Analysis of resulting data from estrogen receptor reporter gene assays

Screening methods for detecting single chemicals and chemical mixtures that have endocrine effects are of increasing importance. One such assay used by many laboratories to screen for potential estrogenicity is the estrogen receptor (ER) transcriptional activation assay, also known as an ER “reporter gene assay.” When applied in screening situations or to mechanistic studies, the assay appears to be quite reliable in giving a qualitative indication of a compound’s potential to activate ER-regulated target genes. Reporter gene assays also are advantageous for more complex applications, such as the analysis of chemical mixtures, but such applications present some challenges for statistical analysis. In particular, the need to transiently transfect cells with DNA constructs each time a reporter gene experiment is run may be a major factor contributing to a significant amount of interexperiment variability in strictly reproducing concentration-effect curves. This article reports on the use of nonlinear mixed models to account for the different sources of variability in analyzing such data. A population-averaged model is selected for use in the analysis of mixtures of chemicals. The models are illustrated with data from an ER reporter gene assay used to analyze tertiary mixtures of chemicals expected to exhibit additivity or synergy based on prior scientific reports. The models yielded the expected conclusions, and thus, validated the statistical approach.