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

这个PFI: AIR技术翻译项目的重点是翻译基于概率目标抑制图的联合治疗的数学模型和设计，以满足开发功能和基因组信息个性化癌症治疗的未满足临床需求。目标是通过直接解决药物协同作用和疾病复发来改善治疗结果。概率目标抑制图创新的成功实施有望对社会产生重大影响，为已经失败或想要替代一线和二线治疗的癌症患者提供治疗设计的替代方法。即使化疗和放疗取得了进展，仅在美国就有超过45万人死于实体瘤癌症；这导致了对癌症患者的个性化药物组合的替代方法的巨大需求。该项目将为概率目标抑制图在协同药物组合设计中的应用提供概念验证。概率目标抑制图谱框架具有以下特点：(i)在模型生成中整合功能和基因组数据，（ii）比现有技术提高预测准确性，（iii）从fda批准的靶向药物中优化选择药物组合。这种方法将提供快速、循证、低毒性的个性化治疗，从而提高治疗效果，降低复发机会。由此产生的技术将不同于市场上现有的精确癌症治疗方法，与同类方法相比将具有很强的竞争力。该项目解决了从研究发现到商业应用的以下技术差距：(a)通过结合现有的单个药物的副作用数据来预测预期的系统级毒性，并结合其他化合物水平和患者水平的数据来确定潜在的意外毒性问题，从而表征联合药物的毒性；(b)设计药物组合选择的优化算法，包括毒性评估和(c)整合突变数据并将靶点映射到已知的蛋白质-蛋白质相互作用（PPI）网络，为概率靶点抑制图框架阐明的靶点的重要性提供进一步的证据。此外，参与该项目的研究生将学习如何通过解决技术差距和成为知识产权开发过程的一部分，将基础研究转化为商业上可行的产品。该项目吸引儿童参与。美国癌症治疗发展研究所和犹他大学提供实验验证能力和商业化专业知识，将这项技术从研究发现转化为商业现实。

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.