Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

PregMedNet: Multifaceted maternal medication impacts on neonatal complications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月10日

查看全文
  • 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

摘要

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.