Drug development programs with biomarker-defined patient selection have higher probability of success
Sources: Estimated Clinical Trial Rates in Oncology, 2019, by Wong Siah, and Lo; Estimation of the Percentage of US Patients With Cancer Who Benefit From Genome-Driven Oncology, JAMA Oncol. 2018;4(8):1093-1098. doi:10.1001/jamaoncol.2018.1660
Engine’s Approach
Gene interaction and network biology focus enables: 1) target ID within clinically-relevant genetic contexts, 2) patient selection biomarkers for therapeutics
→ De-risking of target/drug candidate, increasing probability of success
→ Earlier clarity on development path saves time/effort
→ Acceleration of timeline to clinical data and FDA approval
→ Smaller patient cohorts enable more efficient trials
→ Enhanced commercial potential with more patients
Clinically validated application of gene interactions: Synthetic lethality in cancer enables effective medicines in selective genetic contexts
Mutation of Gene A (Biomarker) makes diseased cells more vulnerable to inhibition of Gene B (Target) than normal cells
Enormous opportunity to stratify patients for precision medicine
>3,000 genes have mutational prevalence of >=5% in a cancer
Problem and opportunity
90% of patients do not have an approved precision medicine
Only 1% of these gene mutations are used to direct an approved precision medicine
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