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物质。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。为此，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，寻求在最大限度地减少第一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。稳健准则规定，使用未加权和加权的非线性最小二乘(NLS和WNLS)回归，响应分布在统计上具有显著意义。NLS对所有数据点进行平均加权，因此不能区分具有沿渐近线的数据的轮廓和由单个点支持的轮廓。WNLS基于1/S2对每个响应点进行加权，其中S是从包含感兴趣的响应点的定义浓度范围内的所有响应数据估计的样本标准偏差，从而使具有相似响应水平的邻近数据点比具有非常不同响应的邻近数据点受到更大的影响。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。第三个阶段也是最后一个阶段，将统计上有意义的个人资料与缺乏统计上令人信服的支持或不活跃的回应分开。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。 我们通过广泛的模拟对这种三阶段分类算法进行了评估(Shockley，2012)。受试者工作特征曲线下面积(AUC)用于评估治疗效果。利用AUC统计，我们的算法优于整体F检验，比较希尔方程与水平线(无反应)的拟合，当半最大反应(AC50)的浓度小于0.1微摩尔时。当AC50大于0.001微摩尔时，它在检测已知活性物质方面也优于t检验方法。当反应在模拟分析的可检测区域(阳性对照的25%)时，对于14点浓度-反应曲线，三阶段决策策略产生了良好的(AUC&#8805；0.75)到高(AUC&#8805；0.9)的性能。当测试反应至少是阳性对照反应的50%时，我们的方法能够检测到相对有效的物质(例如，AC50=0.001微摩尔)，只需4个数据点。

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 substances. 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. For that reason, 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 robust criterion specifies that response profiles are statistically significant using both unweighted and weighted non-linear least squares (NLS and WNLS) regression. NLS weights all data points equally and, consequently, may not discriminate between a profile with data along both asymptotes and a profile supported by a single point. WNLS weights each response point based on 1/s2, where s is the sample standard deviation estimated from all response data within a defined concentration range containing the response point of interest, so that more influence is given to neighboring data points with similar response levels than neighboring data points with very different responses. 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 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. We evaluated this three-stage classification algorithm via extensive simulations (Shockley, 2012). The area under receiver operating characteristic curves (AUC) was used to assess performance. Using AUC statistics, our algorithm outperformed overall F-tests comparing the fit of the Hill equation to a horizontal line (no response) when the concentration for half maximal response (AC50) was less than 0.1micro molar. It also outperformed t-test approaches in detecting known actives when the AC50 was greater than 0.001 micro molar. The three-stage decision strategy yielded good (AUC ≥ 0.75) to high (AUC ≥ 0.9) performance for 14 point concentration-response curves when the response was in the detectable region of the simulated assay (>25% of the positive control). Our approach was able to detect relatively potent substances (e.g., AC50 = 0.001 micro molar) with as few as 4 data points when the tested response was at least 50% of the positive control response.