Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques

Recent advances in chiral selectors immobilization and chiral mobile phase additives in liquid chromatographic enantio-separations: A review

For decades now, the separation of chiral enantiomers of drugs has been gaining the interest and attention of researchers. In 1991, the first guidelines for development of chiral drugs were firstly released by the US-FDA. Since then, the development in chromatographic enantioseparation tools has been fast and variable, aiming at creating a suitable environment where the physically and chemically identical enantiomers can be separated. Among those tools, the immobilization of chiral selectors (CS) on different stationary phases and the chiral mobile phase additives (CMPA) which have been progressed and studied extensively. This review article highlights the major advances in immobilization of CS together with their different recognition mechanisms as well as CMPA as a cheaper and successful alternative for chiral stationary phases. Moreover, the role of molecular modeling tool as a pre-step in the choice of CS for evaluating possible interactions with different ligands has been pointed up. Illustrations of reported methods and updates for immobilized CS and CMPA have been included.

Modelling the enantiorecognition of structurally diverse pharmaceuticals on O-substituted polysaccharide-based stationary phases

This study aims to develop models to predict the retention, separation and elution sequence of the enantiomers of structurally diverse pharmaceuticals. More specifically, Quantitative Structure Retention Relationships (QSRR) models are built that describe the relationship between molecular descriptors and retention. Eighteen structurally diverse chiral mixtures, each consisting of a pair of enantiomers, were analyzed on two polysaccharide chiral stationary phases, Chiralcel OD-RH (cellulose tris(3,5-dimethylphenylcarbamate)) and Lux amylose-2 (amylose tris(5-chloro-2-methylphenylcarbamate)), applying either a basic or an acidic mobile phase, and their retention factor and elution sequence were determined. Both achiral and, in-house defined, chiral descriptors were used as descriptive variables to build the models. Linear regression techniques, i.e. stepwise multiple linear regression (sMLR) and partial least squares (PLS) regression, were applied to model the retention or separation as a function of the descriptors. In a first step, models were built with only achiral descriptors to model the global retention of both enantiomers of a chiral molecule. Subsequently, models were built with only chiral descriptors to predict the enantioseparation and elution sequence, and finally, models were considered with both descriptor types to predict the retention, the separation and the elution sequence of the enantiomers. The global retention was predicted well by the sMLR models with only achiral descriptors. The models with only chiral descriptors were not found suitable to predict the enantioseparation and elution sequence. Finally, the models containing both chiral and achiral descriptors allowed predicting the retention well, but their ability to predict the elution sequence and separation of the enantiomers differed widely for the chromatographic systems considered.

Recognition in the Domain of Molecular Chirality: From Noncovalent Interactions to Separation of Enantiomers

It is not a coincidence that both chirality and noncovalent interactions are ubiquitous in nature and synthetic molecular systems. Noncovalent interactivity between chiral molecules underlies enantioselective recognition as a fundamental phenomenon regulating life and human activities. Thus, noncovalent interactions represent the narrative thread of a fascinating story which goes across several disciplines of medical, chemical, physical, biological, and other natural sciences. This review has been conceived with the awareness that a modern attitude toward molecular chirality and its consequences needs to be founded on multidisciplinary approaches to disclose the molecular basis of essential enantioselective phenomena in the domain of chemical, physical, and life sciences. With the primary aim of discussing this topic in an integrated way, a comprehensive pool of rational and systematic multidisciplinary information is provided, which concerns the fundamentals of chirality, a description of noncovalent interactions, and their implications in enantioselective processes occurring in different contexts. A specific focus is devoted to enantioselection in chromatography and electromigration techniques because of their unique feature as "multistep" processes. A second motivation for writing this review is to make a clear statement about the state of the art, the tools we have at our disposal, and what is still missing to fully understand the mechanisms underlying enantioselective recognition.

Molecular Recognition of the HPLC Whelk-O1 Selector towards the Conformational Enantiomers of Nevirapine and Oxcarbazepine

The presence of stereogenic elements is a common feature in pharmaceutical compounds, and affording optically pure stereoisomers is a frequent issue in drug design. In this context, the study of the chiral molecular recognition mechanism fundamentally supports the understanding and optimization of chromatographic separations with chiral stationary phases. We investigated, with molecular docking, the interactions between the chiral HPLC selector Whelk-O1 and the stereoisomers of two bioactive compounds, the antiviral Nevirapine and the anticonvulsant Oxcarbazepine, both characterized by two stereolabile conformational enantiomers. The presence of fast-exchange enantiomers and the rate of the interconversion process were studied using low temperature enantioselective HPLC and VT-NMR with Whelk-O1 applied as chiral solvating agent. The values of the energetic barriers of interconversion indicate, for the single enantiomers of both compounds, half-lives sufficiently long enough to allow their separation only at critically sub-ambient temperatures. The chiral selector Whelk-O1 performed as a strongly selective discriminating agent both when applied as a chiral stationary phase (CSP) in HPLC and as CSA in NMR spectroscopy.

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation

In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher.

Freely Accessible Chemical Database Resources of Compounds for In Silico Drug Discovery

BACKGROUND

In silico drug discovery has been proved to be a solidly established key component in early drug discovery. However, this task is hampered by the limitation of quantity and quality of compound databases for screening. In order to overcome these obstacles, freely accessible database resources of compounds have bloomed in recent years. Nevertheless, how to choose appropriate tools to treat these freely accessible databases is crucial. To the best of our knowledge, this is the first systematic review on this issue.

OBJECTIVE

The existed advantages and drawbacks of chemical databases were analyzed and summarized based on the collected six categories of freely accessible chemical databases from literature in this review.

RESULTS

Suggestions on how and in which conditions the usage of these databases could be reasonable were provided. Tools and procedures for building 3D structure chemical libraries were also introduced.

CONCLUSION

In this review, we described the freely accessible chemical database resources for in silico drug discovery. In particular, the chemical information for building chemical database appears as attractive resources for drug design to alleviate experimental pressure.

Machine learning to predict the specific optical rotations of chiral fluorinated molecules

A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.

Recognition Mechanisms of Chiral Selectors: An Overview

Stereospecific recognition of chiral molecules plays an important role in nature as the basis of the interaction of chiral bioactive compounds with the chiral target structures. In separation sciences such as chromatographic and capillary electromigration techniques, interactions between chiral analytes and chiral selectors, i.e., the formation of transient diastereomeric complexes in thermodynamic equilibria, are the basis for chiral separations. Due to the large structural variety of chiral selectors, different structural features contribute to the overall chiral recognition process. This introductory chapter briefly summarizes the present understanding of the structural enantioselective recognition processes for various types of chiral selectors.

Chiral Cliffs: Investigating the Influence of Chirality on Binding Affinity

Chirality is understood by many as a binary concept: a molecule is either chiral or it is not. In terms of the action of a structure on polarized light, this is indeed true. When examined through the prism of molecular recognition, the answer becomes more nuanced. In this work, we investigated chiral behavior on protein-ligand binding: when does chirality make a difference in binding activity? Chirality is a property of the 3D structure, so recognition also requires an appreciation of the conformation. In many situations, the bioactive conformation is undefined. We set out to address this by defining and using several novel 2D descriptors to capture general characteristic features of the chiral center. Using machine-learning methods, we built different predictive models to estimate if a chiral pair (a set of two enantiomers) might exhibit a chiral cliff in a binding assay. A set of about 3800 chiral pairs extracted from the ChEMBL23 database was used to train and test our models. By achieving an accuracy of up to 75 %, our models provide good performance in discriminating chiral cliffs from non-cliffs. More importantly, we were able to derive some simple guidelines for when one can reasonably use a racemate and when an enantiopure compound is needed in an assay. We critically discuss our results and show detailed examples of using our guidelines. Along with this publication we provide our dataset, our novel descriptors, and the Python code to rebuild the predictive models.

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy

Predicting whether a chiral column will be effective is a daily task for many analysts. Moreover, finding the best chiral column for separating a particular racemic compound is mostly a matter of trial and error that may take up to a week in some cases. In this study we have developed a novel prediction approach based on combining a random forest classifier and an optimized discretization method for dealing with enantioselectivity as a continuous variable. Using the optimization results, models were trained on data sets divided into four enantioselectivity classes. The best model performances were achieved by over-sampling the minority classes (α ≤ 1.10 and α ≥ 2.00), down-sampling the majority class (1.2 ≤ α < 2.0), and aggregating multicategory predictions into binary classifications. We tested our method on 41 chiral stationary phases using layered fingerprints as descriptors. Experimental results show that this learning methodology was successful in terms of average area under the Receiver Operating Characteristic curve, Kappa indices and F-measure for structure-based prediction of the enantioselective behavior of 34 chiral columns.

Predicting CYP2C19 catalytic parameters for enantioselective oxidations using artificial neural networks and a chirality code

Cytochromes P450 (CYP for isoforms) play a central role in biological processes especially metabolism of chiral molecules; thus, development of computational methods to predict parameters for chiral reactions is important for advancing this field. In this study, we identified the most optimal artificial neural networks using conformation-independent chirality codes to predict CYP2C19 catalytic parameters for enantioselective reactions. Optimization of the neural networks required identifying the most suitable representation of structure among a diverse array of training substrates, normalizing distribution of the corresponding catalytic parameters (k(cat), K(m), and k(cat)/K(m)), and determining the best topology for networks to make predictions. Among different structural descriptors, the use of partial atomic charges according to the CHelpG scheme and inclusion of hydrogens yielded the most optimal artificial neural networks. Their training also required resolution of poorly distributed output catalytic parameters using a Box-Cox transformation. End point leave-one-out cross correlations of the best neural networks revealed that predictions for individual catalytic parameters (k(cat) and K(m)) were more consistent with experimental values than those for catalytic efficiency (k(cat)/K(m)). Lastly, neural networks predicted correctly enantioselectivity and comparable catalytic parameters measured in this study for previously uncharacterized CYP2C19 substrates, R- and S-propranolol. Taken together, these seminal computational studies for CYP2C19 are the first to predict all catalytic parameters for enantioselective reactions using artificial neural networks and thus provide a foundation for expanding the prediction of cytochrome P450 reactions to chiral drugs, pollutants, and other biologically active compounds.

Probing the chiral separation mechanism and the absolute configuration of malathion, malaoxon and isomalathion enantiomers by chiral high performance liquid chromatography coupled with chiral detector-binding energy computations

Chiral separation mechanism determination and absolute configuration assignment are fundamental to the development of chiral stationary phases (CSPs) and the evaluation of both the enantioselective bioactivity and fate of chiral compounds. This work investigated the process of chiral separation and the assignment of the absolute configurations of malathion, malaoxon, and isomalathion using chiral high performance liquid chromatography (HPLC) coupled with chiral detector-binding energy computations. Hydrogen bonding was found to be a very important factor in the chiral separation of isomalathion on Chiralpak AD, although it did not exhibit a significant effect on the chiral separation of malathion and malaoxon on Chiralcel OJ. Based on the sign of a chiral detector, the relationships between the cotton effect, optical dispersion and absolute configuration were established for individual enantiomers of malathion, malaoxon, and isomalathion. The elution orders of the enantiomers of malathion and malaoxon on Chiralcel OJ and the stereoisomers of isomalathion on Chiralpak AD predicted by binding energy computations were found to coincide precisely with those observed in the chiral separation experiments. The result suggests that binding energy computations can be used to assign the absolute configuration of the enantiomers of chiral compounds eluted on CSPs.

Chiral recognition in separation science: an overview

Chiral recognition phenomena play an important role in nature as well as analytical separation sciences. In separation sciences such as chromatography and capillary electrophoresis, enantiospecific interactions between the enantiomers of an analyte and the chiral selector are required in order to observe enantioseparations. Due to the large structural variety of chiral selectors applied, different mechanisms and structural features contribute to the chiral recognition process. This chapter briefly illustrates the current models of the enantiospecific recognition on the structural basics of various chiral selectors.

Differentiation of enantiomers by capillary electrophoresis

Capillary electrophoresis (CE) has matured to one of the major liquid phase enantiodifferentiation techniques since the first report in 1985. This can be primarily attributed to the flexibility as well as the various modes available including electrokinetic chromatography (EKC), micellar electrokinetic chromatography (MEKC), and microemulsion electrokinetic chromatography (MEEKC). In contrast to chromatographic techniques, the chiral selector is mobile in the background electrolyte. Furthermore, a large variety of chiral selectors are available that can be easily combined in the same separation system. In addition, the migration order of the enantiomers can be adjusted by a number of approaches. In CE enantiodifferentiations the separation principle is comparable to chromatography while the principle of the movement of the analytes in the capillary is based on electrophoretic phenomena. The present chapter will focus on mechanistic aspects of CE enantioseparations including enantiomer migration order and the current understanding of selector-selectand structures. Selected examples of the basic enantioseparation modes EKC, MEKC, and MEEKC will be discussed.

BCL::EMAS--enantioselective molecular asymmetry descriptor for 3D-QSAR

Stereochemistry is an important determinant of a molecule’s biological activity. Stereoisomers can have different degrees of efficacy or even opposing effects when interacting with a target protein. Stereochemistry is a molecular property difficult to represent in 2D-QSAR as it is an inherently three-dimensional phenomenon. A major drawback of most proposed descriptors for 3D-QSAR that encode stereochemistry is that they require a heuristic for defining all stereocenters and rank-ordering its substituents. Here we propose a novel 3D-QSAR descriptor termed Enantioselective Molecular ASymmetry (EMAS) that is capable of distinguishing between enantiomers in the absence of such heuristics. The descriptor aims to measure the deviation from an overall symmetric shape of the molecule. A radial-distribution function (RDF) determines a signed volume of tetrahedrons of all triplets of atoms and the molecule center. The descriptor can be enriched with atom-centric properties such as partial charge. This descriptor showed good predictability when tested with a dataset of thirty-one steroids commonly used to benchmark stereochemistry descriptors (r² = 0.89, q² = 0.78). Additionally, EMAS improved enrichment of 4.38 versus 3.94 without EMAS in a simulated virtual high-throughput screening (vHTS) for inhibitors and substrates of cytochrome P450 (PUBCHEM AID891).

Computational modeling of capillary electrophoretic behavior of primary amines using dual system of 18-crown-6 and β-cyclodextrin

Using capillary electrophoresis (CE) three chiral primary amine compounds 1-aminoindan (AI), 1-(1-naphthyl)ethylamine (NEA) and 1,2,3,4-tetrahydro-1-naphthylamine (THAN), exhibited only partial or no separation when β-cyclodextrin (βCD) was used as chiral selector. The use of 18-crown-6 (18C6) as a second additive with βCD resulted in an enhanced separation. A molecular modeling study, using molecular mechanics and the semiempirical PM6 calculations, was used to help explaining the mechanism of the enantiodifferentiation and to predict the separation process. Optimization of the structures of the complexes by the PM6 method indicate that the poor separation obtained in the presence of the βCD chiral selector alone is due to the small binding energy differences (ΔΔE) of 4.7, 1.1 and 1.2 kcal mol(-1) for AI, NEA and THAN, respectively. In the presence of 18C6 it was suggested that a sandwich compound between 18C6, amine and βCD is formed. Theoretical calculations show that a significant increase in the binding energy is obtained for the sandwich compounds indicating strong hydrophobic and van der Waals interactions that show enhanced enantiodifferentiation.

Fundamental aspects of chiral electromigration techniques and application in pharmaceutical and biomedical analysis

Capillary electromigration techniques are often considered ideal methods for the analysis of chiral compounds due to the high resolution power and flexibility of the technique. Therefore, especially capillary electrophoresis using a chiral selector in the background electrolyte, also termed electrokinetic chromatography, has found widespread acceptance in analytical enantioseparations of drug compounds in pharmaceuticals and biological media. Moreover, mechanistic studies on analyte complexation by the chiral selectors have continuously been conducted in an effort to rationalize enantioseparation phenomena. These studies combined capillary electrophoresis with spectroscopic techniques such as nuclear magnetic resonance and/or molecular modeling. The present review focuses on recent examples of mechanistic aspects of capillary electromigration enantioseparations and summarizes recent applications of chiral pharmaceutical and biomedical analysis published between January 2009 and August 2010.

Chiral recognition by enantioselective liquid chromatography: mechanisms and modern chiral stationary phases

An overview of the state-of-the-art in LC enantiomer separation is presented. This tutorial review is mainly focused on mechanisms of chiral recognition and enantiomer distinction of popular chiral selectors and corresponding chiral stationary phases including discussions of thermodynamics, additivity principle of binding increments, site-selective thermodynamics, extrathermodynamic approaches, methods employed for the investigation of dominating intermolecular interactions and complex structures such as spectroscopic methods (IR, NMR), X-ray diffraction and computational methods. Modern chiral stationary phases are discussed with particular focus on those that are commercially available and broadly used. It is attempted to provide the reader with vivid images of molecular recognition mechanisms of selected chiral selector-selectand pairs on basis of solid-state X-ray crystal structures and simulated computer models, respectively. Such snapshot images illustrated in this communication unfortunately cannot account for the molecular dynamics of the real world, but are supposed to be helpful for the understanding. The exploding number of papers about applications of various chiral stationary phases in numerous fields of enantiomer separations is not covered systematically.