Department of Burns, Changhai Hospital, Second Military Medical University, Shanghai, China.
The Centre for Big Data Research in Burns and Trauma, Fuzhou University, Fujian Province, China.
Biomed Res Int. 2020 May 19;2020:4795140. doi: 10.1155/2020/4795140. eCollection 2020.
Drug discovery is a costly process which usually takes more than 10 years and billions of dollars for one successful drug to enter the market. Despite all the safety tests, drugs may still cause adverse reactions and be restricted in use or even withdrawn from the market. Drug-induced liver injury (DILI) is one of the major adverse drug reactions, and computational models may be used to predict and reduce it. To assess the computational prediction performance of DILI, we curated DILI endpoints from three databases and prepared drug features including chemical descriptors, therapeutic classifications, gene expressions, and binding proteins. We trained machine-learning models to predict the various DILI endpoints using different drug features. Using the optimal feature sets, the top-performing models obtained areas under the receiver operating characteristic curve (AUC) around 0.8 for some DILI endpoints. We found that some features, including therapeutic classifications and proteins, have good prediction performance towards DILI. We also discovered that the severity of DILI endpoints as well as the selection of negative samples may significantly affect the prediction results. Overall, our study provided a comprehensive collection, curation, and prediction of DILI endpoints using various drug features, which may help the drug researchers to better understand and prevent DILI during the drug discovery process.
药物发现是一个成本高昂的过程，通常需要超过 10 年的时间和数十亿美元的资金，才能使一种成功的药物进入市场。尽管进行了所有的安全测试，药物仍然可能引起不良反应，并限制使用，甚至从市场上撤回。药物性肝损伤 (DILI) 是主要的药物不良反应之一，可以使用计算模型来预测和减少它。为了评估 DILI 的计算预测性能，我们从三个数据库中整理了 DILI 终点，并准备了药物特征，包括化学描述符、治疗分类、基因表达和结合蛋白。我们使用不同的药物特征训练机器学习模型来预测各种 DILI 终点。使用最佳特征集，针对某些 DILI 终点，表现最佳的模型获得了接近 0.8 的接收器操作特征曲线 (AUC) 下面积。我们发现，一些特征，包括治疗分类和蛋白质，对 DILI 具有良好的预测性能。我们还发现，DILI 终点的严重程度以及阴性样本的选择可能会显著影响预测结果。总的来说，我们的研究使用各种药物特征对 DILI 终点进行了全面的收集、整理和预测，这可能有助于药物研究人员在药物发现过程中更好地理解和预防 DILI。
