National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA.
School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA.
Expert Opin Drug Metab Toxicol. 2024 Jul;20(7):665-684. doi: 10.1080/17425255.2024.2377593. Epub 2024 Jul 10.
Cardiotoxicity is a major cause of drug withdrawal. The hERG channel, regulating ion flow, is pivotal for heart and nervous system function. Its blockade is a concern in drug development. Predicting hERG blockade is essential for identifying cardiac safety issues. Various QSAR models exist, but their performance varies. Ongoing improvements show promise, necessitating continued efforts to enhance accuracy using emerging deep learning algorithms in predicting potential hERG blockade.
Using a large training dataset, six individual QSAR models were developed. Additionally, three ensemble models were constructed. All models were evaluated using 10-fold cross-validations and two external datasets.
The 10-fold cross-validations resulted in Mathews correlation coefficient (MCC) values from 0.682 to 0.730, surpassing the best-reported model on the same dataset (0.689). External validations yielded MCC values from 0.520 to 0.715 for the first dataset, exceeding those of previously reported models (0-0.599). For the second dataset, MCC values fell between 0.025 and 0.215, aligning with those of reported models (0.112-0.220).
The developed models can assist the pharmaceutical industry and regulatory agencies in predicting hERG blockage activity, thereby enhancing safety assessments and reducing the risk of adverse cardiac events associated with new drug candidates.
心脏毒性是药物撤药的主要原因。调节离子流的 hERG 通道对心脏和神经系统功能至关重要。其阻断是药物开发中的一个关注点。预测 hERG 阻断对于识别心脏安全性问题至关重要。存在各种 QSAR 模型，但它们的性能有所不同。正在进行的改进显示出前景，需要使用新兴的深度学习算法不断努力提高预测潜在 hERG 阻断的准确性。
使用大型训练数据集，开发了六个独立的 QSAR 模型。此外，还构建了三个集成模型。所有模型均通过 10 折交叉验证和两个外部数据集进行评估。
10 折交叉验证得出的 Matthews 相关系数（MCC）值在 0.682 到 0.730 之间，超过了同一数据集上报告的最佳模型（0.689）。外部验证得出的第一个数据集的 MCC 值在 0.520 到 0.715 之间，超过了之前报告的模型（0-0.599）。对于第二个数据集，MCC 值在 0.025 到 0.215 之间，与报告的模型一致（0.112-0.220）。
所开发的模型可以帮助制药行业和监管机构预测 hERG 阻断活性，从而增强安全性评估并降低与新候选药物相关的不良心脏事件的风险。
