Robust computational framework for predictive ADME-Tox modeling

用于预测 ADME-Tox 建模的强大计算框架

• 批准号: 7433931
• 负责人: Alexander Tropsha
• 金额: $ 32.21万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-06-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7433931
• 关键词: Academia; Acute; Address; Area; Biological Availability; Cardiotoxicity; Cell Membrane Permeability; Chemicals; Chronic; Clinical; Collaborations; Communities; Computer Simulation; Computer software; Computers; Consensus; Cytochrome P450; Data; Data Collection; Data Set; Databases; Descriptor; Development; Drug Kinetics; End Point; Ensure; Environment; Goals; Health; Hepatotoxicity; Human; Individual; Internet; Intestinal Absorption; Knowledge; Laboratories; Lead; Learning; Letters; Lung; Machine Learning; Metabolic; Metabolism; Methodology; Methods; Modeling; Nature; Online Systems; Organ; Pharmacologic Substance; Postdoctoral Fellow; Property; Protocols documentation; Quantitative Structure-Activity Relationship; Records; Recruitment Activity; Research; Research Design; Research Infrastructure; Research Personnel; Scheme; Scientist; Screening procedure; Secure; Source; Specialist; Standardization; Standards of Weights and Measures; Statistical Models; Statistically Significant; Structure; Students; Techniques; Technology; Testing; Toxic effect; Toxicology; Training; United States Environmental Protection Agency; United States Food and Drug Administration; United States National Institutes of Health; Validation; base; carcinogenicity; career; cluster computing; combinatorial; computer framework; data mining; design; drug discovery; experience; genotoxicity; innovation; knowledge of results; member; method development; neurotoxicity; novel; open source; pre-clinical; programs; protocol development; reproductive; skills; small molecule libraries; software development; tool; virtual; water solubility

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.

描述（由申请人提供）： 该提案旨在基于严格的定量结构活动/属性关系（QSAR/QSPR）建模建立普遍适用且可靠的预测性ADME-TOX建模框架。在QSPR方法论开发和应用于实验数据集的领域多年的研究过程中，该框架已经进行了完善，这导致了新的分析方法，描述符，模型验证方案，整体QSPR工作流程设计以及多个终点研究。该提案着重于优化QSPR方案的设计，以开发至关重要的ADME-TOS特性的可靠预测指标。 ADME性质将包括但不限于水溶性，膜渗透性，P450代谢抑制和诱导，代谢稳定性，人类肠道吸收，生物利用度，转运蛋白和PK数据；将探索各种对人类健康至关重要的毒理学终点；它们可以从有关毒性数据的开发和标准化的最新计划中获得，例如美国FDA，NIEHS和EPA DSS-TOX和其他数据库项目。该项目的最终目标是通过基于Web的预测性ADME-TOX门户与研究社区共享建模软件和专业预测指标。将通过QSPR方法（特定目标1）的并发开发，建立高度预测性，可靠的QSPR模型的已知ADME-TOX属性（特定AIM 2）以及通过专业的Web-portal（特定AIM 3）的部署来实现项目目标。为了实现该项目的目标，重点是开发和交付专业工具和严格的预测指标，我们组建了一个由高级研究人员组成的研究团队，具有免费技能和在计算药物发现，实验毒理学，统计建模以及软件开发以及软件开发和整合领域的成就记录；两名团队成员在过渡到学术界之前曾拥有最近的工业经验。据我们所知，该提案的结果将导致第一个在Silico Adme-Tox建模框架和预测指标中公开可用的结果，这些框架可以被研究界使用，这些框架可以使用，这些框架可用于分析任何集合的化学品（即虚拟和实际复合集）。该框架将对复合优先级，化学库设计以及临床前和临床开发的候选选择产生重大影响。

项目成果

A new structure-based QSAR method affords both descriptive and predictive models for phosphodiesterase-4 inhibitors.

• DOI: 10.2174/1875397300802010029
• 发表时间: 2008-11-06
• 期刊: Current chemical genomics
• 作者: Dong, Xialan;Zheng, Weifan
• 通讯作者: Zheng, Weifan

Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.

• DOI: 10.1021/tx900326k
• 发表时间: 2010-01
• 期刊: CHEMICAL RESEARCH IN TOXICOLOGY
• 影响因子: 4.1
• 作者: Fourches, Denis;Barnes, Julie C.;Day, Nicola C.;Bradley, Paul;Reed, Jane Z.;Tropsha, Alexander
• 通讯作者: Tropsha, Alexander

Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.

化学信息学辅助药物警戒：在史蒂文斯-约翰逊综合征中的应用。

• DOI: 10.1093/jamia/ocv127
• 发表时间: 2016
• 期刊: Journal of the American Medical Informatics Association : JAMIA
• 作者: Low,YenS;Caster,Ola;Bergvall,Tomas;Fourches,Denis;Zang,Xiaoling;Norén,GNiklas;Rusyn,Ivan;Edwards,Ralph;Tropsha,Alexander
• 通讯作者: Tropsha,Alexander