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计算机毒理学模型和数据库作为 FDA 关键路径倡议工具包。

In silico toxicology models and databases as FDA Critical Path Initiative toolkits.

机构信息

Office of Pharmaceutical Science, Center for Drug Evaluation and Research, US Food and Drug Administration, White Oak 51, Room 4128, 10903 New Hampshire Avenue, Silver Spring, MD 20993-0002, USA.

出版信息

Hum Genomics. 2011 Mar;5(3):200-7. doi: 10.1186/1479-7364-5-3-200.

Abstract

In silico toxicology methods are practical, evidence-based and high throughput, with varying accuracy. In silico approaches are of keen interest, not only to scientists in the private sector and to academic researchers worldwide, but also to the public. They are being increasingly evaluated and applied by regulators. Although there are foreseeable beneficial aspects--including maximising use of prior test data and the potential for minimising animal use for future toxicity testing--the primary use of in silico toxicology methods in the pharmaceutical sciences are as decision support information. It is possible for in silico toxicology methods to complement and strengthen the evidence for certain regulatory review processes, and to enhance risk management by supporting a more informed decision regarding priority setting for additional toxicological testing in research and product development. There are also several challenges with these continually evolving methods which clearly must be considered. This mini-review describes in silico methods that have been researched as Critical Path Initiative toolkits for predicting toxicities early in drug development based on prior knowledge derived from preclinical and clinical data at the US Food and Drug Administration, Center for Drug Evaluation and Research.

摘要

计算机毒理学方法具有实用性、基于证据和高通量的特点,其准确性也有所不同。计算机方法不仅受到私营部门科学家和全球学术研究人员的关注,也受到公众的关注。监管机构越来越多地对它们进行评估和应用。尽管可以预见具有一定的有益方面,包括最大限度地利用先前的测试数据和未来毒性测试中动物使用量最小化的潜力,但在药物科学中,计算机毒理学方法的主要用途是作为决策支持信息。计算机毒理学方法有可能补充和加强某些监管审查过程的证据,并通过支持更明智的决策,为研究和产品开发中的额外毒性测试设定优先级,从而加强风险管理。这些不断发展的方法也存在一些挑战,显然必须加以考虑。这篇小型综述描述了美国食品和药物管理局药物评价和研究中心基于临床前和临床数据的先验知识,作为早期药物开发毒性预测的关键路径倡议工具包的研究中的计算机方法。

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