In Vitro Effects of the Endocrine Disruptor p,p’-DDT on Human Follitropin Receptor

BACKGROUND: 1-chloro-4-[2,2,2-trichloro-1-(4-chlorophenyl)ethyl]benzene (p,p\u27-DDT) is a persistent environmental endocrine disruptor (ED). Several studies have shown an association between p,p\u27-DDT exposure and reproductive abnormalities. OBJECTIVES: To investigate the putative effects of p,p\u27-DDT on the human follitropin receptor (FSHR) function. METHODS AND RESULTS: We used Chinese hamster ovary (CHO) cells stably expressing human FSHR to investigate the impact of p,p\u27-DDT on FSHR activity and its interaction with the receptor. At a concentration of 5 μM p,p\u27-DDT increased the maximum response of the FSHR to follitropin by 32 ± 7.45%. However, 5 μM p,p\u27-DDT decreased the basal activity and did not influence the maximal response of the closely related LH/hCG receptor to human chorionic gonadotropin (hCG). The potentiating effect of p,p\u27-DDT was specific for the FSHR. Moreover, in cells that did not express FSHR, p,p\u27-DDT had no effect on cAMP response. Thus, the potentiating effect of p,p\u27-DDT was dependent on the FSHR. In addition, p,p\u27-DDT increased the sensitivity of FSHR to hCG and to a low molecular weight agonist of the FSHR, 3-((5methyl)-2-(4-benzyloxy-phenyl)-5-{[2-[3-ethoxy-4-methoxy-phenyl)-ethylcarbamoyl]-methyl}-4-oxo-thiazolidin-3-yl)-benzamide (16a). Basal activity in response to p,p\u27-DDT and potentiation of the FSHR response to FSH by p,p\u27-DDT varied among FSHR mutants with altered transmembrane domains (TMDs), consistent with an effect of p,p\u27-DDT via TMD binding. This finding was corroborated by the results of simultaneously docking p,p\u27-DDT and 16a into the FSHR transmembrane bundle. CONCLUSION:p,p\u27-DDT acted as a positive allosteric modulator of the FSHR in our experimental model. These findings suggest that G protein-coupled receptors are additional targets of endocrine disruptor

Rational design of isonicotinic acid hydrazide derivatives with antitubercular activity: Machine learning, molecular docking, synthesis and biological testing.

The problem of designing new antitubercular drugs against multiple drug‐resistant tuberculosis (MDR‐TB) was addressed using advanced machine learning methods. As there are only few published measurements against MDR‐TB, we collected a large literature data set and developed models against the non‐resistant H37Rv strain. The predictive accuracy of these models had a coefficient of determination q2 = .7–.8 (regression models) and balanced accuracies of about 80% (classification models) with cross‐validation and independent test sets. The models were applied to screen a virtual chemical library, which was designed to have MDR‐TB activity. The seven most promising compounds were identified, synthesized and tested. All of them showed activity against the H37Rv strain, and three molecules demonstrated activity against the MDR‐TB strain. The docking analysis indicated that the discovered molecules could bind enoyl reductase, InhA, which is required in mycobacterial cell wall development. The models are freely available online (http://ochem.eu/article/103868) and can be used to predict potential anti‐TB activity of new chemicals