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Tools for Prediction of ADME-Tox Properties

ADME-Tox 特性预测工具

基本信息

批准号：
10262292
负责人：
MARC NICKLAUS
金额：
$ 3.37万
依托单位：
DIVISION OF BASIC SCIENCES - NCI
依托单位国家：
美国
项目类别：
财政年份：
资助国家：
美国
起止时间：
至
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10262292
关键词：
Algorithms Anti-HIV Agents Area Benchmarking Biochemistry Biological Biological Assay Biotechnology Cactaceae Cellular biology Chemical Structure Chemicals Collaborations Communicable Diseases Computer Assisted Computer software Computers Cytochrome P450 Data Data Set Data Sources Databases Department of Defense Dependence Descriptor Development Drug Design Excretory function Future Goals Half-Life Health protection Hepatocyte High Performance Computing Human In Vitro Institutes Investigational Drugs Isoenzymes Joints Journals Lead Liver Microsomes Measures Medical Research Metabolic Metabolism Modeling Molecular Target Paper Pharmaceutical Chemistry Pharmaceutical Preparations Pharmacology Phase Positioning Attribute Preclinical Drug Development Property PubChem Publications Publishing Quantitative Structure-Activity Relationship Reaction Research Research Institute Resources Rodent Services Side Site Statistical Methods Structure Technology Telemedicine Testing Toxic effect Training Validation Value of Life Work absorption anti-cancer therapeutic base computational platform drug development improved in vivo inhibitor/antagonist interest member open source pathogen predictive modeling programs small molecule tool web server web services

项目摘要

This project was started as part of a joint project of the CADD Group with several groups at the Department of Defense (DoD), with the title Computational platforms for transforming small molecules into investigational new drugs. The projects lead PI on the DoD side was Dr. S. Anders Wallqvist, Tri-Service Biotechnology High-Performance Computing Software Applications Institute for Force Health Protection (BHSAI), Telemedicine and Advanced Technology Research Center (TATRC), U.S. Army Medical Research and Materiel Command (USAMRMC), 2405 Whittier Drive, Suite 200, Frederick, MD 217602. Other participating groups were at the Department of Biochemistry, Walter Reed Army Institute of Research (WRAIR), and the Department of Cell Biology and Biochemistry, U.S. Army Medical Research Institute for Infectious Diseases (USAMRIID). The aim of the overall project was to integrate three fundamental aspects of the preclinical drug development phase, i.e., structure-based drug design, analysis and prediction of pharmacological data, and the prediction of adverse and off-target effects, in particular those related to drug metabolization, from chemical structures. The most important aspect of Dr. Pugliese's work concerned metabolism and metabolites. The work having effectively started in early 2010, the former CADD Group member Dr. Pugliese worked on implementing a resource for successful prediction of metabolism and metabolites of drug-like small molecules as part of our computer-aided drug design capabilities, until his departure from NCI for a permanent position in June, 2011. While the initial tests and application of these resources were done in the context of pathogens of interest to DoD, the general capability of predicting metabolic stability, metabolization profile and specific metabolites of a small molecule is applicable to all types of drug development, and therefore is very useful in the development of anti-cancer therapeutics aiming at molecular targets of high interest to NCI, as well as in, e.g., NCI's anti-HIV drug design projects. The project has therefore been continued even after the completion of the formal collaboration with the DoD groups in summer 2011. The first phase of this project, consisting of canvassing the field for predictive computer tools as well as data sets that can be used to test these tools and develop (better) predictive models, has been successfully completed. Both commercial and free resources have been compiled or acquired. A comparison and benchmark study with appropriate publication was completed, submitted, and is in press. In this part of the project, we focused on (prediction of) metabolic stability data such as half-life values in Human Liver Microsome or Human Hepatocyte assays. This paper also includes a small benchmark study of predictions of cytochrome P450 interactions (substrates, inhibitors, and inducers). In the second, more-applied, phase of the project, we developed QSAR models for metabolic stability of compounds, based on in vitro half-life assay data measured in human liver microsomes. A variety of QSAR models were generated using different statistical methods and descriptor sets implemented in both open-source and commercial programs (KNIME, GUSAR, StarDrop). The models obtained were compared using four different external validation sets from public and commercial data sources, including two smaller sets of in vivo half-life data in humans. The most predictive models were used for predicting the metabolic stability of compounds from the Open NCI Database, the results of which have been made publicly available on the NCI/CADD Group web server (http://cactus.nci.nih.gov). Both this study and the paper mentioned above have been published in the journal Future Medicinal Chemistry. Current efforts focus on broadening our predictive capabilities to models of all types of properties in the area of absorption, distribution, metabolism, excretion, and toxicities (ADME/Tox) of small molecules. Recently, Dr. Alexey Zakharov has made available a suite of predictive models for physicochemical properties, toxicities, as well as some biological activities in the form of the Chemical Activity Predictor (CAP) web service on the NCI/CADD Group web server. In the context of this topic, CADD Group members have also developed and improved general QSAR-related approaches and algorithms, as well as analyzed (Q)SAR models' dependency on mix-and-match'ability of assay data coming both from specific projects and large public databases such as PubChem and ChEMBL. Further analyses of Q(SAR) data and approaches have been performed with our Russian colleagues, which includes several papers of "mix-and-match" issue analyses. Also, large ADME-Tox computations are being performed for molecules from the SAVI project (Project 6).

这个项目是CADD集团与国防部(DoD)几个小组联合项目的一部分，标题是将小分子转化为研究新药的计算平台。在国防部领导PI的项目是S.Anders Wallqvist博士，他是三军生物技术高性能计算软件应用研究所的部队健康保护机构、远程医疗和先进技术研究中心、美国陆军医学研究和物资司令部(USAMRMC)、惠蒂尔大道2405号、Suite200，马里兰州弗雷德里克，邮编：217602。其他参加小组是沃尔特里德陆军研究所(WRAIR)的生物化学系和美国陆军传染病医学研究所(USAMRIID)的细胞生物学和生物化学系。整个项目的目的是整合临床前药物开发阶段的三个基本方面，即基于结构的药物设计、药理数据的分析和预测以及从化学结构预测不良和非靶点影响，特别是与药物代谢有关的影响。Pugliese博士工作中最重要的方面与新陈代谢和代谢物有关。这项工作实际上从2010年初开始，前CADD小组成员Pugliese博士致力于实施成功预测类药物小分子代谢和代谢物的资源，作为我们计算机辅助药物设计能力的一部分，直到2011年6月离开NCI担任长期职位。虽然这些资源的初始测试和应用是在DOD感兴趣的病原体的背景下进行的，但预测小分子的代谢稳定性、代谢谱和特定代谢物的一般能力适用于所有类型的药物开发，因此在针对NCI高度关注的分子靶点的抗癌治疗药物的开发以及例如NCI的抗HIV药物设计项目中非常有用。因此，即使在2011年夏季完成与国防部各小组的正式协作之后，该项目仍在继续。该项目的第一阶段已顺利完成，其中包括在实地搜寻预测计算机工具以及可用于测试这些工具和开发(更好的)预测模型的数据集。商业资源和免费资源都已汇编或获得。完成了一项比较和基准研究，并出版了适当的出版物，并已提交，正在出版中。在该项目的这一部分，我们关注(预测)代谢稳定性数据，例如人肝微粒体或人肝细胞检测中的半衰期。这篇论文还包括一项预测细胞色素P450相互作用(底物、抑制剂和诱导剂)的小型基准研究。在该项目的第二个更多应用的阶段，我们基于在人肝微粒体中测量的体外半衰期分析数据，开发了化合物代谢稳定性的QSAR模型。使用开源和商业程序(Knime、GUSAR、StarDrop)中实现的不同统计方法和描述符集生成了各种QSAR模型。使用来自公共和商业数据源的四个不同的外部验证集对获得的模型进行比较，其中包括两个较小的人体体内半衰期数据集。最具预测性的模型用于从Open NCI数据库预测化合物的代谢稳定性，其结果已在NCI/CADD Group Web服务器(http://cactus.nci.nih.gov).)上公布这项研究和上面提到的论文都发表在《未来药物化学》杂志上。目前的工作重点是将我们的预测能力扩展到小分子的吸收、分布、新陈代谢、排泄和毒性(ADME/Tox)领域的所有类型属性的模型。最近，Alexey Zakharov博士在NCI/CADD Group Web服务器上以化学活性预测(CAP)Web服务的形式提供了一套物理化学性质、毒性以及一些生物活性的预测模型。在这一主题的背景下，CADD小组成员还开发和改进了与QSAR相关的一般方法和算法，并分析了(Q)SAR模型对来自特定项目和大型公共数据库(如PubChem和ChEMBL)的分析数据的混合匹配能力的依赖性。与我们的俄罗斯同事一起对Q(SAR)数据和方法进行了进一步的分析，其中包括几篇“混合匹配”问题分析的论文。此外，正在对SAVI项目(项目6)中的分子进行大规模的ADME-TOX计算。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors.

DOI：
10.3389/fchem.2018.00133
发表时间：
2018
期刊：
Frontiers in chemistry
影响因子：
5.5
作者：
Pogodin PV;Lagunin AA;Rudik AV;Filimonov DA;Druzhilovskiy DS;Nicklaus MC;Poroikov VV
通讯作者：
Poroikov VV

Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software.