Derivation and application of relative potency estimates based on in vitro bioassay results

效力 生物测定 统计 航程(航空) 标准差 样品(材料) 平行性(语法) 体外 数学 学位(音乐) 集合(抽象数据类型) 化学 样本量测定 相对标准差 EC50型 生物 计算机科学 计量经济学 色谱法 生态学 物理 并行计算 材料科学 复合材料 程序设计语言 声学 检出限 生物化学
作者
Daniel L. Villeneuve,Alan L. Blankenship,John P. Giesy
出处
期刊:Environmental Toxicology and Chemistry [Wiley]
卷期号:19 (11): 2835-2843 被引量:257
标识
DOI:10.1002/etc.5620191131
摘要

Abstract Relative potency (REP) estimates are widely used to characterize and compare the potency of a wide variety of samples analyzed using in vitro bioassays. Relative potency estimates are generally calculated as a simple ratio: the EC50 of a well‐characterized standard divided by the EC50 of a sample. Such estimates are valid only when the dose‐response curves for the sample and standard are parallel and exhibit the same maximum achievable response (efficacy). These conditions are often either violated or cannot be demonstrated. As a result, there is a need to calculate and present REPs in a manner that addresses the potential uncertainties caused by violation of the assumptions of parallelism and equal efficacy. Multiple point estimates, over the range of responses from EC20 to EC80, can be used to derive relative potency ranges (REP 20–80 range). The width of a REP 20–80 range is directly proportional to the degree of deviation from parallelism between sample and standard dose‐response curves. Thus, REP 20–80 ranges both test the assumption of parallelism and characterize the amount of uncertainty in an REP estimate resulting from deviation from parallelism. Although uncertainties due to unequal efficacy cannot be easily characterized mathematically, a systematic method for evaluating sample efficacy has been developed into a framework to guide the derivation and application of REP estimates based on in vitro bioassay results. Use of the systematic framework and REP 20–80 ranges was illustrated using three sample data sets. It is hoped that the framework and discussion presented will facilitate the use of bioassay‐derived REP estimates to characterize samples of both known and unknown composition without ignoring the assumptions underlying REP estimation.
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