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Matthew E. on Friday, October 7th at 1 PM ET.

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 ➤

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GPCR Podcast << Back to podcast list Brian Shoichet About Dr. Brian Shoichet BSc in Chemistry from MIT, Ph.D. with Tack Kuntz at UCSF; Postdoc with Brian Matthews Brian Shoichet on the web Google Scholar Shoichet Lab Twitter Dr. GPCR Ecosystem Enjoying the Dr.

Brian Bender About Dr. Brian Bender Dr. Between undergraduate and graduate school Brian worked as a technician in an academic lab before moving Brian is involved in organizing the GRC/GRS Molecular Pharmacology meeting, which has been postponed Brian is one of the organizers of the upcoming Transatlantic ECI GPCR Symposium . Dr. Brian Bender on the web LinkedIn Twitter ResearchGate Dr.GPCR Member Google Scholar Dr.

Brian Hudson About Brian Hudson Brian is a lecturer in the School of Molecular Biosciences at the University Brian Hudson on the web University of Glasgow Enjoying the Dr. GPCR Podcast? Leave a Review.