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.
Excelra supported the program using its Computational Biology Services and Scientific Application Development for drug discovery expertise to run cancer cohort analytics and predictive modeling, ensuring the analysis aligned with broader precision-medicine goals and application of bioinformatics for cancer research.
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.
To build robust predictive models we combined curated public datasets using Excelra’s Data Curation services and followed FAIR data principles for drug discovery & development to enable data fairification and reuse across downstream analytic pipelines.
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.
Machine learning and advanced analytics workflows — guided by our Selecting and Preparing Data for AI/ML Predictive Modeling
whitepaper — were applied to enable cancer cohort analytics and patient stratification; key biomarker signals were cross-referenced against Excelra’s Custom Biomarker Knowledgebase and results were presented using BioVisualizer and our Visualization services, helping the client translate predictive insights into hypothesis-driven follow-up studies.
