An Innovative Method to Classify SERMs Based on the Dynamics of Estrogen Receptor Transcriptional Activity in Living Animals

Abstract

Abstract Using a mouse model engineered to measure estrogen receptor (ER) transcriptional activity in living organisms, we investigated the effect of long-term (21 d) hormone replacement on ER signaling by whole-body in vivo imaging. Estrogens and selective ER modulators were administered daily at doses equivalent to those used in humans as calculated by the allometric approach. As controls, ER activity was measured also in cycling and ovariectomized mice. The study demonstrated that ER-dependent transcriptional activity oscillated in time, and the frequency and amplitude of the transcription pulses was strictly associated with the target tissue and the estrogenic compound administered. Our results indicate that the spatiotemporal activity of selective ER modulators is predictive of their structure, demonstrating that the analysis of the effect of estrogenic compounds on a single surrogate marker of ER transcriptional activity is sufficient to classify families of compounds structurally and functionally related. For more than one century, the measure of drug structure-activity relationships has been based on mathematical equations describing the interaction of the drug with its biological receptor. The understanding of the multiplicity of biological responses induced by the drug-receptor interaction demonstrated the limits of current approach and the necessity to develop novel concepts for the quantitative analysis of drug action. Here, a systematic study of spatiotemporal effects is proposed as a measure of drug efficacy for the classification of pharmacologically active compounds. The application of this methodology is expected to simplify the identification of families of molecules functionally correlated and to speed up the process of drug discovery.