Analysis of Quantitative High Throughput Screening Data

通过分析广泛浓度（或剂量）范围内的反应曲线，而不是通过评价单一浓度（或剂量）下发生的效应，可以更好地了解物质诱导毒理学反应的能力。体外qHTS试验是多浓度实验，在NTP努力将毒理学从疾病特异性模型水平的主要观察性科学推进到基于广泛纳入靶点特异性、基于机制的生物学观察的主要预测性科学方面发挥重要作用。对qHTS数据的分析在很大程度上是由药物应用的保守焦点（即，最小化I型错误的风险），并且通常依赖于逻辑学而不是统计测试来进行活动调用。在这里，我们开发了一种多阶段活性调用算法，并将其应用于来自九种基于细胞的核受体激动剂测定（AR、ER、FXR、GR、PPARa、PPARg、RXR、TRb、VDR）的qHTS数据。将数据拟合到四参数Hill方程，并对每种化学品计算比较Hill方程和水平线（无响应）的最佳拟合的总体F检验。在第一阶段确定了具有强有力的剂量反应的物质。在第二阶段中，通过比较测量的响应分布与对照值，评价在第一阶段中未检测到活性的化合物在最低剂量下的最大响应。在第三阶段确定了具有弱剂量反应的化学品，最后阶段将在最低剂量下表现出细胞毒性反应的物质与非活性化合物分离。我们的模型确定了更多的活性化合物比以前利用的启发式方法。

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. Here, we developed a multiple-stage activity call algorithm and applied it to qHTS data from nine cell-based nuclear receptor agonist assays (AR, ER, FXR, GR, PPARa, PPARg, RXR, TRb, VDR). Data 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) was calculated for each chemical. Substances with a robust dose-response were identified in the first stage. In the second stage, compounds not detected as active in the first stage were evaluated for a maximal response at the lowest dose by comparing the distribution of measured responses to a control value. Chemicals with a weak dose-response were identified in the third stage, and the final stage separated substances exhibiting a cytotoxic response at the lowest dose from inactive compounds. Our model identified more active compounds than a previously utilized heuristic approach.