CERSI Collaborators: Mathangi Gopalakrishnan, M.Pharm, PhD, FCP; Katrina Mark, MD

FDA Collaborators: Qi Liu, M.Stat, FCP, PhD; Yuching Yang, PhD; Hao Zhu, PhD; Gilbert Burckart, FCP, Pharm.D

Project Start Date: May 24, 2023

Regulatory Science Framework

Regulatory Science Priority Area: 3D, Women’s Health Research Roadmap Priority Area: 3

Regulatory Science Challenge

Up to 80% of pregnant persons use prescription medications during their pregnancy but more than 90% of approved drugs lack the appropriate labeling information for pregnant persons. Differences in how drugs behave in the general versus pregnant population, arising from pregnancy related alterations in absorption, distribution, metabolism, and elimination, underscore the necessity for dose optimization studies in this vulnerable population.

Conducting clinical studies to determine precise drug dosages during pregnancy for every drug is time-consuming, resource-intensive, and ethically challenging. Consequentially there is a compelling need to develop a strategic approach to prioritize which medications warrant further investigation and clinical studies. Establishing a predictive model-based strategy to prioritize drugs for pregnancy studies could streamline the process of addressing the labeling gap, benefiting both healthcare providers and expectant mothers.

Project Description and Goals

University of Maryland (M-CERSI) investigators aim to provide a strategy for prioritizing the need to conduct pregnancy-specific clinical studies on drugs used during pregnancy. To achieve this, they will utilize the TriNetX data query tool to gather information on the most prescribed drugs for pregnant persons in the U.S. from the electronic health records of persons who gave birth in the University of Maryland Medical System. System parameters with pregnancy-induced physiological changes and drug specific parameters will be used to develop and validate a pregnancy physiologically based pharmacokinetic (p-PBPK) model for these drugs. These models will then be used to predict the exposure of each of these drugs in pregnant persons. These exposures will be combined with drug specific details to establish a prioritization framework for the study of medications in pregnant persons.