温州大学段炼小组的最新研究发现妊娠期间通路失调可预测神经发育障碍（NDD）的候选药物。2025年2月6日出版的《神经科学通报》杂志发表了这项成果。

研究人员收集并人工整理了子通路和通路（子/通路）以及药物信息，提出了一个预测候选药物的分析程序。该程序将子通路/通路活性与基因转录数据得出的药物反应评分联系起来，并应用于人类胎儿大脑发育和六种NDDs。此外，研究人员还利用熵鉴定了特异性和多效应子/通路/药物，并结合逻辑回归和随机森林模型分析了性别偏差。

研究发现了19种与疾病相关的候选药物和256种针对NDDs风险子/途径的区域多效性和特异性候选药物，这些药物在胎儿发育过程中显示出时间或空间上的变化。

此外，在填入性别标签后，有5443种不同的药物子/通路表现出性别差异。研究人员建立了一个用户友好型NDDP可视化网站 (https://ndd-lab.shinyapps.io/NDDP)，以便研究人员和临床医生轻松访问和检索数据。该程序克服了数据缺口，并在六种疾病和胎儿发育轨迹中发现了许多多效性和特异性候选药物。这将极大地促进孕期药物发现并可应用于多种性状。

研究人员表示，孕期母亲的健康状况直接影响后代患神经发育障碍的风险和严重程度，尤其是在接触毒品的情况下。然而，由于伦理限制和药物使用因素，在评估孕期药物暴露风险方面的进展甚微。

附：英文原文

Title: Dysregulated Pathways During Pregnancy Predict Drug Candidates in Neurodevelopmental Disorders

Author: Yin, Huamin, Wang, Zhendong, Wang, Wenhang, Liu, Jiaxin, Xue, Yirui, Liu, Li, Shen, Jingling, Duan, Lian

Issue&Volume: 2025-02-06

Abstract: Maternal health during pregnancy has a direct impact on the risk and severity of neurodevelopmental disorders (NDDs) in the offspring, especially in the case of drug exposure. However, little progress has been made to assess the risk of drug exposure during pregnancy due to ethical constraints and drug use factors. We collected and manually curated sub-pathways and pathways (sub-/pathways) and drug information to propose an analytical framework for predicting drug candidates. This framework linked sub-/pathway activity and drug response scores derived from gene transcription data and was applied to human fetal brain development and six NDDs. Further, specific and pleiotropic sub-/pathways/drugs were identified using entropy, and sex bias was analyzed in conjunction with logistic regression and random forest models. We identified 19 disorder-associated and 256 regionally pleiotropic and specific candidate drugs that targeted risk sub-/pathways in NDDs, showing temporal or spatial changes across fetal development. Moreover, 5443 differential drug-sub-/pathways exhibited sex-biased differences after filling in the gender labels. A user-friendly NDDP visualization website (https://ndd-lab.shinyapps.io/NDDP) was developed to allow researchers and clinicians to access and retrieve data easily. Our framework overcame data gaps and identified numerous pleiotropic and specific candidates across six disorders and fetal developmental trajectories. This could significantly contribute to drug discovery during pregnancy and can be applied to a wide range of traits.

DOI: 10.1007/s12264-025-01360-0

Source: https://link.springer.com/article/10.1007/s12264-025-01360-0