An innovative method to classify SERMs based on the dynamics of estrogen receptor transcriptional activity in living animals

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.

Figure 1

Uninterrupted drug administration induces discontinuous reporter accumulation. Profile of photon emission in 21 d in skeletal (A), hepatic (B), and genital (C) areas of single, representative, OVX ERE-Luc mice after treatment with vehicle (veh), E2, or LAS. Drugs were administered via a dorsal implant of a continuous-release pellet delivering the compounds at a fixed concentration. Photon emission in discrete body regions was segmented in a Matlab environment using an algorithm previously described (25) and defined as the number of counts per second per centimeter square (cts/cm²s) corrected for instrument efficiency. In a parallel experiment, photon emission was measured daily in individual animals treated with vehicle (veh) or E2; groups of six mice were euthanized at the high (H) or low (L) photon emission (D and E), blood was collected, and uterus and liver tissues were dissected. F, Luciferase enzymatic activity in liver tissue extracts. G, E2 content was measured in samples of plasma pooled from two mice using gas chromatography mass spectrometry (see Materials and Methods). The analysis was done in duplicate on a total of three samples for each experimental condition. H, Weight of uterus frozen tissue. Bars represent mean ± sem (n = 6). *, P = 0.043 (D); *, P = 0.043 (E); **, P = 0.002 (F); **, P = 0.003 (H) (ANOVA followed by Bonferroni). I, ERα protein content as measured by Western blot in liver tissue extracts (n = 6).

Figure 2

Comparative analysis of the effect of treatment with selected SERMs on luciferase accumulation in skeletal and genital areas of ERE-Luc OVX mice. A–D, OVX females ERE-Luc mice were treated daily with 6 μg/kg sc E2, 3 mg/kg per os CE, 10 mg/kg per os BZA, 50 μg/kg sc LAS, 2 mg/kg sc OSP, 2 or 10 mg/kg per os RAL, or 0.8 mg/kg per os TAM. Photon emission measured in individual ERE-Luc mice was plotted against time (21 d). Bars represent the mean ± sem of a minimum of five animals per group. A, Peak number. Data were scored from the second-derivative plot using GraphPad Prism. On the basis of the variability of photon counting (coefficient of variation 12%), peaks less than the 15% of the distance between the minimum and the maximum are ignored [*, cycling (cyc) P = 0.023; LAS P = 0.004; CE P = 0.021; BZA P = 0.036; BZA+CE P = 0.028]. B, Peak amplitude. Photon emission was centered on the y-axis by the first derivative, and photon emission rates were then processed by fast Fourier transformation into its component sine waves with a 64 zero padding. Analyzed spectra were windowed at bins 3 and 10. The amplitude, estimating the degree of displacement from the resting state, was calculated as the square root of the 95th percentile of the power spectra (*, cyc P = 0.002; E2 P = 0.001; TAM P = 0.011; CE P = 0.003). C, Peak period. Periodicity is estimated by the inverse of the frequencies under the amplitude previously calculated (*, LAS P = 0.019). D, AUC. The AUC of the plot of photon emission in the 21 d treatment was calculated using GraphPad Prism by a trapezoidal approximation (*, genital area cyc P = 0.012, E2 P = 0.001, and CE P = 0.004; skeletal area cyc P = 0.001, E2 P = 0.001, and BZA P = 0.002). E, Groups of six ERE-Luc OVX mice were treated as in A, and photon emission in individual areas was measured 6 h after drug administration (*, genital area cyc P = 0.021, OSP P = 0.038, and E2 P = 0.017; skeletal area cyc P = 0.020, E2 P = 0.008, and CE P = 0.013). Bars represent mean ± sem (n = 6). Statistical analysis was done with ANOVA followed by Bonferroni comparing the effect of each experimental group with OVX and cycling mice.

Figure 3

Extent of functional correlation among each descriptor of drug activity. A, Correlation analysis was done for each pair of descriptors to verify the degree of redundancy of the parameters selected for the clustering analysis; B, degree of correlation in each pair of drug activity descriptors as measured by Pearson’s R².

Figure 4

Phenetics of drug action. Each descriptor (peak number, amplitude, period, AUC, and drug potency) was normalized on the average calculated on intact cycling (cyc) females (considered equal to 100). A matrix was built for each anatomical area: each column contains one descriptor, and each cell contains the descriptor averages for each experimental group. Agglomerative hierarchical clustering (27) was computed with a Manhattan metric and a complete linkage method with an R code available online (Agglomerative Nesting version 1.0.2, Office for Research Development and Education; http://www.wessa.net/). In the dendrogram, distances between branch lengths represents the distance of the menopause model (OVX) vs. the physiology model (cyc); hormone replacement efficacy is measured by its ability to mimic ER activity in the cycling mice. A and B, SERM classification (hierarchical clustering) in the genital area (A) and in skeletal area (B); C, multidimensional imaging descriptors from all anatomical areas measured (genital, skeletal, hepatic, abdominal, and thymic) are clustered as above; dendrogram branches group families of structurally related compounds.