Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, USA.
Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, USA.
Sci Rep. 2017 Oct 9;7(1):12839. doi: 10.1038/s41598-017-12943-x.
Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation - called FDA 'category C' drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacological similarity with drugs of known fetal effect) and empirical data (i.e., derived from Electronic Health Records). Our fetal loss cohort contains 14,922 affected and 33,043 unaffected pregnancies and our congenital anomalies cohort contains 5,658 affected and 31,240 unaffected infants. We trained a random forest to classify drugs of unknown pregnancy class into harmful or safe categories, focusing on two distinct outcomes: fetal loss and congenital anomalies. Our models achieved an out-of-bag accuracy of 91% for fetal loss and 87% for congenital anomalies outperforming null models. Fifty-seven 'category C' medications were classified as harmful for fetal loss and eleven for congenital anomalies. This includes medications with documented harmful effects, including naproxen, ibuprofen and rubella live vaccine. We also identified several novel drugs, e.g., haloperidol, that increased the risk of fetal loss. Our approach provides important information on the harmfulness of 'category C' drugs. This is needed, as no FDA recommendation exists for these drugs' fetal safety.
许多在怀孕期间常用的药物缺乏胎儿安全性建议——被称为 FDA“类别 C”药物。本研究旨在使用化学生物学（即与已知具有胎儿效应的药物的药理学相似性）和经验数据（即源自电子健康记录）将这些药物分为有害和安全两类。我们的胎儿丢失队列包含 14922 例受影响和 33043 例未受影响的妊娠，我们的先天畸形队列包含 5658 例受影响和 31240 例未受影响的婴儿。我们训练了一个随机森林来将未知妊娠类别的药物分为有害或安全类别，重点关注两个不同的结果：胎儿丢失和先天畸形。我们的模型对胎儿丢失的出袋准确率为 91%，对先天畸形的准确率为 87%，优于空模型。五十七种“类别 C”药物被归类为对胎儿丢失有害，十一种对先天畸形有害。这包括有记录的有害影响的药物，如萘普生、布洛芬和风疹活疫苗。我们还发现了几种新的药物，如氟哌啶醇，会增加胎儿丢失的风险。我们的方法提供了有关“类别 C”药物危害性的重要信息。由于这些药物的胎儿安全性没有 FDA 建议，因此这是必要的。
