Computational and Multiscale Approaches in Cardiovascular Drug Discovery

Cardiovascular diseases, encompassing functional, structural, and metabolic disorders, remain a leading cause of morbidity and mortality worldwide. Despite significant advances, the discovery of safe and effective therapies continues to face major scientific and translational challenges. Digital twin approaches can bridge molecular and cellular mechanisms, tissue- and organ-level physiology, patient variability, and clinically meaningful outcomes, providing a comprehensive representation of pathophysiology and treatment. While elements of these approaches are already used in healthcare (e.g., for guiding ablation or designing implantable devices such as pacemakers and defibrillators), challenges remain that limit their adoption in drug development. Integrative strategies that combine experimental pharmacology, human-relevant data, agentic AI, and computational modeling and simulation are increasingly essential for improving target identification and optimization, efficacy prediction, and cardiovascular safety assessment.This Research Topic aims to bring together emerging integrative frameworks and methodologies in cardiovascular drug discovery, with particular emphasis on how multiscale and human-based strategies can accelerate therapeutic innovation. It will include contributions spanning mechanistic pharmacology, in silico modeling, multiscale simulations, translational physiology, and data-driven prediction to advance understanding of cardiovascular disease mechanisms and support the faster development of safer and more effective therapies.Submissions are encouraged to demonstrate how integrative methods can strengthen cardiovascular drug discovery and development across the pipeline, from early mechanistic studies to preclinical evaluation and translational decision-making. Particular interest will be given to studies that combine computational and experimental approaches to refine disease models, predict cardiotoxicity and cardiac safety liabilities, support patient stratification, reduce dependence on animal models, and inform regulatory science.By bringing together work from academia, industry, and clinical research, the collection will provide a timely platform to showcase interdisciplinary methods that are reshaping cardiovascular pharmacology and enabling more predictive, precise, and clinically relevant drug discovery.This Research Topic welcomes topics including, but not limited to:• Agentic AI and machine learning-enabled approaches in cardiovascular pharmacology• Multiscale and human-based models in cardiovascular drug discovery• Cardiac electrophysiology, electromechanics, cardiac metabolism, and vascular/smooth muscle pharmacology• In silico approaches for efficacy and safety assessment• Integration of clinical, experimental, and computational data• Mechanistic studies in structural remodeling, ischemic heart disease, and heart failure• Virtual populations, digital twins, and patient stratification• Subpopulation characterization (sex differences, aging, comorbidities, drug–drug interactions)• Translational and regulatory applications of predictive cardiovascular models Topic editors: Cristian Trovato, Xin Zhou Source: https://www.frontiersin.org/research-topics/80898/computational-and-multiscale-approaches-in-cardiovascular-drug-discovery