Extracting Clinical Significance for Drug-Gene Interactions using FDA Label Packages

The drug-gene interaction database (DGIdb) is a resource that aggregates interaction data from over 40 different resources into one platform with the primary goal of making the druggable genome accessible to clinicians and researchers. By providing a public, computationally accessible database, DGIdb enables therapeutic insights through broad aggregation of drug-gene interaction data.

As part of our aggregation process, DGIdb preserves data regarding interaction types, directionality, and other attributes that enable filtering or biochemical insight. However, source data are often incomplete and may not contain the therapeutic relevance of the interaction. In this report, we address these missing data and demonstrate a pipeline for extracting physiological context from free-text sources. We apply existing large language models (LLMs) to tag and extract indications, cancer types, and relevant pharmacogenomics from free-text, FDA approved labels. We are then able to utilize the Variant Interpretation for Cancer Consortium (VICC) normalization services to ground extracted data back to formally grouped concepts.

In a preliminary test set of 355 FDA labels, we were able to normalize 86.5% of extracted chemical entities back to ontologically-grounded therapeutic concepts. We can link this therapeutic context data back to interaction records already searchable within DGIdb. By using LLMs to extract this data set, we can supplement our existing interaction data with relevant indications, pharmacogenomic data and mutational statuses that may inform the therapeutic relevance of a particular interaction. Inclusion of these data will be invaluable for variant interpretation pipelines where mutational status can lead to the identification of a lifesaving therapeutic.