Andrew Butler, Essam Kerwash, Susan Cole
Medicines and Healthcare products Regulatory Agency
Introduction: Breastmilk provides the optimum nutrition and immune protection for infants and the World Health Organisation therefore recommends exclusive breastfeeding for the first six months of life, followed by transitioning into a combination of foods and breastmilk [1, 2]. Breastfeeding has been recognised as the single most cost-effective child health intervention, making it particularly beneficial in developing nations [3]. Such nations represent those in which malaria is most prevalent [2].
Many medications are excreted into the breastmilk when taken during the postpartum period. As the safety of anti-malarial medications in developing infants is unknown, women must choose between pausing breastfeeding during malaria treatment to guarantee infant safety or risking infant exposure by continuing to breastfeed [2].
The quantity of a drug which passes into the breastmilk is dependent upon the physicochemical properties of both the drug and the breastmilk, as generally only the unbound and unionised fraction is able to cross cell membranes. Thus, if the ionisation state in the milk differs from that of the plasma, the ratio of a drug in the milk compared to that in the plasma (milk-to-plasma [M/P] ratio) may differ from 1. Similarly, changes in milk fat content (creamatocrit) may affect the accumulation of a drug in the breast milk [4].
Throughout the literature there is a scarcity of data informing on the concentration of different medicines within the breastmilk [2]. Where data is available, postpartum time is often inadequately reported or data across a wide range of postpartum timepoints pooled for analysis. As such, published M/P ratios may not reflect those which would be recorded at a different postpartum timepoints. Mathematical models for predicting M/P are available however and can be used to predict infant exposure via the breast milk [5, 6].
Objectives: To investigate whether the postpartum changes in breastmilk properties may affect partitioning of malaria medications using mathematical models.
Methods: 19 anti-malarial medications were selected for analysis. The Fleishaker (FLE) [7] and Atkinson/Begg (AtB) [8] models of milk partitioning were interrogated using MATLAB (MathWorks). Postpartum changes in the properties of breast milk were extracted from the literature as utilised in Simcyp v22 (Certara). All drug properties were collated previously [9] apart from LogD7.2, which was predicted using the Simcyp Prediction Toolbox [10].
Results: The FLE model predicted the M/P ratio of all drugs to be ≤1.35 at all postpartum timepoints. The M/P ratios predicted by the AtB model were similar to, or larger than, those predicted by the FLE model. For drugs with a high LogD7.2 (e.g. atovaquone, artemether and lumefantrine), M/P ratios predicted by the AtB model were orders of magnitude larger. Despite these differences, the trends recorded across postpartum time were virtually identical between the two models.
For 8/19 drugs (including primaquine, artemether and doxycycline), postpartum time had little effect on the M/P ratio, with <40% difference between the maximum and minimum value predicted. For 7 drugs (including cycloguanil and atovaquone), the M/P ratio was ~2.5-fold larger at its predicted peak than at the lowest timepoint, and for the remaining four drugs M/P ratios were ~5.4 (DEAQ), ~6.5 (piperaquine) and ~7.0 (pyronaridine and chloroquine) fold larger at their peak than their respective troughs. The most significant changes in M/P ratios occurred during the fist few weeks postpartum as milk transitions from colostrum (pH ~7.6) to immature milk (pH ~7.15). After this initial rapid change, M/P ratios gradually returned towards values present in the immediate postpartum period as pH increased. Predicted changes in ionisation state were the primary driver for the change in M/P ratios across postpartum time.[SC1]
Conclusions: For drugs with a pKa close to the pH of breastmilk, milk pH and therefore postpartum time may have a significant effect on accumulation within the milk which could have serious consequences for infant safety. In order to appropriately interpret clinical studies of these medicines, milk pH and/or postpartum time needs to be accurately defined. Collection of these data is also necessary for the continued development of physiological models.
The authors are grateful to the Bill and Melinda Gates Foundation for funding this research.
References:
[1] Dieterich, C.M., et al., Breastfeeding and health outcomes for the mother-infant dyad. Pediatr Clin North Am, 2013. 60(1): p. 31-48.
[2] Ojara, F.W., A.N. Kawuma, and C. Waitt, A systematic review on maternal-to-infant transfer of drugs through breast milk during the treatment of malaria, tuberculosis, and neglected tropical diseases. PLOS Neglected Tropical Diseases, 2023. 17(7): p. e0011449.
[3] Balogun, O.O., et al., Factors influencing breastfeeding exclusivity during the first 6 months of life in developing countries: a quantitative and qualitative systematic review. Matern Child Nutr, 2015. 11(4): p. 433-51.
[4] Abduljalil, K., et al., Prediction of drug concentrations in milk during breastfeeding, integrating predictive algorithms within a physiologically-based pharmacokinetic model. CPT Pharmacometrics Syst Pharmacol, 2021. 10(8): p. 878-889.
[5] Pan, X., et al., Supplementing clinical lactation studies with PBPK modeling to inform drug therapy in lactating mothers: Prediction of primaquine exposure as a case example. CPT Pharmacometrics Syst Pharmacol, 2023.
[6] Begg, E.J. and H.C. Atkinson, Modelling of the passage of drugs into milk. Pharmacology & Therapeutics, 1993. 59(3): p. 301-310.
[7] Fleishaker, J.C., N. Desai, and P.J. McNamara, Factors affecting the milk-to-plasma drug concentration ratio in lactating women: physical interactions with protein and fat. J Pharm Sci, 1987. 76(3): p. 189-93.
[8] Atkinson, H.C. and E.J. Begg, Prediction of drug distribution into human milk from physicochemical characteristics. Clin Pharmacokinet, 1990. 18(2): p. 151-67.
[9] Abla, N., et al., Development and application of a PBPK modeling strategy to support antimalarial drug development. CPT: Pharmacometrics & Systems Pharmacology, 2023. 12(9): p. 1335-1346.
[10] Ezuruike, U., et al., Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator. CPT: Pharmacometrics & Systems Pharmacology, 2022. 11(7): p. 805-821.
Reference: PAGE 32 (2024) Abstr 11003 [www.page-meeting.org/?abstract=11003]
Poster: Drug/Disease Modelling - Paediatrics