Our platform is enabled by our ability to efficiently analyze high-throughput molecular-level biochemical assays, including transcriptomics, genomics and/or proteomics, collectively referred to as Omics data. These different types of biochemical assays each provide us with unique information about the molecular mechanisms of disease biology and drug response. Since our inception, we have partnered with industry-leading pharmaceutical and biotechnology companies to perform a variety of analyses that utilize our expertise in translational bioinformatics. Examples publicly disclosed by our partners include our analyses of ibrutinib, ipilimumab, daratumumab, glatiramer acetate and pridopidine.
Underlying each of these elements is our rigorous quality control and ability to analyze complex biological datasets. We are one of the few oncology companies that has been involved in defining best practices for robustly analyzing bioinformatics data, as evidenced by co-authorship on journal articles together with regulatory experts as well as writing invited reviews to educate the scientific community on this topic. This attention to rigorous quality control pervades all of our analyses, and we believe this enables us to extract meaningful information from our own data as well as from a variety of databases of human data.
In early 2018, we began applying our proprietary platform and approach to internally develop our wholly owned pipeline of orally administered small molecule drug programs. Key elements of our platform include:
- Insights from Human Data. Compare distinct groups of individuals who differ in a certain aspect of disease or response to a particular therapy, or identify new patient subsets. Our platform has enabled us to conduct multiple projects that involve stratifying patients into novel subsets. We associate transcriptomic profiles with each subset, which can then be directly inputted into DCT to identify novel targets specific to a given patient subset.
- Novel Biology. Identify novel targets and new ways to drug existing targets using our Disease Cancelling Technology and/or our insights into mechanisms of response. Additional biologic context is derived from quantifying the extent to which different time points, concentrations and perturbations (e.g., inhibition and overexpression) may cancel a disease signal more effectively than existing drug targets.
- Novel Chemistry. Rapidly identify small molecules that selectively bind to a target of interest using our proprietary Fluency deep learning AI technology, and/or engineer PK to achieve optimal signaling dynamics. Fluency identifies the most attractive drug candidates within a library by making ranked predictions of binding affinity for all compounds, and can be run on any library containing millions of compounds.