hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Journal of pharmacological and toxicological methods Pub Date : 2023-09-01 DOI:10.1016/j.vascn.2023.107293
Aziza El Harchi, Jules C. Hancox
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Abstract

Pharmacological blockade of the IKr channel (hERG) by diverse drugs in clinical use is associated with the Long QT Syndrome that can lead to life threatening arrhythmia. Various computational tools including machine learning models (MLM) for the prediction of hERG inhibition have been developed to facilitate the throughput screening of drugs in development and optimise thus the prediction of hERG liabilities. The use of MLM relies on large libraries of training compounds for the quantitative structure-activity relationship (QSAR) modelling of hERG inhibition. The focus on inhibition omits potential effects of hERG channel agonist molecules and their associated QT shortening risk. It is instructive, therefore, to consider how known hERG agonists are handled by MLM. Here, two highly developed online computational tools for the prediction of hERG liability, Pred-hERG and HergSPred were probed for their ability to detect hERG activator drug molecules as hERG interactors. In total, 73 hERG blockers were tested with both computational tools giving overall good predictions for hERG blockers with reported IC50s below Pred-hERG and HergSPred cut-off threshold for hERG inhibition. However, for compounds with reported IC50s above this threshold such as disopyramide or sotalol discrepancies were observed. HergSPred identified all 20 hERG agonists selected as interacting with the hERG channel. Further studies are warranted to improve online MLM prediction of hERG related cardiotoxicity, by explicitly taking into account channel agonism as well as inhibition.

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hERG激动剂对基于网络的预测药物hERG通道相互作用的机器学习方法提出了挑战
临床使用的多种药物对IKr通道(hERG)的药理学阻断与长QT综合征有关,该综合征可导致危及生命的心律失常。已经开发了包括用于预测hERG抑制的机器学习模型(MLM)在内的各种计算工具,以促进正在开发的药物的吞吐量筛选,从而优化hERG负债的预测。MLM的使用依赖于用于hERG抑制的定量构效关系(QSAR)建模的训练化合物的大型文库。对抑制的关注忽略了hERG通道激动剂分子的潜在作用及其相关的QT缩短风险。因此,考虑传销如何处理已知的hERG激动剂是有指导意义的。在这里,探索了两种高度开发的用于预测hERG责任的在线计算工具Pred-hERG和HergSPred检测作为hERG相互作用物的hERG激活剂药物分子的能力。总共用这两种计算工具测试了73种hERG阻滞剂,对hERG阻断剂给出了总体良好的预测,报告的IC50低于Pred-hERG和HergSPred的hERG抑制截止阈值。然而,对于报告的IC50高于该阈值的化合物,如吡喃二胺或索他洛尔,观察到差异。HergSPred鉴定了所有20种被选择为与hERG通道相互作用的hERG激动剂。通过明确考虑通道激动和抑制,有必要进行进一步的研究,以改进hERG相关心脏毒性的在线MLM预测。
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来源期刊
Journal of pharmacological and toxicological methods
Journal of pharmacological and toxicological methods PHARMACOLOGY & PHARMACY-TOXICOLOGY
CiteScore
3.60
自引率
10.50%
发文量
56
审稿时长
26 days
期刊介绍: Journal of Pharmacological and Toxicological Methods publishes original articles on current methods of investigation used in pharmacology and toxicology. Pharmacology and toxicology are defined in the broadest sense, referring to actions of drugs and chemicals on all living systems. With its international editorial board and noted contributors, Journal of Pharmacological and Toxicological Methods is the leading journal devoted exclusively to experimental procedures used by pharmacologists and toxicologists.
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