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In his work, Serafini emphasizes a phenotype-driven, patient-relevant perspective , where animal models This approach reflects his focus on developing models that behave  like patients before molecular exploration "I'm particularly interested in model development... seeing if we can bring preclinical models much closer Takeaway We don’t need better in vitro data — we need better models of reality. Dr. ___ Keyword Cloud: GPCR podcast , pain modeling , GPCR online course , translational research , neuroimmune

Watch Episode 170 What We’re Missing in Pain Research In GPCR drug discovery, receptors typically steal Signaling (RGS proteins)  might hold some of the most untapped therapeutic opportunities, particularly in pain Venetia Zachariou  introduced him to the power of RGS proteins — particularly RGS4  — in modulating pain models and hints that RGS proteins could modulate pain chronification itself . Serafini highlighted that in modern pain drug development, the field has remained too focused on ion

This is a story about how chronic pain doesn't just shape lives — it reshapes careers. Mike Caterina on pain mechanisms in the peripheral nervous system. His interest in model development, in capturing the lived human experience through preclinical systems accurate model systems. , pain neuroscience

CXCR4 takes on a nuclear role in red blood cell maturation CXCL13/CXCR5 emerges as a high-potential pain target ST171, a biased 5‑HT1A agonist, delivers selective pain relief in preclinical models Dr.GPCR Terry’s Corner gives you timeless and timely tools to improve selectivity, model efficacy, and design Blocking this chemokine axis reduces pain  hypersensitivity by dampening neuroinflammation and glial preclinical models.

“We’ve seen these modulators rescue opioid function where it completely fails in neuroma models. .” – Ben Clements By combining chronic pain models with receptor-level pharmacology, Ben is bridging molecule to model, and model to patient. Beyond Acute Pain: Into the Chronic Unknown Opioids work well for acute pain. Chronic pain? to model and molecule to patient.

TRV045 is the Company’s novel S1P1 receptor modulator being developed as a potential treatment for diabetic neuropathic pain (DNP).

Terry Kenakin’s newest foundational lesson in Terry's Corner  cuts straight to it:   Why models are vital for translating lab data into clinical forecasts The 4 types of pharmacologic models (and when to use each) The truth about linear models: useful or misleading? How the Mass Action Law underpins nearly every model Future of Receptor Theory: Linkage vs. Unlock "Pharmacologic Models" now

November 22, 2021 JUPITER, FL—Scientists at Scripps Research in Florida have created a collection of new pain-relieving

Watch Episode 175 From failed assays to breakthroughs in GPCR modeling , Dr. A professor highlighted Carlsson’s talent in molecular modeling, a skill he hadn’t yet recognized as Predictive GPCR-Ligand Modeling Carlsson's work quickly shifted from curiosity to impact. Embracing Complexity and Failure Carlsson’s approach to modeling is rooted in scientific humility. It's not enough to run simulations or generate models.

GPCRs demanded something different: the integration of modeling, medicinal chemistry, and pharmacology Predictions from modeling inform chemistry. Chemistry fuels assays. Assay data flows back into models. The cycle only works because every part is connected .   His group is trained to explain exactly what a model can predict and where its limits are. He treats modeling as part of a continuum rather than a separate discipline.

Models are the bridge. With a model, you don’t guess. You project. It’s signal—once the model interprets it. Models as Experimental Guides Models don’t just interpret results. They design experiments. Are Models Ever Proven? Here’s the uncomfortable truth: you can never prove a model “right.”

of curiosity, persistence, and using science to meet unmet clinical needs, especially in the chronic pain Clifford Woolf, a leader in pain biology. There, Ajay expanded his understanding of neurobiology and translational research models, further refining slow down or prevent good science from reaching patients — particularly in underfunded fields like pain The company’s goal is to model receptor dynamics — including biased signaling — to predict drug behavior

Watch Episode 175 If your model can’t predict the future of GPCR drug discovery, why build it at all? Carlsson’s group integrates modeling with mechanistic pharmacology. For scientists, this is an intellectual challenge: demand more from your models.  The next frontier in GPCR drug discovery isn’t more structures—it’s smarter models. Want to go deeper into GPCR modeling? Dr.

Must-read publications:  β-arrestin2-biased allosteric modulator for pain beyond opioids & GPR3 regulated by a negative allosteric modulator Terry's Corner – Determine GPCR MoA Early (and Right) Early discovery This week’s Terry’s Corner lesson shows how to replace inference with models that disentangle mechanism—so You’ll apply a model-first workflow to classify orthosteric vs. allosteric behavior, stress-test assumptions What you’ll gain—immediately relevant to your pipeline: Stop costly misreads:  Distinguish orthosteric

Terry's Corner - New Course on Pharmacologic Models The latest Terry’s Corner unlocks clinical forecasting   ✅ Avoid costly errors:  Master vital models to confidently forecast outcomes, before it’s too late Sokhom Pin delivers brutally honest insights.  

Watch Episode 167 Today’s GPCR scientists don’t want to study one interaction, they want to model the Validated antibodies + digital search platform Entire library hosted on Addgene This wasn’t a flash-in-the-pan visiting scientists are already expanding this work, bringing in computational layers  like AlphaFold to model

John Streicher talks about his work on terpenes found in cannabis as these may be a novel way to treat pain These compounds may be a novel way to treat pain without the negative side effects of cannabinoids or

a variety of potential clinical applications with a great interest in the treatment of neuropathic pain optimization, this series of compounds could represent potential clinically useful S1R ligands for pain

Batman ) and master the Hall model  to decode the thermodynamics behind complex binding behavior . Real-world case studies showing binding-function dissociation and residual signal effects ✅ Tools to model Thermodynamics > Intuition: Why the Hall Cube Matters To untangle complex binding behaviors, you need a model Kenakin introduces the Hall model : a cube mapping allosteric and orthosteric interactions, layered with This lecture gives you the language, models, and mindset  to interpret these systems correctly and act

Therapeutics Doses First Subjects In Phase 1 Clinical Trial Of CFTX-1554 For The Treatment Of Neuropathic Pain CFTX-1554 is being developed as a non-opioid approach to the treatment of neuropathic pain, a debilitating

October 2022 Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold for GPCRs with the most widely used template-based software-Modeller. If only looking at each program's top-scored structure, Modeller had the smallest average modeling RMSD 73 cases with the top-scored model, respectively, where no good templates were available for Modeller

their transducer coupling, biased angiotensin receptor ligands, and circuit-selective analgesia in pain models. Separate effect size from time:  Use allosteric modulators to expand therapeutic index and reduce overdose

vitro and in situ chemical crosslinking, disulfide pattern analysis, and mutation data with molecular modeling to generate experimentally driven full-length models. These models provide insights into the interface, important side-chain interactions, and activation mechanism The models are expected to allow for testable hypotheses about signal transduction and drug development

September 2022 Dynamics of tumor-associated macrophages in a quantitative systems pharmacology model of immunotherapy in triple-negative breast cancer "Quantitative systems pharmacology (QSP) modeling is immuno-suppressive tumor microenvironment, we incorporated the dynamics of TAMs into our previously published QSP model We show that through proper calibration, the model captures the macrophage heterogeneity in the tumor

Changes the Game in Drug Discovery For decades, drug discovery pipelines have relied on the orthosteric model But that model is no longer sufficient. Unexpected activity profiles, SAR that breaks your QSAR model, and missed opportunities to design more You’ll walk away with tools to: Interpret probe-dependent effects Model and anticipate longer equilibration Outdated models can mislead your team, waste your resources, and cost your pipeline months of progress

Enter AlphaFold: Predicting the “Face” of a Receptor When Alessandro began his PhD, structural models of struggling to predict structures from scratch, Alessandro and others could now use AI-generated models “…now you have a plethora of 400 models that you can start with molecular dynamics, docking, virtual By iteratively refining the models with docking and mutagenesis data, they developed predictive pipelines Want to level up your modeling skills?

October 2022 Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model

Additionally, we detect a shortening of PC in PINK1-deficient human cellular and mouse models of familial Furthermore, in sPD models, the shortening of PC is accompanied by increased Sonic Hedgehog (SHH) signal

Watch Episode 171 Some careers are planned. Alessandro Nicoli’s wasn’t. Born near Venice and trained as a pharmaceutical chemist, Alessandro never imagined himself working at the cutting edge of computational GPCR research. But one academic spark—and the right mentor—changed his trajectory forever. In this blog post, we dive into his story of scientific curiosity, chance opportunities, and the unlikely road that led him to the Technical University of Munich. The Early Days: Molecules and Mentors Alessandro began his academic life studying pharmaceutical chemistry at the University of Padua. While fascinated by organic chemistry and the idea of “building molecules,” he didn’t see the big picture—until he encountered Prof. Moro , a medicinal chemist who introduced him to the interplay between molecular structure and biological function. “It was not just a molecule—it was a partner interacting with a protein or DNA… that opened a new world.” —Alessandro Nicoli This perspective changed everything. Suddenly, chemistry wasn’t just synthetic—it was strategic. He began exploring how small modifications could radically alter biological outcomes, an insight that later fueled his move toward computational research. Falling for Computational Chemistry Alessandro’s master’s thesis brought him face to face with his “second academic love”: computational chemistry . Studying cancer-related proteins (BCL2 family), he combined NMR and molecular docking to explore drug candidates. The realization that he could ask—and answer—complex biological questions with a computer sealed the deal. “Doing every day the results, I was getting in love with the topic and decided—yeah, I want to do this in my life.” —Alessandro Nicoli After graduation, a serendipitous email from Prof. Moro led him to an opportunity in Prof. Antonella Di Pizio’s lab  in Munich. Within weeks, he moved to Germany and became her first PhD student—helping build a lab from the ground up. From Empty Lab to GPCR Discovery Starting in an empty lab with just a shared desk, Alessandro and Antonella began their mission: to computationally study olfactory GPCRs , a massively under-characterized group of receptors. With hundreds of subtypes and limited ligand data, olfactory GPCRs represent a high-risk, high-reward challenge —perfect for a PhD built on curiosity and courage. “Choosing one specific receptor is difficult… they’re unique because they can bind many different molecules. Let’s embrace the challenge to study all of them.”  —Alessandro Nicoli Want to explore computational GPCR science yourself? Explore the   GPCR University  or enroll in Terry's Corner for GPCR Courses led by Dr. Terry Kenakin. ________ Keyword Cloud: #GPCRresearch #DrugDiscovery #ComputationalChemistry #MolecularModeling #MolecularDynamics #AlphaFold #StructuralBiology #InSilicoBiology

But in Melbourne, a quiet experiment challenged that model — and it worked. Funding models rewarded independence, not shared resources. Engineering Collaboration: The Monash Lab Model Traditional academic labs mirror feudal structures. The Monash model flipped that logic: Shared infrastructure, not duplicated equipment Centralized core What Changed After This Data The pooled funding model turned the lab into a magnet: postdocs, visiting