In silico methods are often used for predicting pharmacokinetic properties of drugs due to their simplicity and cost-effectiveness. This study evaluates the penetration of 83 active pharmaceutical ingredients into human breast milk with an experimental milk-to-plasma ratio (M/P) obtained from the literature. Multiple linear regression (MLR), partial least squares (PLS) and random forest (RF) regression methods were compared to uncover the relationship between physicochemical, pharmacokinetic and membrane crossing properties of active pharmaceutical ingredients (APIs) using their rapid reference measurement value (R_f values), thin-layer chromatography (TLC) data from albumin-impregnated plates. Molecular descriptors of APIs proven to be important for their crossing into breast milk, including protein binding, ionisation state and lipophilicity and TLC data, have been included in the development of the prediction models. The best regression results were achieved by MLR (r ² = 0.83 and r ² = 0.86, n = 28) and RF (r ² = 0.85, n = 58). In addition, the discriminant function analysis (DFA) was performed on acidic, basic and neutral drugs separately and showed a prediction accuracy of 93%, with M/P included as the discriminating variable.

由于其简单性和成本效益，计算机方法通常用于预测药物的药代动力学特性。这项研究以从文献中获得的实验性乳浆比（M / P）评估了83种活性药物成分在人乳中的渗透性。比较了多元线性回归（MLR），偏最小二乘（PLS）和随机森林（RF）回归方法，利用其快速参考测量值（R）揭示了活性药物成分（API）的理化，药代动力学和膜交叉特性之间的关系_F值），白蛋白浸渍板的薄层色谱（TLC）数据。已证明对API进入母乳非常重要的分子描述符，包括蛋白质结合，电离状态和亲脂性以及TLC数据，已包含在预测模型的开发中。通过MLR（r ²  = 0.83和r ²  = 0.86，n = 28）和RF（r ²  = 0.85，n = 58）可获得最佳回归结果。此外，对酸性，碱性和中性药物分别进行了判别功能分析（DFA），预测准确性为93％，其中M / P作为判别变量。