Key result
Simulated high- and low-risk drug combinations alter TdP biomarkers nonlinearly based on concentration.
Why the study?
While CiPA assesses single-drug TdP risk, multiple medication therapies are commonly used, necessitating an expansion of TdP risk assessment to drug combinations.
Does in silico modeling of drug-drug interactions under the CiPA paradigm predict the TdP risk of combined medications?
Does in silico modeling of drug-drug interactions under the CiPA paradigm predict the TdP risk of combined medications?
In silico modeling of drug-drug interactions using the CiPA paradigm demonstrates that combining drugs with different TdP risk profiles can nonlinearly alter proarrhythmic risk, highlighting the need for computational assessment of polypharmacy.
Nonlinear TdP biomarker shifts with drug combos suggest polypharmacy caution; leaves open CiPA model validation for clinical risk prediction.
Researchers have recently proposed the Comprehensive In-vitro Proarrhythmia Assay (CiPA) to analyze medicines’ TdP risks. Using the TdP metric known as qNet, numerous single-drug effects have been studied to classify the medications as low, intermediate, and high-risk. Furthermore, multiple medication therapies are recognized as a potential method for curing patients, mainly when a limited number of drugs are available. This work expands the TdP risk assessment of drugs by introducing a CiPA-based in silico analysis of the TdP risk of combined drugs. The cardiac cell model was simulated using the population of models approach incorporating drug-drug interactions (DDIs) models for various two-drug combinations. Action potential duration (APD90), qNet, and calcium duration (CaD90) were computed and analyzed as features. The drug combination maps were also utilized to illustrate the impact of DDIs on the TdP risk of combined medicines. We found that the DDIs of the combined drugs alter cell responses in terms of biomarkers such as APD90, qNet, and CaD90 in a highly nonlinear manner. The results also revealed that combinations of high-risk with low-risk and intermediate-risk with low-risk drugs could result in compounds with varying TdP risks depending on the drug concentrations.
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Qauli et al. (2022) studied Drug-induced Torsade de pointes (TdP). Drug-drug combinations (12 CiPA drugs) vs. Single drug effects was evaluated on qNet, APD90, and CaD90 (TdP risk metrics). In silico simulations of drug-drug interactions demonstrated that combining high-risk and low-risk drugs alters TdP risk biomarkers like qNet and APD90 in a highly nonlinear manner depending on drug concentrations.
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