Literature-based predictions of Mendelian disease therapies

• Published in: American journal of human genetics Vol. 110; no. 10; pp. 1661 - 1672
• Main Authors: Deisseroth, Cole A., Lee, Won-Seok, Kim, Jiyoen, Jeong, Hyun-Hwan, Dhindsa, Ryan S., Wang, Julia, Zoghbi, Huda Y., Liu, Zhandong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 05.10.2023; Elsevier
• Subjects: Databases, Factual; drug repurposing; drug screening; Humans; literature search; natural language processing; NLP; pathway analysis; PubMed; text mining; literature search; NLP; drug repurposing; natural language processing; drug screening; text mining; pathway analysis
• ISSN: 0002-9297; 1537-6605
• DOI: 10.1016/j.ajhg.2023.08.018

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. To facilitate the investigation of Mendelian disease therapeutics, we introduce a prediction tool called parsing modifiers via article annotations (PARMESAN). This tool extracts known gene-gene and drug-gene relationships from the biomedical literature, predicts unknown relationships, and prioritizes the predictions based on the amount of supporting and opposing evidence.