Tools for Prediction of ADME-Tox Properties

该项目是作为CADD小组与国防部（DoD）的几个小组的联合项目的一部分开始的，标题为将小分子转化为研究性新药的计算平台。国防部方面的项目负责人是S. Anders Wallqvist博士，三军生物技术高性能计算软件应用研究所（BHSAI），远程医疗和先进技术研究中心（TATRC），美国陆军医学研究和装备司令部（USAMRMC）， 2405 Whittier Drive, Suite 200, Frederick, MD 217602。其他参与小组来自沃尔特·里德陆军研究所生物化学系（WRAIR）和美国陆军传染病医学研究所细胞生物学和生物化学系（USAMRIID）。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脱靶效应，特别是与药物代谢相关的不良和脱靶效应。普格列斯博士研究的最重要方面是新陈代谢和代谢物。这项工作于2010年初有效开始，前CADD小组成员Pugliese博士致力于实现一种资源，用于成功预测药物样小分子的代谢和代谢物，作为我们计算机辅助药物设计能力的一部分，直到他于2011年6月离开NCI担任永久职位。虽然这些资源的初始测试和应用是在国防部感兴趣的病原体背景下进行的，但预测小分子代谢稳定性、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NCI高度感兴趣的分子靶点的抗癌治疗开发中非常有用，以及在NCI的抗hiv药物设计项目中。因此，即使在2011年夏季与国防部小组完成正式合作后，该项目仍在继续。该项目的第一阶段已经成功完成，包括对预测计算机工具以及可用于测试这些工具和开发（更好的）预测模型的数据集进行实地调查。商业和免费资源都已汇编或获得。一项比较和基准研究已完成，已提交，并已出版。在项目的这一部分，我们专注于（预测）代谢稳定性数据，如人肝微粒体或人肝细胞测定中的半衰期值。本文还包括细胞色素P450相互作用（底物，抑制剂和诱导剂）预测的小型基准研究。在项目的第二个应用阶段，我们基于在人肝微粒体中测量的体外半衰期测定数据，开发了化合物代谢稳定性的QSAR模型。使用不同的统计方法和描述符集生成各种QSAR模型，这些方法和描述符集在开源和商业程序（KNIME， GUSAR, StarDrop）中实现。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了比较，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预测Open NCI数据库中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）上公开提供。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、代谢、排泄和毒性（ADME/Tox）领域的所有类型的特性模型。最近，Alexey Zakharov博士在NCI/CADD集团网络服务器上以化学活动预测器（CAP）网络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动的预测模型。在本主题的背景下，CADD小组成员还开发和改进了一般qsar相关的方法和算法，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。我们与俄罗斯同事一起对Q（SAR）数据和方法进行了进一步分析，其中包括几篇“混合匹配”问题分析论文。此外，正在对SAVI项目（项目6）中的分子进行大型ADME-Tox计算。

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).