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Gutkind, and Francesco Raimondi for their work on The landscape of cancer-rewired GPCR signaling axes Affiong Ika Oqua, Dr.

In this session, you’ll gain:   ✅ A framework for understanding when apparent multiple affinities really Every day spent misunderstanding the meaning of apparent affinity differences can slow your project’s The interpretation of high and low affinity binding sites  on GPCRs.   High and Low Affinity: What’s Really Going On? It’s common to observe two apparent affinities for a ligand under certain experimental conditions.

The interpretation of high and low affinity binding sites on GPCRs. At first glance, when a ligand binds at two different affinities in the same system, it seems logical Under certain experimental conditions, what appears to be a second high-affinity site may simply reflect Sites Slows You Down Traditional models that shaped affinity analysis were designed for simpler systems Every day spent misunderstanding apparent affinity differences delays key milestones. 🗣️ “Thank you

In GPCR drug discovery, a single mistaken assumption can derail an entire program. When pharmacologists misinterpret how an antagonist interacts with its receptor, the consequences ripple across assay development, SAR interpretation, and clinical translation. This week in Terry’s Corner, Dr. Terry Kenakin reframes one of pharmacology’s most foundational ideas: the difference between competitive and non-competitive antagonism. It’s a practical, kinetic problem with real implications for compound evaluation and mechanism-of-action (MOA) studies. Why Misreading Antagonism Delays GPCR Drug Discovery If a dose-response curve shifts to the right, you might call it competitive antagonism. If the maximal response drops, non-competitive seems like the obvious answer. But Terry explains why those assumptions don’t always hold. The real story lies in the rate at which antagonists bind and unbind . Fast offset kinetics allow agonists to outcompete and restore full activity—hallmarks of competitive behavior. But if the antagonist binds tightly and dissociates slowly, the receptor remains blocked, even at high agonist concentrations. This mimics non-competitive behavior, even if the antagonist occupies the orthosteric site. In other words, you can’t diagnose mechanism from curve shape alone . Inside the Lesson: Antagonist Behavior by the Numbers Terry walks through both the pharmacologic and kinetic signatures of antagonism and explains why: Orthosteric antagonism  blocks the receptor completely via steric hindrance, unlike partial allosteric inhibition. Competitive antagonism  occurs when agonist and antagonist re-equilibrate rapidly during the experiment. DR curves shift to the right with no loss of maximal effect. Non-competitive antagonism  results from slow offset kinetics . Antagonist “hogs” the receptor, depressing the response, even if more agonist is added. Schild and Gaddum equations  offer early frameworks for quantifying these effects but modern curve-fitting provides more accurate assessments. Common misconceptions  arise when irreversible binding, receptor reserve, or allosteric effects mimic classical patterns —leading scientists to assign the wrong mechanism. This lecture helps you not only understand these differences, but also know when and why they matter. Beyond the Curve: Questions This Lesson Helps You Answer This lesson pushes you to go deeper than surface-level interpretations and ask the questions that truly shape mechanistic understanding. How do the binding kinetics  of an antagonist influence what we actually observe in a dose-response experiment? Why is competitive antagonism  considered reversible and surmountable, while non-competitive antagonism  often results in persistent receptor blockade? Terry also explores the experimental constraints —like timing and equilibrium—that determine whether you can confidently assign mechanism. He challenges the idea that curve shape alone is diagnostic, pointing out how features like receptor reserve  or irreversible binding  can mislead interpretation. And through historical context, he explains how the classic models of Schild and Gaddum  continue to inform modern analysis, while also highlighting the need for contemporary curve-fitting methods in a post-linear era. Unlock “Competitive Antagonism” Now Only in Terry’s Corner Why Terry’s Corner For discovery-phase teams, time is the scarcest resource. Misinterpreted mechanisms delay decision-making and weaken downstream results. Terry’s Corner  is built to help you avoid those traps. Each subscription includes: Weekly deep dives from Dr. Terry Kenakin Monthly Ask‑Me‑Anything sessions with live Q&A An expanding on‑demand library for novices to expert drug hunters Influence over upcoming topics Who it’s for:  drug discovery teams, pharmacologists refining their toolkit, and scientists who need trusted, curated insights to keep pipelines moving efficiently. Why now:  GPCR research is accelerating—and the projects that succeed will be those built on clear, accurate models of receptor behavior. 40 years of expertise at your fingertips:   Explore the complete library ➤ Want to know what’s inside?   Read the latest articles ➤ Not ready for full membership? Start by challenging your assumptions. Consider this: Two dose-response curves, side by side. One shifts neatly to the right without dropping the maximum. The other shows a clear depression in maximal effect. You’d be tempted to label the first “competitive” and the second “non-competitive.” But Terry cautions: “These patterns are consistent with, but not proof of, mechanism. The real determinant lies in the kinetics of antagonist binding and unbinding.” This is the kind of insight that makes the difference between a dead-end program and a well-informed decision. The stakes are too high to rely on assumptions. If you want to avoid costly misinterpretations and strengthen your pharmacology toolkit, Terry’s Corner is your edge. Keep yourself and your team updated on the newest releases. Subscribe to The Kenakin Brief today  ➤   #GPCRantagonism #CompetitiveAntagonists #NonCompetitiveAntagonists #DoseResponseCurve #Pharmacology #SchildEquation #AntagonistAffinity #Kinetics #DrugReceptorInteractions #GPCRblockade

It infers the affinity (Kᵦ)  of an antagonist by linking concentration shifts to receptor occupancy. In the full lecture, you will learn how to extract affinity even in this dual-behavior scenario: Focus Even when maximal responses are altered, affinity constants remain extractable  from correctly chosen That’s where pA₂ values  come in—a quick estimate of antagonist affinity from a single concentration. It sharpens interpretation—distinguishing real affinity  from apparent effect.

this assay was applied to an ion channel target with its agonists, of which the determined binding affinity

near-physiological conditions while simultaneously generating image-based evidence to support numerical affinity highlights how HCS can be used both to triage compound series and to extract quantitative structure–affinity In this article, you’ll learn:   How live-cell HCS provides physiologically relevant affinity measurements When medicinal chemists evaluate affinity jumps between analogues, image data can help resolve questions Quantitative affinity estimates are transparent and reproducible.  

  lecture delivers exclusive, real-world insight designed to equip you to move beyond surface-level affinity drug’s rate of binding onset and offset matter most for in vivo success, and when static equilibrium affinity Yet most discovery teams still rely on classic affinity metrics alone, often overlooking how kinetic Why the Myth of “Potency = Affinity” Could Be Slowing Your Pipeline Many workflows still operate on the Kinetic measurements reveal hidden liabilities or advantages that static affinity numbers cannot.

Their binding affinity was assessed through radioligand binding, showing a nanomolar affinity for both Comparison of CELT-335 affinity for CB1 and CB2 receptors measured by competition radioligand binding The high affinity of CELT-335 for CB1R was maintained in the Tag-lite® assay (Ki=44.8nM in radioligand ; ( b ) CB2R binding affinities. It highlights the difference in affinity of compounds such as SCRAs and other synthetic cannabinoids.

CELT-419 has for D3R, and prove it has enough affinity for FA assays, the radioligand binding method Using the FA method, CELT-419 binding affinity and kinetics were determined using budded baculovirus Overall the determined affinities agree well with the affinities from previous studies.[13] Besides traditional dopaminergic ligands, the affinities of the P-165 (CELT-419 pharmacophore), and PL-384 (pharmacophore Structure-Based Design of High-Affinity Fluorescent Probes for the Neuropeptide Y Y1 Receptor. J.

concepts related to orthosteric binding: Langmuir Adsorption Isotherm : This concept teaches us about affinity A high affinity means the drug will bind more easily and effectively. This knowledge can guide modifications to improve binding affinity and bioavailability.

If you’ve read our previous post, you probably know what CELT-335 is and its high affinity for the cannabinoid It has shown high affinity for the CB1R, and the binding affinity values obtained for natural cannabinoids These assays probide a reliable way to measure binding affinities for CB1R in live cells and the PHERAstar

This lecture is a roadmap for understanding why two drugs with similar affinity may behave completely Why “Affinity Alone” Isn’t Enough—Again Just like binding kinetics require us to go beyond static Kd Affinity tells part of the story. But without understanding how GPCR state changes  influence that affinity—or vice versa—drug discovery

Orthosteric sites are zero-sum: highest affinity or concentration wins. How Conformational Changes Drive Affinity and Efficacy A key takeaway from this session: affinity and A high-affinity ligand often has higher efficacy, but the relationship is not linear. That perspective helps explain why two compounds with similar affinities can deliver very different clinical

discovery-phase decision you make shapes your entire pipeline trajectory—and understanding when equilibrium affinity Terry’s Corner: Why Binding Kinetics Matter More Than Affinity In drug discovery today, time wasted is Yet too many programs rely solely on affinity metrics—Kd—as proxies for in vivo behavior, missing a critical interpret kinetic binding experiments and recognize when a drug’s rate of onset and offset matter more than affinity Prevent wasted cycles:  Avoid costly delays caused by false positives from static affinity readouts.

Using this method, binding affinity, kinetics and selectivity can all be measured and used to establish both radioligands and FP assays using CELT-228 A3AR fluorescent antagonist provide similar binding affinity Comparison of hA3 binding affinities or percentage of displacement at 1 µM measured for different compounds

PAMs assumed therapeutically viable without verifying whether they amplify affinity or efficacy. One carries broad off-target risk; the other behaves more like a high-affinity ligand with slow dissociation Lower-affinity alternatives may produce deeper, more therapeutically relevant coverage. EC₅₀ and Emax uncouple affinity and efficacy, making cross-agonist comparison unreliable. Dr.

Affinity snapshots alone won’t save your pipeline. Kinetics will. Not just the one with higher affinity, but the one that gets there faster and leaves slower. Think of it like catching a train: two passengers have tickets (affinity), but only the one who sprints

The observed differences in the binding affinity and cooperativity arise from the functional groups that membrane lipids as an integral component of transmembrane sites for accurate characterization, binding-affinity

With a model, it becomes explainable: if the dimer form has higher affinity, then adding ligand actually It’s a ratio of affinity and efficacy. But if you deconvolute into affinity and efficacy, a rich SAR emerges.

We found that the affinity of Fzd for Dvl was not affected by Wnt ligands, in contrast to other members of the GPCR superfamily for which the binding of extracellular ligands affects the affinity for downstream

Same Affinity, Different Outcomes: Why Residence Time Matters More Two ligands. Identical in vitro affinity. One with rapid offset, the other with slow offset.

these, four fragment- and seven lead-like compounds were confirmed to bind to the allosteric site with affinities The four compounds with the highest affinities were demonstrated to be negative allosteric modulators

these, four fragment- and seven lead-like compounds were confirmed to bind to the allosteric site with affinities The four compounds with the highest affinities were demonstrated to be negative allosteric modulators

the gold standard for verifying true competition — and why misclassification propagates error across affinity Quantify affinity you can defend.  

functional sites They bind to the active site of the receptor, which also proves kinetic and binding affinity CELT-240 in flow cytometry binding assays is suitable to measure the affinity of compounds for the D2

They found that AF2 models achieved accurate side-chain predictions and successfully docked high-affinity The highest affinity compounds (15 to 24 nM) were identified from AF2 docking.

This week’s edition links ligand mechanism to the decisions that shape affinity, efficacy, selectivity focuse on the distinction between orthosteric and allosteric mechanisms and the impact of this choice on affinity Win on kinetics, not just Kd:  Dynamic binding means “affinity” moves—design readouts and decisions that

Key results: Eight additional MQs were identified with nanomolar affinities for the V2R, all antagonists The variant MQ1-K39A showed a higher affinity for the hV2R, but not for the rat V2R.

have been identified to exist in at least two distinct states: an active state characterized by high affinity for agonists when coupled to G proteins, and an inactive state in which their affinity for agonists discovery of allosteric modulators for GPCRs which possess the capability to modulate and fine-tune the affinity