Shun Tong Ying, Lazo John S, Sharlow Elizabeth R, Johnston Paul A
University of Pittsburgh Drug Discovery Institute, Pittsburgh, PA, USA.
J Biomol Screen. 2011 Jan;16(1):1-14. doi: 10.1177/1087057110389039. Epub 2010 Dec 15.
High-throughput screening (HTS) has achieved a dominant role in drug discovery over the past 2 decades. The goal of HTS is to identify active compounds (hits) by screening large numbers of diverse chemical compounds against selected targets and/or cellular phenotypes. The HTS process consists of multiple automated steps involving compound handling, liquid transfers, and assay signal capture, all of which unavoidably contribute to systematic variation in the screening data. The challenge is to distinguish biologically active compounds from assay variability. Traditional plate controls-based and non-controls-based statistical methods have been widely used for HTS data processing and active identification by both the pharmaceutical industry and academic sectors. More recently, improved robust statistical methods have been introduced, reducing the impact of systematic row/column effects in HTS data. To apply such robust methods effectively and properly, we need to understand their necessity and functionality. Data from 6 HTS case histories are presented to illustrate that robust statistical methods may sometimes be misleading and can result in more, rather than less, false positives or false negatives. In practice, no single method is the best hit detection method for every HTS data set. However, to aid the selection of the most appropriate HTS data-processing and active identification methods, the authors developed a 3-step statistical decision methodology. Step 1 is to determine the most appropriate HTS data-processing method and establish criteria for quality control review and active identification from 3-day assay signal window and DMSO validation tests. Step 2 is to perform a multilevel statistical and graphical review of the screening data to exclude data that fall outside the quality control criteria. Step 3 is to apply the established active criterion to the quality-assured data to identify the active compounds.
在过去20年里，高通量筛选（HTS）在药物发现中占据了主导地位。HTS的目标是通过针对选定的靶点和/或细胞表型筛选大量不同的化合物来识别活性化合物（命中物）。HTS过程包括多个自动化步骤，涉及化合物处理、液体转移和检测信号捕获，所有这些都不可避免地导致筛选数据中的系统变异。挑战在于从检测变异性中区分出生物活性化合物。传统的基于板对照和非对照的统计方法已被制药行业和学术领域广泛用于HTS数据处理和活性鉴定。最近，引入了改进的稳健统计方法，减少了HTS数据中系统行/列效应的影响。为了有效且恰当地应用这些稳健方法，我们需要了解它们的必要性和功能。本文展示了6个HTS案例历史的数据，以说明稳健统计方法有时可能会产生误导，并且可能导致更多而非更少的假阳性或假阴性。在实践中，没有一种方法是适用于每个HTS数据集的最佳命中检测方法。然而，为了帮助选择最合适的HTS数据处理和活性鉴定方法，作者开发了一种三步统计决策方法。第一步是确定最合适的HTS数据处理方法，并从3天检测信号窗口和二甲基亚砜（DMSO）验证测试中建立质量控制审查和活性鉴定的标准。第二步是对筛选数据进行多级统计和图形审查，以排除不符合质量控制标准的数据。第三步是将既定的活性标准应用于质量保证的数据，以识别活性化合物。
