Each of the programs in our oncology pipeline are designed to cause cyclical disruption of abnormal activation of the MAPK and other relevant signaling pathways while limiting drug-related toxicity. Traditional drug approaches have been designed to sustain pathway inhibition, which can cause on-target drug-related toxicity and limit clinical durability as a result of drug holidays or treatment discontinuation. Based on insights derived from our translational bioinformatics platform, our differentiated approach is to design drugs with short half-lives that provide enhanced mechanistic control of the target of interest and break tumor addiction, which is the tumor’s ability to indefinitely self-replicate, metastasize and evade the host’s immune system, among others capabilities, through deep cyclic disruption of these pathways (i.e., signaling dynamics). By cyclically disrupting these core oncogenic signaling pathways in cancer cells, we believe we can create novel therapeutics that maximize therapeutic activity in broad patient populations while providing an improved tolerability profile. We believe we are pioneers in this unique approach of leveraging signaling dynamics against tumor addiction.
Our platform allows us to leverage human biological data in new and creative ways, which provides counterintuitive insights that are not constrained by the inherent limitations of conventional approaches or prevailing scientific views.
Leveraging our history in translational bioinformatics, we have built a clinical-stage oncology company that incorporates our expertise into every step of our process to discover and develop novel product candidates. Our goal is to meaningfully improve patient outcomes as compared to drugs developed through traditional drug discovery approaches. Our integrated approach has already yielded programs that have exhibited preclinical activity against a broad range of clinically challenging solid tumors, with our lead product candidate IMM-1-104 currently being evaluated in a Phase 1/2a clinical trial.