Virtual clinical QT exposure-response studies - A translational computational approach

A comprehensive review of 20 years of progress in nonclinical QT evaluation and proarrhythmic assessment

The assessment of drug-induced QT interval prolongation and associated proarrhythmic risks, such as Torsades de Pointes (TdP), has evolved significantly over the past decades. This review traces the development of nonclinical QT evaluation, highlighting key milestones and innovations that have shaped current practices in cardiac safety assessment. The emergence of regulatory guidelines, including International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) S7B, established a nonclinical framework for evaluating drug effects on cardiac repolarization, addressing concerns raised by drug withdrawals in the 1990s. Advances in in vitro, in vivo, and in silico models have enhanced the predictive accuracy of nonclinical studies, with the hERG assay and telemetry-based animal models becoming gold standards. Recent initiatives, such as the Comprehensive in vitro Proarrhythmia Assay (CiPA) and the Japan iPS Cardiac Safety Assessment (JiCSA), emphasize integrating mechanistic insights from human-derived cardiomyocyte models and computational approaches to refine risk predictions. The 2020s mark a shift toward integrated nonclinical-clinical risk assessments, as exemplified by the ICH E14/S7B Questions and Answers. These highlight the need of best practices for study design, data analysis, and interpretation to support regulatory decision-making. Furthermore, the adoption of New Approach Methodologies (NAMs) and reinforced adherence to 3Rs principles (Reduce, Refine, Replace) reflect a commitment to ethical and innovative safety science. This review underscores the importance of harmonized and translational approaches in cardiac safety evaluation, providing a foundation for advancing drug development while safeguarding patient safety. Future directions include further integration of advanced methodologies and regulatory harmonization to streamline nonclinical and clinical risk assessments.

Fast and accurate prediction of drug induced proarrhythmic risk with sex specific cardiac emulators

In silico trials for drug safety assessment require many high-fidelity 3D cardiac simulations to predict drug-induced QT interval prolongation, which is often computationally prohibitive. To streamline this process, we developed sex-specific emulators for a fast prediction of QT interval, trained on a dataset of 900 simulations. Our results show significant differences between 3D and 0D single-cell models as risk levels increase, underscoring the ability of 3D modeling to capture more complex cardiac responses. The emulators demonstrated an average error of 4% compared to simulations, allowing for efficient global sensitivity analysis and fast replication of in silico clinical trials. This approach enables rapid, multi-dose drug testing on standard hardware, addressing critical industry challenges around trial design, assay variability, and cost-effective safety evaluations. By integrating these emulators into drug development, we can improve preclinical reliability and advance the practical application of digital twins in biomedicine.

On the relationship between hERG inhibition and the magnitude of QTc prolongation: An in vitro to clinical translational analysis

Assessing the magnitude of QTc prolongation is crucial in drug development due to its association with Torsades de Pointes. Inhibition of the hERG channel, pivotal in cardiac repolarization, is a key factor in evaluating this risk. In this study, the relationship between hERG inhibition and QTc prolongation magnitude was investigated, with the aim to derive simple guidance on the required hERG margin to avoid a large (>20 ms) QTc prolongation.

METHODS

Data from literature and FDA sources were searched for compounds with hERG IC50 values alongside clinical QTc data with paired plasma concentrations, or compounds demonstrating a clinical concentration-QTc relationship. Relationships between hERG inhibition, hERG IC50 margin to unbound plasma Cmax, and QTc prolongation magnitude were calculated.

RESULTS

Analysis of 148 clinical QTc observations from 98 compounds revealed that compounds associated with QTc prolongation >10 ms typically exhibited hERG margins of ≤33-fold, while those exceeding 20 ms were generally associated with margins of ≤24-fold. QTc increases above 10 ms were not observed at hERG margins >100-fold. Based on 53 clinical concentration-QTc datasets, modest hERG inhibition levels of ∼4-6 % correlated with a 10 ms QTc prolongation, while ∼10-13 % inhibition corresponded to a 20 ms prolongation.

CONCLUSIONS

This study enhances understanding of the relationship between hERG inhibition and QTc prolongation magnitude, by conducting analysis across a wide range of 98 compounds. This information can be used to determine the optimal hERG margin, particularly for drug discovery projects with limited scope to completely design-out hERG activity.

A comprehensive stroke risk assessment by combining atrial computational fluid dynamics simulations and functional patient data

Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like theCHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.