Adopting a Practical Statistical Approach for Evaluating Assay Agreement in Drug Discovery

Abstract

The authors assess the equivalence of 2 assays and put forward a general approach for assay agreement analysis that can be applied during drug discovery. Data sets generated by different assays are routinely compared to each other during the process of drug discovery. For a given target, the assays used for high-throughput screening and structure-activity relationship studies will most likely differ in their assay reagents, assay conditions, and/or detection technology, which makes the interpretation of data between assays difficult, particularly as most assays are used to measure quantitative changes in compound potency against the target. To better quantify the relationship of data sets from different assays for the same target, the authors evaluated the agreement between results generated by 2 different assays that measure the activity of compounds against the same protein, ALK5. The authors show that the agreement between data sets can be quantified using correlation and Bland-Altman plots, and the precision of the assays can be used to define the expectations of agreement between 2 assays. They propose a scheme for addressing issues of assay data equivalence, which can be applied to address questions of how data sets compare during the lead identification and lead optimization processes in which assays are frequently added and changed. (Journal of Biomolecular Screening 2005:508-516)

Microscale screening of antibody libraries as maytansinoid antibody-drug conjugates

“…Simultaneous 6-day continuous exposure cytotoxicity assays were run comparing the potency of the microscale and research scale ADCs, using both antigen-positive and antigen-negative cell lines. Comparison of the targeted IC 50 s showed good agreement and Bland-Altman analysis 21,22 measured the average bias between methods as 2.6-fold, within the practical range for cell-based screening assays ( Fig. 6A, Fig.…”

Simple Colorimetric Trypanothione Reductase-Based Assay for High-Throughput Screening of Drugs against Leishmania Intracellular Amastigotes

“…The categorization of screening assay quality by the value of the Z= factor has been described elsewhere (29). Typically, a Z= factor value Ͼ0.5 is defined as acceptable for an assay to be used in compound testing (26).…”

“…Transformed data from the two assays were used for obtaining a scatter plot in which one set of assay results was plotted against the other on a log-log scale. The level of correlation between these data was assessed by the Pearson's correlation test (GraphPad Prism), in which the null hypothesis stated that no linear relationship linked the measurements obtained by the two methods (26). To determine the level of agreement between the two tests, the transformed data from both assays were graphed in a Bland-Altman plot (27) (GraphPad Prism), where the difference of IC 50 values obtained for each compound by the two assays was plotted as a function of the mean IC 50 between microscopy and the TryR-based assay.…”

The Minimum Significant Ratio: A Statistical Parameter to Characterize the Reproducibility of Potency Estimates from Concentration-Response Assays and Estimation by Replicate-Experiment Studies

“…Note that in many instances of interassay comparisons, it is required not only that the 2 assays have a high correlation but also that the affinity estimates are equal in the 2 assays. In those cases, one can use the interlaboratory assessment techniques described in the 4th section and in Sun and others' work 7 to assess this requirement.…”