A Multi-Objective Approach for Drug Repurposing in Preeclampsia

Area of Science:

• Pharmacology and Obstetrics
• Computational Biology and Drug Discovery

Background:

• Preeclampsia is a serious pregnancy complication causing high blood pressure and leading to maternal and fetal mortality.
• Its exact cause remains unknown, necessitating research into novel therapeutic targets and treatments.

Purpose of the Study:

• To identify potential therapeutic targets for preeclampsia by analyzing drugs in clinical trials.
• To discover novel drug candidates for preeclampsia treatment through virtual screening.

Main Methods:

• Extracted potential preeclampsia targets and off-targets from DrugBank and ChEMBL databases.
• Developed classification models using TPOT pipeline and optimized virtual screening with genetic algorithms.
• Screened the DrugBank database for potential therapeutic agents.

Main Results:

• Achieved high prediction accuracy (0.830 for targets, 0.850 for off-targets) for classification models.
• Identified key targets (AR, VDR, SLC6A2, NOS3, CHRM4) and off-targets (ABCG2, ERBB2, CES1, REN).
• Discovered potential treatments including estradiol, vitamins E and D, and simvastatin.

Conclusions:

• This study provides a computational framework for identifying preeclampsia targets and drug candidates.
• Identified specific proteins and compounds with potential for preeclampsia treatment, offering new avenues for clinical research.