PregMedNet: Multifaceted maternal medication impacts on neonatal complications
Year: 2026
Type: article
Study design: Observational Study
Abstract: While medication use is common among pregnant women, medication safety remains insufficiently characterized because studies in pregnant women are challenging due to safety concerns. The recent digitization of healthcare databases and advances in computational methods have created new opportunities for large-scale, retrospective drug safety evaluations. Here, we present PregMedNet, a platform that characterizes multifaceted maternal medication associations on neonatal outcomes during pregnancy, covering more than 27,000 drug-disease pairs across 1,152 medications and 24 outcomes. These results encompass known and additional odds ratios (ORs), adjusted ORs, and drug-drug interactions, systematically analyzed using nationwide claims data and an advanced machine learning pipeline. Notably, one of the associations identified in this study is supported by in vivo experiments, increasing confidence in PregMedNet's findings and highlighting the utility of claims data and machine learning for perinatal medication safety studies. Additionally, potential biological mechanisms underlying the associations are explored using a graph learning method, providing candidate pathways for future mechanistic investigations. We expect that PregMedNet will contribute to advancing maternal medication safety and improving neonatal outcomes by providing extensive, multifaceted drug safety information on this previously underrepresented population.
Source: Nature Communications
Authors: Yeasul Kim, Ivana Marić, Chloe M. Kashiwagi, Lichy Han, Philip Chung, Jonathan D. Reiss, Lindsay D. Butcher, Kaitlin J. Caoili, Eloïse Berson, Lei Xue, Camilo Espinosa, Tomin James, Sayane Shome, Feng Xie, Marc Ghanem, David Seong, Alan L. Chang, S. Momsen Reincke, Samson Mataraso, Chi-Hung Shu, Davide De Francesco, Martin Becker, Wasan M. Kumar, Ronald J. Wong, Brice Gaudilliere, Martin S. Angst, Gary M. Shaw, Brian T. Bateman, David K. Stevenson, Lawrence S. Prince, Nima Aghaeepour
Institutions: Stanford Medicine, Stanford University, Inserm, Université Paris Sciences et Lettres, Centre de Bio-Informatique, École Nationale Supérieure des Mines de Paris, Institut Curie, University of Minnesota, Charité – Universitätsmedizin Berlin, Philipps University of Marburg, Technische Universität Darmstadt, Hessian Center for Artificial Intelligence, University of Rostock
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $6,990
Awards: Machine Learning for Integrative Modeling of the Immune System in Clinical Settings, G2041607039, Stanford University - to support multiomics integrated analysis to advance understanding and treatment of pregnancy complications, Stanford Center for Clinical & Translational Education and Research (Spectrum), G4818529163, G8863475353, Mechanisms Regulating Inflammatory Phenotypes in Fetal Macrophages
OpenAlex ID: W4407573854