PFI:AIR - TT: Design of functionally-tested, genomics-informed personalized cancer therapy drug treatment plans

Texas Tech University, Lubbock TX

Investigators

Abstract

This PFI: AIR Technology Translation project focuses on translating mathematical modeling and design of combination therapy based on Probabilistic Target Inhibition Maps to fulfill the unmet clinical need of developing functional and genomic-informed personalized cancer therapy. The goal is to improve treatment outcomes by directly addressing drug synergy and disease recurrence. Successful implementation of the Probabilistic Target Inhibition Map innovation is expected to have a significant impact on society by providing an alternative approach to therapy design for cancer patients who have failed, or want alternatives to, first and second line therapies. Even with advances in chemotherapy and radiation, there are over 450,000 deaths attributed to solid tumor cancers in the U.S. alone; resulting in a significant need for alternative approaches involving personalized drug combinations for cancer patients failing standard of care treatments. The project will result in proof of concept validation for application of Probabilistic Target Inhibition Maps to synergistic drug combination design. The Probabilistic Target Inhibition Map framework has the unique features of (i) integrating functional and genomic data in model generation, (ii) increased prediction accuracy over existing techniques and (iii) optimized selection of drug combinations from FDA-approved targeted drugs. This approach will provide rapid, evidenced-based, reduced toxicity personalized therapies, leading to greater treatment efficacy and lower chances of recurrence. The resulting technology will be unlike existing precision cancer therapy approaches available in the market, and will be very competitive with comparable approaches. This project addresses the following technology gaps as it translates from research discovery towards commercial application: (a) characterizing combination drug toxicities by incorporating existing side effects data of individual drugs to predict expected system-level toxicity, and integrate additional compound-level and patient-level data to identify potentially unexpected toxicity issues, (b) design of optimization algorithms for selection of drug combinations incorporating toxicity estimation and (c) integrating mutation data and mapping targets to known Protein-Protein Interaction (PPI) networks for providing further evidence for the significance of targets elucidated by the Probabilistic Target Inhibition Map framework. In addition, graduate students involved in this project will learn about translating fundamental research to commercially viable product by addressing technology gaps and being part of the intellectual property development process. The project engages Children?s Cancer Therapy Development Institute and University of Utah to provide experimental validation capabilities and commercialization expertise in this technology translation effort from research discovery towards commercial reality.