Discovery Technology Laboratories , Mitsubishi Tanabe Pharma Corporation , 2-2-50 Kawagishi , Toda , Saitama 335-8505 , Japan.
Mol Pharm. 2018 Nov 5;15(11):5302-5311. doi: 10.1021/acs.molpharmaceut.8b00785. Epub 2018 Sep 27.
Predicting the fraction unbound in plasma provides a good understanding of the pharmacokinetic properties of a drug to assist candidate selection in the early stages of drug discovery. It is also an effective tool to mitigate the risk of late-stage attrition and to optimize further screening. In this study, we built in silico prediction models of fraction unbound in human plasma with freely available software, aiming specifically to improve the accuracy in the low value ranges. We employed several machine learning techniques and built prediction models trained on the largest ever data set of 2738 experimental values. The classification model showed a high true positive rate of 0.826 for the low fraction unbound class on the test set. The strongly biased distribution of the fraction unbound in plasma was mitigated by a logarithmic transformation in the regression model, leading to improved accuracy at lower values. Overall, our models showed better performance than those of previously published methods, including commercial software. Our prediction tool can be used on its own or integrated into other pharmacokinetic modeling systems.
预测血浆中游离分数可以更好地了解药物的药代动力学性质，有助于在药物发现的早期阶段选择候选药物。这也是降低后期淘汰风险和优化进一步筛选的有效工具。在这项研究中，我们使用免费软件构建了人类血浆中游离分数的计算预测模型，旨在特别提高低值范围的准确性。我们采用了几种机器学习技术，并在有史以来最大的 2738 个实验值数据集上训练了预测模型。分类模型在测试集中对低游离分数类的真阳性率达到了 0.826。通过对回归模型进行对数变换，可以缓解血浆中游离分数的严重偏态分布，从而提高低值的准确性。总的来说，我们的模型比以前发表的方法（包括商业软件）表现更好。我们的预测工具可以单独使用，也可以集成到其他药代动力学建模系统中。
