Clinical trials in a dish: cardiometabolic drug development with biological and digital twins

Figure 1. Regulatory milestones enabling NAMs and the decision-making framework for cardiometabolic drug development. (A) Timeline of key regulatory milestones from 2022 to 2026, including the FDA Modernization Act 2.0, ISTAND Liver-on-Chip Letter of Intent (LOI) acceptance, FDA Science Board six-point NAM recommendations, the 2025 FDA Roadmap to Reducing Animal Testing, and the 2026 year-one progress and milestone. (B) NAM tools for regulatory submission. Biological twin platforms (iPSC-CMs, ECs, hepatocytes, adipocytes, multicellular organoids, and MPSs) and digital twin platforms (AI/ML ADMET, drug screening, PBPK, and target prediction) provide a weight-of-evidence package supporting CMD safety assessment, drug-induced liver injury (DILI) risk evaluation, and IND/NDA submissions. (C) The NAM-based decision framework has been mapped across CMDs, from metabolic stress to metabolic dysfunction and structural remodeling to clinical outcomes. NAM platforms are assigned to each stage: hazard identification, mechanistic understanding, dose–response and exposure modeling, and cardiac/vascular safety evaluation. CAD, coronary artery disease.

Figure 2. Biological twin platforms for CMD modeling from patient to population level. (A) Platforms are arranged along axes of throughput and physiological relevance from 2D monoculture and coculture inserts through bioreactors, 3D bioprinting, organoids, and EHTs to organ-on-chip and fully integrated MPSs. Cellular constituents include cardiomyocytes, ECs, hepatocytes, adipocytes, fibroblasts, and immune cells from iPSCs or primary patient sources. Insets show microfluidic chip architecture and gut-heart and gut-liver multiorgan configurations relevant to oral drug absorption, first-pass hepatic metabolism, and downstream cardiac exposure assessment. ALI, air-liquid interface. (B) Organoid village strategy for population-scale biological twin modeling. Patient-derived iPSCs and primary cells obtained from PBMCs and tissue biopsies are stored in a multidonor biobank representing diverse genetic backgrounds and CMD states. These are differentiated into organ-specific organoids and cultured as mixed-donor organoid villages. Single-cell transcriptomics and computational demultiplexing enable population-scale drug response profiling that captures interindividual variability in a single…

Figure 3. Detecting cardiotoxicity, proarrhythmia risk, and microvascular dysfunction under cardiometabolic stress. (A) Liver-heart MPS for metabolite-mediated cardiotoxicity detection. Drugs administered to metabolically stressed hepatic organoids generate disease-specific metabolites delivered via microfluidic flow to the cardiac compartment. Cardiac readouts include calcium transient amplitude, contractility, viability, troponin release, and oxidative injury markers. (B) Integrated heart/liver axis arrhythmia risk assessment combining MEA recording with the CiPA framework. Drugs administered to MASH-stressed hepatocytes generate disease-specific metabolites delivered to iPSC-CMs simultaneously exposed to glucotoxic and lipotoxic stress. MEA captures CiPA readouts of altered field potential, beat rate variability, action potential duration (APD) morphology, and early afterdepolarization. In silico action potential modeling using ion channel binding kinetics integrates with MEA data to generate mechanistic proarrhythmia risk scores, capturing metabolite-mediated and disease state–amplified arrhythmia risk beyond the resolution of standard hERG testing. (C) Four converging disease…

Figure 4. Integrated biological and digital twin framework for cardiometabolic drug safety and regulatory submission. (A) AI/ML-driven digital twin pipeline. iPSC-derived cardiac, vascular, and liver organoids from both healthy donors and cardiometabolic patients are exposed to drug candidates. Morphological feature extraction from high-content staining images and multiomics data streams — including proteomics, transcriptomics, epigenomics, metabolomics, and secretome analysis — are integrated with publicly available clinical datasets to train AI prediction models. These models enable high-throughput screening (HTS) of large-compound libraries, mechanism-of-action (MOA) deconvolution, lead optimization with functional group addition, validation, and drug repurposing, collectively bridging organoid-scale phenotyping to patient-scale pharmacological prediction. (B) A defined patient population anchors disease-conditioned biological twin experiments across liver-heart MPS, vascularized cardiac organoids, and coronary microvascular chips. Functional readouts feed three digital twin components (AI/ML ADMET prediction, cardiac electrophysiology simulation, and PBPK modeling) to generate a…