AIRES
Artificial Intelligence-enabled design of KRAS inhibitors that resist RESistance
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Despite major advances, drug resistance eventually occurs in nearly all advanced cancers – limiting survival benefits even with the most advanced drugs. Second generation inhibitors circumvent primary resistance, but secondary resistance mechanisms nearly always emerge. In colorectal cancer (CRC), the second cause of cancer mortality, KRAS mutations affect 30-40% of patients. However, treatment with KRAS inhibitors has limited benefits due to the rapid onset of drug resistance, with only 29% of patients responding for 6 months. Combining this drug with an EGFR inhibitor enabled to increase the response time to only 8 months.
Solution
Small molecules have the inherent capacity to inhibit more than one protein – a behavior often termed polypharmacology that is rarely exploited in drug discovery. It has been shown that we can use machine learning to predict the targets of small molecules. It has also been shown that poypharmacology evolves during drug discovery, setting the foundations to exploit it in prospective drug discovery. Now, we propose to leverage artificial intelligence (AI) to rationally design multi-target small-molecules that simultaneously inhibit KRAS and kinases that have been identified to modulate KRAS resistance in the clinic, such as EGFR, MEK, or RET, to block resistance from the outset.
Objective
The ultimate, long-term deliverable of this project would be a new multi-target KRAS inhibitor approved for the treatment of cancer patients. This project initially focuses in CRC, the second cause of cancer mortality. Accessing a drug that delays resistance would increase the life expectancy and tremendously improve the quality of life of these CRC patients. However, other cancers representing high unmet medical needs could also benefit from the multi-target KRAS inhibitor developed.