(13)C NMR quantitative spectrometric data-activity relationship (QSDAR) models of steroids binding the aromatase enzyme

Computational prediction models for assessing endocrine disrupting potential of chemicals

Endocrine disrupting chemicals (EDCs) mimic natural hormones and disrupt endocrine function. Humans and wildlife are exposed to EDCs might alter endocrine functions through various mechanisms and lead to an adverse effects. Hence, EDCs identification is important to protect the ecosystem and to promote the public health. Leveraging in-vitro and in-vivo experiments to identify potential EDCs is time consuming and expensive. Hence, quantitative structure-activity relationship is applied to screen the potential EDCs. Here, we summarize the predictive models developed using various algorithms to forecast the binding activity of chemicals to the estrogen and androgen receptors, alpha-fetoprotein, and sex hormone binding globulin.

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

CERAPP: Collaborative Estrogen Receptor Activity Prediction Project

BACKGROUND

Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program.

OBJECTIVES

We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing.

METHODS

CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies.

RESULTS

Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.

CONCLUSION

This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.

CITATION

Mansouri K, Abdelaziz A, Rybacka A, Roncaglioni A, Tropsha A, Varnek A, Zakharov A, Worth A, Richard AM, Grulke CM, Trisciuzzi D, Fourches D, Horvath D, Benfenati E, Muratov E, Wedebye EB, Grisoni F, Mangiatordi GF, Incisivo GM, Hong H, Ng HW, Tetko IV, Balabin I, Kancherla J, Shen J, Burton J, Nicklaus M, Cassotti M, Nikolov NG, Nicolotti O, Andersson PL, Zang Q, Politi R, Beger RD, Todeschini R, Huang R, Farag S, Rosenberg SA, Slavov S, Hu X, Judson RS. 2016.

CERAPP

Collaborative Estrogen Receptor Activity Prediction Project. Environ Health Perspect 124:1023-1033; http://dx.doi.org/10.1289/ehp.1510267.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Advances in nuclear magnetic resonance for drug discovery

Nuclear Magnetic Resonance (NMR) techniques are widely used in the drug discovery process. The primary feature exploited in these investigations is the large difference in mass between drugs and receptors (usually proteins) and the effect this has on the rotational or translational correlation times for drugs bound to their targets. Many NMR parameters, such as the diffusion coefficient, spin diffusion, nuclear Overhauser enhancement, and transverse and longitudinal relaxation times, are strong functions of either the overall tumbling or translation of molecules in solution. This has led to the development of a wide variety of NMR techniques applicable to the elucidation of protein and nucleic acid structure in solution, the screening of drug candidates for binding to a target of choice, and the study of the conformational changes which occur in a target upon drug binding. High-throughput screening by NMR methods has recently received a boost from the introduction of sophisticated computational techniques for reducing the time needed for the acquisition of the primary NMR data for multidimensional studies.

Topical corticosteroids, structure-activity and the glucocorticoid receptor: discovery and development--a process of "planned serendipity"

This is a personal recounting of the way in which the original steroid chemists and biologists worked closely together, often by trial and error, to use cortisol as the template to develop increasingly improved systemic glucocorticoids. In doing this, they learned how certain chemical functional groups affected efficacy and safety negatively and positively. When the more promising systemic glucocorticoids were subsequently applied topically, the skin barrier impaired their activity. This led to new research, this time employing in vitro percutaneous absorption evaluations coupled with in vivo vasoconstrictor studies, to screen and develop effective new topical delivery systems. A subsequent stage of this glucocorticoid research revealed that these molecules had to "fit" into receptor sites and the approximate spatial structure of such receptor sites. It also disclosed the way that the various chemical functional groups affected that "fit" and the resulting effect upon safety and efficacy.

Whole-molecule calculation of log p based on molar volume, hydrogen bonds, and simulated 13C NMR spectra

The prediction of Log P is usually accomplished using either substructure or whole-molecule approaches. However, these methods are complicated, and previous whole-molecule approaches have not been successful for the prediction of Log P in very complex molecules. The observed chemical shifts in nuclear magnetic resonance (NMR) spectroscopy are related to the electrostatics at the nucleus, which are influenced by solute-solvent interactions. The different solvation effects on a molecule by either water or methanol have a strong effect on the NMR chemical shift value. Therefore, the chemical shift values observed in an aqueous and organic solvent should correlate to Log P. This paper develops a rapid, objective model of Log P based on molar volume, hydrogen bonds, and differences in calculated 13C NMR chemical shifts for a diverse set of compounds. A partial least squares (PLS) model of Log P built on the sum of carbon chemical shift differences in water and methanol, molar volume, number of hydrogen bond donors and acceptors in 162 diverse compounds gave an r2 value of 0.88. The average r2 for 10 training models of Log P made from 90% of the data was 0.87+/-0.01. The average q2 for 10 leave-10%-out cross-validation test sets was 0.87+/-0.05.

Computational modeling of biologically active molecules using NMR spectra

The molecular structure and NMR chemical shift information of a compound can be combined to form powerful models of biological activity. NMR spectral data and structure information can be combined on a structural template analogous to 3D-QSAR methodology or orientation independently in spectral space. Surprisingly, quantitative spectrometric data-activity relationship (QSDAR) models built on structure templates are inferior to multi-dimensional QSDAR models built in spectral space. 3D-QSDAR modeling could be useful for estimating chemical toxicity, risk assessment of environmental contaminants and drug lead-compound identifications.

Prediction of anti-plasmodial activity of Artemisia annua extracts: application of NMR spectroscopy and chemometrics

We describe the application of 1H NMR spectroscopy and chemometrics to the analysis of extracts of Artemisia annua. This approach allowed the discrimination of samples from different sources, and to classify them according to anti-plasmodial activity without prior knowledge of this activity. The use of partial least squares analysis allowed the prediction of actual values for anti-plasmodial activities for independent samples not used in producing the models. The models were constructed using approximately 70% of the samples, with 30% used as a validation set for which predictions were made. Models generally explained >90% of the variance, R(2) in the model, and had a predictive ability, Q(2) of >0.8. This approach was also able to correlate 1H NMR spectra with cytotoxicity (R2=0.9, Q2=0.8). This work demonstrates the potential of NMR spectroscopy and chemometrics for the development of predictive models of anti-plasmodial activity.

Combining NMR spectral and structural data to form models of polychlorinated dibenzodioxins, dibenzofurans, and biphenyls binding to the AhR

A three-dimensional quantitative spectrometric data-activity relationship (3D-QSDAR) modeling technique which uses NMR spectral and structural information that is combined in a 3D-connectivity matrix has been developed. A 3D-connectivity matrix was built by displaying all possible assigned carbon NMR chemical shifts, carbon-to-carbon connections, and distances between the carbons. Two-dimensional 13C-13C COSY and 2D slices from the distance dimension of the 3D-connectivity matrix were used to produce a relationship among the 2D spectral patterns for polychlorinated dibenzofurans, dibenzodioxins, and biphenyls (PCDFs, PCDDs, and PCBs respectively) binding to the aryl hydrocarbon receptor (AhR). We refer to this technique as comparative structural connectivity spectral analysis (CoSCoSA) modeling. All CoSCoSA models were developed using forward multiple linear regression analysis of the predicted 13C NMR structure-connectivity spectral bins. A CoSCoSA model for 26 PCDFs had an explained variance (r2) of 0.93 and an average leave-four-out cross-validated variance (q(2)4) of 0.89. A CoSCoSA model for 14 PCDDs produced an r2 of 0.90 and an average leave-two-out cross-validated variance (q(2)2) of 0.79. One CoSCoSA model for 12 PCBs gave an r2 of 0.91 and an average q(2)2 of 0.80. Another CoSCoSA model for all 52 compounds had an r2 of 0.85 and an average q(2)2 of 0.52. Major benefits of CoSCoSA modeling include ease of development since the technique does not use molecular docking routines.

Comparative structural connectivity spectra analysis (CoSCoSA) models of steroids binding to the aromatase enzyme

A method that combines NMR spectral and structural information into a constructed three-dimensional (3D)-connectivity matrix is developed for modeling biological binding activity of small molecules. The 3D-connectivity matrix for a molecule is defined by associating the distances between all possible carbon-to-carbon connections with their assigned carbon NMR chemical shifts. In this project we selected from the total 3D-connectivity matrix a subset, the two-dimensional (2D) (13)C-(13)C COSY and a theoretical long range 2D (13)C-(13)C distance connectivity spectral plane. Patterns of (13)C chemical shifts observed at these two relative distances for 50 steroids were used to produce a mathematical relationship for the steroids' relative binding affinity (pK(i)) to the aromatase enzyme. We call this technique comparative structural connectivity spectra analysis (CoSCoSA) modeling. Using combinations of the 2D COSY and 2D long-range distance spectra as modeling parameters, we built four CoSCoSA models. One model was made from the 2D COSY spectra alone and another was developed using only the 2D long-range distance spectra. Then the COSY and long-distance spectra were combined in two different ways: starting with the combined principal components (PCs) from the separately calculated COSY and distance spectra or using the combined raw spectra (3D). The best CoSCoSA model was based on the combined PCs from COSY and distance spectra. This model had an r(2) of 0.96 and a leave-one-out cross-validation (q(2)) of 0.92. In general CoSCoSA modeling combines the quantum mechanical information inherent in NMR chemical shifts with internal molecular atom-to-atom distances to give a reliable and straightforward basis for predictive modeling. The technique has the flexibility and accuracy to outperform not only the cross-validated variance q(2) of previously published quantitative structure-activity relationships (QSAR) but also those obtained by related quantitative spectral data-activity relationships (QSDARs) lacking connectivity dimensions.