Prediction of drug pro-arrhythmic cardiotoxicity using a semi-physiologically based pharmacokinetic model linked to cardiac ionic currents inhibition

多非利特 基于生理学的药代动力学模型 索他洛尔 QT间期 心脏毒性 药理学 尖端扭转 药代动力学 药品 药效学 心肌细胞 心肌细胞 医学 药物开发 化学 内科学 毒性 心房颤动
作者
Han Zhou,Zexin Zhang,Liang Zhu,Ping Li,Shijin Hong,Li Liu,Xiaodong Liu
出处
期刊:Toxicology and Applied Pharmacology [Elsevier]
卷期号:457: 116312-116312 被引量:3
标识
DOI:10.1016/j.taap.2022.116312
摘要

Drug-induced torsades de pointes (TdP) risks are responsible for the withdrawal of many drugs from the market. Nowadays, assessments of drug-induced TdP risks are mainly based on maximum effective free therapeutic plasma concentration (EFTPCmax) and cardiac ionic current inhibitions using the human ventricular myocytes model (Tor-ORd model). Myocytes are targets of drug-induced TdP. The TdP risks may be directly linked to myocyte drug concentrations. We aimed to develop a semi-physiologically based pharmacokinetic (Semi-PBPK) model linked to cardiac ionic current inhibition (pharmacodynamics, PD) (Semi-PBPK-PD) to simultaneously predict myocyte drug concentrations and their TdP risks in humans. Alterations in action potential duration (ΔAPD90) were simulated using the Tor-ORd model and ionic current inhibition parameters based on myocyte or plasma drug concentrations. The predicted ΔAPD90 values were translated into in vivo alterations in QT interval(ΔQTc) induced by moxifloxacin, dofetilide, or sotalol. Myocyte drug concentrations of moxifloxacin, dofetilide, and sotalol gave better predictions of ΔQTc than plasma. Following validating the developed semi-PBPK-PD model, TdP risks of 37 drugs were assessed using ΔAPD90 and early afterdepolarization occurrence, which were estimated based on 10 × EFTPCmax and 10 × EFTMCmax (maximum effective free therapeutic myocyte concentration). 10 × EFTMCmax gave more sensitive and accurate predictions of pro-arrhythmic cardiotoxicity and the predicted TdP risks were also closer to clinic practice than 10 × EFTPCmax. In conclusion, pharmacokinetics and TdP risks of 37 drugs were successfully predicted using the semi-PBPK-PD model. Myocyte drug concentrations gave better predictions of ΔQTc and TdP risks than plasma.
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