Search Results

The Legacy of Schild Analysis Schild analysis was born from an elegantly simple equation published by At its heart, Schild analysis isn’t about math—it’s about validation. Partial agonists don’t break Schild analysis—they refine it. Schild analysis still applies—if used carefully. Schild analysis translates that language.

Schild analysis is one of the few tools that tells you whether your “competitive antagonist” is actually Terry’s Corner: Schild Analysis — Why It Matters Most assays show a clean rightward shift and we assume Kenakin breaks down why Schild analysis remains the gold standard for verifying true competition — and Gives You an Edge Premium delivers a clear, noise-free stream of GPCR intelligence every week: deeper analysis

How Fluorescent Ligands Transform Flow Cytometry in GPCR Analysis GPCRs are a tough target in traditional

September 2022 PLC-IP3-ORAI pathway participates in the activation of the MRGPRB2 receptor in mouse peritoneal caused by MRGPRB2 activation were blocked by U73122 (PLC blocker) or 2-APB (IP3 blocker) or synta66 (ORAI Our results indicated the involvement of the PLC-IP3-ORAI signaling pathway and CACCS in MRGPRB2-mediated

October 2022 Design and validation of recombinant protein standards for quantitative Western blot analysis Replacement of radioligand binding assays with antibody-antigen interaction-based approaches for quantitative analysis C-terminal tail for use as standard and negative control, respectively, in quantitative Western blot analysis

With power analysis , you’ll stop guessing. Once you learn to design with power analysis, you’ll never run blind again. Enter ANOVA (Analysis of Variance).   What used to be a gray area— “maybe these lines are different?” Unlock “Statistical Analysis” now Only in Terry’s Corner Why Terry’s Corner Your work demands clarity

We have carried out MARTINI coarse-grained molecular dynamics simulations of SMO in POPC and in ciliary These analyses have also helped to identify and define a strict cholesterol consensus motif (CCM), which Structural analysis of SMO domains shows significant changes in the CRD and ICD, during the course of Further detailed analysis of the dynamics of the TMD reveals the movements of TM5, TM6, and TM7, linked In addition, our analysis also shows that phosphatidylinositol-4-phosphate (PI4P), along with some ICD

agonist determined by serial femtosecond crystallography (SFX) at the Pohang Accelerator Laboratory X-Ray

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

2026 GPCR Pharmacology AMA: Receptor Theory, Biased Signaling & Assay Interpretation The first GPCR Pharmacology AMA of 2026  at Terry’s Corner will take place on: Thursday, February 26 at 1 PM EST Dr. Kenakin will address receptor theory, assay interpretation, biased signaling, and practical drug discovery challenges — driven by questions from the community. These sessions focus on real scientific uncertainty, not rehearsed presentations. Terry’s Corner Expands to YouTube Terry’s Corner is now on YouTube. Three videos are already live, and the channel will expand regularly. The objective is straightforward: Make mechanistic pharmacology easier to access, revisit, and apply across research teams. Short conceptual breakdowns Focused receptor theory discussions Clear explanations reinforcing disciplined interpretation As the archive grows, it becomes a searchable extension of Terry’s teaching — designed for repeated exposure rather than one-time viewing. Subscribe to stay current as new videos are released: ▶️ https://www.youtube.com/@TerryPharmacologyCorner New White Paper on GPCR Biased Signaling Terry Kenakin, Ph.D., Professor of Pharmacology at the University of North Carolina School of Medicine, has authored a new white paper in collaboration with Eurofins DiscoverX: Assess GPCR Biased Signaling of Agonists Using Functional Cell-Based Assays The paper explores: Detection and quantification of signaling bias Influence of biased signaling on therapeutic profiles Application of quantitative tools such as transduction coefficients (log(τ/KA) or log(max/EC50)) Systematic comparison of biased agonists using modern functional assays For scientists working in GPCR programs, this connects functional assay data directly to translational decision-making — moving beyond descriptive bias claims toward quantitative rigor. Access the white paper here Why Terry’s Pharmacology Corner Mechanistic understanding evolves. What appears settled under one experimental condition may require refinement under another. What seems definitive during early screening can shift as assay systems, receptor expression levels, or kinetics change. Pharmacology does not drift because data are missing. It drifts when interpretation becomes casual. Terry’s Pharmacology Corner provides a structured environment to maintain interpretive discipline: Weekly advanced pharmacology lectures Monthly live AMAs for real-time scientific discussion A continually expanding on-demand archive Sustained exposure to quantitative receptor theory and mechanistic reasoning The value lies not in a single explanation, but in preserving rigor as programs mature. Forty years of pharmacological expertise — organized into a year-round framework for serious GPCR scientists. Stay in the Know If you want updates on future AMAs, new YouTube releases, white papers, and ongoing pharmacology insights, join Terry Kenakin’s Brief . Concise. Focused. Mechanistic. 👉 Sign up here Continue the Work Live sessions are one layer. Sustained exposure is where judgment sharpens. If you want structured, year-round access to Terry’s full library — including advanced lectures, archived AMAs, and quantitative pharmacology deep dives: 👉 Access Terry’s Corner Free for 7 Days Strengthen Your Mechanistic Thinking

The differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA) were performed using R software, and functional enrichment analysis and protein–protein interaction (PPI) network were Module analysis was performed using Cytoscape.

The analysis below emerged from a recent live Ask Me Anything (AMA) session, where members brought forward Interpreting Schild Plots — Curves and Slopes Schild analysis remains foundational, but interpretation Demonstrating changes in association or dissociation rates moves analysis beyond functional shifts toward Robust controls distinguish mechanism from artifact Multipathway analysis reduces false confidence Neglecting

This week's highlight includes congrats to: Miles Thompson , Alexander Hauser , Caroline Gorvin ,   et

Kenakin and Hoare on '   Applying Pharmacology to Drug Discovery ' and ' Advanced Data Analysis for GPCR Materials Access to a Private Community Certificate of Participation for each course Don’t miss out—grab This week's highlight includes congrats to: Miles Thompson ,   Alexander Hauser ,   Caroline Gorvin ,

Miles Thompson, Alexander Hauser, Caroline Gorvin et al. for their research on GPCR gene variants and

Grab your flux capacitors, GPCR time travelers! This Week’s Highlights: G protein-coupled receptor (GPCR) pharmacogenomics Miles D Thompson , David protein subtype selectivity GPCRs in Cardiology, Endocrinology, and Taste Integrated bioinformatics analysis

Source: Nørskov-Lauritsen L, Thomsen AR, Bräuner-Osborne H. G protein-coupled receptor signaling analysis using homogenous time-resolved Förster resonance energy Molecules. 2023 Dec 15;28(24):8107. doi: 10.3390/molecules28248107 Nørskov-Lauritsen L, Thomsen AR, G protein-coupled receptor signaling analysis using homogenous time-resolved Förster resonance energy

PLCβ3, or pharmacological inhibition of its upstream regulators, Gβγ or Gq, ex vivo in periaqueductal gray

Drugs, and more Predicting allosteric sites using fast conformational sampling as guided by coarse-grained activity-induced, equilibrative nucleoside transporter-dependent, somatodendritic adenosine release revealed by a GRAB

This simple advice, passed on to Ben in grad school, has helped him stay sane, focused, and impactful Data analysis. Teaching. The pressure to stay productive never stops.

Two-photon microscopy revealed a glowing islet in a living mouse — a moment JB calls the “Holy Grail”

For teams seeking fine-grained control over receptor output, these GPCR pharmacology insights determine

cytokine release and (ii) the consequence of its pharmacological inhibition by using the inverse agonist GRA2

It starts by noticing what grabs your attention, what sparks those quiet “aha” moments during a lecture

Grab this chance to deepen your understanding of pharmacology and take your knowledge to the next level exploring valuable resources such as ' Applying Pharmacology to Drug Discovery ' and ' Advanced Data Analysis

articulate a trajectory, they categorize you as “interesting but unready.” 3️⃣ Internal Alignment Frays

Striatal Spiny Neurons to Brain-Derived Neurotrophic Factor GPCRs in Oncology and Immunology Comparative Analysis of the GNAI Family Genes in Glioblastoma through Transcriptomics and Single-Cell Technologies Methods

Gain invaluable skills in data analysis under Dr. Hoare's expert guidance. neurogliosis in female Alzheimer's mice The association of GNB5 with Alzheimer disease revealed by genomic analysis Itaconate in host inflammation and defense Methods & Updates in GPCR Research Improved green and red GRAB LSDV isolates from 2022 outbreak in Indonesia through phylogenetic networks and whole-genome SNP-based analysis

out on insightful resources like '   Applying Pharmacology to Drug Discovery ' and ' Advanced Data Analysis Grab your spot today and score a 25% early bird discount—exclusively for premium members. basis for the ligand recognition and G protein subtype selectivity of kisspeptin receptor Structural analysis of the human C5a-C5aR1 complex using cryo-electron microscopy Structural analysis of the human C5a-C5aR1

activity-induced, equilibrative nucleoside transporter-dependent, somatodendritic adenosine release revealed by a GRAB