Machine learning approaches for assessing medication transfer to human breast milk.

The human milk/plasma (M/P) drug concentration ratio is crucial in pharmacology, especially for breastfeeding mothers undergoing treatment. It determines the extent to which drugs ingested by the mother pass into breast milk, potentially affecting the infant. This study conducted a comprehensive evaluation of multiple machine learning algorithms to assess their effectiveness in predicting the M/P ratio. The dataset consists of 162 drugs and 11 predictor variables. M/P ratios were categorized into two groups of (0, 1) and (≥ 1), and a refined three-category system: (0, < 0.5), (0.5, < 1), and (≥ 1). The ML techniques utilized include K-Nearest Neighbors (KNN), Random Forest, Support Vector Machine (SVM), and Neural Networks. We implied the five-fold cross-validation to ensure the model's robustness and Principal Component Analysis (PCA) was applied for data visualization. Bayesian Information Criterion (BIC) was used in the KNN model selection to balance complexity and explanatory power. In our study, KNN achieved average accuracies of 79% for the two-category system and 60% for the three-category. Random Forest models show 77 and 64% average accuracy, respectively. SVM achieved similar results with 78 and 67%, while Neural Networks have the overall best result among the other models with average accuracies of 82 and 76% accuracy. The study highlights the potential of machine learning (ML) techniques in predicting M/P ratios, offering valuable insights for risk assessment during drug development. These predictive models can serve as a valuable tool for estimating drug transfer into breast milk, helping to bridge knowledge gaps in drug safety for lactating individuals. Further validation and refinement by incorporating larger datasets can enhance their reliability and applicability. Advancing these techniques can support safer medication use and informed clinical decision-making for lactating individuals.