Thyroid Receptor Ligands. 3. Design and Synthesis of 3,5-Dihalo-4-alkoxyphenylalkanoic Acids as Indirect Antagonists of the Thyroid Hormone Receptor

Prediction of binding affinity and efficacy of thyroid hormone receptor ligands using QSAR and structure-based modeling methods

The thyroid hormone receptor (THR) is an important member of the nuclear receptor family that can be activated by endocrine disrupting chemicals (EDC). Quantitative Structure-Activity Relationship (QSAR) models have been developed to facilitate the prioritization of THR-mediated EDC for the experimental validation. The largest database of binding affinities available at the time of the study for ligand binding domain (LBD) of THRβ was assembled to generate both continuous and classification QSAR models with an external accuracy of R(2)=0.55 and CCR=0.76, respectively. In addition, for the first time a QSAR model was developed to predict binding affinities of antagonists inhibiting the interaction of coactivators with the AF-2 domain of THRβ (R(2)=0.70). Furthermore, molecular docking studies were performed for a set of THRβ ligands (57 agonists and 15 antagonists of LBD, 210 antagonists of the AF-2 domain, supplemented by putative decoys/non-binders) using several THRβ structures retrieved from the Protein Data Bank. We found that two agonist-bound THRβ conformations could effectively discriminate their corresponding ligands from presumed non-binders. Moreover, one of the agonist conformations could discriminate agonists from antagonists. Finally, we have conducted virtual screening of a chemical library compiled by the EPA as part of the Tox21 program to identify potential THRβ-mediated EDCs using both QSAR models and docking. We concluded that the library is unlikely to have any EDC that would bind to the THRβ. Models developed in this study can be employed either to identify environmental chemicals interacting with the THR or, conversely, to eliminate the THR-mediated mechanism of action for chemicals of concern.

Only subtle protein conformational adaptations are required for ligand binding to thyroid hormone receptors: simulations using a novel multipoint steered molecular dynamics approach

Thyroid hormone receptors (TR) are hormone-dependent transcription regulators that play a major role in human health, development, and metabolic functions. The thyroid hormone resistance syndrome, diabetes, obesity, and some types of cancer are just a few examples of important diseases that are related to TR malfunctioning, particularly impaired hormone binding. Ligand binding to and dissociation from the receptor ultimately control gene transcription and, thus, detailed knowledge of binding and release mechanisms are fundamental for the comprehension of the receptor's biological function and development of pharmaceuticals. In this work, we present the first computational study of ligand entry into the ligand binding domain (LBD) of a nuclear receptor. We report molecular dynamics simulations of ligand binding to TRs using a generalization of the steered molecular dynamics technique designed to perform single-molecule pulling simulations along arbitrarily nonlinear driving pathways. We show that only gentle protein movements and conformational adaptations are required for ligand entry into the LBDs and that the magnitude of the forces applied to assist ligand binding are of the order of the forces involved in ligand dissociation. Our simulations suggest an alternative view for the mechanisms ligand binding and dissociation of ligands from nuclear receptors in which ligands can simply diffuse through the protein surface to reach proper positioning within the binding pocket. The proposed picture indicates that the large-amplitude protein motions suggested by the apo- and holo-RXRalpha crystallographic structures are not required, reconciling conformational changes of LBDs required for ligand entry with other nuclear receptors apo-structures that resemble the ligand-bound LBDs.

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TRbeta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TRbeta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor beta but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity.

Thyroid receptor ligands. Part 8: Thyromimetics derived from N-acylated-α-amino acid derivatives displaying modulated pharmacological selectivity compared with KB-141

Based on the scaffold of the pharmacologically selective thyromimetic 2b, structurally a close analog to KB-141 (2a), a number of novel N-acylated-alpha-amino acid derivatives were synthesized and tested in a TR radioligand binding assay as well as in a reporter cell assay. On the basis of TRbeta(1)-isoform selectivity and affinity, as well as affinity to the reporter cell assay, 3d was selected for further studies in the cholesterol-fed rat model. In this model 3d revealed an improved therapeutic window between cholesterol and TSH lowering but decreased margins versus tachycardia compared with 2a.

2D QSAR studies on thyroid hormone receptor ligands

2D QSAR studies were carried out for a series of 55 ligands for the Thyroid receptors, TRalpha and TRbeta. Significant cross-validated correlation coefficients (q(2)=0.781 (TRalpha) and 0.693 (TRbeta)) were obtained. The models' predictive abilities were proved more valuable than the classical 2D-QSAR, and were further investigated by means of an external test set of 13 compounds. The predicted values are in good agreement with experimental values, suggesting that the models could be useful in the design of novel, more potent TR ligands. Contribution map analysis identified a number of positions that are promising for the development of receptor isoform specific ligands.

Novel thyroid hormone receptor antagonists with an N-alkylated diphenylamine skeleton

Thyroid hormones play important roles in growth, development and homeostasis, and disruption of their functions induces serious disease, so novel synthetic thyroid hormone analogues are candidates for clinical application. We designed and synthesized novel diphenylamine derivatives with a thiazolidinedione moiety as the terminal polar group as thyroid hormone receptor (TR) antagonists. Compounds bearing an appropriately sized N-alkyl group showed antagonistic activities towards both the hTRalpha1 and hTRbeta1 subtypes.

Prediction of binding affinities to β1 isoform of human thyroid hormone receptor by genetic algorithm and projection pursuit regression

Quantitative structure-activity relationship (QSAR) has been applied to a set of thyroid hormone receptor beta(1) (TRbeta(1)) antagonists, which are of special interest because of their potential role in safe therapies for nonthyroid disorders while avoiding the cardiac side effects. Using the calculated structural descriptors by CODESSA program, principal component analysis (PCA) was performed on the whole compounds to assist the separation of the data into the training set and the test set in QSAR analysis. Six molecular descriptors selected by genetic algorithm (GA) were used as inputs for a projection pursuit regression (PPR) study to develop a more accurate QSAR model. The PPR model performs well both in the fitting and prediction capacity. For the test set, it gave a predictive correlation coefficient (R) of 0.9450, root mean square error (RMSE) of 0.4498, and absolute average relative deviation (AARD) of 4.19%, respectively, confirming the ability of PPR for the prediction of the binding affinities of compounds to beta(1) isoform of human thyroid hormone receptor (TRbeta(1)).

Thyroid receptor ligands. Part 7: Indirect antagonists of the thyroid hormone receptor with improved affinity

Based on the concept of 'indirect antagonism' of nuclear receptors, a series of thyroid hormone receptor (TR) antagonists were prepared with improved affinity compared with what was previously described. The results of a binding assay for the human TR and reporter cell assay revealed, within this series, that an m-bromobenzoyl substituent (11f) was optimal in terms of affinity and antagonist activity. Compared with already reported TR antagonists, their affinities are within the same range, thus potentially representing useful approach to novel and high-affinity TR-antagonists.

Simulating α/β Selectivity at the Human Thyroid Hormone Receptor: Consensus Scoring Using Multidimensional QSAR

We present a consensus-scoring study on the human thyroid hormone receptor alpha and beta using two receptor-modeling concepts (software Quasar and Raptor) that are based on multidimensional QSAR and allow for the explicit simulation of induced fit. The binding mode of 82 agonists and indirect antagonists, spanning an activity range of seven orders of magnitude in K(i), was identified through flexible docking to the respective X-ray crystal structures (Yeti software) and represented by a 4D data set with up to four conformations per compound. The receptor surrogates for the thyroid alpha receptor converged at a cross-validated r(2) of 0.846/0.919 (64 training compounds; for Quasar and Raptor, respectively) and yielded a predictive r(2) of 0.812/0.814 (18 test compounds); the models for the thyroid beta receptor resulted in a cross-validated r(2) of 0.823/0.909 and a predictive r(2) of 0.665/0.796, respectively. Consensus was achieved as, on average, the calculated activities of the training set differ only by a factor of 2.2 in K(i) and those of the test set by a factor of 2.8 when predicted by Quasar and Raptor, respectively.