Within complex biological networks, key interactions between multiple genes drive diseases.  Identifying these causal relationships are critical to enable precision medicine.  These scientific concepts have directed successful clinical and commercial medicines.  We are motivated to leverage these validated concepts for next-generation precision medicines.

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