A Translational Framework for Target Validation in Genetic Cardiomyopathy

Genetic cardiomyopathies, encompassing hypertrophic cardiomyopathy and dilated cardiomyopathy, represent two of the most extensively characterized inherited cardiovascular disorders. Despite decades of mechanistic insight into sarcomeric dysfunction, calcium handling abnormalities, stress-responsive signaling cascades, and fibrotic remodeling, the translation of this knowledge into durable therapeutic success has remained uneven. A central but underappreciated challenge is the assumption that clinical pathogenicity inherently confers molecular stability that a variant classified as pathogenic will produce consistent downstream molecular perturbations across independent patient cohorts, disease stages, and biological contexts. We examine genetic cardiomyopathy biology through a translational lens, arguing that molecular stability and cross-cohort reproducibility must function as explicit development gates alongside mechanistic plausibility. We synthesize evidence across sarcomeric biology, calcium signaling, fibrosis, metabolic remodeling, and immune crosstalk, and critically appraise how biological heterogeneity, incomplete penetrance, and model limitations introduce translational risk. The expanding roles of multi-omics platforms and artificial intelligence-driven discovery are evaluated for both their promise and methodological fragility. Based on the available data and prevailing practices, a seven-step structured translational framework is proposed, operationalized through a five-domain Molecular Concordance Scoring Matrix that translates stability assessment into a scored, development-ready criterion. By reframing stability as a property of mechanism rather than a statistical afterthought, this framework aims to reduce late-stage development failure, improve biomarker reliability, and align therapeutic platform selection with the biological realities of genetically complex cardiac disease.