Developing physiologically based pharmacokinetic models to understand lactation exposure to medications: Gaps in available data and call for action

Although a significant proportion of postpartum women use medication whilst breastfeeding, information regarding infant exposure to maternal medication via the breast milk is typically limited. In addition to growing pressures for the generation of clinical lactation data from regulators, interest in the use of alternative techniques, such as physiologically based pharmacokinetic (PBPK) modelling, to support clinical studies in this underserved population is increasing. This is consistent with the current interest in model-informed drug development. For lactation models to contribute to clinical decision making, the uncertainties underpinning these models must be understood and minimized where possible. This review therefore aimed to investigate the breast milk distribution models used to support PBPK modelling in the lactation context and to characterize the sources and extent of uncertainty associated with these models. Three models were identified throughout the literature, termed as the Phase Distribution, Log-Transformed Phase Distribution, and Semi-Mechanistic models. Model performance, as compared against observed data, was broadly consistent between each of the three models but significant limitations were identified. Specifically, there is a paucity of clinical lactation data in which breast milk constituents are adequately defined to allow validation of mechanistic models; in vitro data inconsistently support model assumptions; and limited data have been generated using samples from a range of women to offer an understanding of population variability. These gaps in available data are clearly addressable, and further work should be conducted to better support the use of modelling and help address the inequities in safe access to medicine currently faced by women who breastfeed their infants.