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

Abstract

Background and purpose A recent paradigm shift in proarrhythmic risk assessment suggests that the integration of clinical, non-clinical, and computational evidence can be used to reach a comprehensive understanding of the proarrhythmic potential of drug candidates. While current computational methodologies focus on predicting the incidence of proarrhythmic events after drug administration, the objective of this study is to predict concentration-response relationships of QTc as a clinical endpoint. Experimental approach Full heart computational models reproducing human cardiac populations were created to predict the concentration-response relationship of changes in the QT interval as recommended for clinical trials. The concentration-response relationship of the QT-interval prolongation obtained from the computational cardiac population was compared against the relationship from clinical trial data for a set of well-characterized compounds: moxifloxacin, dofetilide, verapamil, and ondansetron. Key results Computationally derived concentration–response relationships of QT interval changes for three of the four drugs had slopes within the confidence interval of clinical trials (dofetilide, moxifloxacin and verapamil) when compared to placebo-corrected concentration-ΔQT and concentration-ΔQT regressions. Moxifloxacin showed a higher intercept, outside the confidence interval of the clinical data, demonstrating that in this example, the standard linear regression does not appropriately capture the concentration-response reulsts at very low concentrations. The concentrations corresponding to a mean QTc prolongation of 10 ms were consistently lower in the computational model than in clinical data. The critical concentration varied within an approximate ratio of 0.5 (moxifloxacin and ondansetron) and 1 times (dofetilide, verapamil) the critical concentration observed in human clinical trials. Notably, no other in silico methodology can approximate the human critical concentration values for a QT interval prolongation of 10 ms. Conclusion and implications Computational concentration-response modelling of a virtual population of high-resolution, 3-dimensional cardiac models can provide comparable information to clinical data and could be used to complement pre-clinical and clinical safety packages. It provides access to an unlimited exposure range to support trial design and can improve the understanding of pre-clinical-clinical translation.

JA-S is funded by a Ramon y Cajal fellowship (RYC-2017-22,532), Ministerio de Ciencia e Innovacion, Spain; and by Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion (meHeart ME PID2019-104356RB-C44). CB is funded by the Torres Quevedo Program (PTQ2018–010290), Ministerio de Ciencia e Innovacion, Spain. AA is funded by the Torres Quevedo Program (PTQ2020–011408), Ministerio de Ciencia e Innovacion, Spain. JA-S, and MV are supported by the European Union's Horizon 2020 research and innovation programme under grant agreements No. 823712 (CompBioMed project, phase 2) and No. 951773 (PerMedCoE). Elem Biotech research staff was supported by the RED.ES Spanish programme, under grant reference No 2021/C005/00149823, during the development phase of the project.

Peer Reviewed

Postprint (published version)