Application of Lactation Physiologically Based Pharmacokinetic Modeling to Predict Milk Exposure of Passively...

Physiologically based pharmacokinetic (PBPK) models have gained interest as a tool for predicting drug transfer to human milk and assessing the exposure levels in infants. Our group previously developed an integrated lactation PBPK model framework to understand the transfer of drugs into milk and the resulting exposure in infants. As a part of the framework, the current paper focuses on performance of lactation PBPK models to predict maternal plasma and milk concentrations for drugs that are not substrates of P-glycoprotein (P-gp) or breast cancer resistance protein (BCRP) transporters-atenolol, escitalopram, and alprazolam. PBPK models for healthy adult subjects were developed for atenolol and escitalopram in Simcyp v23 and verified against clinical data. For alprazolam, the Simcyp library model was used. The built-in lactation compartment in Simcyp was utilized for the three drugs. Atenolol and escitalopram adult healthy PBPK models showed agreement of plasma concentration-time profiles with respective clinical data. The lactation PBPK models predicted milk concentration profiles with reasonable agreement for all three drugs as well. The predicted milk concentration-time profile, milk pharmacokinetic parameters and milk-to-plasma (M/P) ratio of atenolol, alprazolam, and escitalopram were within 2-fold of the reported values, suggesting agreement between simulation and clinical data. The current work shows the potential of lactation PBPK models to predict drug exposure in human milk for drugs that are not P-gp or BCRP substrates. The approach will be further evaluated for drugs that are substrates for these active transporters known to be present in mammary tissues.