Introduction
Breastfeeding is highly recommended by the WHO, more specifically exclusive breastfeeding up to 6 months after birth and continued partially, in combination with complementary feeding, until 2 years of age. This is because breastfeeding has a positive impact on the development and general health of children. Breastfed children are less likely to suffer from childhood infections, at least in part due to antibodies in human milk, and have a lower risk of being overweight.1 According to Castro et al 2 breastfeeding is also related to higher intelligence, with a possible increase up to seven intelligence quotient points and a reduction in child morbidity and mortality.2 In addition, breastfeeding is also beneficial for maternal health, for example, by reducing the risk of developing breast or ovarian carcinoma.3
At least 50% of women need pharmacotherapy in the postpartum period, and this proportion has been rising due to the increasing prevalence of chronic diseases and later-age pregnancies.4 Meanwhile, an immense information gap regarding the safety of medicines during lactation still exists today, complicating evidence-based decisions on the use and selection of medicines during breastfeeding.5 6 This often results in unnecessary cessation of breastfeeding or poor adherence to or avoidance of pharmacological treatment.4 7 Furthermore, the majority of medicines are used off-label during breastfeeding, which may put the child at risk for unknown side effects.8
To assess the transfer of medicines in human milk, the relative infant dose (RID) and the milk-to-plasma (M/P) ratio are commonly used as parameters in clinical lactation studies. M/P ratios reported in the literature are often based on single time point assessments. However, as the concentration profiles in human milk and plasma vary individually over time and may not be exactly the same, the M/P ratio may differ significantly depending on the timing of sampling. Ideally, the M/P ratio should be based on the (24-hour) area under the concentration-time curve (AUC) in human milk and plasma.9 Assuming that only the unbound and unionised medicine fraction will cross the blood–milk barrier, the M/P ratio can be calculated based on the physicochemical properties of a medicine (eg, pH partitioning, protein binding and distribution into milk lipid), for instance, using the non-clinical phase distribution model.10 11
Koshimichi et al 12 have developed an empirical model to predict the in vivo M/P ratio based on the physicochemical properties of the medicine (ie, polar surface area, molecular weight, lipophilicity as logP and logD7.4 and hydrogen bond donors). However, medicines that are dependent on transporters (efflux and uptake) for their active secretion into human milk are not sufficiently well captured by this method.12 Moreover, an issue with approaches using exposure indexes such as the RID is the arbitrary cut-off point (typically 10%), indicating safe versus non-safe exposure.9 Importantly, the safety of the child does not only depend on the dosage that the child receives via human milk but also on the toxicity of the medicine and its major metabolites, as well as the absorption, distribution, metabolism and excretion (ADME) profile of the medicine and its metabolites in the child.
Besides the above-mentioned methods to study medicine transfer in human milk, non-clinical (in vitro and in vivo animal) experiments have been developed. Unfortunately, these experiments were often not successful in predicting human milk medicine concentrations due to species-specific differences.13 14
Recently, some studies on the prediction of medicine exposure in the child via breastfeeding have been performed using an in silico method, that is, physiologically based pharmacokinetic (PBPK) modelling.15 16 PBPK modelling allows bottom-up predictions of pharmacokinetics (PK) based on the integration of population-specific physiology data with medicine-specific data on ADME and physicochemistry. These models have been accepted by regulatory authorities and the pharmaceutical industry to predict drug–drug interactions and population-specific PK.17 Although there are limitations, these models indicate that PBPK modelling is a feasible approach predicting child exposure to maternal medicine via breastfeeding.18 Lactation and paediatric PBPK models are currently being developed within the Innovative Medicine Initiative (IMI) project ConcePTION1, a European public–private partnership aiming to establish an ecosystem to generate evidence-based information on the exposure and effects of medicines during pregnancy and lactation.
Breastfeeding women and their breastfed children are often excluded from clinical trials due to ethical and practical reasons, which result in a therapeutic orphan population and a huge knowledge gap.19 Due to this existing information gap, performing clinical lactation studies is highly needed and can provide valuable information. However, a broad variability in conducting and reporting on clinical lactation studies exists where, for example, essential PK data might be missing, such as dosage or time of medicine intake relative to the sampling time.20 21 In addition, obtaining ethics approval for samples of each individual woman and each specific compound makes conducting clinical lactation studies time-consuming, while the breastfeeding period is generally limited in time. However, the collection of in vivo human data is essential for the evaluation of lactation PBPK models. These thoroughly evaluated PBPK models will lead to a generic set of breastfeeding-specific population parameters, enabling the construction of structural PBPK frameworks for breastfeeding women and breastfed infants. Such frameworks might subsequently be applied with increasing certainty to similar medicines for which in vivo data are still lacking.
The UmbrelLACT study is a prospective, observational, clinical lactation study to collect data on human milk transfer of maternal medicines and, subsequently, data on child intake and systemic exposure through human milk and the general health outcome of the child. In addition, the results will be used to evaluate the predictive performance of lactation and paediatric PBPK models.