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

基本信息

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
项目类别：
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

项目摘要

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.

迫切需要开发新的替代方法（NAM）来减少化学物质的动物测试，化妆品和药品，用于评估化学毒性的“6 组”监管检测。（急性经口毒性、急性经皮毒性、急性吸入毒性、皮肤刺激和腐蚀、眼睛刺激和腐蚀和皮肤过敏）是化学产品必须经过的一系列测试才能达到能够准确估计实验结果的计算方法。测试可以为我们的团队和其他几个团队之前的体内研究提供强大的替代方案。一些研究小组已经为其中一些终点开发了模型，但使用的数据有限，或者在某些情况下缺乏数据报告数据的管理和模型验证策略的严格性。我们最近成立了 Predictive, LLC，以实现商业和分销的开发和发行。预测重要毒性终点的监管强度模型在第一阶段 STTR 应用中，我们打算生成所有“6 包”检测的经过严格验证的模型，将这些模型转移到 Predictive, LLC，并且将这些模型集成到名为 StopTox（系统和局部毒性）预测器的软件产品中。将通过关注以下具体目标来实现这一目标： 1. 制定先进的目标。 “6 组”测试的模型我们将使用新数据并开发新的共识模型。我们还将使用多种类型的描述符和先进的建模技术，包括深度学习方法。生成一个贝叶斯模型，应用每个独特模型的单独预测作为描述符，这可以评估某种化合物在任何 6 组测试中是否具有活性：具体目标 2：模型解释和阐明不良后果途径 (AOP)。我们将为模型启用协议和工具解释，无论是在PF化学特征方面，都是监管决策支持的重要组成部分负责毒性，相应的 AOP 将以图形形式实现。预测片段贡献的可视化，使用户能够更安全地解释预测和设计在并行的工作中，我们将研究 AOP 的问题，这对于机械论来说非常重要。了解新化学品的毒性机制和监管接受度具体目标 3：STOPTox。 Predictive, LLC 将在本地运行的软件中实现所有模型。测试将由内部和外部用户独立进行并在安全的门户网站上进行。对于单个模型，智能共识贝叶斯模型以及预测的片段贡献将显示在屏幕上，用户将能够下载包含结果和摘要的报告模型的特征和有助于解释结果的说明该提案的最终目标。是通过创建一个软件来利用“6包”监管分析中测试的化合物的公共数据知识平台（STOPTox）作为服务商业化或授权给商业用户。

项目成果

期刊论文数量（1）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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