Cyclica

Project Title: Genetic variation and structure-based drug polypharmacology: multiscale structural pharmacogenomics
Website: Cyclica Inc
Platform: Cloud Analytics

Health

Over the past 25 years, drug discovery efforts have been aimed at identifying small organic molecules that exert a strong effect on a single protein that corresponds to the biological target of a disease. Although designed to fulfill a single biological function, small molecule drugs can incidentally interact with hundreds of the ~20,000 known human gene products. These off-target interactions may lead to side effects, new therapeutic effects, or otherwise affect drug response. Mapping a drug to all of its human protein interaction partners is known as polypharmacology.

Thus, it is advantageous to understand the polypharmacology of each drug in development prior to clinical testing in order to better anticipate off-target effects that could potentially cause toxicity. However, to address drug polypharmacology experimentally is prohibitively expensive in time and monetary cost. Predicting a drug’s polypharmacology is Cyclica’s central mandate. Our drug discovery platform is based on proteome-wide screening, and centered around PROBEx, a cloud-based software solution that simulates a drug’s interaction with 100,000’s of proteins on the basis of their molecular structures. PROBEx maps out the possible off-target protein partners and consequently pharmacology for any pharmaceutical or nutraceutical compounds, to service academic research institutes, hospitals, and pharmaceutical companies developing new disease therapies.

Pharmacogenomics is the study of how genes affect a person’s response to drugs by combining pharmacology (the science of drugs), and genomics (the study of genes and their functions) to develop effective, safe medications that are tailored to a person’s genetic makeup. This field, commonly termed “personalized medicine” has recently emerged and gained significant attention from many companies, including IBM Watson Life Sciences. To our knowledge however, information pertaining to the structural proteome and its relation to drug binding, has not yet been systematically leveraged to improve pharmacogenetic analyses. This technology gap represents a unique opportunity for Cyclica to contribute to personalized medicine, by working with SOSCIP and the IBM cloud analytics platform. Cyclica can integrate its drug polypharmacology predictions with clinical and genomic data to build models that can explain and predict variation in drug response between different individuals. Through Industrial Partner: Cyclica Inc. this program, we will create new tools that match patients to the pharmaceuticals best suited for their distinct genetic features. Ultimately, the effort will improve outcomes and limit the loss of valuable time associated with trial-and-error medicinePage.