Search

The emergence of oral targeted anticancer agents transformed several cancers into chronic conditions with a need for long-term oral treatment. Although cancer is a life-threatening condition, oncology medication adherence-the extent to which a patient follows the drug regimen that is intended by the prescriber-can be suboptimal in the long term, as in any other chronic disease. Poor adherence can impact negatively on clinical outcomes, notably because most of these drugs are given as a standard non-individualized dosage despite marked inter-individual variabilities that can lead to toxic or inefficacious drug concentrations. This has been especially studied with the prototypal drug imatinib. In the context of therapeutic drug monitoring (TDM), increasingly advocated for oral anticancer treatment optimization, unreported suboptimal adherence affecting drug intake history may lead to significant bias in the concentration interpretation and inappropriate dosage adjustments. In the same way, suboptimal adherence may also bias the results of pharmacokinetic modeling studies, which will affect in turn Bayesian TDM interpretation that relies on such population models. Detailed knowledge of the influence of adherence on plasma concentrations in pharmacokinetic studies or in routine TDM programs is however presently missing in the oncology field. Studies on this topic are therefore eagerly awaited to better pilot the treatment of cancer with the new targeted agents and to find their optimal dosage regimen. Hence, the development and assessment of effective medication adherence programs are warranted for these treatments. [ABSTRACT FROM AUTHOR]

Aims: Sertraline is frequently prescribed for mental health conditions in both pregnant and breastfeeding women. According to the limited available data, only small amounts of sertraline are transferred into human milk, yet with a large amount of unexplained interindividual variability. This study aimed to develop a population pharmacokinetic (popPK) model to describe the pharmacokinetics of sertraline during the perinatal period and explain interindividual variability., Methods: Pregnant women treated with sertraline were enrolled in the multicenter prospective cohort SSRI-Breast Milk study. A popPK model for sertraline maternal plasma and breast milk concentrations was developed and allowed estimating the milk-to-plasma ratio (MPR). An additional fetal compartment allowed cord blood concentrations to be described. Several covariates were tested for significance. Ultimately, model-based simulations allowed infant drug exposure through placenta and breast milk under various conditions to be predicted., Results: Thirty-eight women treated with sertraline were included in the study and provided 89 maternal plasma, 29 cord blood and 107 breast milk samples. Sertraline clearance was reduced by 42% in CYP2C19 poor metabolizers compared to other phenotypes. Doubling milk fat content increased the MPR by 95%. Simulations suggested a median daily infant dosage of 6.9 μg kg -1 after a 50 mg maternal daily dose, representing 0.95% of the weight-adjusted maternal dose. Median cord blood concentrations could range from 3.29 to 33.23 ng mL -1 after maternal daily doses between 25 and 150 mg., Conclusions: Infant exposure to sertraline, influenced by CYP2C19 phenotype and breast milk fat content, remains low, providing reassurance regarding the use of sertraline during pregnancy and breastfeeding., (© 2024 The Author(s). British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Medication intake during the postpartum period is common with discontinuation of breastfeeding sometimes unnecessarily recommended for fear of adverse effects in the breastfed infant, while exposure through human milk is generally low. The assessment of risks associated with medication intake during breastfeeding is based, among other things, on the little clinical evidence available in specialized sources of information, and on pharmacokinetic principles. A decision-making support is presented to facilitate communication with mothers, foster medication adherence and prevent unnecessary interruption of breastfeeding., Competing Interests: Les auteur-es n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Introduction: Despite many research efforts, current data on the safety of medicines during breastfeeding are either fragmented or lacking, resulting in restrictive labeling of most medicines. In the absence of pharmacoepidemiologic safety studies, risk estimation for breastfed infants is mainly derived from pharmacokinetic (PK) information on medicine. This manuscript provides a description and a comparison of the different methodological approaches that can yield reliable information on medicine transfer into human milk and the resulting infant exposure., Area Covered: Currently, most information on medicine transfer in human milk relies on case reports or traditional PK studies, which generate data that can hardly be generalized to the population. Some methodological approaches, such as population PK (popPK) and physiologically based PK (PBPK) modeling, can be used to provide a more complete characterization of infant medicine exposure through human milk and simulate the most extreme situations while decreasing the burden of sampling in breastfeeding women., Expert Opinion: PBPK and popPK modeling are promising approaches to fill the gap in knowledge of medicine safety in breastfeeding, as illustrated with our escitalopram example.