Combination feasibility prediction for checkpoint inhibitors for a biologic

The client, a Biotech company based out of Europe, had a large molecule in the development pipeline for cancer indications. They were interested in combining their proprietary molecule with already approved immune check-point inhibitors to improve therapeutic efficacy.

Client’s requirement

To prioritize cancer indications based on their sensitivity towards the combination of the biologic with a check point inhibitor (anti-PD-1/PDL-1). Publicly available data on successful and failed drug combinations was used for building predictive models.

Our approach

Machine learning models were built using to assess the sensitivity of cancer indications as well as patients to the drug combination. Based on the analysis, some cancer indications were prioritized for further assessment. A biological hypothesis was built to establish the synergistic role of the combination
partners for cancer treatment.