Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within ± 0.5959. The prediction proportions within 0.8–1.25-fold, 0.67–1.5-fold, and 0.5–2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients’. This model enables potential DDI evaluation before clinical trials, which will save time and cost.

许多机器学习技术为药物-药物相互作用 (DDI) 提供了简单的预测。但是，不存在系统构建的具有药代动力学 (PK) DDI 信息的数据库，也没有一个机器学习模型可以用它来数值预测 PK 倍数变化 (FC)。因此，我们提出了一个 PK DDI 预测 (PK-DDIP) 模型，用于高精度的定量 DDI 预测，同时构建了一个高度可靠的 PK-DDI 数据库。3,627 个 PK DDI 的可靠信息由 3,587 种药物使用 38,711 个食品和药物管理局 (FDA) 药物标签构建而成。该 PK-DDIP 模型预测时间浓度曲线 (AUC) 下面积的 FC 在 ± 0.5959 范围内。AUC在0.8-1.25倍、0.67-1.5倍和0.5-2倍内的预测比例分别为75.77、86.68和94.76%。两项外部验证证实了新更新的 FDA 标签和来自患者的 FC 的良好预测性能。该模型可以在临床试验之前进行潜在的 DDI 评估，这将节省时间和成本。