Safety Data Mining Based on Chemical Structures

基于化学结构的安全数据挖掘

• 批准号: 6865093
• 负责人: DAVID M FRAM
• 金额: $ 17.95万
• 依托单位: LINCOLN TECHNOLOGIES, INC.
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-02-01 至 2006-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6865093
• 关键词: Internet; chemical information system; chemical structure function; clinical research; computer program /software; computer system design /evaluation; drug adverse effect; drug screening /evaluation; human data; information retrieval; mathematical model; method development; model design /development; statistics /biometry

DESCRIPTION (provided by applicant): Predicting and understanding drug toxicity in humans remains a major obstacle to new drug development programs. Pharmacovigilance databases of spontaneous adverse event reports from marketed drugs represent an important source of human toxicity information for addressing this challenge, especially if the associations identified in these databases between various adverse events (types of toxicity) and specific drugs can be understood in terms of the relationship of the adverse events to the underlying chemical structure of the drugs. In Phase I, the focus is on the integration of structural chemistry information into the applicant's existing "'WEBVDME" safety data mining system to support initial investigation of relationships between drug toxicity Profiles (as expressed in spontaneous adverse event data) and underlying chemical structures and substructures (2-D fragments). Phase II will also investigate the feasibility and value of incorporating other types of information (3-D molecular descriptors, pharmacokinetic and metabolic parameters) for further elucidation of patterns and issues in drug toxicity. The key benefit of the project to public health is an improved ability to predict drug toxicity, leading to significant reductions in the financial and human costs of drug development and to improved safety profiles for the drugs that reach the market.

描述（由申请人提供）：预测和了解人体药物毒性仍然是新药开发计划的主要障碍。来自上市药物的自发不良事件报告的药物警戒数据库代表了解决这一挑战的人类毒性信息的重要来源，特别是如果这些数据库中确定的各种不良事件（毒性类型）与特定药物之间的关联可以根据不良事件与药物基础化学结构的关系来理解。在I期，重点是将结构化学信息整合到申请人现有的“WEBVDME”安全性数据挖掘系统中，以支持药物毒性特征（如自发不良事件数据所示）与基础化学结构和亚结构（2-D片段）之间关系的初步研究。第二阶段还将调查纳入其他类型的信息（三维分子描述符，药代动力学和代谢参数）的可行性和价值，以进一步阐明药物毒性的模式和问题。该项目对公共卫生的主要好处是提高了预测药物毒性的能力，从而大大降低了药物开发的财务和人力成本，并改善了进入市场的药物的安全性。