STopTox: A comprehensive in silico platform for predicting systemic and topical toxicity

StopTox：用于预测全身和局部毒性的综合计算机平台

• 批准号: 10324720
• 负责人: Alexander Tropsha
• 金额: $ 25.55万
• 依托单位: PREDICTIVE, LLC
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-13 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10324720
• 关键词: Acute; Address; Animal Testing; Animals; Bayesian Modeling; Biological Assay; Characteristics; Chemical Structure; Chemicals; Collection; Communities; Computer Models; Computer software; Consensus; Corrosion; Cosmetics; Data; Data Reporting; Data Set; Dermal; Descriptor; Development; Eye; Fees; Funding; Hazard Identification; In Vitro; Individual; Ingestion; Inhalation; Instruction; Interagency Coordinating Committee on the Validation of Alternative Methods; Investigation; Knowledge; Label; Laboratories; Letters; Licensing; Mammals; Maps; Medical; Methods; Modeling; Oral; Paste substance; Pathway interactions; Pesticides; Pharmacologic Substance; Phase; Privacy; Probability; Protocols documentation; Publishing; Quantitative Structure-Activity Relationship; Reporting; Research; Running; Secure; Services; Skin; Small Business Technology Transfer Research; Techniques; Testing; Toxic effect; Validation; Visualization; Work; acute toxicity; adverse outcome; analog; computerized tools; deep learning; design; hazard; improved; in silico; in vivo; irritation; learning strategy; nephrotoxicity; novel; novel strategies; phase 2 study; predictive modeling; preservation; skin irritation; software as a service; software development; tool; user friendly software; web app; web portal

There is a strong need to develop New Alternative Methods (NAMs) to reduce animal testing of chemical, cosmetic, and pharmaceutical products to evaluate chemical toxicity. “6-pack” battery of regulatory assays (acute oral toxicity, acute dermal toxicity, acute inhalation toxicity, skin irritation and corrosion, eye irritation and corrosion, and skin sensitization) is a collection of tests that chemical products must go through to achieve regulatory approval. Computational approaches that can accurately estimate the results of the experimental testing can provide a powerful alternative to in vivo investigations. Previously, both our group and several other groups have developed models for some of these endpoints but using limited data or, in some cases, lacking rigor in both curation of the reported data and model validation strategies. This project addresses these deficiencies. We recently formed Predictive, LLC, to enable the development and distribution of commercial and regulatory strength models to predict important toxicity endpoints. In this Phase I STTR application, we intend to produce rigorously validated models of all “6-pack” assays, transfer these models to Predictive, LLC, and integrate these models into a software product termed STopTox (Systemic and Topical Toxicity) Predictor. We will achieve this objective by focusing on the following Specific Aims. Specific Aim 1. Develop advanced models for the “6-pack” battery of tests. We will ingest new data and develop new consensus models using multiple types of descriptors and advanced modeling techniques, including deep learning methods. We will also generate a Bayesian model applying individual predictions of each unique model as descriptors, which could assess if a compound would be active in any of the 6-pack tests. Specific Aim 2: Model interpretation and elucidation of adverse outcomes pathways (AOPs.) We will enable protocols and tools for model interpretation, which is an important part of regulatory decision support, both in terms of pf chemical features responsible for toxicity, and respective AOPs. Predictive probability maps will be implemented as a graphical visualization of the predicted fragment contribution, allowing the user to interpret the prediction and design safer compounds. In a parallel effort, we will work on the issue of AOPs, which is very important for a mechanistic understanding of toxicity mechanisms and regulatory acceptance of new chemicals. Specific Aim 3: STopTox platform development. Predictive, LLC, will implement all models in a software that will run both locally standalone and on a secure web portal. Testing will be done both internally and by external users. Predictions for individual models, the smart-consensus Bayesian models, as well as predicted fragment contributions, will be displayed on the screen and the user will be able to download a report with the results and a summary of characteristics of the models and instructions to help interpret the results. The ultimate objective of this proposal is to leverage public data knowledge on compounds tested in “6-pack” regulatory assays by creating a software platform (STopTox) to be commercialized as a service or licensed to commercial users.

强烈需要开发新的替代方法（NAM）来减少化学动物测试， 化妆品和药品以评估化学毒性。 “ 6包”的监管测定 （急性口服毒性，急性皮肤毒性，急性吸入毒性，皮肤刺激和腐蚀，眼睛刺激和 腐蚀和皮肤敏感性）是化学产品必须进行的测试集合的集合 监管批准。可以准确估计实验结果的计算方法 测试可以为体内调查提供有力的替代方法。以前，我们的小组和其他几个 小组为其中一些终点开发了模型，但使用有限的数据或在某些情况下缺乏 在报告的数据和模型验证策略的策划中严格。这个项目解决了这些 缺陷。我们最近成立了预测性有限责任公司，以使商业的开发和分配和分布 调节强度模型预测重要的毒性终点。在此阶段I sttr应用程序中，我们打算 生成了所有“ 6包”测定法的严格验证模型，将这些模型转移到Predictive，LLC和 将这些模型集成到称为stoptox（系统性和局部毒性）预测因子的软件产品中。我们 将通过关注以下特定目标来实现这一目标。特定目标1。发展高级 “ 6包”测试电池的型号。我们将摄入新数据并使用 多种类型的描述符和高级建模技术，包括深度学习方法。我们也会 生成一个贝叶斯模型，该模型应用每个唯一模型作为描述符的个人预测，可以 评估化合物是否在任何6件包测试中都活跃。特定目标2：模型解释和 阐明不良结果途径（AOPS）。我们将启用模型的协议和工具 解释是监管决策支持的重要组成部分，既在PF化学特征方面 负责毒性和相对AOP。预测概率图将作为图形实现 可视化预测的片段贡献，使用户可以解释预测和设计更安全 化合物。在平行的努力中，我们将研究AOP的问题，这对于机械方面非常重要 了解毒性机制和新化学物质的调节接受。特定目标3：stoptox 平台开发。 Predictive，LLC将在将在本地运行的软件中实现所有模型 独立并在安全的Web门户上。测试将在内部和外部用户进行。预测 对于单个模型，智能传记的贝叶斯模型以及预测的碎片贡献将 显示在屏幕上，用户将能够下载带有结果的报告和摘要 模型和指示的特征，以帮助解释结果。该提议的最终目标 是通过创建软件来利用在“ 6包”调节案中测试的化合物的公共数据知识 平台（StopTox）将作为服务商业化或获得商业用户的许可。

项目成果

STopTox: An in Silico Alternative to Animal Testing for Acute Systemic and Topical Toxicity.

• DOI: 10.1289/ehp9341
• 发表时间: 2022-03
• 期刊: Environmental health perspectives
• 影响因子: 10.4
• 作者: Borba JVB;Alves VM;Braga RC;Korn DR;Overdahl K;Silva AC;Hall SUS;Overdahl E;Kleinstreuer N;Strickland J;Allen D;Andrade CH;Muratov EN;Tropsha A
• 通讯作者: Tropsha A