Novel Cell-Based Assays and Screening Technologies in Drug Discovery

Cell-based assay innovation is reshaping how we discover, validate, and de-risk therapeutic candidates across disease areas. This Research Topic invites original research, methods, brief reports, and reviews that showcase next-generation cellular models and screening technologies that deliver translatable pharmacology, robust quantitation, and actionable mechanistic insight. We seek contributions that close the gap between molecular hypotheses and clinically relevant biology, emphasizing rigor, scalability, and experimental validation.We welcome the following subtopics:Advancements in Receptor Signaling and Target PharmacologyContributions should deploy state-of-the-art readouts to resolve signaling complexity across diverse target classes (e.g., GPCRs, ion channels, RTKs, nuclear receptors), including biased signaling, allosteric modulation, probe dependence, and spatiotemporal dynamics, while using engineered or native cellular contexts to interrogate receptor coupling diversity (e.g., G protein/β-arrestin pathways, kinase cascades, channel gating), receptor oligomerization, trafficking, and receptor-receptor crosstalk. We invite work that introduces biosensors (e.g., BRET/FRET, conformational, second messenger, voltage/ion indicators, or proximity-based sensors), multiplexed transcriptional reporters, and single-cell approaches that enhance sensitivity and pathway resolution, as well as quantitative frameworks (kinetic/operational models) that improve potency/efficacy estimation and enable translatable pharmacodynamic predictions across different receptor families.Automation and High Throughput in Drug DiscoveryContributions should demonstrate miniaturized, robust, and cost-efficient high-throughput and high-content screening workflows (including CRISPR-enabled genetic screens, image-based phenotypic profiling, and microphysiological systems), and integrate laboratory automation (liquid handling, robotics, closed-loop experimentation) to accelerate design–build–test cycles and improve assay reproducibility. We welcome work that validates advanced readouts such as multiplexed omics, imaging cytometry, label-free technologies, and real-time kinetic measurements in screening-compatible formats, and provides best practices for assay development (Z′, S:B, DRC quality, MSR), QC/QA pipelines, data governance, and hit triage to reduce false discovery and improve downstream success.AI/ML in Drug DiscoveryContributions should combine predictive modeling (e.g., generative design, activity/clogP/ADME-Tox prediction, polypharmacology profiling) with experimentally verified cell-based assays, and employ active learning, Bayesian optimization, or reinforcement learning to guide compound selection, with prospective experimental validation demonstrating cycle-time reduction and improved hit quality. We also invite work that applies interpretable ML to connect cellular phenotypes with mechanism-of-action, target engagement, or pathway modulation, including multimodal fusion of imaging, transcriptomics, and functional readouts, and that shares open, FAIR datasets and reproducible computational-experimental pipelines that promote community benchmarking.Model systems of interest include primary cells, iPSC-derived cells, 3D/organoid cultures, immune co-cultures, and microphysiological systems that recapitulate human biology. We also encourage translational studies linking cellular pharmacology to in vivo biomarkers and safety/efficacy signals.Important note: Please note that submissions lacking experimental validation of in silico findings will not be considered. Topic editors: Madhu Sudhan Ravindran, Andrew Huber Source: https://www.frontiersin.org/research-topics/79832/novel-cell-based-assays-and-screening-technologies-in-drug-discovery