Every year billions of dollars are invested in new drug development in the USA, and over 30,000 patients enroll in National Cancer Institute sponsored clinical oncology drug trials. Economic and health-related risks inherent in antineoplastic drug development impact both public health and economic stability. The ability to predict antineoplastic drug efficacy and toxicity based on preclinical and early clinical antineoplastic drug data could reduce these risks, resulting in more efficient and robust drug development pipelines. We have developed a drug development forecasting approach that utilizes preclinical data and available early clinical data to predict phase I, II, and III trial outcomes. Our overall hypothesis is that integrating pharmacologic data and clinical trial outcomes will yield commercially successful decision support software for drug development. Our objective is to develop novel database integration tools and prototype decision support software that will reduce the risks and costs of developing new antineoplastic drugs. Our unique approach uses pre-clinical and early clinical pharmacologic data to inform decisions for the initial and ongoing financial support of Phase I, II and III clinical oncology trials. We will accomplish our objective via two Specific Aims: (1) To develop comprehensive and novel database integration tools for pharmacologic data, and clinical trial event data predictive of Phase I/II/III antineoplastic compound outcomes, and (2) to leverage the database developed in Specific Aim 1 to identify, evaluate and implement Bayesian network models of pharmacologic markers that predict oncology trial events. This project will deliver comprehensive proof-of-concept for prototype software tools that can (a) integrate diverse data sources, and (b) predict Phases I, II and III clinical oncology trial outcomes, with the ultimate goal of reducing the risk and cost of developing new antineoplastic drugs. Upon successful completion of this project, we will expand the database to include all antineoplastic drugs available for analysis, and we will extend the development and application of the software tools to other disease mechanisms.
Public Health Relevance: Every year billions of dollars are invested in new drug development in the USA, and over 30,000 patients enroll in National Cancer Institute sponsored clinical oncology drug trials. The ability to use early drug development data to predict antineoplastic drug efficacy and safety could reduce these risks. We have developed a drug development prediction approach that utilizes preclinical data and available early clinical data to predict trial outcomes. Our overall hypothesis is that integrating pharmacologic data and clinical trial outcomes will yield commercially successful decision support software for drug development. Our objective is to develop novel database tools and decision support software that will reduce the risks and costs of developing new antineoplastic drugs. Our unique approach uses pre-clinical and early clinical pharmacologic data to inform decisions for the initial and ongoing financial support of clinical oncology drug trials.
Public Health Relevance: This Public Health Relevance is not available.
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