This study replicates human cardiac safety clinical trials using computational models, which include diurnal hormonal placebo effects, aiming to create fast and efficient drug screening tools to assess QT interval prolongation preclinically. A virtual cardiac population that simulates sex-specific electrophysiological variability influenced by diurnal hormonal cycles was created to closely mirror human physiology. The goal is to assess the impact of these hormone dynamics on placebo-induced responses and evaluate the virtual population's accuracy in reflecting clinical trial outcomes. The study showed that the virtual population model successfully replicated about 50% of the observed QT variability seen in placebo groups. The computational framework facilitated direct comparison with human clinical trials, achieving critical concentrations, i.e., concentrations that cause a mean QTc effect of 10 ms, within a 0.6-fold range of clinical results. Additionally, the exposure-response slopes exhibited relative errors averaging 43%, which translates into mean absolute errors of 6-7 ms in comparison to clinical data. These results highlight its potential to reproduce human physiology and provide results consistent with real-world human clinical trials. Additionally, a comparison with a previously published statistical placebo model further supports this approach. By approximating the diurnal hormonal variations, as a component of the mechanistic basis of placebo, this method offers a robust decision-making tool for early-stage drug safety assessment.

M.V. is CTO and co-founder of ELEM Biotech. Elem Biotech owns the commercial rights to Alya, the computational finite element solver employed in this study.

This work was supported by the Partnership for Advanced Computing in Europe (grant number EHPC-REG-2022R01-038-EuroHPC) awarded to J.A.

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I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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The methodology can be replicated using any finite element solver given all the parameterization information provided in this paper. Quantified biomarkers can be made available upon request.