Literature-Based Predictions of Mendelian Disease Therapies

Authors

Cole A Deisseroth
Won-Seok Lee
Jiyoen Kim
Hyun-Hwan Jeong
Ryan S Dhindsa
Julia Wang
Huda Y Zoghbi
Zhandong Liu

Publication Date

10-5-2023

Journal

American Journal of Human Genetics

DOI

10.1016/j.ajhg.2023.08.018

PMID

37741276

PMCID

PMC10577072

PubMedCentral® Posted Date

9-22-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, PubMed, Databases, Factual

Abstract

In the effort to treat Mendelian disorders, correcting the underlying molecular imbalance may be more effective than symptomatic treatment. Identifying treatments that might accomplish this goal requires extensive and up-to-date knowledge of molecular pathways-including drug-gene and gene-gene relationships. To address this challenge, we present "parsing modifiers via article annotations" (PARMESAN), a computational tool that searches PubMed and PubMed Central for information to assemble these relationships into a central knowledge base. PARMESAN then predicts putatively novel drug-gene relationships, assigning an evidence-based score to each prediction. We compare PARMESAN's drug-gene predictions to all of the drug-gene relationships displayed by the Drug-Gene Interaction Database (DGIdb) and show that higher-scoring relationship predictions are more likely to match the directionality (up- versus down-regulation) indicated by this database. PARMESAN had more than 200,000 drug predictions scoring above 8 (as one example cutoff), for more than 3,700 genes. Among these predicted relationships, 210 were registered in DGIdb and 201 (96%) had matching directionality. This publicly available tool provides an automated way to prioritize drug screens to target the most-promising drugs to test, thereby saving time and resources in the development of therapeutics for genetic disorders.