Analysis of Quantitative High Throughput Screening Data

成千上万的化学品在广泛的商业用途和环境中尚未测试对人类的不利影响。因此，需要改进体内毒性测试的化学优先级，并最终找到用于评估大量潜在有害化合物的基于细胞的替代品。定量高通量筛选（qHTS）试验是多浓度实验，在国家毒理学计划的努力中发挥着重要作用，以应对这些测试挑战，并将毒理学从主要的观察性科学推进到主要的预测性科学。qHTS可以在一个广阔的化学空间内同时分析数千种化学物质，降低每种物质的成本。 先前用于从qHTS数据进行活动调用的方法是基于寻求最小化假阳性的制药应用，并且通常依赖于化学分析而不是统计测试来进行活动调用。我们开发了一种三阶段算法，将来自qHTS数据的物质分类为与毒理学评价相关的统计学支持的活性类别，寻求提高灵敏度，同时最大限度地降低I类错误率（Shockley，2012）。我们的方法的第一阶段拟合四参数Hill方程，以找到在测试浓度范围内具有稳健浓度响应曲线的活性物质。第二阶段寻找在最低测试浓度下具有实质性活性的相对强效物质，即第一阶段未捕获的物质。该算法的第三阶段也是最后一阶段将统计上显著的特征与缺乏统计上令人信服的支持或不活跃的响应分开。该框架可容纳大量的qHTS数据，容忍丢失的数据，并且不需要重复测量。 上述三阶段算法基于希尔方程模型。然而，浓度-响应数据可能很复杂，并且在数据中找到不基于S形曲线拟合的替代模式可能更具信息性。根据qHTS实验中生成的数据拟合的非线性回归模型得出的参数估计值可能伴有较大的不确定性（Shockley，2015）。因此，我们开发了加权熵分数（WES）作为平均活性水平的度量，以在qHTS实验中对化学品进行排名（Shockley，2014）。WES评分可用于在没有预先指定的模型结构的情况下对测试库中的所有化学品进行排名，或者WES可用于通过对返回的“命中”进行排名来补充现有方法。在qHTS研究的典型模拟条件的全范围内，WES优于基于AC 50（半数最大反应的估计浓度）的排名。使用基于WES的非参数方法估计qHTS曲线中的效价，其中效价估计为产生加权熵最大变化率的浓度（Shockley，2016）。新的效价估计量（出发点，PODWES）可适应任何浓度-响应模式，并且不依赖于任何预先指定的浓度-响应模型。在基于Hill方程模型和钟形增益-损失模型的模拟研究中，与传统的AC 50参数相比，PODWES以更高的精度和更小的偏差估计效力。此外，PODWES产生的Tox 21 II期雌激素受体激动剂体外数据集的重现性比AC 50更好。 Tox 21 qHTS实验生成每种测试化合物的至少三个（但可能多达51个）浓度-响应曲线。单一化合物的反应模式可能彼此相似或不同。我们开发了一种基于ANOVA的方法来标记具有不同响应模式的化合物，该方法可用于在实验或单个化合物水平上对qHTS实验进行质量控制。这种方法可以可靠地将化合物聚类为噪声、同质响应或异质响应。我们的新方法，称为使用ANOVA的亚组聚类分析（CASANOVA），将化合物特异性反应模式聚类成统计支持的聚类，从而得到可信的效力估计（Shockley et al.，2019年）。

Thousands of chemicals in wide commercial use and the environment have not been tested for adverse effects on humans. Accordingly, there is a need to improve chemical prioritization for in vivo toxicity testing and, ultimately, to find cell-based alternatives for evaluating the large inventory of potentially harmful compounds. Quantitative high throughput screening (qHTS) assays are multiple-concentration experiments with an important role in the efforts of the National Toxicology Program to meet these testing challenges and advance toxicology from a predominantly observational science to a predominantly predictive science. qHTS can simultaneously assay thousands of chemicals over a wide chemical space with reduced cost per substance. Previous approaches for making activity calls from qHTS data were based on pharmaceutical applications seeking to minimize false positives and usually relied on heuristics rather than statistical tests to make activity calls. We developed a three-stage algorithm to classify substances from qHTS data into statistically supported activity categories relevant to toxicological evaluation, seeking to improve sensitivity while minimizing Type I error rate (Shockley, 2012). The first stage of our approach fits a four-parameter Hill equation to find active substances with a robust concentration-response profile within the tested concentration range. The second stage finds relatively potent substances with substantial activity at the lowest tested concentration, substances not captured in the first stage. The third and final stage of the algorithm separates statistically significant profiles from responses that lack statistically compelling support, or inactives. This framework accommodates large volumes of qHTS data, tolerates missing data, and does not require replicate measurements. The three-stage algorithm described above is based on the Hill equation model. However, concentration-response data can be complex, and it may be more informative to find alternative patterns in the data not based on fits to sigmoidal curves. Parameter estimates derived from nonlinear regression model fits to data generated in qHTS experiments may accompany large uncertainties (Shockley, 2015). Therefore, we developed a weighted entropy score (WES) as a measure of average activity level to rank chemical in qHTS experiments (Shockley, 2014). WES scores can be used to rank all chemicals in a tested library without a pre-specified model structure, or WES can be used to complement existing approaches by ranking returned "hits". WES outperforms rankings based on AC50 (estimated concentration of half-maximal response) across the full range of simulated conditions that are typical of qHTS studies. A nonparametric approach based on WES was used to estimate potency in qHTS profiles, where potency is estimated as the concentration producing the maximal rate of change in weighted entropy (Shockley, 2016). The new potency estimator (Point of Departure, PODWES) can accommodate any concentration-response pattern and does not depend on any pre-specified concentration-response model. In simulation studies based on the Hill equation model and the bell-shaped gain-loss model, PODWES estimates potency with greater precision and less bias compared to the conventional AC50 parameter. Also, PODWES produced more reproducible potency estimates than AC50 for a Tox21 Phase II estrogren receptor agonist in vitro data set. Tox21 qHTS experiments generate at least three (but possibly as many as 51) concentration-response profiles for each tested compound. The response patterns for a single compound may be similar or dissimilar with each other. We have developed an ANOVA-based method to flag compounds that have dissimilar response patterns that can be used for quality control of qHTS experiments at the level of the experiment or individual compounds. This approach can reliably cluster compounds into noise, homogeneous responses, or heterogeneous responses. Our novel method, called Cluster Analysis by Subgroups using ANOVA (CASANOVA), clusters compound-specific response patterns into statistically supported clusters that lead to trustworthy potency estimates (Shockley et al., 2019).