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Juan Jose Fung, Principal Scientist, at GPCR Therapeutics.

foundational lesson from Terry Kenakin’s Pharmacology Vault , we unlock the visual language of pharmacology: dose-response In classic Terry style, the lecture walks you through: What dose-response curves are and why they’re 👉 Discover why dose-response curves are the backbone of all pharmacology. Unlock "Dose-Response Curves" now

activity becomes so dominated by experiments, data nuance, and scientific discourse that the organization loses

October 2022 Genome-scale CRISPR screening reveals that C3aR signaling is critical for rapid capture of fungi by macrophages "The fungal pathogen Histoplasma capsulatum (Hc) invades, replicates within, and destroys and release of small peptide fragments such as C3a, a role for C3aR in macrophage interactions with fungi phagocytosis of bacteria and latex beads, it is critical for optimal macrophage capture of pathogenic fungi Hc-macrophage interactions and uncover a novel and specific role for C3aR in macrophage recognition of fungi

.- Jan Steyaert, scientific director of the VIB-VUB Center for Structural Biology, Vlaams Instituut Biotechnologie Jan Steyaert will be presented with the Gabbay Award at Brandeis University on October 27 when he will

March 2022 Inversago Pharma Announces Dosing of First Participant with Metabolic Syndrome in Phase 1B clinical stage biotech company with a unique portfolio of CB1 inverse agonists, announced today the dosing

Why Misreading Antagonism Delays GPCR Drug Discovery If a dose-response curve shifts to the right, you How do the binding kinetics  of an antagonist influence what we actually observe in a dose-response experiment Consider this: Two dose-response curves, side by side.

Selectivity is one of the most overused—and misunderstood—terms in drug discovery. A compound shows no response in one assay, and we call it “selective.” Another produces a larger shift in EC₅₀ in one system than another, and we assume we’ve found therapeutic separation. But as Dr. Kenakin demonstrates in this session of Terry’s Corner, what we often measure is not receptor selectivity. It’s a partnership between ligand efficacy and the sensitivity of the cellular system used to detect it. In this session, you’ll gain: A framework for cancelling cell effects to isolate true receptor selectivity Practical methods for calculating system-independent selectivity Clear distinction between receptor selectivity and signaling bias Quantifying Receptor Selectivity Requires Canceling the Cell In early discovery, comparing EC₅₀ values across compounds is a common first-pass strategy for ranking potency. But raw potency differences are not pure reflections of affinity or efficacy. They also encode: Receptor expression levels Coupling efficiency Signal amplification Assay sensitivity Observed agonism is never “just the ligand.” It is always ligand × system. In the full lecture, Dr. Kenakin reveals how two compounds tested in different systems can appear to differ by thousands-fold in selectivity—until the system contribution is mathematically cancelled. What remains may be a much smaller, but far more meaningful, difference. True receptor selectivity must transcend the cell line. Canceling the Cell If observed potency reflects both drug properties and system sensitivity, then system effects must be neutralized. The strategy is conceptually simple: Measure full concentration–response curves Calculate relative potencies within each system Perform a ratio-of-ratios comparison By comparing the relative potency of two agonists across two receptor systems—and then comparing those ratios to each other—system-dependent factors cancel out. What remains reflects differences in: Affinity Efficacy And importantly, this value becomes portable. It should hold regardless of receptor density or assay format. This is not just mathematical elegance. It is strategic clarity. It prevents discovery teams from advancing compounds based on artifacts of expression systems rather than intrinsic pharmacology. Why Full Curves Matter Selectivity measurements require full concentration–response curves. Not single concentrations. Not partial windows. Not truncated data. Why? Because the absence of observed agonism does not prove absence of efficacy . A low-efficacy agonist tested in a low-sensitivity system may show no visible response—even at concentrations where 50% receptor occupancy occurs. Move that same ligand into a more sensitive assay, and a curve appears. Dr. Kenakin uses a lever analogy where: Efficacy is the weight applied. System sensitivity determines whether the lever moves enough to be seen. If the assay threshold is too high, real pharmacology becomes invisible. This has direct implications: A “non-selective” compound may simply be under-detected. A “silent” ligand may be system-limited. A development decision may hinge on assay sensitivity rather than molecular behavior. Without full curves, you cannot separate drug properties from detection limitations. Comparing Full and Partial Agonists Discovery programs rarely enjoy the simplicity of comparing two full agonists. More often, one ligand is partial. Now EC₅₀ values alone are insufficient. Maximal response differs. Potency scales distort. Dr. Kenakin outlines a practical solution: use a potency metric that incorporates both efficacy and EC₅₀ (log maximal response divided by EC₅₀). This approach: Corrects distortions introduced by differing maximal responses Allows comparison across full and partial agonists Preserves system independence When handled correctly, partial agonists can be quantified on equal footing with full agonists—without biasing interpretation. Confidence, Not Just Ratios Selectivity is not just a number. It is an estimate with uncertainty. Delta–delta comparisons can be repeated, generating: Standard errors 95% confidence intervals And this is where interpretation sharpens. If confidence intervals include zero, selectivity is not statistically significant. If they exclude zero, the separation is real. In the full lecture, you will learn how this approach removes subjectivity from interpretation. No more “it looks selective.” Statistics decide. For teams under pressure to nominate leads, this discipline matters. It prevents overinterpretation of noise as biology. Receptor Selectivity vs Signaling Bias Here is where the conversation becomes more nuanced. Receptor selectivity involves concentration separation. A compound binds receptor A and produces a response at one range of concentrations. Only at much higher concentrations does receptor B become engaged. Bias is different. Bias occurs simultaneously with receptor binding. When the ligand engages the receptor, multiple signaling pathways initiate: G protein activation β-arrestin recruitment Calcium signaling Other downstream cascades But they do not activate with equal intensity . Bias reflects differential pathway amplification at the same receptor—not concentration separation across receptors. This distinction is critical: Receptor selectivity separates by concentration window. Bias separates by signaling strength. You will learn how the same ratio-of-ratios framework can be applied within a receptor to quantify pathway bias. But caution is required. If pathway-specific readouts are used to define receptor selectivity, then both agonists must be evaluated using the same pathway. Otherwise, bias contaminates the selectivity calculation. Whole-Cell Responses: A Historical Complication Historically, pharmacology relied on whole-cell or tissue responses. These are integrated outputs. They blend multiple pathways into a single functional readout. This has advantages: Physiological relevance Functional integration But it obscures pathway-specific behavior. Modern assays allow isolation of discrete signaling nodes. This precision is powerful—but it introduces complexity. Each pathway can produce a different apparent selectivity profile. In the end, what matters therapeutically is the integrated response. But during discovery, pathway dissection can clarify mechanism and reveal hidden liabilities. The key is consistency: define which pathway defines “selectivity,” and stay faithful to it. Strategic Implications for Drug Hunters Quantifying receptor selectivity correctly does more than refine pharmacological metrics. It changes decisions. It prevents false negatives caused by insensitive assays. It avoids overestimating subtype separation. It clarifies whether differentiation is receptor-based or pathway-based. It creates transportable, system-independent numbers. In a world of increasingly complex GPCR modulation, these distinctions are not academic. They define risk. Compounds fail when assumptions about selectivity prove wrong in vivo. Often, the error began at the assay stage. Quantification—done correctly—protects pipelines. Why Terry’s Corner Terry’s Pharmacology Corner delivers weekly lectures from Dr. Terry Kenakin, monthly live AMAs, and a growing on-demand library built for scientists who need clarity fast. It is designed for: Pharmacologists sharpening foundational tools Discovery teams solving assay bottlenecks Leaders making mechanism-driven portfolio decisions GPCR innovation is accelerating. Those who master system-independent thinking today will define tomorrow’s breakthroughs. 40 years of expertise at your fingertips: Explore the full library and trailers ➤ https://www.ecosystem.drgpcr.com/terry-corner

March 2022 Confo Therapeutics Doses First Subjects In Phase 1 Clinical Trial Of CFTX-1554 For The Treatment medicines targeting G-protein coupled receptors (GPCRs), today announced that the first subjects have been dosed

March 2022 ERNEST has established an Emergency Fund for Ukrainian researchers.

However, blind assessments of ligand pose quality and affinity prediction have thus far not provided Median ligand RMSD values for top-scored poses were 1.2 Å and 2.0 Å for self-docking and StateMatch/FunctionMatch

promoted to partner at Third Rock Ventures as the firm raised just over $600 million for its fourth fund Since then, Celniker has helped usher in an additional fund and headed a few startups as interim CEO. Coming on its 15th year, Third Rock Ventures announced its sixth fund today — and largest one by far That money has gone to some 60 biotechs, much of it as early funding."

That choice determines whether you risk over- or under-dosing, miss safety windows, or miss therapeutic shifts, signaling outcomes, and efficacy profiles. ✅ Practical Perspective:  Why this knowledge reshapes dose–response is not just cleaner assay design but a sharper decision framework for selecting, prioritizing, and dosing Translational Relevance: From Bench to Clinic Misjudging orthosteric vs allosteric behavior can derail dose Recognizing how your ligand interacts with the receptor lets you predict safety margins, dose–response

Funding models rewarded independence, not shared resources. The Power of GPCR Collaboration: Funding as a Force Multiplier Collaboration sounds warm and fuzzy. Shared funding gave us leverage. This changed not just what got funded, but what was possible . What mattered was how leadership leveraged luck into durable structure  — through funding strategies,

In a LPS-induced lung inflammation model in mice, ACT-660602 led to significantly reduced recruitment of the CXCR3+ CD8+ T cell in the bronchoalveolar lavage compartment when administered orally at a dose

Solubility poses significant challenges. of adjustments, a scientist on his team presented a data set that transformed everything: a clean, dose-dependent The biological validation came from Jean-Philippe Pin’s group at the Genomic Functional Institute in

“We did a full dose-response and saw antagonism—all in one plate-based assay. layered collaborations—with Durham University chemist David Parker on the probe chemistry, and with Jean-Philippe

Kenakin argues, every downstream variable becomes distorted —from preclinical modeling to dose selection pharmacokinetic strategy reduces to four deceptively simple questions: How much of the administered dose How frequently must it be dosed to maintain effective exposure? Across therapeutic classes, Dr. Drug-Like Properties: The Real Starting Point PK does not begin at dosing—it begins with physicochemical

Their “aha” moment came when they ran a dose-response with Exendin-4 on GLP-1 receptors—and saw clean → Discovery of pH-sensitive dual response (brightness + lifetime) → Application to GPCR models with Jean-Philippe The fun is what you do with the other 10%. 🎯 From Launch to Legacy For Dr.

discovery teams, this means a shorter exposure can yield longer efficacy windows—opening doors to lower dosing Dose prediction models need kinetic nuance—not just Cmax and AUC. For teams running early-stage programs, recognizing this decoupling early can sharpen dose optimization Dosing regimens aligned with biology, not just exposure.

Where’s your dose justification?   How consistent is your manufacturing?   2️⃣ Dose selection logic: Is there a clear, mechanistic, and empirical rationale for how you plan to dose in trials? future label claims , not just scientific curiosity You design preclinical studies that support your dose

It quantifies the degree of agonist blockade using dose ratios . analysis turns receptor pharmacology into detective work, spotlighting mechanistic fingerprints buried in dose–response Derive dose ratios  from those sections only.

In addition, Structure Therapeutics has completed dosing in a single ascending dose (SAD) Phase 1 study

is designing covalent or tight-binding candidates, these principles reduce surprises and accelerate dose missteps by: Preventing kinetic traps —spot PK/PD decoupling early so washouts and C_max don’t mislead dosing

of max in calcium assays to rapidly rank offset rates, and predict in vivo coverage before you ever dose Suddenly, at higher doses, you see an exaggerated ‘bang’ of effect.

bioinformatics analysis to construct a risk model for ovarian cancer prognosis GprC of the nematode-trapping fungus Arthrobotrys flagrans activates mitochondria and reprograms fungal cells for nematode hunting Role and Biophysicists decipher functionality of adrenaline-binding receptor Tectonic plates more than $130m in new funding

ORs are highly expressed by olfactory sensory neurons of the nose where they are activated by different analyses, have indicated that OR genes are also expressed in non-olfactory tissues including the testis, lung epithelial cells proliferation via activation by β-ionone which initiates prostate cancer cell cycle arrest (Jones In non-small-cell lung cancer OR2J3 activation induced apoptosis and inhibited cell proliferation and

June 2022 "Published on juin 22, 2022 On June 22, 2022, Michel Bouvier, Chief Executive Officer of IRIC

This week in Terry’s Corner , we go beyond the dose-response curves to explore the kinetic and pharmacologic Bridge science and strategy:  Ensure data and decision-making align with funding and milestones.

The logic seemed obvious: Higher potency → lower dose → fewer side effects. The good news is that modern technologies—from delivery systems to alternative dosing routes—are dramatically