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Next on the Dr. GPCR Podcast, we have Dr. Nicola Smith, Molecular Pharmacologist, lab head, and senior lecturer at UNSW Sydney. Subscribe to the podcast today and get notified when new episodes are released! Please share! https://www.ecosystem.drgpcr.com/dr-gpcr-podcast/ #gpcr #drgpcr #podcast

March 2022 "Congratulations to Richard J. Law, our newly named Chief Business Officer.

If you’ve ever stared at a dose–response curve and wondered, “Is this partial agonism? Or something allosteric?” —you already know the trap. In discovery, different pathways may look identical at first glance. This week in Terry's Corner you'll learn how model-based thinking helps you determine a drug’s mechanism of action and turn assay snapshots into real predictions. And here’s the danger: if you can’t tell how  a drug is working, every downstream decision—SAR, lead optimization, even clinical strategy—rests on shaky ground. You don’t need more data—you need a way to translate snapshots into predictions. This session gives you exactly that. By the end, you’ll know how to use pharmacological models to separate lookalikes, explain puzzling outcomes, and make predictions that guide discovery forward, not sideways. In This Session, You’ll Gain: ✅ How to turn descriptive snapshots into predictive insights ✅ Tools to distinguish orthosteric vs. allosteric mechanisms ✅ A framework for using models to design better experiments From Observation to Prediction You’ve run your experiment. A compound shifts the curve. It elevates the baseline. But what does that really mean? The first trap in discovery is stopping at description. You can say what the drug “seems” to do, but not what it will do elsewhere. Models are the bridge. They take descriptive data from one system and translate it into parameters that can be applied to others. Suddenly, your snapshot becomes a forecast . Instead of saying, “This looks like partial agonism” , you can ask: What happens if concentration increases further? What happens when receptor expression is different? What happens in vivo? With a model, you don’t guess. You project. When Two Mechanisms Look the Same Some of the most difficult calls in pharmacology happen when two different mechanisms look identical in a single assay . Without the right model, it’s like staring at identical twins—you can’t tell them apart until you see them move. Take the example Terry highlights: an agonist curve with a rightward shift and elevated baseline. That could be: An orthosteric partial agonist , or An allosteric partial agonist The raw data won’t tell you which. But the model will . Extend the concentration range, and the predictions diverge: Orthosteric? The shift continues linearly. Allosteric? The shift plateaus once the allosteric site saturates. With the right model, you can separate lookalikes and prevent an entire program from being misclassified. When Binding Increases Instead of Decreases Another trap: paradoxical results. You add a non-radioactive analog of a ligand, expecting it to displace binding. Instead, binding increases. Without a framework, this looks like an assay error. With a model, it becomes explainable: if the dimer form has higher affinity, then adding ligand actually drives up bound species. This isn’t noise. It’s signal—once the model interprets it. Models as Experimental Guides Models don’t just interpret results. They design experiments. In HIV entry studies, purified gp120 was too expensive for routine assays. The question was: could crude gp120 supernatant be used instead, without corrupting results? A model answered: yes—provided CD4 concentrations stayed low and gp120 concentrations high. The outcome? Reliable results at a fraction of the cost. When data alone are ambiguous, models tell you which parameters to control, which conditions to vary, and where to focus your resources. Think of models as your GPS—they don’t just explain where you are, they guide you to the next best turn. Are Models Ever Proven? Here’s the uncomfortable truth: you can never prove a model “right.” But you can build confidence through iteration. The cycle is simple: Experiment → Model → Prediction → Experiment. Each loop tightens the fit. Internal checks (like requiring a Schild regression slope of unity for competitive antagonism) add further discipline. The goal isn’t perfection. The goal is reliability : enough confidence in the model to make predictions that hold across systems. Garbage In, Garbage Out Even the best models can only work with the data they’re fed. Poor-quality data in means poor-quality predictions out. Potency (EC₅₀) is a prime example. It’s a ratio of affinity and efficacy. If you stop at potency, structure–activity relationships (SARs) may look flat. But if you deconvolute into affinity and efficacy, a rich SAR emerges. Chemists suddenly have meaningful levers to pull. The lesson: models don’t just need data. They need the right data . A Case That Seemed Impossible In one real program, a compound produced four completely different assay signatures depending on the system: Sometimes it shifted curves left Sometimes it raised baseline activity Sometimes it boosted the maximum response Chemists were left asking: Which effect should we believe? Only when the data were fit to the right model did the picture snap into focus. What looked like four conflicting behaviors turned out to be one coherent mechanism , hidden in plain sight. That’s the power of model-driven thinking—it takes chaos and reveals consistency. What You’ll Walk Away With By the end of this session, you won’t just “know about models.” You’ll know how to use them to sharpen discovery decisions. Specifically, you’ll be able to: Convert descriptive assay snapshots into predictive insights Differentiate orthosteric vs. allosteric mechanisms with confidence Apply models to design cost-efficient, informative experiments This is more than learning concepts. It’s learning how to design, interpret, and decide with models built in . Determining Mechanism of Action: Your Edge If you’re still relying on descriptive observations—“looks like a shift,” “seems like baseline activation”—you’re leaving risk on the table. With the right models, you’ll know (not guess) how your drug works, how it differs from others, and how it will behave across systems. This isn’t just another lecture. It’s a shift in how you approach discovery. Model-driven discovery accelerates timelines, prevents misclassifications, and gives your team sharper levers to pull. That’s your competitive edge. Unlock “Mechanism of Action” now Only in Terry’s Corner   Why Terry’s Corner When early choices determine which programs advance, you can’t afford vague models or slow learning. Terry’s Corner is designed to give you the edge. Join for: Weekly, faculty-grade lessons that sharpen techniques you actually deploy A continuously expanding, searchable on-demand library Monthly Ask-Me-Anything sessions  (first one coming in the next few weeks!) Subscriber-driven topics,  so the next lesson addresses your bottleneck. Built for discovery-phase teams, pharmacologists refining fundamentals, scientists challenging legacy assumptions, and leaders who need decision-ready intelligence. The pace of GPCR innovation is accelerating—teams acting on today’s insights will set tomorrow’s standards while others play catch-up. Stay current. Stay confident. Stay ahead. 🟢 40 years of expertise at your fingertips: Explore the complete library ➤ ✳️ Want to know what’s inside? Read the latest articles ➤ Stay sharp between lectures. Subscribe to The Kenakin Brief today ➤

Context matters.

Terry’s Corner: Why Binding Kinetics Matter More Than Affinity In drug discovery today, time wasted is framework to interpret kinetic binding experiments and recognize when a drug’s rate of onset and offset matter Alex Serafini, whose unconventional path—from patient to scientist—exemplifies why personal mission matters

Itself Drug discovery pipelines often stall not because the target is wrong—but because the chemical matter Informatics Expands the Search Space for Chemical Drug Matter Advances in chemoinformatics  introduced New therapeutic hypotheses “Off-target” effects that may be on-target opportunities This expands drug matter Biologics Are Now Chemical Drug Matter, Too Proteins, peptides, and antibodies are no longer niche. What matters now is not the category , but the fit: Does the chemical matter support the therapeutic

Exploring the kinetic factors that enhance in vivo efficacy beyond traditional potency metrics, as presented by Dr. GPCR. Hey GPCR Fans, This week's breakthroughs are crucial for staying ahead in the rapidly evolving landscape of GPCR research and drug discovery. Dr. Terry Kenakin's insights on target residence time can reshape how you evaluate and advance lead compounds, potentially saving your team from costly late-stage failures. That's exactly what Dr. GPCR delivers every week: practical tools and critical intelligence to elevate your science and sharpen your decisions. Breakthroughs this week: Novo Nordisk cuts Ozempic® cost; Nxera launches obesity pipeline; Superluminal–Lilly cardiometabolic partnership; New GPCR allosteric sites; GPCR signaling potentiation by ATP and sugars. 🔍 This Week in Dr. GPCR Premium: Sneak Peek Get a glimpse of the in-depth intelligence available exclusively to our Premium Members this week: Industry insights:  Discover the latest strategic moves in the pharmaceutical sector, from new pipelines targeting obesity to significant collaborations in cardiometabolic disease, and gain insights into novel approaches in neurodegeneration and antibody therapeutics. Upcoming events:  Stay informed about key global conferences and symposia focusing on GPCRs, neuropharmacology, drug discovery, and biophysics, ensuring you don't miss crucial networking and learning opportunities. Career opportunities:  Explore a selection of high-level job openings in high-throughput screening, research, biologics development, clinical operations, and biostatistics within leading organizations. Must-read publications:  Stay updated on cutting-edge research, including the potentiation of GPCR signaling by ATP and sugar monophosphates and the identification of a novel allosteric site on the vasopressin V2 receptor. Terry's Corner - Unlock the Power of Target Residence Time in Your GPCR Drug Discovery Pipeline Gain a critical edge by understanding the in vivo efficacy drivers overlooked by traditional potency metrics. Are your promising in vitro results failing to translate into real-world clinical success? Dr. Terry Kenakin’s latest insights delve into target residence time, revealing why kinetic persistence often trumps binding affinity for in vivo efficacy. Discover how factors like restricted tissue diffusion and receptor density can dramatically alter drug action, potentially unlocking the true potential of your lead compounds. Problem Solved:  Eliminate the blind spots in your drug evaluation process, moving beyond simple potency measures to understand the dynamic interactions that govern in vivo effectiveness. Competitive Edge:  Identify high-value compounds that might be missed by traditional screening methods, gaining a first-mover advantage in developing more effective therapeutics. Threat Avoided:  Prevent costly late-stage failures by incorporating kinetic modeling early in your pipeline, ensuring your candidates have the persistence needed for clinical impact. ➡️  Premium Members get 50%+ discount when they join Terry’s Corner. Access this week’s key insight ➤ Dr GPCR Podcast – Decoding the Deadly Duo: Xylazine, Fentanyl, and Respiratory Depression Understand the synergistic mechanisms driving the escalating opioid crisis and the crucial role of GPCR pharmacology. The opioid crisis is evolving with the dangerous combination of fentanyl and the veterinary sedative xylazine. This week’s featured podcast episode with Catherine Demery explores the distinct yet lethal mechanisms by which these drugs impair respiration. Learn how fentanyl slows inhalation via opioid receptors, while xylazine prolongs exhalation through alpha-2 adrenergic receptors, creating a synergistic effect that drives overdose deaths. Catherine’s research, blending GPCR signaling studies with public health data, offers critical insights into this urgent crisis. Problem Solved:  Gain a deeper understanding of the pharmacological underpinnings of opioid overdose, informing the development of more effective intervention strategies. Competitive Edge:  Stay informed on emerging public health threats and the scientific research aimed at addressing them, positioning your work at the forefront of critical biomedical challenges. Threat Avoided:  Recognize the growing prevalence and dangers of xylazine-laced opioids, enabling you to contribute to solutions and understand the broader impact on public health. Listen now to understand how two mechanisms intersect—and why pharmacologists are critical in addressing this crisis ➤ Call for Papers – GPCRs: Signal Transduction Volume II With over 21,000 views  and 7,785 downloads  from Volume I, the Signal Transduction  Research Topic is back. Volume II invites experts to deepen our collective understanding of GPCR pathways in health and disease. Manuscript summary deadline: 24 September 2025 . Final submissions: 12 January 2026 . Why contribute: Join a global, like-minded GPCR community. Shape the next generation of cellular biochemistry research. Amplify your work with high-impact visibility. Submit your paper today to secure your work in Volume II ➤ Why Dr. GPCR Premium Membership Gives You an Edge Every week, Premium delivers noise-free intelligence : expert-led courses, classified industry insights, curated events, exclusive job opportunities, and insider commentary. Designed for GPCR scientists and translational teams, Premium keeps you informed on the science, careers, and business moves shaping drug discovery. Unlike fragmented feeds and endless searches, Premium is structured to help you move faster, smarter, and with greater clarity. FAQ: Premium Membership 🔹 What’s included? The complete Weekly News digest, curated jobs, upcoming events, classified GPCR publications, exclusive on-demand expert lectures, and member-only discounts. 🔹 Who is it for? GPCR scientists, translational pharmacologists, biotech discovery teams, and decision-makers who need career-relevant intelligence to stay ahead. 🔹 Why now? 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Michel Bouvier, J Silvio Gutkind, and team found that Gαs is essential for GRK selectivity and gene regulation Matthew Eddy and lab research on GPCR signaling mechanisms using NMR spectroscopy with labeled receptors cardiometabolic diseases GPCR Events, Meetings, and Webinars October 2 - 3, 2023 | Celebrating Robert J.

J., Fung, J. J., Pardon, E., Casarosa, P., Chae, P. S., Devree, B. T., Rosenbaum, D. M., Thian, F. H., Pautsch, A., Steyaert, J., Weis, W. I., & Kobilka, B. K. (2011). M., Manglik, A., Hu, J., Hu, K., Eitel, K., Hübner, H., Pardon, E., Valant, C., Sexton, P. C., Gmeiner, P., Steyaert, J., Weis, W. I., Garcia, K. C., Wess, J., & Kobilka, B. K. (2013). Nature, 504(7478), 101–106. https://doi.org/10.1038/nature12735 Burg, J. S., Ingram, J.

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Patrick Sexton, and Matthew Belousoff et al. for their work on Lipid-Dependent Activation of the Orphan

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GPCR Virtual Cafe with Matthew Eddy scheduled for tomorrow (Sep 29th, 2022) due to Hurricane Ian that

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antihistamines: A clinical trial is needed Jillian G Baker ,  Erica K Sloan ,   Kevin Pfleger ,  Peter J Trung Duong Nguyen ,  Ziaurrehman Tanoli ,  Saad Hassan , Umut Onur Özcan ,   Jimmy Caroli ,   Albert J physiological ligand complexes Luis P Taracena Herrera , Søren N Andreassen ,   Jimmy Caroli ,   Albert J a human bitter taste GPCR Lior Peri ,  Donna Matzov ,  Dominic R Huxley ,   Peter Gmeiner ,  Peter J

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