Medical Scientist Training Program, Baylor College of Medicine, Houston, TX 77030, USA; Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA.
Jan and Dan Duncan Neurological Research Institute, Texas Children's Hospital, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA; Integrative Molecular and Biomedical Sciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, USA.
Am J Hum Genet. 2023 Oct 5;110(10):1661-1672. doi: 10.1016/j.ajhg.2023.08.018. Epub 2023 Sep 22.
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.
在治疗孟德尔疾病的过程中，纠正潜在的分子失衡可能比对症治疗更有效。要确定可以实现这一目标的治疗方法，需要广泛而最新的分子途径知识，包括药物-基因和基因-基因关系。为了应对这一挑战，我们提出了“通过文章注释解析修饰物”（PARMESAN），这是一种计算工具，它可以在 PubMed 和 PubMed Central 中搜索信息，将这些关系组合到一个中央知识库中。PARMESAN 然后预测潜在的新的药物-基因关系，并为每个预测分配一个基于证据的分数。我们将 PARMESAN 的药物-基因预测与 Drug-Gene Interaction Database（DGIdb）显示的所有药物-基因关系进行比较，并表明得分较高的关系预测更有可能与该数据库指示的方向性（上调与下调）相匹配。PARMESAN 对超过 3700 个基因进行了超过 200,000 次评分超过 8 的药物预测（作为一个示例截止值）。在这些预测的关系中，有 210 个在 DGIdb 中注册，并且有 201 个（96%）具有匹配的方向性。这个公开可用的工具提供了一种自动的方法来优先筛选药物，以针对最有前途的药物进行测试，从而在开发针对遗传疾病的治疗方法时节省时间和资源。
