Structural and chemical basis for enhanced affinity and potency for a large series of estrogen receptor ligands: 2D and 3D QSAR studies

Human Estrogen Receptor α Antagonists. Part 1: 3-D QSAR-Driven Rational Design of Innovative Coumarin-Related Antiestrogens as Breast Cancer Suppressants through Structure-Based and Ligand-Based Studies

The estrogen receptor α (ERα) represents a 17β-estradiol-inducible transcriptional regulator that initiates the RNA polymerase II-dependent transcriptional machinery, pointed for breast cancer (BC) development via either genomic direct or genomic indirect (i.e., tethered) pathway. To develop innovative ligands, structure-based (SB) three-dimensional (3-D) quantitative structure-activity relationship (QSAR) studies have been undertaken from structural data taken from partial agonists, mixed agonists/antagonists (selective estrogen receptor modulators (SERMs)), and full antagonists (selective ERα downregulators (SERDs)) correlated with either wild-type or mutated ERα receptors. SB and ligand-based (LB) alignments allow us to rule out guidelines for the SB/LB alignment of untested compounds. 3-D QSAR models for ERα ligands, coupled with SB/LB alignment, were revealed to be useful tools to dissect the chemical determinants for ERα-based anticancer activity as well as to predict their potency. The herein developed protocol procedure was verified through the design and potency prediction of 12 new coumarin-based SERMs, namely, 3DQ-1a to 3DQ-1e, that upon synthesis turned to be potent ERα antagonists by means of either in vitro or in vivo assays (described in the second part of this study).

Ecotoxicological QSAR modeling of endocrine disruptor chemicals

This study reports highly robust externally predictive quantitative structure-toxicity relationship (QSTR) and interspecies quantitative structure-toxicity-toxicity (i-QSTTR) models developed using toxicity data of endocrine disruptor chemicals (EDCs) towards 14 different species falling in four different trophic levels. Genetic algorithm followed by Partial Least Squares (PLS) regression was used in model development following the strict OECD guidelines. The models were developed using 2D descriptors having definite physicochemical meaning and validated by several internationally accepted validation metrics. The scope of predictions was defined by estimating applicability domain of the models. Presence of halogens, sulfur and phosphorus in the molecules greatly influenced the toxicity of EDCs as suggested by continuous repetition of 2D atom pair descriptors. Lipophilic contributions as calculated by logP terms (mainly ALOGP2 and XlogP) were the second most important feature controlling the EDC hazards. Hydrophilic moiety such as functionalities like esters, aliphatic ethers, branching and higher oxygen content reduced the EDC toxicity. Interspecies models were employed in data gap filling following the hierarchy of different species. The reliability of predictions was calculated by the "prediction reliability indicator" tool.

Synthesis of Sulfur Perfluorophenyl Compounds Using a Pentafluorobenzenesulfonyl Hypervalent Iodonium Ylide

A novel pentafluorobenzenesulfonyl hypervalent iodonium ylide 3 was designed and synthesized as a useful tool for the preparation of sulfur pentafluorophenyl compounds containing a C₆F₅S or C₆F₅SO₂ unit. Electrophilic pentafluorophenylthiolation of enamines, formal [3+2] cycloaddition reaction of nitriles and alkynes, and intramolecular S_NAr cyclization were achieved using iodonium ylide 3. The fluoro-click reaction was also demonstrated using one of the products via an intermolecular S_NAr reaction with heterocentered nucleophiles.

Metal-free O-H/C-H difunctionalization of phenols by o-hydroxyarylsulfonium salts in water

An environmentally benign method for C-H/O-H difunctionalization of phenols with sulfoxides under mild conditions has been developed. The reaction process is mediated by an electrophilic aromatic substitution and subsequent selective aryl or alkyl migration, involving C-S and C-O bond formations with broad substrate scope.

3D-QSAR analysis of a series of S-DABO derivatives as anti-HIV agents by CoMFA and CoMSIA

In this study, we retrieved a series of 59 dihydroalkylthio-benzyloxopyrimidine (S-DABO) derivatives, which is a class of highly potent HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) reported from published articles, and analysed them with comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). Statistically significant three-dimensional quantitative structure-activity relationship (3D-QSAR) models by CoMFA and CoMSIA were derived from a training set of 46 compounds on the basis of the rigid body alignment. Further, the predictive ability of the QSAR models was validated by a test set of 13 compounds. Based on the information derived from CoMFA and CoMSIA contour maps, we have identified some steric and electrostatic features for improving the activities of these inhibitors, and we validated the 3D-QSAR results by a molecular docking method. On the basis of the obtained results, we designed a new series of S-DABO derivatives with high activities. Therefore, this study could be utilized to design more potent S-DABO analogues as anti-HIV agents.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure-activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

Computational studies of TGF-βRI (ALK-5) inhibitors: analysis of the binding interactions between ligand-receptor using 2D and 3D techniques

ALK-5 (Activin-Like Kinase 5) is a biological receptor involved in a variety of pathological processes such as cancer and fibrosis. ALK-5 receptor propagates an intracellular signaling that forms a protein complex capable of reaching the nucleus and modulating the gene transcription. In the present study, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent ALK-5 inhibitors. Significant correlation coefficients (CoMFA, r(2)=0.99 and q(2)=0.85; HQSAR, r(2)=0.92 and q(2)=0.72) were obtained, indicating the predictive potential of the 2D and 3D models for untested compounds. The models were then used to predict the potency of a test set, and the predicted values from the HQSAR and CoMFA models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D (steric and electrostatic) contour maps and 2D contribution maps, can be useful for the design of novel bioactive ligands.

QSAR studies for the computational prediction of HMG-CoA reductase inhibitors by genetic function approximation technique

Two-dimensional quantitative structure−activity relationship (2D-QSAR) models are useful in understanding how chemical structure is related to the biological activity of natural and synthetic chemicals. Also, they could be usefully employed for designing newer and better therapeutics. A 2D-QSAR study was performed for 52 compounds of a series of thiophenyl quinolines and α-asarone derivatives as potential hypocholesterolemic inhibitors using different types of physicochemical descriptors, which correlated significantly with the activity. Linear QSAR models were developed using multiple linear regression, where the genetic algorithm (genetic function approximation technique) was adopted for selecting the most appropriate descriptors. The results are discussed on the basis of regression data and the cross-validation technique. Model A is the best 2D-QSAR model describing the inhibition efficiency of HMG-CoA reductase with cross-validated squared correlation coefficient (Q 2 = 0.700) and the squared correlation coefficient (R 2 = 0.752), which is able to describe 70% of the variance in the experimental activity. The good agreement between the experimental and the predicted values of pIC50 (micromoles per litre) (R = 0.876) confirms the reliability and the predictability of the proposed model. The results obtained from the present QSAR study explained the importance of the electronic, structural, spatial, and electrotopological descriptors in enhancing the biological activity of the investigated inhibitors.

Discovery of estrogen receptor modulators: a review of virtual screening and SAR efforts

IMPORTANCE OF THE FIELD

Virtual screening (VS) coupled with structural biology is a significantly important approach to increase the number and enhance the success of projects in lead identification stage of drug discovery process. Recent advances and future directions in estrogen therapy have resulted in great demand for identifying the potential estrogen receptor (ER) modulators with more activity and selectivity.

AREAS COVERED IN THIS REVIEW

This review presents the current state of the art in VS and structure-activity relationship of ER modulators in recent discovery, and discusses the strengths and weaknesses of the technology.

WHAT THE READER WILL GAIN

Readers will gain an overview of the current platforms of in silico screening for discovery of ER modulators; they will learn which structural information is significantly correlated with the bioactivity of ER modulators and what novel strategies should be considered for the creation of more effective chemical structures.

TAKE HOME MESSAGE

With the goal of reducing toxicity and/or improving efficacy, challenges to the successful modeling of endocrine agents are proposed, providing new paradigms for the design of ER inhibitors.

Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities

The determination of binding affinities for the estrogen receptor (ER) is used extensively to assess potential hazards to human health and the environment arising from chemicals that can interfere with natural hormone homeostasis. Given the great number of chemicals to which humans and wildlife are exposed, (quantitative) structure-activity relationship (Q)SAR models for the characterization of ER disruptors represent a fast and cost-efficient alternative to experimental testing. In this toxicological context, the freely available Organisation for Economic Co-operation and Development (OECD) (Q)SAR Application Toolbox provides a profiler for the categorical profiling of chemicals according to their ER binding propensities. The aim of this study was to evaluate the predictive performances of this profiler. To achieve such a purpose, prediction results with the ER-profiler were compared with experimental binding affinities relative to two large datasets of chemicals (rat and human). The resulting Cooper statistics indicated that the binding affinities of the majority of chemicals included in the retained datasets could be correctly predicted.

Recent advances in fragment-based QSAR and multi-dimensional QSAR methods

This paper provides an overview of recently developed two dimensional (2D) fragment-based QSAR methods as well as other multi-dimensional approaches. In particular, we present recent fragment-based QSAR methods such as fragment-similarity-based QSAR (FS-QSAR), fragment-based QSAR (FB-QSAR), Hologram QSAR (HQSAR), and top priority fragment QSAR in addition to 3D- and nD-QSAR methods such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis (CoMSIA), Topomer CoMFA, self-organizing molecular field analysis (SOMFA), comparative molecular moment analysis (COMMA), autocorrelation of molecular surfaces properties (AMSP), weighted holistic invariant molecular (WHIM) descriptor-based QSAR (WHIM), grid-independent descriptors (GRIND)-based QSAR, 4D-QSAR, 5D-QSAR and 6D-QSAR methods.

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT1A receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds.

Structure-based approach for the study of estrogen receptor binding affinity and subtype selectivity

Estrogens exert important physiological effects through the modulation of two human estrogen receptor (hER) subtypes, alpha (hERalpha) and beta (hERbeta). Because the levels and relative proportion of hERalpha and hERbeta differ significantly in different target cells, selective hER ligands could target specific tissues or pathways regulated by one receptor subtype without affecting the other. To understand the structural and chemical basis by which small molecule modulators are able to discriminate between the two subtypes, we have applied three-dimensional target-based approaches employing a series of potent hER-ligands. Comparative molecular field analysis (CoMFA) studies were applied to a data set of 81 hER modulators, for which binding affinity values were collected for both hERalpha and hERbeta. Significant statistical coefficients were obtained (hERalpha, q(2) = 0.76; hERbeta, q(2) = 0.70), indicating the internal consistency of the models. The generated models were validated using external test sets, and the predicted values were in good agreement with the experimental results. Five hER crystal structures were used in GRID/PCA investigations to generate molecular interaction fields (MIF) maps. hERalpha and hERbeta were separated using one factor. The resulting 3D information was integrated with the aim of revealing the most relevant structural features involved in hER subtype selectivity. The final QSAR and GRID/PCA models and the information gathered from 3D contour maps should be useful for the design of novel hER modulators with improved selectivity.

Quantitative structure-activity relationships for a series of inhibitors of cruzain from Trypanosoma cruzi: molecular modeling, CoMFA and CoMSIA studies

Human parasitic diseases are the foremost threat to human health and welfare around the world. Trypanosomiasis is a very serious infectious disease against which the currently available drugs are limited and not effective. Therefore, there is an urgent need for new chemotherapeutic agents. One attractive drug target is the major cysteine protease from Trypanosoma cruzi, cruzain. In the present work, comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) studies were conducted on a series of thiosemicarbazone and semicarbazone derivatives as inhibitors of cruzain. Molecular modeling studies were performed in order to identify the preferred binding mode of the inhibitors into the enzyme active site, and to generate structural alignments for the three-dimensional quantitative structure-activity relationship (3D QSAR) investigations. Statistically significant models were obtained (CoMFA, r2=0.96 and q2=0.78; CoMSIA, r2=0.91 and q2=0.73), indicating their predictive ability for untested compounds. The models were externally validated employing a test set, and the predicted values were in good agreement with the experimental results. The final QSAR models and the information gathered from the 3D CoMFA and CoMSIA contour maps provided important insights into the chemical and structural basis involved in the molecular recognition process of this family of cruzain inhibitors, and should be useful for the design of new structurally related analogs with improved potency.

Fragment-based QSAR: perspectives in drug design

Drug design is a process driven by innovation and technological breakthroughs involving a combination of advanced experimental and computational methods. A broad variety of medicinal chemistry approaches can be used for the identification of hits, generation of leads, as well as to accelerate the optimization of leads into drug candidates. Quantitative structure-activity relationship (QSAR) methods are among the most important strategies that can be applied for the successful design of small molecule modulators having clinical utility. Hologram QSAR (HQSAR) is a modern 2D fragment-based QSAR method that employs specialized molecular fingerprints. HQSAR can be applied to large data sets of compounds, as well as traditional-size sets, being a versatile tool in drug design. The HQSAR approach has evolved from a classical use in the generation of standard QSAR models for data correlation and prediction into advanced drug design tools for virtual screening and pharmacokinetic property prediction. This paper provides a brief perspective on the evolution and current status of HQSAR, highlighting present challenges and new opportunities in drug design.

Two- and three-dimensional quantitative structure-activity relationships studies on a series of liver x receptor ligands

Liver X receptor (LXR) is an attractive drug target for the development of novel therapeutic agents for the treatment of dyslipidaemia and cholestasis. In the present work, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent LXR ligands. Significant correlation coefficients (CoMFA, r(2) = 0.98 and q(2) = 0.69; HQSAR, r(2) = 0.99 and q(2) = 0.85) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted values obtained from the 2D and 3D models were in good agreement with the experimental results. The final QSAR models, along with the information obtained from 3D steric and electrostatic contour maps and 2D contribution maps should be useful for the design of novel LXR ligands having improved potency.

Prediction of binding affinity for estrogen receptor alpha modulators using statistical learning approaches

The estrogen receptor (ER), an important drug target for the therapy of breast cancers, received a great deal of attention during recent years. This work aimed at finding more potent and selective ER modulators through the investigations of multiple ligand-receptor interactions by exploring the relationship between the experimental and predicted pIC50 values using in silico methods. A Bayesian-regularized neural network combined with principal component analysis has been conducted on a set of ERalpha modulators (127 molecules), resulting in the correlation coefficients of 0.91 +/- 0.02, 0.87 +/- 0.04 and 0.90 +/- 0.02 for the training set (64 molecules), cross-validation set (32 molecules) and independent test (31 molecules), respectively. Meanwhile, a multiple linear regression (MLR) method has also been applied in order to explore the most important variables related to the biological activities. The proposed MLR model obtains a reasonable predictivity of pIC50 (R = 0.72, Q = 0.79) and makes use of four molecular descriptors, namely, Xvch6, nelem, SsssCH and SaaN. All these results prove the reliabilities of the in silico models, which should be useful not only for the screening but also for the rational design of novel ERalpha modulators with improved potency.

Hologram QSAR model for the prediction of human oral bioavailability

A drug intended for use in humans should have an ideal balance of pharmacokinetics and safety, as well as potency and selectivity. Unfavorable pharmacokinetics can negatively affect the clinical development of many otherwise promising drug candidates. A variety of in silico ADME (absorption, distribution, metabolism, and excretion) models are receiving increased attention due to a better appreciation that pharmacokinetic properties should be considered in early phases of the drug discovery process. Human oral bioavailability is an important pharmacokinetic property, which is directly related to the amount of drug available in the systemic circulation to exert pharmacological and therapeutic effects. In the present work, hologram quantitative structure-activity relationships (HQSAR) were performed on a training set of 250 structurally diverse molecules with known human oral bioavailability. The most significant HQSAR model (q(2)=0.70, r(2)=0.93) was obtained using atoms, bond, connection, and chirality as fragment distinction. The predictive ability of the model was evaluated by an external test set containing 52 molecules not included in the training set, and the predicted values were in good agreement with the experimental values. The HQSAR model should be useful for the design of new drug candidates having increased bioavailability as well as in the process of chemical library design, virtual screening, and high-throughput screening.