Analysis of Quantitative High Throughput Screening Data

数以千计的广泛商业使用的化学品尚未经过对人类有害影响的测试，但它们存在于环境中。因此，有必要改进体内毒性测试的化学优先顺序，并最终找到基于细胞的替代品来评估大量潜在有害化合物。定量高通量筛选(QHTS)分析是一种多浓度实验，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，并将毒理学从以观察为主的科学发展为以预测为主的科学。QHTS可以在广阔的化学空间内同时检测数千种化学物质，同时降低每种物质的成本。 以前从qHTS数据进行活动调用的方法是基于寻求将误报降至最低的制药应用程序，并且通常依赖启发式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QHTS数据中的物质分类到与毒理学评估相关的统计支持的活动类别中，寻求在最大限度减少I类错误率的同时提高敏感性(Shockley，2012)。我们方法的第一阶段符合一个四参数Hill方程，以寻找在测试的浓度范围内具有稳健的浓度-响应曲线的活性物质。第二阶段发现相对有效的物质，在测试的最低浓度下具有相当的活性，第一阶段没有捕捉到的物质。该算法的第三个也是最后一个阶段将统计上有意义的简档与缺乏统计上令人信服的支持或不活跃的响应分开。该框架可以容纳大量的qHTS数据，允许丢失数据，并且不需要重复测量。 上述三阶段算法是基于Hill方程模型的。然而，浓度响应数据可能很复杂，在数据中找到不基于对S型曲线的拟合度的替代模式可能更有信息量。从非线性回归模型得到的参数估计与qHTS实验中产生的数据相匹配，可能会伴随着巨大的不确定性(Shockley，2015)。因此，我们开发了一个加权熵分数(WES)作为衡量qHTS实验中平均活性水平的指标来对化学物质进行排名(Shockley，2014)。WES分数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物质进行排名，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下，WES的表现优于基于AC50(半最大响应的估计浓度)的排名。基于WES的非参数方法被用来估计qHTS剖面中的效力，其中效力被估计为产生最大加权熵变化率的浓度(Shockley，2016)。新的效价估计器(起点，PODWES)可以适应任何浓度-反应模式，并且不依赖于任何预先指定的浓度-反应模型。在基于Hill方程模型和钟形损益模型的仿真研究中，PODWES比传统的AC50参数具有更高的估计精度和更小的偏差。此外，在体外数据集中，PODWES为Tox21第二阶段雌激素受体激动剂产生了比AC50更具重复性的效力估计。 Tox21 qHTS实验为每种被测化合物生成至少三个(但可能多达51个)浓度响应曲线。单一化合物的响应模式可能彼此相似，也可能不同。我们开发了一种基于方差分析的方法来标记具有不同响应模式的化合物，可用于在实验水平上或单个化合物的QHTS实验的质量控制。这种方法可以可靠地将化合物聚集成噪声、同质响应或异质响应。

Thousands of chemicals in wide commercial use have not been tested for adverse effects on humans, but are present in the environment. 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.