PDF(Pubmed)
Abstract：
Drug-induced liver injury (DILI) poses a significant challenge for the pharmaceutical industry and regulatory bodies. Despite extensive toxicological research aimed at mitigating DILI risk, the effectiveness of these techniques in predicting DILI in humans remains limited. Consequently, researchers have explored novel approaches and procedures to enhance the accuracy of DILI risk prediction for drug candidates under development. In this study, we leveraged a large human dataset to develop machine learning models for assessing DILI risk. The performance of these prediction models was rigorously evaluated using a 10-fold cross-validation approach and an external test set. Notably, the random forest (RF) and multilayer perceptron (MLP) models emerged as the most effective in predicting DILI. During cross-validation, RF achieved an average prediction accuracy of 0.631, while MLP achieved the highest Matthews Correlation Coefficient (MCC) of 0.245. To validate the models externally, we applied them to a set of drug candidates that had failed in clinical development due to hepatotoxicity. Both RF and MLP accurately predicted the toxic drug candidates in this external validation. Our findings suggest that in silico machine learning approaches hold promise for identifying DILI liabilities associated with drug candidates during development.
摘要：
药物诱导的肝损伤（DILI）对制药行业和监管机构提出了重大挑战。尽管广泛的毒理学研究旨在减轻DILI风险，这些技术在预测人类DILI方面的有效性仍然有限。因此,研究人员探索了新的方法和程序,以提高正在开发的候选药物的DILI风险预测的准确性.在这项研究中,我们利用大型人类数据集来开发用于评估DILI风险的机器学习模型。使用10倍交叉验证方法和外部测试集严格评估了这些预测模型的性能。值得注意的是,随机森林（RF）和多层感知器（MLP）模型是预测DILI最有效的模型。在交叉验证期间，RF的平均预测精度为0.631，而MLP的最高马修斯相关系数（MCC）为0.245。要从外部验证模型，我们将其应用于一组因肝毒性而在临床开发中失败的候选药物.RF和MLP在该外部验证中均准确预测了毒性候选药物。我们的研究结果表明，计算机机器学习方法有望在开发过程中识别与候选药物相关的DILI负债。
