Over the last 50 years, the worldwide cost of bringing a drug to market has ballooned more than 100-fold to several billion dollars; yet our ability to predict the outcomes of clinical trials, where most of the money is spent, has not improved and remains less than 10%. Why do trials fail so frequently? In most cases, it is a lack of efficacy, where the patient benefit is no better than an already approved therapy. Our inability to predict efficacy fundamentally distills down to inaccurate and poor understanding of disease biology. Personalized medicine, where therapeutics are designed to exploit disease biology that simultaneously defines patient responder segments, has become a popular approach to mitigate clinical risk while getting a foothold in the market.
At Leapfrog Bio, we have developed a platform that predicts which cancer patients will respond to which drugs with unprecedented accuracy. Validated all the way to clinical outcomes, the heart of our approach is a pharmacogenomic functional screening assay that detects druggable driver-gene dependencies. In other words, we can screen drugs and identify which patients, if any, would respond based on their tumor genetic composition.
The output of these screens is a specific patient subset with enhanced efficacy for the drug being screened. Our platform represents 95% of all cancer patients, and we can screen dozens of drugs per month. Validation includes a recapitulation of efficacy in candidate patients for targeted therapeutics (e.g. PARP inhibitors), alignment with synthetic lethal interaction networks, and, crucially, accurate prediction of clinical outcomes in several dozen patient subset-drug combinations.
We believe our platform will have a game-changing impact on patient selection for oncology assets. This includes screening preclinical development candidates to identify promising initial indications, exploring non-oncology assets for treating defined cancer subtypes, and repurposing failed clinical-stage assets. We are actively pursuing all three avenues and are open to partnership opportunities.
Cis-regulatory mutations with driver hallmarks in major cancers.
Cheng Z, Vermeulen M, Rollins-Green M, DeVeale B, Babak T.
iScience. 2021 Feb 4;24(3):102144.
A one-step tRNA-CRISPR system for genome-wide genetic interaction mapping in mammalian cells.
Zhao Y, Tyrishkin K, Sjaarda C, Khanal P, Stafford J, Rauh M, Liu X, Babak T, Yang X.
Sci Rep. 2019 Oct 10;9(1):14499. doi: 10.1038/s41598-019-51090-3.