Analysis of Quantitative High Throughput Screening Data

通过分析大范围浓度(或剂量)的反应曲线，而不是通过评估在单一浓度(或剂量)下发生的影响，可以更好地了解一种物质诱导毒理学反应的能力。体外QHTS分析是一种多浓度实验，在NTPs的努力中发挥着重要作用，以推动毒理学从疾病特定模型水平上的主要观察性科学发展为基于广泛纳入靶标特异性、基于机制的生物学观察的主要预测性科学。对qHTS数据的分析在很大程度上是出于对药物应用的保守关注(即，将I类错误的风险降至最低)，并且通常依赖启发式而不是统计测试来进行活动调用。为了评估qHTS研究中的活性，我们建立了一个两阶段决策树统计模型，并将其应用于12种基于细胞的激动剂核受体分析(AR、ER、FXR、GR、LXR、PPARd、PPARg、RXR、TRB、VDR、PXR人、PXR大鼠)以及P53和ELG1激动剂的浓度-反应数据。我们还将该模型应用于核受体拮抗剂的测定(AR、FXR、GR、LXR、PPARd、PPARg、RXR、TRB、VDR)和不同鸡细胞系的细胞毒性测定。在第一阶段，从1408种物质获得的数据符合四参数Hill方程和总体F检验，比较最佳拟合Hill方程和水平线(无反应)，每种物质表现出至少25%的效力，使用不同的显著阈值，在测试的浓度范围(5×10-10M到10-4M)内识别出活性化合物。在第二阶段，对在第一阶段中未被检测为活性的化合物进行评估，方法是将测量的响应分布与阿尔法值为0.05的控制值进行比较。使用这种方法，我们在每次化验中识别出比以前使用的启发式方法更多的活性化合物。

The ability of a substance to induce a toxicological response is better understood by analyzing the response profile over a broad range of concentrations (or doses) rather than by evaluating effects that occur at a single concentration (or dose). In vitro qHTS assays are multiple-concentration experiments that play an important role in NTPs efforts to advance toxicology from a predominantly observational science at the level of disease-specific models to a predominantly predictive science based on broad inclusion of target-specific, mechanism-based, biological observations. The analysis of qHTS data has largely been motivated by the conservative focus of pharmaceutical applications (i.e., minimizing the risk of Type I error) and generally has relied on heuristics rather than statistical tests to make activity calls. To evaluate the activity within qHTS studies, we developed a two-stage decision tree statistical model and applied it to normalized concentration-response data from twelve cell-based agonist nuclear receptor assays (AR, ER, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR, PXR-human, PXR-rat), and agonist assays for p53 and ELG1. We also applied the model to nuclear receptor antagonist assays (AR, FXR, GR, LXR, PPARd, PPARg, RXR, TRb, VDR) and cytotoxicity assays from various chicken cell lines. In the first stage, data obtained from 1408 substances were fit to a four-parameter Hill equation and an overall F-test comparing the best fit to the Hill equation and a horizontal line (no response) for each substance exhibiting at least 25% efficacy, using different significance thresholds, identified active compounds within the tested concentration range (5 x 10-10 M to 10-4 M). In the second stage, compounds not detected as active in the first stage were evaluated by comparing the distribution of measured responses to a control value, at an alpha value of 0.05. Using this approach, we identified a greater number of active compounds in each assay than a previously utilized heuristic approach.