A cancer therapeutics company has a high volume in-vitro system that mimics tumor microenvironments. Their scientists need to identify novel drug targets, and if they have been previously studied. The challenge was to create a data-driven process to identify and prioritize which therapeutic candidates should move forward in the drug development pipeline. A platform is needed to analyze large libraries of therapeutic candidates against the body of scientific literature and public data to provide context and insights.

Our team developed a comprehensive target identification (multiomics informatics) and validation (Biomedical NLP) platform. The multiomics informatics pipeline was built using publicly available -omics databases in order to generate signatures for pathways analysis to identify novel targets for drug discovery. The biomedical NLP engine extracts gene to disease relationships from ~2 TB of scientific literature. Our platform enabled their scientists to prioritize the highest quality targets for further research. As a result, our clients were able to sign collaboration agreements with new pharmaceutical partners based on the quality of the drug targets as highlighted in the data platform.