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Machine learning-based prediction of drug lactation risk: Bridging molecular features and breastfeeding safety
Peineng Liu1, Shaokai Huang1, Xiaochun Xie1
1Department of Pharmacy, Jieyang People's Hospital, Jieyang, 522000, China.
Background:
Breastfeeding medication safety assessment presents critical challenges due to limited clinical evidence and ethical constraints on lactation studies. While existing computational models focus on predicting pharmacokinetic surrogates such as milk-to-plasma ratios, these endpoints lack direct clinical actionability for therapeutic decision-making.
Methods:
We developed LRCpredictor, a computational framework for predicting drug lactation risk based on Dr. Thomas W. Hale's evidence-based Lactation Risk Categories (LRC) system. A dataset of 391 drugs (179 high-risk L4/L5, 212 low-risk L1/L2) was characterized using three complementary molecular representations. Systematic feature engineering with four selection algorithms identified optimal feature subsets. Five ensemble machine learning algorithms were evaluated through cross-validation and independent testing. Multi-level SHAP analysis and structural alert mining were employed to elucidate molecular determinants of lactation risk.
Results:
The optimal Gradient Boosting Decision Tree (GBDT) model using 35 selected features achieved robust cross-validated performance (AUC = 0.80, MCC = 0.52) and demonstrated notably superior discrimination for extreme risk categories (L1 vs. L5: AUC = 0.85, MCC = 0.62). Analysis revealed lactation risk is governed by the complex interplay of electronic properties, structural topology, electrotopological characteristics, polarizability and drug-likeness attributes. Matched molecular pair analysis demonstrated how specific structural modifications translate into interpretable risk changes. We further identified 18 structural alerts, including five exclusive to high-risk drugs, enabling rapid toxicophore recognition during risk assessment and drug design.
Conclusions:
LRCpredictor provides the first interpretable framework for clinically actionable lactation risk prediction with mechanistic molecular insights. The freely accessible web platform (https://lrcpredictor.streamlit.app/) with integrated visual explanations supports evidence-based decision-making for healthcare providers and drug design optimization.
