In silico assessment on TdP risks of drug combinations under CiPA paradigm

Dosing Time of Day Impacts the Safety of Antiarrhythmic Drugs in a Computational Model of Cardiac Electrophysiology

Circadian clocks regulate many aspects of human physiology, including cardiovascular function and drug metabolism. Administering drugs at optimal times of the day may enhance effectiveness and reduce side effects. Certain cardiac antiarrhythmic drugs have been withdrawn from the market due to unexpected proarrhythmic effects such as fatal Torsade de Pointes (TdP) ventricular tachycardia. The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a recent global initiative to create guidelines for the assessment of drug-induced arrhythmias that recommends a central role for computational modeling of ion channels and in silico evaluation of compounds for TdP risk. We simulated circadian regulation of cardiac excitability and explored how dosing time of day affects TdP risk for 11 drugs previously classified into risk categories by CiPA. The model predicts that a high-risk drug taken at the most optimal time of day may actually be safer than a low-risk drug taken at the least optimal time of day. Based on these proof-of-concept results, we advocate for the incorporation of circadian clock modeling into the CiPA paradigm for assessing drug-induced TdP risk. Since cardiotoxicity is the leading cause of drug discontinuation, modeling cardiac-related chronopharmacology has significant potential to improve therapeutic outcomes.

Cardiotoxicity evaluation of two-drug fixed-dose combination therapy under CiPA: a computational study

The Comprehensive In Vitro Proarrhythmia Assay (CiPA) evaluates drug-induced torsade de pointes (TdP) risk, with qNet commonly used to classify drugs into low-, intermediate-, and high-risk categories. While most studies focus on single-drug effects, 2-drug fixed-dose combination (FDC) therapy is widely used for cardiovascular disease management. We aimed to develop the CiPA-based methodology to predict adverse effects of FDC therapy. A human ventricular cell model was stimulated under the effects of various drug combinations from twelve well-characterized compounds suggested by CiPA at 1 to 4 maximum plasma concentration, and the qNetavg biomarker as a function of the ratio of two drugs was used to evaluate the TdP risk of combined compounds. Results showed that high-risk and intermediate-risk drug combinations often yielded lower qNetavg than individual drugs, suggesting increased TdP risk. Conversely, combinations involving low-risk drugs tended to reduce TdP risk by raising qNetavg above individual drug levels. Also, we found that the interplay of some major ionic channels caused variations on qNetavg. These findings highlight the importance of evaluating FDC cardiotoxicity to predict risks that may not appear in single-drug analysis.

Deconvoluting and derisking QRS complex widening to improve cardiac safety profile of novel plasmepsin X antimalarials

Quinoline-related antimalarial drugs have been associated with cardiotoxicity risk, in particular QT prolongation and QRS complex widening. In collaboration with Medicines for Malaria Venture, we discovered novel plasmepsin X (PMX) inhibitors for malaria treatment. The first lead compounds tested in anesthetized guinea pigs (GPs) induced profound QRS widening, although exhibiting weak inhibition of NaV1.5-mediated currents in standard patch clamp assays. To understand the mechanism(s) underlying QRS widening to identify further compounds devoid of such liability, we established a set of in vitro models including CaV1.2, NaV1.5 rate-dependence, and NaV1.8 patch clamp assays, human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM), and Langendorff-perfused isolated GP hearts. Six compounds were tested in all models including anesthetized GP, and 8 additional compounds were tested in vitro only. All compounds tested in anesthetized GP and isolated hearts showed a similar cardiovascular profile, consisting of QRS widening, bradycardia, negative inotropy, hypotension, and for some, QT prolongation. However, a left shift of the concentration-response curves was noted from in vitro to in vivo GP data. When comparing in vitro models, there was a good consistency between decrease in sodium spike amplitude in hiPSC-CM and QRS widening in isolated hearts. Patch clamp assay results showed that the QRS widening observed with PMX inhibitors is likely multifactorial, primarily due to NaV1.8 and NaV1.5 rate-dependent sodium blockade and/or calcium channel-mediated mechanisms. In conclusion, early de-risking of QRS widening using a set of different in vitro assays allowed to identify novel PMX inhibitors with improved cardiac safety profile.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

In Silico Human Cardiomyocyte Action Potential Modeling: Exploring Ion Channel Input Combinations

In silico modeling offers an opportunity to supplement and accelerate cardiac safety testing. With in silico modeling, computational simulation methods are used to predict electrophysiological interactions and pharmacological effects of novel drugs on critical physiological processes. The O'Hara-Rudy's model was developed to predict the response to different ion channel inhibition levels on cardiac action potential duration (APD) which is known to directly correlate with the QT interval. APD data at 30% 60% and 90% inhibition were derived from the model to delineate possible ventricular arrhythmia scenarios and the marginal contribution of each ion channel to the model. Action potential values were calculated for epicardial, myocardial, and endocardial cells, with action potential curve modeling. This study assessed cardiac ion channel inhibition data combinations to consider when undertaking in silico modeling of proarrhythmic effects as stipulated in the Comprehensive in Vitro Proarrhythmia Assay (CiPA). As expected, our data highlight the importance of the delayed rectifier potassium channel (IKr) as the most impactful channel for APD prolongation. The impact of the transient outward potassium channel (Ito) inhibition on APD was minimal while the inward rectifier (IK1) and slow component of the delayed rectifier potassium channel (IKs) also had limited APD effects. In contrast, the contribution of fast sodium channel (INa) and/or L-type calcium channel (ICa) inhibition resulted in substantial APD alterations supporting the pharmacological relevance of in silico modeling using input from a limited number of cardiac ion channels including IKr, INa, and ICa, at least at an early stage of drug development.