Prediction of basic drug exposure in milk using a lactation model algorithm integrated within a physiologically based...

Area of Science:

• Pharmacology
• Toxicology
• Maternal-Fetal Medicine

Background:

• Medication use during breastfeeding poses risks due to infant drug exposure via breast milk.
• Limited data exists on the safety of many medications for breastfeeding infants.
• Accurate prediction of drug levels in breast milk is crucial for neonatal risk assessment.

Purpose of the Study:

• To evaluate an integrated lactation model within a Physiologically Based Pharmacokinetic (PBPK) platform.
• To assess the model's predictive performance for drug concentrations in breast milk.
• To estimate infant drug exposure from maternal medication use.

Main Methods:

• Utilized a 'log transformed phase-distribution' lactation model integrated into a PBPK platform.
• Incorporated physicochemical properties of four basic drugs: tramadol, venlafaxine, fluoxetine, and paroxetine.
• Used the Simcyp Simulator V20 to predict milk-to-plasma (M/P) ratios and infant doses.

Main Results:

• PBPK models accurately predicted maternal plasma exposure, M/P ratios, and infant daily doses within a two-fold margin.
• The integrated model successfully predicted drug exposure in breast milk for the studied compounds.
• The model demonstrated strong predictive performance for basic drugs passively diffusing into milk.

Conclusions:

• Integrating lactation models into PBPK platforms enhances prediction of drug exposure in breast milk.
• The developed PBPK model serves as a valuable tool for neonatal risk assessment and lactation study design.
• This approach supports informed decisions regarding maternal medication use during breastfeeding.