I-Corps: Translation Potential of Artificial Intelligence (AI)-Informed Custom Biomarkers from Genomic Dark Matter

The broader impact of this I-Corps project is the development of technology for drug development that enables more precise and personalized medical care. The technology uses computational algorithms including artificial intelligence (AI), along with insights gained from underutilized regions of the genome to provide expanded biomarker development targets. Failure rates in drug development are high, in part, due to the generic treatment of a broad population of patients. This broad generic treatment also results in non-responsiveness and adverse effects for individual patients/patient groups. Current biomarkers are primarily based on output from less than 3% of the human genome, with the remainder being considered dark matter - untapped genomic sequences that provide a vast resource of patient-specific information. This information may provide patient- and/or disease-specific biomarker signatures that classify patients into risk categories for specific therapies and predict patient response. In addition, this drug development platform is customizable based on patient information, and can be adapted for individual drug development companies, assay developers, and/or industries that are focused on meeting the growing demand for precision medicine for human or animal health. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. The solution is based on the development of a suite of computational tools including artificial intelligence (AI) and machine learning-based approaches that may provide a drug development platform that includes insight into segments of understudied genomic sequences. Biomarkers are typically developed from proteins involved in or associated with disease processes. These proteins are produced from a very small proportion of human genomic sequence. This means that a vast portion of the human genome, considered dark matter, remains untapped from a biomedical and biotechnological perspective. Using underutilized sequences identified by computational approaches, the goal is to increase the number of candidates entering the biomarker development pipeline, and significantly expand the biomarker candidate pool. This expansion may support precision medicine approaches through the identification and development patient-specific biomarkers and disease-specific therapies. In addition, the expansion may optimize the drug dose for suitable clinical responses, predict patience responses to drugs, and facilitate early detection of adverse effects. This technology may increase the efficiency and reduce drug development failure rates by reducing the time and costs of drug development. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.