Pharmaceutical research · 2026
Development of a Mechanistic Distribution Model for Predicting Drug Partitioning into Human Milk.
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Deferm Neel, Murphy Anna, Gardner Iain, Abduljalil Khaled
Аннотация
PURPOSE/OBJECTIVE: Limited clinical data on drug safety during lactation complicate benefit-risk assessment for breastfeeding women who require pharmacotherapy. Mechanistic prediction of the milk-to-plasma ratio within physiologically-based pharmacokinetic frameworks offers a complementary and efficient approach to traditional clinical lactation studies. We aimed to develop a mechanistic milk partitioning model for milk-to-plasma ratio prediction that explicitly accounts for protein binding, pH-dependent ionization, lipid partitioning, electrostatic interactions with acidic phospholipids present in milk fat globule membranes, and membrane potential effects on drug disposition.
METHODS: Model performance was compared with both the phase distribution model and its log-transformed variant using a verification dataset of 107 observations from 57 compounds with diverse physicochemical properties.
RESULTS: For acids, moderate-to-strong bases and ampholytes, the milk partitioning model demonstrated the best performance (absolute average fold error (AAFE): 1.99, 1.70 and 3.34), outperforming both comparator models. For weak bases, the milk partitioning model achieved comparable accuracy to the phase distribution model and its log-transformed variant (AAFE: 1.63 vs. 1.62 and 1.64) while showing lower bias (average fold error (AFE): 0.92 vs. 0.72 and 0.73). Finally, for neutral compounds, the phase distribution model showed superior performance to the milk partitioning model (AAFE: 2.35 vs. 2.60).
CONCLUSIONS: When integrated within a physiologically-based pharmacokinetic framework, the milk partitioning model provides a mechanistically robust and practical tool to support quantitative assessment of drug transfer into human milk and inform safer dosing strategies for breastfeeding populations.
METHODS: Model performance was compared with both the phase distribution model and its log-transformed variant using a verification dataset of 107 observations from 57 compounds with diverse physicochemical properties.
RESULTS: For acids, moderate-to-strong bases and ampholytes, the milk partitioning model demonstrated the best performance (absolute average fold error (AAFE): 1.99, 1.70 and 3.34), outperforming both comparator models. For weak bases, the milk partitioning model achieved comparable accuracy to the phase distribution model and its log-transformed variant (AAFE: 1.63 vs. 1.62 and 1.64) while showing lower bias (average fold error (AFE): 0.92 vs. 0.72 and 0.73). Finally, for neutral compounds, the phase distribution model showed superior performance to the milk partitioning model (AAFE: 2.35 vs. 2.60).
CONCLUSIONS: When integrated within a physiologically-based pharmacokinetic framework, the milk partitioning model provides a mechanistically robust and practical tool to support quantitative assessment of drug transfer into human milk and inform safer dosing strategies for breastfeeding populations.