INTELLIGENT TOXICOLOGY PREDICTION SYSTEM

智能毒理学预测系统

• 批准号: 2309227
• 负责人: J C BHANDARI
• 金额: $ 4.98万
• 依托单位: COLUMBIA CASCADE, INC.
• 依托单位国家: 美国
• 财政年份: 1993
• 资助国家: 美国
• 起止时间: 1993-09-30 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/2309227
• 关键词: INTELLIGENT; TOXICOLOGY; PREDICTION; SYSTEM

The overall objective of SBIR Topic 32 is also the major goal of an NIEHS project from which it emanates, to develop predictive toxicology methods that support decision making and provide insight into pertinent mechanisms of toxic action. Fundamentally different methods are needed to assess the extent to which any one of them applies to the multifactorial prediction space of interest; accordingly, their simultaneous development is essential to achievement of significant progress in this research area. The specific objective of Phase I is to demonstrate the feasibility of reducing the representation of specific-organ toxicity datasets using symbolic artificial intelligence (SAI) methodology, so as to enable their more effective utilization in research aimed at predicting the carcinogenic potential of noncongeneric chemicals, other environmental agents, and mixtures. Realistically, little is known about relationships between specific-organ toxicity and carcinogenicity, or about how to utilize the extensive but complicated NTP database of specific-organ toxicity effects to predict carcinogenicity. Therefore, the Phase I research is viewed as a major challenge by the NIEHS, especially in view of the short, six-month period of the contract. The successful product of Phase I is expected to be a report that describes experiments performed, evaluates results obtained, and demonstrates the applicability of the SAI approach chosen for solving the specific-organ toxicity data analysis problem. Any Phase 2 support for longer-range research to develop a particularly promising approach for predicting carcinogenicity will depend on the extent to which the Phase I objective is accomplished.

SBIR 主题 32 的总体目标也是 NIEHS 的主要目标 它的起源项目，开发预测毒理学方法 支持决策并提供对相关机制的洞察 的毒性作用。 需要采用根本不同的方法来评估 其中任何一个适用于多因素预测的程度 兴趣空间；因此，它们的同步发展至关重要 以期在该研究领域取得重大进展。 第一阶段的具体目标是证明可行性 使用减少特定器官毒性数据集的代表性 符号人工智能（SAI）方法论，从而使他们能够 在旨在预测致癌物质的研究中更有效地利用 非同类化学品、其他环境因素的潜力，以及 混合物。 事实上，人们对两者之间的关系知之甚少。 特定器官毒性和致癌性，或如何利用 广泛但复杂的特定器官毒性效应 NTP 数据库 来预测致癌性。 因此，第一阶段研究被视为 NIEHS 面临的重大挑战，特别是考虑到短暂的六个月 合同期限。 一期产品的成功预计 是一份描述所进行的实验、评估结果的报告 获得，并证明了所选择的 SAI 方法的适用性 解决特定器官毒性数据分析问题。 任何第 2 阶段 支持长期研究以开发特别有前途的 预测致癌性的方法将取决于致癌性的程度 第一阶段的目标已经完成。