resources

The Challenge:
To understand the molecular effect of a ph-3 drug (Drug A) which showed moderate outcomes and to recommend next steps to increase response rates.

The Strategy:
The drug MoA component of the platform maps a given drug’s molecular effect and projects it onto the disease’s biological and clinical landscape. Using this method, one can also monitor response groups, residual disease activity, and resistance mechanisms. We can apply this on any data uploaded to the platform, as well as on our collection of SOC treatments, specific for each indication. With these features pre-computed into our models, we suggest beneficial combinations, based on complementary mechanisms.

In this case, the analysis of gene modules (gene modules are sets of co-regulated genes that emerge from network analysis) yielded some interesting insights.

These modules are:

• Data-driven and thus proprietary to our models
• Calculated on the adjusted gene space which accounts for cell compositional changes, and therefore represents common regulatory events with a distinct functionality
• Tissue and indication-specific, which increases their biological relevance as compared to known pathway collections