From raw data to meaningful information: a robust but flexible method to assess in vitro assay responses—lessons learned from a novel Dicentrarchus labrax estrogen screen test

Abstract

Statistical analysis of in vitro assay data is a critical step towards a good interpretation of biological responses. However, it is still frequently undermined due to inappropriate statistical procedures, misinterpretation, insufficient statistical power—often resulting from small sample sizes—or poorly defined methodologies, in case of standardized tests. In line with practices commonly adopted in clinical studies and more recently in biomonitoring research, the use of thresholds for interpreting results may improve the robustness of conclusions. This work presents the application of a methodology for defining thresholds using a normal distribution–based approach. As a case study, these thresholds were applied to analyze data obtained from the DLES test ( Dicentrarchus labrax estrogen screen test), an in vitro screening tool designed to detect interactions between chemicals and nuclear estrogen receptors in D. labrax . The results were subsequently compared with methods derived from the OECD TG 455, as well as with non-parametric statistical analyses. By applying normal distribution–based thresholds, data analysis was simplified and the reliability of the DLES test results was increased, especially when compared with hypothesis tests. Also, this was especially true when studying non-model species, for which standard reference substances are rarely available. However, special attention should be paid to the size of the initial dataset used to define the thresholds. The methodology implemented here could provide insight for other in vitro assays. Overall, this article encourages the reflection on approaches to in vitro data analysis.

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