Repurposing Drugs for Cancers of the Breast, Colon, and Ovary using Active Machine Learning and Biobank of DTC’s

Population growth, aging populations and increases in cancer incidence have necessitated new approaches to identifying effective treatments.

One such approach involves the repositioning, or repurposing, of drugs already FDA approved in either non-oncology disease states and/or approved in a limited number of tumor types. The repurposing of these drugs for new indications represents an opportunity to circumvent the time and investment that must go into the discovery, development, and approval of new compounds (1).

An expedient workflow for identifying drugs that are good candidates for repurposing is demonstrated in this paper. The Predictive Oncology machine learning approach paired with our proprietary biobank of frozen dissociated tumor cells (DTCs) offers a screening opportunity for many repurposing candidates to be assessed, with outputs from this effort reflective of both confident predictions (Machine Learning) and wet lab results.