Predicting exposure of neonates to maternal medicines via breastfeeding

As we know Clinical practice doesn’t have enough scientific support to initiate the decision regarding the adjustment of drug doses involving different physiological conditions 1,2 Obviously, ethical premises prevent the voluntary and unnecessary exposure of vulnerable patients. However, currently, virtual reality, non-clinical tools, can help to simulate patients with these characteristics of vulnerability, allowing the assessment of the exposure of virtual vulnerable patients, as is the case of children and pregnant women and breastfeeding3–5  The project came from a consortium of eight work projects, approved IMI project ConcePTION with the main goal to reduce uncertainty about the edicts of medication used during pregnancy and breastfeeding. The project for this vacancy is the work project (WP3) that aim to focus on in vitro animal models and modelling & simulation. Development of a robust PBPK framework to predict drug concentration profiles in human breast milk and neonatal physiological factors determining drug exposure in breastfed infants based on PBPK modelling. Objectives: The main goal of this work project will be to develop, characterize, validate and apply a non-clinical testing platform for reliable prediction of drug concentrations in human breast milk along with systemic drug exposure in breastfed infants. This will be done by developing and implementing powerful high-throughput tools for generating quantitative in vitro human data. We will develop a predictive in vivo animal model for lactation and a PBPK model to provide information on the transfer of medications and their metabolites in the milk for inclusion in the initial label. Sub-objectives • To map applicability and to identify shortcomings of current in vitro and animal models for studying drug distribution to human breast milk and including prediction of systemic infant exposure; • Building on state-of-the-art in vitro models: (i) to develop a validated (and implementable) human in vitro platform for accurate and rapid determination of plasma-milk transfer rates of drugs (including metabolites) and drug candidates; (ii) to explore the impact of drug-specific properties (e.g., physiochemistry, drug-transporter affinities) in determining the extent and rate of drug excretion in human breast milk; • Relying on existing expertise with animal lactation models, to develop a relevant animal lactation model (along with an in vitro model) in a species sufficiently related to human lactation physiology to validate extrapolation of the human in vitro and animal in vivo data to human in vivo predictions; • Relying on non-clinical data generated in this WP, to develop, validate and apply a Physiology-Based Pharmacokinetic (PBPK) modelling platform for predicting drug concentration profiles in human breast milk. PBPK-based predictions will be verified against clinically observed human lactation data (as already generated by EFPIA contributors or generated during this project as deliverables of WP 4). To explore the sensitivity of the PBPK-based predictions regarding drug concentrations in human breast milk to maternal (physiological) factors (e.g., milk composition, drug transporter abundance in mammary epithelial cells); • To identify based on published data neonatal physiological factors (e.g., related to maturation of gastrointestinal drug absorption processes) determining systemic drug exposure in breastfed infants. The established PBPK platform will be expanded with a PBPK modelling strategy in infants that takes these neonatal factors into account; • To evaluate and cross-validate the developed non-clinical and computational approaches for their performance to predict medication concentrations in human breast milk and systemic drug exposure of breastfed infants (by comparison with existing human lactation data; • To propose acceptable (and regulatory recognized) non-clinical data generation protocol for drug milk excretion and breastfed infant exposure (in collaboration with WPs 4 and 6); and • Based on the scope and limitations of the non-clinical and in silico tools generated in this WP, to provide input to other WPs regarding the use of these new predictive tools as best practices for informing risk assessment on medication use during lactation. REFERENCES 1. Dunne J, Rodriguez WJ, Murphy MD, et al. Extrapolation of adult data and other data in pediatric drug-development programs. Pediatrics. 2011;128(5). doi:10.1542/peds.2010-3487 2.  Germovsek E, Barker CIS, Sharland M, Standing JF. Pharmacokinetic–Pharmacodynamic Modeling in Pediatric Drug Development, and the Importance of Standardized Scaling of Clearance. Clin Pharmacokinet. 2019;58(1):39-52. doi:10.1007/s40262-018-0659-0 3.  FDA. Draft Guidance for Industry - Pediatric Study Plans: Content of and Process for Submitting Initial Pediatric Study Plans and Amended Pediatric Study Plans. FDA Guid. 2016;(March):13. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM360507.pdf 4. Purohit VS. Biopharmaceutic Planning in Pediatric Drug Development. AAPS J. 2012;14(3):519-522. doi:10.1208/s12248-012-9364-3 5. Ventrella D, Forni M, Bacci ML, Annaert P. Non-clinical Models to Determine Drug Passage into Human Breast Milk. Curr Pharm Des. 2019;25(5):534-548. doi:10.2174/1381612825666190320165904