Review: A Roadmap to Use Nonstructured Data to Discover Multitarget Cancer Therapies

Abstract

Therapy resistance to single agents has led to the realization that combination therapies could become the cornerstone of cancer treatment. To operationalize the selection of effective and safe multitarget therapies, we propose to integrate chemical and preclinical therapeutic information with clinical efficacy and toxicity data, allowing a new perspective on the drug target landscape. To assess the feasibility of this approach, we evaluated the publicly available chemical, preclinical, and clinical therapeutic data, and we addressed some potential limitations while integrating the data. First, by mapping available structured data from the main biomedical resources, we noticed that there is only a 1.7% overlap between drugs in chemical, preclinical, or clinical databases. Especially, the limited amount of structured data in the clinical domain hinders linking drugs to clinical aspects such as efficacy and side effects. Second, to overcome the abovementioned knowledge gap between the chemical, preclinical, and clinical domain, we suggest information extraction from scientific literature and other unstructured resources through natural language processing models, where BioBERT and PubMedBERT are the current state-of-the-art approaches. Finally, we propose that knowledge graphs can be used to link structured data, scientific literature, and electronic health records, to come to meaningful interpretations. Together, we expect this richer knowledge will lower barriers toward clinical application of personalized combination therapies with high efficacy and limited adverse events.