Dysregulated Pathways During Pregnancy Predict Drug Candidates in Neurodevelopmental Disorders.
10.1007/s12264-025-01360-0
- Author:
Huamin YIN
1
;
Zhendong WANG
2
;
Wenhang WANG
3
;
Jiaxin LIU
3
;
Yirui XUE
4
;
Li LIU
3
;
Jingling SHEN
5
;
Lian DUAN
6
Author Information
1. Central Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
2. Key Laboratory of Interventional Pulmonology of Zhejiang Province, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
3. Institute of Life Sciences, College of Life and Environmental Sciences, Wenzhou University, Wenzhou, 325035, China.
4. Wenzhou Medical University, Wenzhou, 325035, China.
5. Institute of Life Sciences, College of Life and Environmental Sciences, Wenzhou University, Wenzhou, 325035, China. jingling_shen@wzu.edu.cn.
6. Central Laboratory, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China. duanlian@wmu.edu.cn.
- Publication Type:Journal Article
- Keywords:
Entropy;
Fetal brain development;
Neurodevelopmental disorders;
Pathway;
Predicting drug candidates
- MeSH:
Humans;
Female;
Pregnancy;
Neurodevelopmental Disorders/metabolism*;
Male;
Prenatal Exposure Delayed Effects;
Fetal Development/drug effects*;
Drug Discovery/methods*;
Brain/metabolism*
- From:
Neuroscience Bulletin
2025;41(6):987-1002
- CountryChina
- Language:English
-
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.
