? 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. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21GM076059-03
Application #
7433931
Study Section
Special Emphasis Panel (ZGM1-PPBC-0 (TX))
Program Officer
Okita, Richard T
Project Start
2006-06-01
Project End
2010-05-31
Budget Start
2008-06-01
Budget End
2010-05-31
Support Year
3
Fiscal Year
2008
Total Cost
$322,087
Indirect Cost
Name
University of North Carolina Chapel Hill
Department
Pharmacology
Type
Schools of Pharmacy
DUNS #
608195277
City
Chapel Hill
State
NC
Country
United States
Zip Code
27599
Low, Yen S; Caster, Ola; Bergvall, Tomas et al. (2016) Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome. J Am Med Inform Assoc 23:968-78
Wambaugh, John F; Wetmore, Barbara A; Pearce, Robert et al. (2015) Toxicokinetic Triage for Environmental Chemicals. Toxicol Sci 147:55-67
Low, Yen Sia; Sedykh, Alexander Yeugenyevich; Rusyn, Ivan et al. (2014) Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays. Curr Top Med Chem 14:1356-64
Politi, Regina; Rusyn, Ivan; Tropsha, Alexander (2014) Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods. Toxicol Appl Pharmacol 280:177-89
Low, Yen; Sedykh, Alexander; Fourches, Denis et al. (2013) Integrative chemical-biological read-across approach for chemical hazard classification. Chem Res Toxicol 26:1199-208
Sedykh, Alexander; Fourches, Denis; Duan, Jianmin et al. (2013) Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions. Pharm Res 30:996-1007
Zhu, Xiang-Wei; Sedykh, Alexander; Zhu, Hao et al. (2013) The use of pseudo-equilibrium constant affords improved QSAR models of human plasma protein binding. Pharm Res 30:1790-8
Zhang, Liying; Sedykh, Alexander; Tripathi, Ashutosh et al. (2013) Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches. Toxicol Appl Pharmacol 272:67-76
Zhang, Liying; Fourches, Denis; Sedykh, Alexander et al. (2013) Discovery of novel antimalarial compounds enabled by QSAR-based virtual screening. J Chem Inf Model 53:475-92
Tropsha, Alexander (2012) Recent trends in statistical QSAR modeling of environmental chemical toxicity. Exp Suppl 101:381-411

Showing the most recent 10 out of 24 publications