Steinbach Thomas, Gad-McDonald Samantha, Kruhlak Naomi, Powley Mark, Greene Nigel
EPL, Inc, Res Triangle Park, NC, USA
Gad Consulting Services, Raleigh, NC, USA.
Int J Toxicol. 2015 Jul-Aug;34(4):352-4. doi: 10.1177/1091581815584914. Epub 2015 May 15.
A continuing education (CE) course at the 2014 American College of Toxicology annual meeting covered the topic of (Quantitative) Structure-Activity Relationships [(Q)SAR]. The (Q)SAR methodologies use predictive computer modeling based on predefined rules to describe the relationship between chemical structure and a chemical's associated biological activity or statistical tools to find correlations between biologic activity and the molecular structure or properties of a compound. The (Q)SAR has applications in risk assessment, drug discovery, and regulatory decision making. Pressure within industry to reduce the cost of drug development and societal pressure for government regulatory agencies to produce more accurate and timely risk assessment of drugs and chemicals have necessitated the use of (Q)SAR. Producing a high-quality (Q)SAR model depends on many factors including the choice of statistical methods and descriptors, but first and foremost the quality of the data input into the model. Understanding how a (Q)SAR model is developed and applied is critical to the successful use of such a tool. The CE session covered the basic principles of (Q)SAR, practical applications of these computational models in toxicology, how regulatory agencies use and interpret (Q)SAR models, and potential pitfalls of using them.
2014年美国毒理学院年会上的一门继续教育（CE）课程涵盖了（定量）构效关系[(Q)SAR]这一主题。(Q)SAR方法使用基于预定义规则的预测性计算机建模来描述化学结构与化学物质相关生物活性之间的关系，或者使用统计工具来寻找生物活性与化合物分子结构或性质之间的相关性。(Q)SAR在风险评估、药物研发和监管决策中都有应用。制药行业内部降低药物研发成本的压力，以及政府监管机构面对的来自社会的要求对药物和化学品进行更准确、及时的风险评估的压力，使得(Q)SAR的使用成为必要。生成一个高质量的(Q)SAR模型取决于许多因素，包括统计方法和描述符的选择，但首要的是输入模型的数据质量。了解(Q)SAR模型是如何开发和应用的，对于成功使用这样一种工具至关重要。该CE课程涵盖了(Q)SAR的基本原理、这些计算模型在毒理学中的实际应用、监管机构如何使用和解释(Q)SAR模型，以及使用它们可能存在的陷阱。
