Robust computational framework for predictive ADME-Tox modeling

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DESCRIPTION (provided by applicant): This proposal seeks to establish a universally applicable and robust predictive ADME-Tox modeling framework based on rigorous Quantitative Structure Activity/Property Relationships (QSAR/QSPR) modeling. The framework has been refined in the course of many years of our research in the areas of QSPR methodology development and application to experimental datasets that led to novel analytical approaches, descriptors, model validation schemes, overall QSPR workflow design, and multiple end-point studies. This proposal focuses on the design of optimized QSPR protocols for the development of reliable predictors of critically important ADME-Tox properties. The ADME properties will include, but not limited to, water solubility, membrane permeability, P450 metabolism inhibition and induction, metabolic stability, human intestinal absorption, bioavailability, transporters and PK data; a variety of toxicological end-points vital to human health will be explored; they are available from recent initiatives on development and standardization of toxicity data, such as the US FDA, NIEHS, and EPA DSS-Tox and other database projects. The ultimate goal of this project is sharing both modeling software and specialized predictors with the research community via a web-based Predictive ADME-Tox Portal. The project objectives will be achieved via concurrent development of QSPR methodology (Specific Aim 1), building highly predictive, robust QSPR models of known ADME-Tox properties (Specific Aim 2), and the deployment of both modeling software and individual predictors via a specialized web-portal (Specific Aim 3). To achieve the goals of this project focusing on the development and delivery of specialized tools and rigorous predictors, we have assembled a research team of mostly senior investigators with complimentary skills and track records of accomplishment in the areas of computational drug discovery, experimental toxicology, statistical modeling, and software development and integration; two of the team members have had recent industrial experience before transitioning to academia. To the best of our knowledge, the results of this proposal will lead to the first publicly available in silico ADME-Tox modeling framework and predictors that can be used by the research community to analyze any set of chemicals (i.e., virtual and real compound sets). The framework will have a significant impact on compound prioritization, chemical library design, and candidate selection for preclinical and clinical development.