Mitochondrial network organization in skeletal muscle reflects metabolic health and is disrupted in primary mitochondrial myopathies, type 2 diabetes/insulin resistance, and age-related functional decline. Studying these disruptions in vitro using mature human myotubes is, however, complicated by the dense, anisotropic architecture of mitochondria, which poses fundamental challenges for automated segmentation and phenotypic classification. Here we present MitoLatentProfiler, a deep learning framework that combines images of micropatterned primary human myotubes with a topology-preserving U-Net and a Classifier U-Net that jointly optimizes segmentation and phenotype classification, structuring the encoder latent space for downstream analysis. Constraining analysis to troponin-positive myotubes excludes confounding signals from neighboring cells. The resulting embeddings organize along two main axes: a morphological axis (fusion/fission balance, shared by TOMM20 and MitoTracker) and a marker-specific axis (associated with bioenergetic insult, MitoTracker only), enabling hierarchical marker-adaptive classification. The segmentation model achieves Dice =0.82 and clDice =0.86. A hierarchical support vector machine classifier reaches macro F1 up to 0.93, outperforming classical morphological descriptors across donors (n=2), markers, and plates not seen during training. Fragmentation, Hypertubulation, and two Damaged phenotype-scores (induced by oligomycin/antimycin and carbonyl cyanide m-chlorophenyl hydrazone) yield Z' factors of 0.54, 0.38, 0.87, and 0.96 respectively, confirming screening applicability for damage and fragmentation readouts. The modular design allows the encoder to serve as a fixed feature extractor: adapting to new phenotypes requires only retraining the lightweight classifier. This pipeline provides a scalable tool for mechanistic studies and compound screening targeting mitochondrial dysfunction in muscle disease.
Artificial intelligence now guides choices across small-molecule discovery, yet benchmark gains rarely reveal whether a model changed the next compound, assay or project decision. This Perspective organizes public evidence around Design, Make, Test and Analyze (DMTA) handoffs. It distinguishes reproducible benchmark evidence (C1), use-relevant retrospective robustness (C2), executed Make or Test handoffs (C3), prospective workflow comparisons (C4-W) and traceable downstream trajectories (C4-D), with separate Evaluation, Deliverability and Governance profiles. Two authors independently screened 99 public sources and classified a reconciled inventory of 75 claim units. Consensus coding assigned 41 claims to C1, 6 to C2, 19 to C3 and 9 to Context; none met C4-W or C4-D. These counts characterize the purposive public-evidence map. Four pressure tests examine structural prediction, low-data active learning, ultra-large screening and reward hacking. Stronger claims depend on inspectable worklists, denominators, negative outcomes, decision rules and provenance. The proposed reporting framework separates workflow comparison from downstream traceability and supports tiered disclosure when compound details are commercially sensitive.
Acute myeloid leukemia (AML) exhibits pronounced cellular heterogeneity, which contributes to variable therapeutic responses and limits the predictive power of conventional functional assays. While bulk viability measurements provide aggregated readouts, they fail to resolve phenotypic diversity and dynamic cellular states within heterogeneous populations. Here, we established a high-content fluorescence imaging workflow for quantitative drug response profiling (DRP) in AML at single-cell resolution. The assay integrates three non-toxic fluorescent dyes to capture features of nuclear morphology, mitochondrial function, and apoptosis. Automated high-content imaging combined with computational image analysis enables robust segmentation and extraction of phenotypic features across thousands of individual cells. Using a supervised machine learning approach, cells were classified into viable, apoptotic and dead states, enabling quantitative assessment of drug responses through population-normalized metrics. This approach allows direct integration of image-based data into downstream analysis workflows, facilitating the generation of functional dose-response curves and the determination of IC50 values and drug sensitivity scores (DSS) at single-cell resolution. The workflow was validated across seven AML cell lines, including models of acquired and mutation-driven resistance to BCL-2 inhibition. Image-based DRPs generated for venetoclax (VEN) showed strong concordance with established bulk measurements obtained using the ATP-based cell viability readout (CellTiterGlo®, CTG). Furthermore, screening of a 16-compound panel representing diverse mechanisms of action demonstrated robust agreement between image- and CTG-based drug response profiles while providing additional phenotypic information at single-cell resolution. Finally, the workflow was successfully transferred to primary AML samples. A pilot 31-compound drug screen identified BCL-2 inhibitors as the most active compounds, consistent with the patient's molecular profile. Together, this workflow establishes a scalable functional phenomics platform for high-resolution drug profiling and phenotypic stratification in AML. The integration of single-cell imaging with AI-based analysis provides a promising foundation for future functional precision oncology approaches, with potential applications in patient-specific DRP and combination therapy optimization.
Recent advances and increasing adoption of 3-dimensional (3D) model systems, such as tumour spheroids and organoids, attempt to more faithfully recapitulate the pathophysiology and heterogeneity observed in patients' tumours, with the goal of reducing high attrition rates observed in late stage drug development. While established high content imaging systems provide the spatial resolution and throughput necessary to place 3D models at the earliest stages of drug discovery, they yield limited information on disease heterogeneity or drug response at the single cell level in 3D. Improvements in single-cell RNA-Seq are transforming our understanding of disease trajectories and therapy response, however this technology is too expensive and laborious for high throughput screening. Here we demonstrate the high content screening capabilities of a compact and low cost light-sheet fluorescence microscopy platform called dual-view oblique plane microscope (dOPM). We apply the dOPM to screen a small library of compounds in a 3D glioblastoma (GBM) stem cell spheroid model expressing the FUCCI cell cycle reporter. We benchmark the performance and compare the outputs of the dOPM GBM spheroid assay with standard 2D and 3D spheroids assays using established spinning disk confocal high content platforms. In a proof-of-principle small molecule compound screen we demonstrate that the dOPM performs to accepted standards of reproducibility for high throughput screening in a 96-well plate format. We further demonstrate the ability of the dOPM to capture the heterogeneity across multiple spheroids within an individual well and provide single cell level data within each individual 3D spheroid. We propose that further development of the opensource dOPM platform will support the advancement of 3D high content phenotypic screening assays from cell population measurements to highly quantitative single cell analysis across 3D space and time dimensions.
The corneal endothelium executes its crucial role in the maintenance of corneal transparency by regulating the corneal hydration levels. Dysfunction of the endothelial layer leads to corneal edema, i.e. bullous keratopathy, for which an endothelial transplantation is currently the only available treatment option. Nevertheless, the limited availability of donor tissue encourages the development of alternative treatment strategies. This study addresses a pharmacological-based alternative approach focusing on the comprehension and proof-of-concept validation of a hybrid small molecule screening approach. In this regard, a weighted voting-based quantitative structure-activity relationship (QSAR) model was developed for the in silico identification of small molecule building blocks promoting corneal endothelial regeneration. The established QSAR model was applied to the virtual Enamine® Hit Locator Library, composed of 460,160 compounds. Ultimately, the four most effective compounds predicted to stimulate corneal endothelial cell growth in silico, were selected for in vitro validation. All four selected compounds enhanced cell proliferation at nanomolar concentrations. Their effects were comparable to Y-27,632, a ROCK inhibitor commonly used as a positive control to stimulate corneal endothelial cell proliferation and migration, supporting their potential as low-dose small molecule alternatives for corneal endothelial regeneration. These findings validate the predictive performance of our initial QSAR model and highlight the relevance for future screening of other hit locator library compounds, while feeding new biological data into the model for further refinement and accuracy improvement.
Brain metastasis originating from breast cancer is an uncommon but clinically significant complication that poses substantial therapeutic challenges and is associated with poor patient prognosis. Traditional treatment modalities-including surgical resection, whole-brain radiation therapy, stereotactic radiosurgery, and systemic chemotherapy-often fail to achieve satisfactory long-term control due to the protective nature of the blood-brain barrier (BBB), tumor heterogeneity, and the aggressive biology of metastatic lesions. This comprehensive review delves into recent advances in modern therapeutic approaches that aim to enhance the efficacy of conventional treatments for rare brain metastases from breast cancer (BC). We systematically evaluate emerging strategies such as advanced drug delivery technologies, including nanoparticles (NPs), polymeric NPs, and liposomal formulations, which are designed to overcome pharmacokinetic limitations and improve the penetration and retention of chemotherapeutic agents within the central nervous system (CNS). The review also explores the integration of immunotherapies, particularly immune checkpoint inhibitors and adoptive cell therapies-with traditional modalities to potentiate antitumor immune responses in the brain microenvironment. Moreover, we discuss the development and application of novel radiosensitizers and combination regimens aimed at overcoming the inherent and acquired radioresistance of metastatic tumors. The synthesis of current preclinical models and clinical trial data provides critical insights into the potential of these combined approaches to enhance patient survival and quality of life. Finally, we identify key challenges, including the need for personalized treatment protocols and the management of therapy-related toxicities, and propose future directions for research.
Native mass spectrometry (nMS) enables direct analysis of non-covalent protein-ligand interactions, providing information on binding, stoichiometry, and protein oligomeric state. In drug discovery, nMS is widely used for mechanistic protein-ligand characterization and for confirming hits identified by orthogonal biochemical or biophysical assays. However, limited analytical throughput has restricted its use for screening compound libraries for non-covalent protein-ligand binding. Here, we demonstrate the utility of acoustic ejection mass spectrometry (AEMS) coupled to a high-resolution QTOF mass spectrometer as a high-throughput platform for nMS screening using SARS-CoV-2 main protease (Mpro) as a model protein. Well-resolved charge-state envelopes corresponding to the monomeric and dimeric forms of Mpro enabled simultaneous assessment of ligand binding and changes in oligomeric state. Non-covalent binders, including compounds that perturb the monomer-dimer equilibrium, were readily identified directly from native mass spectra. Analysis of a 1536-well screening plate was completed in approximately 1 h with an acquisition time of 2.5 s per well and excellent reproducibility across the plate. These results demonstrate that acoustic ejection nMS is a practical high-throughput screening platform that simultaneously delivers direct molecular information on protein-ligand binding, binding stoichiometry, and oligomeric distribution.
Research engaged with AI has dramatically increased due to a huge improvement of technologies in terms of cost and time-effectiveness as well as decreasing use of animals. High throughput screening (HTS) is a frequently used method in drug discovery. Here, we combined two powerful tools for AI-driven identification of Zika virus (ZIKV) inhibitors. The main complication from ZIKV is microcephaly and other related birth defects when pregnant women become infected with this virus. To date, there are no specific vaccines and antiviral drugs for the treatment of ZIKV infection. We have successfully developed an HTS (1536 well-plate format) cell viability-based zika virus cytopathic effect (CPE) assay. To evaluate an AI-driven method, we implemented the CPE assay into a miniaturized format and screened 1280 unique compounds. In addition, we incorporated an ultra-high throughput imager for 1536 well-plate HTS to rapidly obtain high-quality, brightfield images which were then analyzed by two different platforms. One platform, AVIA, uses deep convolutional neural networks (CNNs) to automate viral infectivity scoring based on CPEs long before signs of infection are visible to the human eye. The second, AutoHCS, utilizes a segmentation-free feature classification approach to develop probabilistic phenotypic profiles of antiviral compounds. Ultimately, AVIA was able to distinguish between infected and uninfected cells with high accuracy as early as 40 hours post infection, demonstrating a meaningful reduction in assay duration compared to the CPE readout. Furthermore, phenotypic profiling of 1280 compounds using AutoHCS phenotypic profiling overlaid with AVIA infectivity scores identified many compounds that showed possible anti-viral effect. Interestingly, 12 out of 20 compounds that were identified as hit compounds in the original cell viability-based CPE assay were found by AVIA and AutoHCS, demonstrating reasonable concordance and reliability of this method. Further, by comparing AVIA infectivity scores against AutoHCS morphological profiles, we were able to also eliminate compounds as false-positives without additional counterscreens or orthogonal assays. Overall, we demonstrate a robust, sensitive assay that produces high quality imaging data which results in promising cost- and time-effective HTS when combined with modern AI/ML tooling.
Real-time cell analysis (RTCA) is a biosensor technology that enables kinetic monitoring of cellular activities via electrical impedance. Among its multiple applications, this label-free method offers a holistic approach to monitoring the activity of G protein-coupled receptors (GPCRs) by capturing integrated cellular responses upon pharmacological modulation. GPCRs represent an important class of drug targets, covered by one third of all therapeutics approved by regulatory authorities. Modern drug discovery efforts targeting GPCRs are often initiated with a high-throughput screening (HTS) campaign, typically relying on the detection of downstream signaling events. This approach is often well-suited for sufficiently characterized receptors; however, it is less applicable for less studied (e.g. orphan) GPCRs, with unknown ligands and signaling pathways. Our study presents the integration of the xCELLigence RTCA HT system into a fully automated HTS system, to enable large-scale screening for GPCR agonists. The results obtained in a “proof-of-concept” pilot HTS demonstrate the compatibility of impedance-based RTCA with robotic HTS platforms, as well as the potential of this method for the identification of GPCR ligands.
Haploinsufficiency disorders arise when loss of function mutations in one allele of a gene reduce gene dosage below the level required for normal physiology. Pharmacologic upregulation of the remaining functional allele represents a promising therapeutic strategy but requires screening assays capable of detecting modest changes in endogenous gene expression. Here we developed and compared three high throughput cell-based assays for identifying small molecule upregulators of JAG1, the gene most frequently mutated in Alagille syndrome (ALGS). The assays measure JAG1 expression at different molecular levels: RNA fluorescence in situ hybridization (RNA FISH) for JAG1 mRNA, immunofluorescence (IF) for endogenous JAG1 protein, and a HiBiT luminescence assay using CRISPR engineered LX-2 hepatic stellate cells expressing HiBiT tagged JAG1. Each assay was optimized in 384-well format and benchmarked using a panel of 32 histone deacetylase inhibitors (HDACi), compounds known to broadly increase gene expression. All three assays detected JAG1 upregulation and identified overlapping sets of active compounds. The homogeneous HiBiT assay showed the most favorable high throughput screening statistics (S/B = 2.7 and Z′ > 0.5) and the lowest well to well variability, whereas the RNA FISH and IF assays provided higher signal to basal ratios and single cell resolution. Entinostat, Mocetinostat, and Chidamide were consistently identified as the most potent JAG1 upregulators across all assays. These complementary assays provide a flexible platform for identifying small molecule modulators of gene dosage and may be broadly applicable to drug discovery efforts targeting haploinsufficiency diseases.
High throughput screening produces large, complex datasets that are difficult to interrogate without programming expertise, making hit selection time-consuming and inflexible. While instrument software and commercial tools offer partial solutions, they often lack adaptability or require costly infrastructure. Interactive dashboards provide an effective alternative by enabling dynamic filtering and integrated visualization within a single interface. Here, we present simple R Markdown-based templates for creating customizable, modular dashboards for screen data analysis. Built using the flexdashboard and crosstalk R packages, and HTML widgets, these lightweight, easy-to-build HTML dashboards require no complex installation process or installation of licensed software. They support linked visualizations, threshold-based filtering (e.g., Z-score, p-value, fold change), and interactive data exploration and are shared as a standalone HTML file. This framework enables rapid, flexible hit selection across diverse high throughput screening applications and is designed for users with basic R experience.
Inositol-tetrakisphosphate 1-kinase (ITPK1) is a pivotal enzyme in the inositol phosphate signaling pathway that functions to maintain the balance of inositol phosphate (IP) species. Dysregulation of this pathway has been linked to human disease, making ITPK1 an attractive therapeutic target. While high-throughput screening (HTS) is a traditional strategy for identifying small molecule inhibitors, integrating computational approaches can significantly speed up and enhance hit rates. Here, we developed a hybrid experimental and virtual approach towards the identification of ITPK1 chemical probe candidates. We first miniaturized an ITPK1 enzymatic assay to 1536-well format and screened ∼19,000 annotated and chemically diverse compounds. We then utilized the resulting dataset to develop Machine Learning (ML) and Pharmacophore (PH4) models to virtually screen a larger library of 120,000 compounds to expand the chemical diversity of the screening set. Importantly, our screening platform included a selectivity assay against PPIP5K2, the closest structural relative of ITPK1. The identified hits were evaluated for ITPK1 binding, including via a novel high-throughput Structure Dynamic Response (SDR) target engagement assay. Hits underwent further confirmation through orthogonal assays and mechanistic investigation, including obtaining a co-crystal structure for one of the hits. This integrated workflow-combining physical HTS with computational modeling-led to the identification of two novel candidate inhibitors. This study demonstrates an efficient, scalable strategy for targeting ITPK1 and offers a promising platform for drug discovery efforts in diseases linked to perturbed inositol phosphate pathways.
Antibody-drug conjugate (ADC) is a novel type of targeted systemic therapy that is changing the current landscape of tumor treatment. By integrating the specificity of antibodies with the cytotoxic effects of their payloads, ADCs facilitate precise tumor killing. In solid tumors such as breast cancer, non-small cell lung cancer (NSCLC), and urothelial carcinoma, ADCs have demonstrated significant efficacy with acceptable toxicity profiles. Colorectal cancer (CRC) ranks as the third most prevalent tumor globally, and there remains an unmet clinical need for targeted treatment options. The approval of T-DXd for HER2-positive (IHC3+) CRC marks ADCs' entry into this treatment arena. This article will focus on the clinical performance of ADCs and explore design considerations and future directions for ADCs in CRC. Significance statement ADCs, a targeted treatment strategy that has emerged over the past two decades, are rapidly evolving. we believe that ADCs will offer more targeted treatment options for CRC patients in the future. This review focuses on the clinical performance of ADCs and explores design considerations and future directions for ADCs in CRC. This study provides a reference for researchers to understand and learn ADC.
Accurately detecting patterns of interest across a large number of images presents a significant challenge in data analysis for high-throughput analytical assays. In many cases, manual image review to some degree is necessary, deviating from a fully automated workflow desired in high-throughput platforms. CellVision, a deep learning-based workflow, was created using recent developments in AI-powered image analysis to enable truly automated and accurate counting of viral plaques. This Python-based workflow uses image processing techniques and a convolutional neural network to identify viral plaques, separate fused plaques, and differentiate plaques from other objects that appear in the same image. By integrating pattern recognition and classification methods, CellVision enables fully automated data analysis for the high-throughput µPlaque assay. Its design accommodates a variety of atypical cases found in real-world images, facilitating the identification of diverse patterns. The high accuracy achieved by CellVision eliminates the need for a manual, visual review of the images, substantially reducing time to data release. CellVision has been fully integrated into the µPlaque assay workflow and has been used to support an investigational, live-attenuated, quadrivalent dengue vaccine in development by Merck & Co., Inc., Rahway, NJ, USA. The accuracy of plaque counting results from CellVision was demonstrated against a diverse dataset verified by multiple analysts, outperforming an alternative commercial tool. CellVision's successful integration into the assay workflow and its potential for generalization to other viral plaque assays signify a new standard in applying AI-powered methods to antiviral vaccine discovery and development.
Lassa virus (LASV) is a hemorrhagic fever arenavirus of significant public health concern, infecting millions of people per year in Africa. Here, we developed a computational strategy to identify specific inhibitors of LASV glycoprotein-mediated virus cell entry, leveraging a previous screen of 297,156 small molecules from the MLPCN library, with the results deposited in the PubChem database. Data mining methods were developed to efficiently select small molecules prioritized for both potency and specificity in inhibiting Lassa virus infection. Cheminformatics classification then identified diverse chemical scaffolds that had not been previously reported. Representatives were evaluated against authentic LASV infection, yielding potencies as low as 10 nM. Investigation of the target mechanism compared vesicular stomatitis virus bearing the GPs of LASV, the distantly related Junín virus, and unrelated Ebola virus. The results identified three distinct chemical scaffolds that demonstrated strong LASV selectivity, each acting at the membrane fusion stage of virus cell entry. Sensitivity was mapped to regions of GP2 known to coordinate pH-triggered conformational rearrangements needed for membrane fusion. Time-of-addition experiments demonstrated loss of activity coincident with endosomal escape, and cell-cell fusion assays confirmed direct inhibition of GP-mediated syncytia formation. Together, these findings characterize each scaffold as an effective LASV fusion inhibitor and highlight the effectiveness of our combined computational and experimental approaches in identifying mechanistically informative antiviral scaffolds.
Some experiments generate data consisting of counts of cells in two states, such as alive or dead, CD3+ or CD3-, etc. These are often converted to percentages and analyzed with parametric statistical tests intended for means like t tests, which assume data are normally distributed around means. This may not be true when percentages are close to either 0 or 100. I compared the performance of a comprehensive set of statistical tests intended for count data to t tests under conditions relevant to common wet-lab experiments, 3-6 replicate with 100-1000 cells per replicate. Only a few did as good a job as t tests at maintaining the rate of false positives, but those offered no advantage at finding differences when they exist. Researchers can use t tests to analyze count data converted to percentages under typical wet-lab conditions confident that this is an appropriate and effective approach.
OBJECTIVE:The rising incidence of Mycobacterium avium (MAV) infection poses significant challenges due to diagnostic delays and refractory treatment. Understanding host immune remodeling-particularly myeloid heterogeneity-is critical for identifying precise therapeutic targets. METHODS:We integrated single-cell RNA sequencing (scRNA-seq) with machine learning to profile peripheral blood from MAV patients. CellChat analysis mapped intercellular communication, while an ensemble of 113 machine learning algorithms screened for diagnostic biomarkers. Network pharmacology and molecular docking were employed to identify host-directed therapies, validated via RT-qPCR. RESULTS:The single-cell atlas revealed a pathological expansion of hyper-inflammatory activated macrophages and neutrophils in progressive disease. Analysis identified a monocyte-derived MIF signaling axis that recruits and "locks" APP-expressing macrophages, driving immune dysregulation. A diagnostic model (Stepglm+GBM) based on five core genes (IL1B, STAT3, TNF, STAT1, TLR4) achieved high accuracy (AUC > 0.88). Furthermore, molecular docking confirmed that Wogonin forms high-affinity bonds with STAT3 and TNF, suggesting its potential as an immunomodulator. CONCLUSION:This study delineates the high-resolution immune landscape of MAV infection, elucidating the MIF-APP axis-driven myeloid reprogramming. We established a robust diagnostic model and identified Wogonin as a potential therapeutic agent, providing novel strategies to break the "immune stalemate" in MAV infection.
Anterior gradient 2 (AGR2) is an endoplasmic reticulum -resident protein belonging to the disulfide isomerase family. AGR2 contributes to protein quality control and unfolded protein response (UPR). In addition to its canonical intracellular role, AGR2 also exists in an extracellular form (eAGR2), which is strongly associated with tumour progression and cancer aggressiveness. The objective was to develop a novel approach for identifying and quantifying AGR2 from a broad range of biological fluids (cell lysates, extracellular medium, serum). Therefore, an amplified luminescent proximity homogeneous assay (EnLIGHT OMEGA ™) was established using a proximity method based on the principles of fluorescence resonance energy transfer and antigen-antibody specific binding. This method is sensitive, rapid, performed without washing, and can detect 0.02-300 ng/mL of AGR2. In conclusion, our study establishes a simple, sensitive, and rapid method for eAGR2 detection in oncology applications, including cancer drug resistance and metastatic progression.
Leukocyte immunoglobulin-like receptor B4 (LILRB4, ILT3) is an inhibitory immune checkpoint expressed on myeloid cells, where it contributes to immunosuppression within the tumor microenvironment. Secretogranin 2 (SCG2) has recently been identified as a functional ligand of LILRB4, yet small molecule modulators of this interaction remain unexplored. Here, we report the development of a high-throughput time-resolved fluorescence resonance energy transfer (TR-FRET) assay to interrogate the LILRB4 (ILT3)-SCG2 interaction. The assay demonstrated robust performance and was validated using a blocking anti-LILRB4 antibody, consistent with orthogonal ELISA measurements. Pilot screening of chemical libraries identified 23 primary hits, of which two compounds, BMS-813,160 and PSB-603, showed reproducible, dose-dependent inhibition with TR-FRET IC₅₀ values of 26.7 ± 1.03 µM and 37.2 ± 2.14 µM, respectively. Activity was confirmed by ELISA, supporting the robustness of the assay. This platform enables high-throughput discovery of first-in-class small molecule modulators of the LILRB4-SCG2 immune checkpoint and provides a foundation for targeting myeloid-driven immunosuppression.
G protein-coupled receptors (GPCRs) represent one of the most important target classes in drug discovery, yet many remain pharmacologically underexplored due to limited biological knowledge and technical challenges associated with their characterisation. Here, we evaluate grating-coupled interferometry (GCI) as a versatile optical biosensor platform for GPCR research. Using the human adenosine A2A receptor (A2AR) as a model system, we demonstrate that GCI provides high-quality kinetic data that correlates with data obtained using well-established Biacore technology. When properly set up, the waveRAPID injection method enables fast and reliable determination of binding kinetics and affinities from single-concentration injections. We further extend this approach to determine thermodynamic parameters for diverse A2AR ligands using minimal protein consumption, by combining waveRAPID injections with the fast temperature changes available on the WAVEdelta system. Finally, we performed a kinetic fragment screening using a 704-member fragment library to identify specific A2AR binders. Nano differential scanning fluorimetry (nanoDSF) was subsequently applied to confirm binding for the identified hits. Collectively, this study establishes GCI-based analysis as a powerful and information-rich platform for kinetic, thermodynamic, and fragment-based discovery on GPCR targets, which is particularly valuable for early-stage drug discovery.
