PDF(Pubmed)
Abstract：
Safety pharmacology examines the potential for new drugs to have unusual, rare side effects such as torsade de pointes (TdP). Recently, as a part of the Comprehensive in vitro Proarrhythmia Assay (CiPA) project, techniques for predicting the development of drug-induced TdP through computer simulations have been proposed and verified. However, CiPA assessment generally does not consider the effect of cardiac cell inter-individual variability, especially related to metabolic status. The study aimed to explore whether rare proarrhythmic effects may be linked to the inter-individual variability of cardiac cells and whether incorporating this variability into computational models could alter the prediction of drugs\' TdP risks. This study evaluated the contribution of two biological characteristics to the proarrhythmic effects. The first was spermine concentration, which varies with metabolic status; the second was L-type calcium permeability that could occur due to mutations. Twenty-eight drugs were examined throughout this study, and qNet was analyzed as an essential feature. Even though there were some discrepancies of TdP risk predictions from the baseline model, we found that considering the inter-individual variability might change the TdP risk of drugs. Several drugs in the high-risk drugs group were predicted to affect as intermediate and low-risk drugs in some individuals and vice versa. Also, most intermediate-risk drugs were expected to act as low-risk drugs. When compared, the effects of inter-individual variability of L-type calcium were more significant than spermine in altering the TdP risk of compounds. These results emphasize the importance of considering inter-individual variability to assess drugs.
摘要：
安全药理学检查了新药具有不寻常的潜力，罕见的副作用，如尖端扭转(TdP)。最近,作为体外心律失常综合检测（CiPA）项目的一部分，已经提出并验证了通过计算机模拟预测药物诱导的TdP发展的技术。然而,CiPA评估通常不考虑心脏细胞间变异性的影响，尤其是与代谢状态有关。本研究旨在探讨罕见的致心律失常效应是否可能与心脏细胞的个体间变异性有关，以及将这种变异性纳入计算模型是否可以改变药物TdP风险的预测。这项研究评估了两种生物学特征对心律失常作用的贡献。第一个是精胺浓度，随代谢状态而变化；第二种是由于突变可能发生的L型钙通透性。在整个研究过程中检查了28种药物，并将qNet作为一个基本特征进行了分析。尽管TdP风险预测与基线模型存在一些差异，我们发现,考虑个体间的变异性可能会改变药物的TdP风险.预测高危药物组中的几种药物在某些个体中作为中危和低危药物发挥作用，反之亦然。此外，大多数中危药物被认为是低危药物.当比较时,在改变化合物的TdP风险方面,L型钙的个体间变异性的影响比精胺更显著.这些结果强调了考虑个体间差异以评估药物的重要性。
