signaling analysis. But the heart of the field hasn’t changed. It’s still driven by scientists asking hard questions — and refusing to accept slow answers. Technology accelerates science, but people direct it. And those who understand both the legacy and the future of GPCR work are the ones shaping the next era of breakthroughs. This isn’t just a history lesson. It’s a call to see your lab bench not as a constraint, but as a launchpad. 🎧  Listen to the full conversation with Michelle Halls on the Dr. GPCR Podcast 🔓 Get tools, deep dives, and exclusive insights with   Dr. GPCR Premium .

Watch Episode 176 She didn’t want to be in the lab. It was supposed to be just a summer project—routine pipetting, repetitive assays, a box to tick before moving on. But something shifted. A single experiment worked. Then another didn’t. And somewhere between the results and the unknown, curiosity turned into obsession. I didn’t expect to love it, says Michelle Halls. But the moment I designed my own experiment, I was hooked. The Accidental Beginning Michelle hadn’t mapped out a scientific empire. She was a student expecting tedium, not inspiration. Yet that summer research placement cracked open a new reality: the thrill of asking questions no one else could answer . Her early work didn’t involve groundbreaking receptor models or million-dollar grants. It involved making sense of messy data and realizing the power of not knowing . This moment—the first taste of scientific ownership—reshaped her trajectory. It wasn’t the result that mattered. It was the fact that it was my  experiment. Many GPCR scientists trace their origin story back to a single unexpected spark. Not a grand plan. A spark. For innovators building tools, platforms, or therapeutics, these origin moments are where tomorrow’s leaders are born. The Moment It Clicked Once the initial spark was lit, Michelle’s curiosity snowballed. Instead of dreading lab time, she found herself chasing questions late into the night. This shift—from passive observer to active investigator—wasn’t about external validation. It was about internal ignition. She moved from “What am I supposed to do?” to “What happens if I try this?” That transition defines every true scientist. It’s not about perfection. It’s about chasing a signal through the noise. For Early-Career Scientists: Your pivotal moment might not feel like fireworks. It might be quiet, subtle—an idea you can’t stop thinking about. Pay attention to that. What began as a quiet obsession soon demanded a bigger stage. Curiosity wasn’t just something Michelle felt — it started steering every decision she made. From Cambridge to Leadership That early curiosity led her to pursue a PhD in Molecular Pharmacology at Monash University, and later to train in single-cell biology at University of Cambridge. She went from reluctant intern to global researcher shaping how we understand GPCR spatial signaling. By 2011, she had established her own group within the Drug Discovery Biology theme at Monash Institute of Pharmaceutical Sciences, exploring how receptors control localised signaling, how disease hijacks these systems, and how to target them for therapeutic gain. Mini Timeline Summer Project  — Unexpected spark PhD at Monash  — From curiosity to expertise Cambridge Fellowship  — Precision meets scale Leadership at MIPS  — Turning questions into impact What Changed After This : Her scientific questions got bigger. Instead of “what happens in this cell,” she began asking “how do cells organize signaling at scale?” This pivot reflects a universal research truth: origin stories evolve—but the spark remains . Luck, Leadership & GPCR Signaling Michelle is clear: luck played a role. But so did choice. She built on chance moments with deliberate moves—grants pursued, labs chosen, collaborations built. She emphasizes leadership not as titles but as creating spaces where science thrives . For her, leadership in GPCR research is about enabling others to find their spark the way she found hers. It’s easy to call it luck. But luck only works if you say yes when the door opens. For innovators and biotech strategists, stories like Michelle’s reveal how scientific leadership emerges. Not from polished plans—but from patterns of curiosity, risk-taking, and mentorship loops. Why GPCR Spatial Signaling Is Changing Drug Discovery Today, Michelle leads the Spatial Organization of Signaling laboratory, asking a deceptively simple question: where  do GPCR signals happen—and how does location change everything? Her work sits at the intersection of fundamental biology and therapeutic strategy. By understanding how signaling is organized in time and space, her team is opening doors to next-generation GPCR drug discovery and precision targeting. Spatial signaling isn’t just a technical detail. It’s a new language for drug discovery. Knowing where  signals occur could unlock new therapeutic strategies, better efficacy, and fewer side effects. Built to Inspire The story of Michelle Halls isn’t just about a career; it’s about a pattern. Curiosity → Ownership → Opportunity → Leadership → Innovation. For young scientists, that summer moment is waiting. For biotech innovators, those sparks are the future workforce and idea engines. For GPCR research, leaders like Michelle are showing what happens when we follow the signal all the way. 🎧 Listen to the full conversation with Michelle Halls on  The Dr. GPCR Podcast 🔓 Want to go deeper? Join   Dr. GPCR Premium  for exclusive tools, deep dives, and expert access.

Innovative Approaches in GPCR Drug Discovery: Designing Precise Solutions for GPCR Challenges. Welcome GPCR Fans, Most pharmacologists are trained to chase targets. But what if the real opportunity lies in the chemical matter we throw at them? That’s exactly what Terry’s Corner delivers this week: a deep dive into molecular creativity, rational design, and the overlooked role of chemistry in innovation. Breakthroughs this week:  Orphan receptor GPRC5B in neurogenesis; Septerna’s pill-based weight-loss strategy; Atrogi’s new CEO announcement. 🔍 This Week in Premium: Sneak Peek Industry insights:  New alliances, pipeline shifts, and platform tech that could reshape metabolic drug development. Upcoming events:  Global GPCR summits and pharmacology forums shaping 2026 priorities. Career opportunities:  Discovery biology roles and training paths in GPCR signaling. Must-read publications:  Emerging targets, signaling dynamics, and acid-sensing receptors in disease. Terry’s Corner: Why Chemistry Still Rules For decades, discovery focused on targets. But drugs aren’t just biology—they’re chemical matter. And that matter shapes everything: selectivity, safety, efficacy, and innovation. In this new course, Dr. Terry Kenakin reveals how drug chemistry defines function, not just fit. What You’ll Learn on molecular creativity in drug discovery: • Why Nature Was First : From opium to antibiotics, nature’s molecules still outperform many designed compounds. • The Power of Structure : How privileged scaffolds and rational design open the door to dual activity and precision. • Cheminformatics to Biologics : GPCR-focused chemical design is evolving—fast. Learn what’s next. 🟢 Premium Members get 50%+ discount when they join Terry’s Corner. 👉 Access this week’s key insight ➤ Dr. GPCR Podcast: Leadership, Impact, and GPCR Signaling with Dr. Michelle Halls This episode goes beyond the bench. Dr. Michelle Halls dissects how spatial GPCR signaling shifts discovery—and how leadership, mentorship, and vision shape translational success. From cAMP to femtomolar ligands, she unpacks a career at the edge of precision signaling. Key Insights: • Receptor Localization Matters : Protein complexes pre-assemble at membranes, altering how ligands trigger responses. • Assay Development Gets Real : Fluorescent tools and real-world biology don’t always match. She explains why. • Training Builds Innovation : Her lab model at Monash is shaping the next generation of GPCR scientists. 👉 Dive into spatial pharmacology ➤ A Note From Yamina: Building the Next Chapter of Dr. GPCR If the past few years were about rhythm, 2025 is about systems. Yamina’s open letter reflects on how Dr. GPCR evolved from a grassroots effort to a global force in GPCR science—one rooted in connection, sustainability, and execution. Highlights: • The Foundry Arrives : R&D meets biotech with real-world acceleration, strategic consulting, and CRO matchmaking. • Premium Expansion : New courses, better UX, and full University access are coming—grandfather pricing ends in 2026. • Inclusive Growth : More access for developing nations, new instructors welcome, and global partnerships with impact. 👉 Read Yamina’s note ➤ Why Dr. GPCR Premium Membership Gives You an Edge Premium delivers curated, noise-free intelligence every week: deep-dive expert lectures, classified industry news, priority event alerts, job opportunities, and insider commentary—designed to help you move faster, smarter. Whether you’re designing the next assay, scouting a new therapeutic angle, or exploring career pivots, Premium helps you stay ready—without the noise. FAQ 🔹 What’s included? The complete Weekly News digest, curated jobs, upcoming events, classified GPCR publications, exclusive on-demand expert frameworks, and member-only discounts. 🔹 Who is it for? GPCR scientists, translational pharmacologists, biotech drug discovery teams, and decision-makers who need fast, curated, career-relevant intelligence to stay ahead. 🔹 Why now? The pace of GPCR innovation is accelerating. Those acting on the right signals today will shape tomorrow’s breakthroughs—and avoid delays others won’t see coming. 👉 Don’t Fall Behind—Access the Edge You Need 👉 Already a Premium Member? Access this week’s full Premium Edition here ➤ Voice of the Community “Thank you for bringing this course with Dr. Kenakin. I wish Dr. GPCR the best for the sake of promoting more educational opportunities that are sorely needed in the field.” No matter where you are in your GPCR journey, Dr. GPCR Premium is here to accelerate your next move. 🧭 Get smarter signal detection, sharper tools, and real-time intelligence—all in one platform. 👉 Become a Premium Member ➤

Pipeline Efficiency Begins With the Chemistry Itself Drug discovery pipelines often stall not because the target is wrong—but because the chemical matter  interacting with that target lacks the right properties to produce meaningful pharmacology. We obsess over target validation, signaling pathways, expression patterns, and disease relevance. Yet, far less time is spent scrutinizing the structural logic and origin  of the molecules we screen in the first place. This lesson asks a deceptively simple question: What if our molecules—not our targets—are limiting discovery? In this lesson, you’ll gain: A strategic view of how chemical scaffolds shape pharmacologic outcomes An understanding of new chemical sources beyond natural agonist analogs Awareness of how GPCR allostery and biased signaling are redefining drug design The Long Arc of Chemical Pharmacology The early history of drug discovery was rooted in nature . Extracts from plants, fungi, bacteria, and environmental microorganisms provided the first potent modulators of physiology. Opium, for example, was used for dysentery and relief of suffering as early as the 3rd century BC; its derivatives — morphine, codeine, papaverine — became cornerstones of modern therapy.  The natural world still holds enormous untapped potential . Less than ~15% of higher plant species, <5% of bacterial and fungal species, and only a fraction of marine organisms have been meaningfully explored.  Yet natural-product scaffolds come with costs: they are structurally complex, expensive to modify, often unpredictable in IP , and sometimes more difficult to optimize for modern pharmacokinetics.  Still, nature remains a treasure map — just one that requires more strategic navigation. The key question Terry raises: If natural scaffolds provided our starting pharmacology, what new scaffolds will define the next 50 years? Building on Known Pharmacology Medicinal chemists learned early that modifying endogenous molecules — hormones, neurotransmitters, and metabolic signals — could yield new drug effects. Adenine-derived scaffolds enabled selective adenosine receptor antagonists; tryptophan modifications led to somatostatin receptor ligands.  From these efforts emerged the concept of privileged structures : chemical backbones that show repeat utility  across GPCR classes and receptor families. Indoles, benzodiazepines, phenethylamines — each recurs because it “fits” biology well.  This was more than trial-and-error. It was early structure-based pharmacology. Dr. Kenakin highlights how hybrid molecules  — combining two pharmacophores into one scaffold — enable dual-modality treatments with unified pharmacokinetics  (instead of juggling two separate drugs with mismatched ADME profiles).  And occasionally, new chemistry emerges from an unexpected source: Side effects. Diuretic action discovered in sulfanilamide derivatives led to furosemide; sedative effects of early antihistamines helped launch antipsychotics.  Lesson:  The structure-response relationship is rarely linear — and observing the unexpected is part of the craft. Informatics Expands the Search Space for Chemical Drug Matter Advances in chemoinformatics  introduced large-scale similarity mapping, such as SEA (Similarity Ensemble Approach), which compares the chemical similarity of ligands , not the protein sequence of targets.  This is a philosophical shift: Instead of asking, “Which proteins are related?” We ask, “Which molecules behave as if they belong together?” This approach reveals: Hidden target overlap New therapeutic hypotheses “Off-target” effects that may be on-target opportunities This expands drug matter beyond the familiar and encourages deliberate exploration of chemical novelty, rather than incremental tuning of existing scaffolds. The molecule, not the receptor, becomes the guiding principle. Allostery and Biased Signaling Change the Game The most profound change in GPCR drug discovery is our expanding understanding of allosteric receptor function . GPCRs are not simple on/off switches. They are allosteric machines , able to shift conformational states in response to multiple binding influences.  This enables: Positive Allosteric Modulators (PAMs) — enhance natural signaling Negative Allosteric Modulators (NAMs) — attenuate signaling Biased agonists  — favor one intracellular pathway over another These are not analogs of natural transmitters. They are structural strategies for tuning physiology. This reroutes discovery toward: Functionally selective ligands Better therapeutic windows More predictable clinical behavior Allosteric modulation allows us to work with  biology’s dynamic systems instead of forcing orthosteric competition. Biologics Are Now Chemical Drug Matter, Too Proteins, peptides, and antibodies are no longer niche. They are mainstream pharmacology. They offer high specificity , favorable safety , and unique mechanisms , including GPCR modulation through agonism, internalization, or ligand scavenging.  Advances in formulation and delivery have overcome earlier pharmacokinetic limitations. Peptide GPCR therapies now address obesity, diabetes, cancer, neuroendocrine disorders, inflammatory diseases, and more.  The boundary between “small molecule” and “biologic” has blurred. What matters now is not the category , but the fit: Does the chemical matter support the therapeutic mechanism? Does it interact with the receptor in a way that biology can use? Core message: The receptor is only half the story. The molecule is the other half. Why Terry’s Corner Terry’s Corner is a continuously growing knowledge platform built for scientists who want sharper decision-making power in discovery pharmacology. Subscribers gain weekly lectures led by Dr. Terry Kenakin, monthly AMA discussions, and on-demand access to a library of expert GPCR teaching sessions. Members can also propose new topics, ensuring relevance to real-world discovery problems. This is strategic, method-proven insight for discovery teams, pharmacologists refining core skills, and R&D leads who need clear reasoning in a rapidly changing field. GPCR innovation is accelerating. Those who learn now will shape the drugs others spend the next decade trying to understand. 40 years of expertise at your fingertips: Explore the full library ➤ Subscribe to the Kenakin Brief to stay in the know ➤

Exploring the Past and Future: Insights from Yamina's Perspective on Dr. GPCR. Dear Dr. GPCR Community, If the past few years have been about finding our rhythm, this year has been about refining our systems  — and stepping confidently into what’s next. When we started Dr. GPCR, it was with a simple goal: to connect people who shared a passion for GPCR science and discovery. What began as a small, volunteer-driven initiative has grown into a vibrant global network — thousands of scientists, founders, students, and partners who now learn, collaborate, and move the field forward together. This year, we focused on building for sustainability  — strengthening operations, expanding partnerships, and refining the systems that make Dr. GPCR stronger, more connected, and more capable of supporting our growing community. We’ve shaped everything we do around three interconnected ecosystems: Free , Premium , and Foundry. Strengthening the Foundation You already know our Free Ecosystem  — the podcast, blogs, and Flash News that make GPCR insights accessible to everyone. These resources continue to bring the latest in GPCR science and industry updates to thousands across the globe. The Premium Ecosystem  — home to the Classified Weekly Newsletter , Dr. GPCR University , the Job Board , and the Event Board  — has evolved into a hub for deeper engagement. It’s where conversations turn into collaborations and learning becomes ongoing. Now, we’re expanding that circle with the Dr. GPCR Foundry  — our R&D and biotech hub designed to bridge GPCR science with execution. The Dr. GPCR Foundry: Science Meets Execution The Foundry is where we bring together biotech innovators, Venture Capital, CRO partners, and GPCR experts to help teams move faster — from idea to discovery — while reducing risk and increasing efficiency. One of its first pillars, Terry’s Pharmacology Corner , led by Dr. Terry Kenakin , continues to grow. Through Terry’s Pharmacology Corner , he shares decades of expertise in an accessible, on-demand format for scientists across academia, biotech, and pharma. Learners can study at their own pace, shape the curriculum, and join live monthly AMA sessions with Dr. Kenakin. We’re fortunate to partner with Dr. Terry Kenakin , a pioneer who helped shape modern pharmacology and deepen our understanding of GPCR function. Premium Members receive exclusive discounted access — making it easier than ever to deepen expertise directly from one of the field’s pioneers. The Foundry will also include Yamina’s Consulting Corner — a place for teams to get tailored scientific and strategic guidance that helps bridge discovery and development. And coming early next year: the CRO Bank , connecting GPCR-focused companies and teams with trusted contract research partners to accelerate progress. Premium: More Value, More Connection Meanwhile, the Premium experience  itself is getting a major upgrade. We’re redesigning the platform for smoother navigation  and preparing to relaunch Dr. GPCR University  next year — with new courses and instructors. For the first time, University courses will be included in Premium Membership , giving members full access to learning, connection, and discovery in one place. As part of these updates, a pricing adjustment is planned for 2026 — the first since Premium launched . Anyone who joins Premium before these changes go live will be grandfathered at their current rate , as long as their membership stays active, and will automatically receive access to all new benefits. We’re also reopening our instructor program  — welcoming scientists from across the community to teach new GPCR-focused courses. Whether you’re an established expert or an early-career researcher, you and your lab will receive a complimentary one-year Premium Membership  when you lead a course. Our access program for researchers in developing countries  continues as well: eligible scientists can apply for a 90% discount on Premium , and every Dr. GPCR University course reserves five complimentary seats for students from developing countries — no Premium membership required. Strategic Partnerships with Purpose Dr. GPCR’s mission has always been about connection — not just between people, but between ideas, technologies, and opportunities. Our strategic partnerships  reflect that mission. They go beyond sponsorship to bring tools and technologies directly to the researchers who use them , empowering scientists to move their work forward with the right resources in hand. Our partnerships with Celtarys Research  and Revvity are great examples — uniting innovation and application to advance GPCR research worldwide. We’re continuing to seek new strategic partners  who share our goal of supporting the GPCR community while strengthening discovery where it truly happens — in the lab. The Dr. GPCR Ambassador Program As we grow, we want the community to grow with us. Our Ambassador Program empowers community members to share Dr. GPCR resources, connect new scientists, and earn rewards  for helping expand access to GPCR knowledge. It’s our way of recognizing those who champion the mission — spreading GPCR insights, connecting peers, and bringing more researchers into the community. We’ll share how to apply — and the details — soon. Community and Connection Dr. GPCR isn’t just a digital platform — it’s a community. This year, we hosted in-person Happy Hours in Boston  (one even sponsored!) that sparked new collaborations and friendships. In 2026, we plan to host more of these gatherings and begin preparing for the first Dr. GPCR Retreat  — our largest in-person event yet, designed to bring together scientists, industry leaders, and innovators in one shared space. Looking Ahead As we move into 2026, our focus remains simple: keep building with purpose — strengthening what works, refining what can grow, and listening closely to our community along the way. Dr. GPCR is evolving — thoughtfully, inclusively, and sustainably. As we grow, we’d love to hear from you — and work with you. Whether you’d like to partner, teach, share tools and technologies, or simply offer feedback , your input helps shape what comes next for Dr. GPCR. You can always reach me at hello@DrGPCR.org  — and yes, we read every email. With appreciation and excitement, Yamina Berchiche Founder & CEO, Dr. GPCR About Dr. GPCR Dr. GPCR is a global platform dedicated to connecting GPCR scientists, industry experts, and innovators through research, education, and community. From free resources like the podcast and blog to our Premium ecosystem and Foundry initiatives, our mission is to advance GPCR science — together. Related Links 🔗 Explore Terry’s Pharmacology Corner → TerryKenakin.com 📧 Connect with us →   Hello@DrGPCR.org 📧  Learn About Partnerships →   Hello@DrGPCR.org

Watch Episode 175 When Jens Carlsson was a PhD student, he thought collaborations slowed science down . While experimentalists struggled for months with crystallography, he could pull a clean protein structure from the Protein Data Bank in a single afternoon and move on. Why wait for someone else’s bottleneck? That perspective changed the moment he entered GPCR research . Suddenly, computational shortcuts weren’t enough. GPCRs demanded something different: the integration of modeling, medicinal chemistry, and pharmacology . Docking scores were meaningless without assays. Predictions went nowhere without synthetic expertise. And most importantly, no single lab could cover the full terrain alone . Today, Carlsson is Professor of Computational Biochemistry at Uppsala University and one of the strongest advocates for an approach that too many labs still resist. In his view, collaboration is not a bonus or an optional supplement—it is the only way GPCR drug discovery works .   From Lone Modeler to Collaborative Architect Carlsson’s lab is built to plug directly into a larger ecosystem . At its core is a team of about ten computational chemists working on molecular docking, molecular dynamics, virtual screening, and more recently, machine learning. Supporting them is a medicinal chemist who can design and synthesize new scaffolds when commercial libraries run out of options. But Carlsson doesn’t try to do everything under one roof. For receptor assays and the biological interpretation of ligands, he relies on a network of collaborators worldwide , each bringing specialized expertise in different GPCR targets. The power of this design lies not in its independence but in its interdependence . Predictions from modeling inform chemistry. Chemistry fuels assays. Assay data flows back into models. The cycle only works because every part is connected .   Closing the Trust Gap If collaboration is so effective, why do so many labs avoid it? Carlsson points to a deeper problem: credibility . Experimentalists often hesitate to rely on computational predictions, especially when burned in the past by models that promised too much and delivered too little. Carlsson has learned that credibility doesn’t come from publishing a paper or showing a binding score. It comes from honesty. His group is trained to explain exactly what a model can predict and where its limits are. If docking can suggest binding modes but can’t resolve nanomolar potency differences, they say so. If a simulation narrows options but doesn’t rank them definitively, they make that clear. That candor changes the tone of collaboration . Pharmacologists know they aren’t being oversold. Chemists understand why certain molecules are worth the effort to synthesize. And experiments are better designed because expectations are realistic. Over time, that transparency has built enduring partnerships , including one of Carlsson’s earliest with Ken Jacobson’s lab at the NIH , which continues to advance GPCR ligand discovery today.   Beyond Academia: Advising Biotech Carlsson’s collaborative philosophy has also found a place in the biotech industry . Through his consulting company , he doesn’t simply sell computational services. Instead, he acts as an advisor, helping research teams decide where modeling can accelerate progress—and where it can’t. This distinction is critical. Many startups chase elaborate simulations that look impressive on paper but do little to move drug discovery forward. Carlsson helps teams avoid that trap by focusing on leverage points : which models can be trusted for a particular GPCR, which predictions justify immediate synthesis, and when the smartest move is to pause until better biological data arrives. In practice, this approach saves companies time, resources, and credibility with investors. It also allows Carlsson to stay plugged into the fast-changing needs of biotech while keeping his academic mission intact: training the next generation of GPCR scientists . The GPCR Challenge What makes collaboration non-negotiable in GPCR research is the biology itself. GPCRs regulate essential functions from vision to mood to cardiovascular control. They offer extraordinary therapeutic promise —but their complexity makes them one of the hardest targets in drug discovery. Biased signaling, allosteric binding sites, and subtype selectivity mean there is rarely a straight line from prediction to medicine. No single discipline can cover all the ground . Structural biologists may capture snapshots of receptor conformations, but lack large-scale screening capacity. Modelers can generate binding hypotheses, but they remain untested until validated experimentally. Medicinal chemists can synthesize molecules, but must choose carefully from infinite possibilities. The path forward depends on knitting these pieces together into a coherent discovery engine. Carlsson’s lab is a proof point : collaboration is not just a cultural preference. It is a technical necessity .   Lessons from the Collaboration Blueprint Carlsson’s approach offers lessons across the spectrum of GPCR science. At the individual level, progress begins with transparency . Overstating the precision of a model or the predictive power of an assay may look persuasive in the short term, but it erodes trust and slows progress in the long term. Equally important is the ability to learn across disciplines . A pharmacologist who understands docking, or a modeler who knows assay constraints, will collaborate more effectively and make fewer wrong turns. Science advances faster when people invest in fluency beyond their own silo . Finally, collaboration should be treated as a deliberate strategy , not an afterthought. The most effective discovery programs are structured like Carlsson’s: modeling designed with downstream chemistry in mind, chemistry connected to assays, and assay data cycling back to refine predictions. Whether in academia or biotech, the future belongs to research groups that can orchestrate ecosystems rather than defend territories .   Why His Model Works What sets Carlsson apart is not just access to computational power or assay platforms. It is the values he applies to every collaboration . He treats modeling as part of a continuum rather than a separate discipline. He resists hype, focusing instead on predictions collaborators can actually use. And he prioritizes shared purpose , designing each project with the next experiment in mind. These values have become the glue that holds his partnerships together . They explain why collaborators return, why projects move forward, and why his model of collaboration is increasingly cited as a template for how modern GPCR science should be done .   GPCR Models Don’t Discover Drugs—People Do In GPCR research, the biggest breakthroughs won’t come from algorithms or crystal structures alone. They will come from how scientists choose to work together . Carlsson’s lesson is simple but profound: the strongest model in GPCR drug discovery isn’t built on a supercomputer. It’s built on trust between scientists. And for the next generation of researchers and biotech leaders, that is the real collaboration advantage . Want to hear how Dr. Carlsson mentors the next generation? 🎧 Listen to the full episode on the Dr. GPCR Podcast 🌱 Build a career with purpose, not just papers The Dr. GPCR Premium Ecosystem  provides early-career scientists with real-world guidance, insider knowledge, and access to leaders in the field. 🎓 Mentorship that matters. 🧭 Decisions with clarity. 💬 Exclusive community.

Watch Episode 175 If your model can’t predict the future of GPCR drug discovery, why build it at all? For decades, GPCRs have been the cornerstone of pharmacology. Nearly one-third of all approved drugs target them. Yet even with the structural biology revolution, one stubborn hurdle persists: prediction.   Can we leverage structural and computational insights not just to explain receptor–ligand interactions after the fact, but to forecast outcomes and design ligands with new properties? That’s the mission of Dr. Jens Carlsson , Professor of Computational Biochemistry at Uppsala University. His lab sits at the crossroads of structure-based modeling, computational chemistry, and drug discovery —rethinking how simulations, docking, and machine learning can transform GPCR research from descriptive science into predictive decision-making. From Static Structures to Testable Hypotheses Carlsson’s philosophy is simple but radical: modeling should generate hypotheses, not just rationalize data. Projects in his group often begin with either: A new GPCR structure  ripe for exploration, or A pharmacological challenge  such as differentiating agonists from antagonists, predicting biased agonism, or achieving receptor subtype selectivity. To tackle these, the lab employs molecular docking, molecular dynamics simulations, and ligand-based screening . Vast chemical libraries—sometimes billions of molecules—are sifted computationally to highlight potential hits. When existing compounds are exhausted, bespoke ligands are designed and synthesized in-house. That medicinal chemistry capacity is unusual for a modeling lab and provides a critical bridge between prediction and validation. Every computational hypothesis moves downstream into experimental assays via close collaborations. For Carlsson, a prediction is only valid if it survives wet-lab testing.   The “Predict, Not Explain” Ethos What sets the Carlsson lab apart is its internal rule : results must be actionable. Instead of rehashing how a known ligand binds to a known receptor, they aim to produce predictions that guide experimental choices —whether in target prioritization, compound selection, or mechanistic exploration. This predictive framing raises the bar. Common questions in the group include: Can we forecast ligand efficacy or selectivity? Can we design compounds that trigger biased signaling? But part of their rigor lies in restraint. If a method can’t resolve subtle potency differences, the team prefers to acknowledge that limitation rather than overpromise. That scientific humility has earned credibility with experimental collaborators , where skepticism of computational work is common.   GPCR Modeling in Context: Then and Now When Carlsson entered the GPCR field in the early 2000s, structural data was scarce. His early Adenosine 2A receptor work, in collaboration with Ken Jacobson, helped establish virtual screening as a viable tool for GPCR ligand discovery. Fast forward to today: dozens of GPCR structures  exist, many in multiple conformations or bound to G proteins and arrestins. Yet functional selectivity, allosteric modulation, and biased agonism  remain major challenges. Carlsson’s group integrates modeling with mechanistic pharmacology. For dopamine receptor studies, they built predictive models before experimental structures were available. When crystal structures were finally solved, the models aligned strikingly well—a rare and powerful validation.   AlphaFold: A Game-Changer with Caveats One of the newest tools in the lab’s arsenal is AlphaFold , DeepMind’s AI-powered protein structure prediction platform. Carlsson’s team has already published cases where AlphaFold-derived GPCR models provided accurate scaffolds for ligand docking—enabling progress in systems without experimental structures. In one instance, an AlphaFold prediction was so strong the group pivoted its entire workflow around it. But AlphaFold is not a panacea. It struggles with ligand-bound conformations, GPCR–G protein complexes, and receptor–peptide interactions. The lab has witnessed both spectacular accuracy and puzzling failures. Carlsson’s takeaway: AlphaFold is a powerful starting point, but domain expertise and experimental grounding remain indispensable.   Translational Impact for Drug Discovery While the Carlsson lab is rooted in academia, its impact extends into biotech and pharma. Carlsson also runs a consulting arm  that advises drug discovery teams on GPCR modeling strategy. Notably, this isn’t contract computational chemistry—it’s strategic guidance  on target selection, ligand feasibility, and prioritization. This dual perspective makes Carlsson a trusted partner for industry teams navigating early-stage pipelines , where skepticism toward in silico hits is high. By focusing on translationally relevant predictions , the lab helps bridge the gap between computation and experiment. For drug discovery executives, the message is clear: predictive modeling can shorten timelines, reduce costs, and expand druggable space—if it’s done with rigor and honesty.   Why Predictive GPCR Modeling Matters Now The convergence of machine learning, improved GPCR structures, and scalable experimental assays is shrinking the gap between modeling and pharmacology. But success depends on researchers who can manage expectations, build trust, and integrate across disciplines. Carlsson’s group does not aim for perfect predictions. Instead, it aims for useful, testable predictions that shape the next experiment. That shift—from describing the past to forecasting the future—is what makes predictive GPCR modeling transformative. For scientists, this is an intellectual challenge: demand more from your models. For industry leaders, this is an operational opportunity: integrate prediction into decision-making.   Modeling That Moves the Field Forward The Carlsson lab exemplifies how c omputational biochemistry can meaningfully impact real-world pharmacology. With rigor, transparency, and cross-disciplinary collaboration, they’re pushing GPCR science toward a predictive era. The next frontier in GPCR drug discovery isn’t more structures—it’s smarter models. Are you demanding enough from yours? Want to go deeper into GPCR modeling? Dr. Jens Carlsson breaks down the tools, challenges, and mindset behind building models that don’t just explain—they predict . 🎧 Listen to the full episode of the Dr. GPCR Podcast Push the boundaries of what's possible 👉 Join the Dr. GPCR Premium Ecosystem  for advanced insights into structure-based modeling, ligand design, and experimental collaboration. 🧠 Learn from the field’s top innovators. 📈 Sharpen your translational impact. 🔗 Connect with scientists shaping the future.