The Hidden Cost of QTc Assessment: A Quantitative Weight of Evidence Framework to Eliminate Structural False Positives and Restore the Lost Opportunity Cost of Abandoned Medicines.

The regulatory framework for QTc assessment has been optimized against false negatives while ignoring the false positives that silently remove efficacious medicines from development. False positives arise from three independent, compounding sources. First, a structural source: sensitive tests applied in low-prior populations generate predominantly false positives as a mathematical inevitability. Second, a measurement artifact: patch-clamp electrophysiology systematically biases hERG IC50 estimates, creating artificially elevated priors. Third, biomarker non-specificity: QTc captures autonomic, thermal, and heart-rate-driven changes that carry no torsadogenic risk. Clinical TdP case series confirm that torsade requires large, concentration-dependent QTc prolongation well above regulatory detection thresholds. The multichannel hypothesis is not supported by the three primary reference agents. Furthermore, competitive equilibrium binding Ki measures the thermodynamic quantity that physiology reflects; it predicts QTc prolongation more consistently than IC50 and yields a more sensitive and specific assessment. We propose a Bayesian weight-of-evidence framework (a quantitative New Approach Methodology suitable for FDA ISTAND qualification) anchored on the hERG Ki margin. The framework updates sequentially with in vivo and clinical QTc data, propagates uncertainty, and produces a posterior probability of clinically meaningful QTc prolongation. This posterior gates a staged assessment, reserving high-sensitivity studies for compounds where the prior warrants them. For peptides and proteins, a Beta-Binomial analysis of class-level evidence from approved therapeutics is sufficient to extinguish the hERG-driven torsade prior without compound-specific assessment. These elements place false positive and false negative costs on the same ledger, providing infrastructure to move cardiac safety assessment from qualitative judgment to a reproducible, decision-rule-based methodology.