CoMFA Study of Piperidine Analogues of Cocaine at the Dopamine Transporter: Exploring the Binding Mode of the 3α-Substituent of the Piperidine Ring Using Pharmacophore-Based Flexible Alignment

Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives

Solute carrier transporters (SLCs) are a class of important transmembrane proteins that are involved in the transportation of diverse solute ions and small molecules into cells. There are approximately 450 SLCs within the human body, and more than a quarter of them are emerging as attractive therapeutic targets for multiple complex diseases, e.g., depression, cancer, and diabetes. However, only 44 unique transporters (∼9.8% of the SLC superfamily) with 3D structures and specific binding sites have been reported. To design innovative and effective drugs targeting diverse SLCs, there are a number of obstacles that need to be overcome. However, computational chemistry, including physics-based molecular modeling and machine learning- and deep learning-based artificial intelligence (AI), provides an alternative and complementary way to the classical drug discovery approach. Here, we present a comprehensive overview on recent advances and existing challenges of the computational techniques in structure-based drug design of SLCs from three main aspects: (i) characterizing multiple conformations of the proteins during the functional process of transportation, (ii) identifying druggability sites especially the cryptic allosteric ones on the transporters for substrates and drugs binding, and (iii) discovering diverse small molecules or synthetic protein binders targeting the binding sites. This work is expected to provide guidelines for a deep understanding of the structure and function of the SLC superfamily to facilitate rational design of novel modulators of the transporters with the aid of state-of-the-art computational chemistry technologies including artificial intelligence.

Singular value decomposition analysis of the torsional angles of dopamine reuptake inhibitor GBR 12909 analogs: effect of force field and charges

Three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis of large, flexible molecules, such as the dopamine reuptake inhibitor GBR 12909 (1), is complicated by the fact that they can take on a wide range of closely-related conformations. The first step in the analysis is to classify the conformers into groups. Over 600 conformers each of a piperazine (2) and piperidine (3) analog of 1 were generated by random search conformational analysis using the Merck Molecular Force Field (MMFF94). Singular value decomposition (SVD) was used to group the conformers of 2 and 3 by the similarity of their non-ring torsional angles. SVD uncovered subtle differences in their conformer populations due to that fact that the conformers separate along different principal components, and ultimately to the fact that different torsional angles are the chief contributors to these components. The results were compared to our previous SVD analysis (Fiorentino, et al., Journal of Computational Chemistry, 2006, 27, 609-620) of conformer populations of 2 and 3 generated by the Tripos force field and Gasteiger-Hückel charges. Except for the dominant contribution of angle B3 to principal component 8 seen with both force fields, the angles which are chiefly responsible for the grouping of the conformers of 2 and 3 are different with both force fields. This illustrates that SVD is useful in identifying unique groupings of conformers in large data sets of flexible molecules-a first step in selecting representative conformers for 3D-QSAR modeling studies.

Comparison data sets for benchmarking QSAR methodologies in lead optimization

2D and 3D QSAR techniques are widely used in lead optimization-like processes. A compilation of 40 diverse data sets is described. It is proposed that these can be used as a common benchmark sample for comparisons of QSAR methodologies, primarily in terms of predictive ability. Use of this benchmark set will be useful for both assessment of new methods and for optimization of existing methods.

Partial charge calculation method affects CoMFA QSAR prediction accuracy

The 3D-QSAR method comparative molecular field analysis (CoMFA) involves the estimation of atomic partial charges as part of the process of calculating molecular electrostatic fields. Using 30 data sets from the literature the effect of using different common partial charge calculation methods on the predictivity (cross-validated R2) of CoMFA was studied. The partial charge methods ranged from the popular Gasteiger and the newer MMFF94 electronegativity equalization methods, to the more complex and computationally expensive semiempirical charges AM1, MNDO, and PM3. The MMFF94 and semiempirical MNDO, AM1, and PM3 methods for computing charges were found to result in statistically significantly more predictive CoMFA models than the Gasteiger charges. Although there was a trend toward the semiempirical charges performing better than the MMFF94 charges, the difference was not statistically significant. Thus, semiempirical partial charge calculation methods are suggested for the most predictive CoMFA models, but the MMFF94 charge calculation method is a very good alternative if semiempirical methods are not available or faster calculation speed is important.

Receptor-based modeling and 3D-QSAR for a quantitative production of the butyrylcholinesterase inhibitors based on genetic algorithm

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models have been constructed using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) for a series of structurally related steroidal alkaloids as butyrylcholinesterase (BuChE) inhibitors. Docking studies were employed to position the inhibitors into the BuChE active site to determine the most probable binding mode. The strategy was to explore multiple inhibitor conformations in producing a more reliable 3D-QSAR model. These multiple conformations were derived using the FlexS program. The conformation selection step for CoMFA was done by genetic algorithm. The genetic algorithm based CoMFA approach was found to be the best. Both CoMFA and CoMSIA yielded significant cross-validated q(2) values of 0.701 and 0.627 and the r(2) values of 0.979 and 0.982, respectively. These statistically significant models were validated by a test set of five compounds. Comparison of CoMFA and CoMSIA contour maps helped to identify structural requirements for the inhibitors and serves as a basis for the design of the next generation of the inhibitor analogues. The results demonstrate that the combination of ligand-based and receptor-based modeling with use of a genetic algorithm is a powerful approach to build 3D-QSAR models. These data can be used for the lead optimization process with respect to inhibition enhancement which is important for the drug discovery and development for Alzheimer's disease.

Pharmacophore design and database searching for selective monoamine neurotransmitter transporter ligands

Neuronal monoamine transporters (MATs) are involved in the pathophysiology and treatment of mental health conditions such as depression, attention deficit hyperactivity disorder, substance abuse and neurodegenerative disorders including Alzheimer's disease and Parkinson's disease. Various structural classes of compounds have been synthesized and tested in vitro for activity against transporters of three monoamine signaling molecules: noradrenaline (NET); serotonin (SERT) and dopamine (DAT). We have developed and validated a number of pharmacophore models describing the interaction of two classes of compounds with each of these three MATs. These pharmacophores explain the selectivity of binding to the MATs for various compound classes and have been used to search in silico databases for novel, potentially selective ligands. These ligands, after confirmation of their activities, will provide tools for investigating the function of MATs as well as the potential for new therapeutic agents in mental health applications. The database searches also retrieved close analogues of known MAT ligands, further validating the approach.

QSAR studies and pharmacophore identification for arylsubstituted cycloalkenecarboxylic acid methyl esters with affinity for the human dopamine transporter

Data from a series of 29 monoamine transport inhibitors were used to generate 2D and 3D QSAR models for their binding affinity to the human dopamine transporter (hDAT). Among the inhibitors were many non-nitrogen containing compounds. The 2D QSAR analysis resulted in the equation -logK(i)=4.00-3.93E(LUMO)-0.67E(HOMO)-3.24sigma(p), which predicted the importance of electron withdrawing groups in the aromatic moiety. However, the model failed to predict the observed poor binding of nitro-substituted compounds. In contrast, a derived 3D QSAR model was capable of predicting these more correctly.

Improved 3D-QSAR CoMFA of the dopamine transporter blockers with multiple conformations using the genetic algorithm

A 3D-QSAR/CoMFA was performed for a series of 42 piperidine-based dopamine transporter (DAT) blockers. The overall process consisted of three major steps: (1) a pharmacophore model was built using the Genetic Algorithm Similarity Program (GASP); (2) the Flexible Superposition (FlexS) technique was applied to generate multiple conformations for each of the ligands based on the pharmacophore; (3) the Genetic Algorithm was employed to optimize the selection of the ligand conformations for the CoMFA modeling. The CoMFA models were found to be more detailed in the putative binding site by exploring multiple conformations of each ligand. The comparison of the contour maps shows that, in general, these models are comparable and the differences between them result from the ability of the flexible 3alpha-substituents of the ligands to adopt multiple conformations satisfying the same pharmacophore model. These findings provide guidance for the design and improvement of compounds with DAT activity, which is important for the development of a treatment of cocaine addiction and certain neurological disorders.

Hierarchical clustering analysis of flexible GBR 12909 dialkyl piperazine and piperidine analogs

Pharmacophore modeling of large, drug-like molecules, such as the dopamine reuptake inhibitor GBR 12909, is complicated by their flexibility. A comprehensive hierarchical clustering study of two GBR 12909 analogs was performed to identify representative conformers for input to three-dimensional quantitative structure-activity relationship studies of closely-related analogs. Two data sets of more than 700 conformers each produced by random search conformational analysis of a piperazine and a piperidine GBR 12909 analog were studied. Several clustering studies were carried out based on different feature sets that include the important pharmacophore elements. The distance maps, the plot of the effective number of clusters versus actual number of clusters, and the novel derived clustering statistic, percentage change in the effective number of clusters, were shown to be useful in determining the appropriate clustering level. Six clusters were chosen for each analog, each representing a different region of the torsional angle space that determines the relative orientation of the pharmacophore elements. Conformers of each cluster that are representative of these regions were identified and compared for each analog. This study illustrates the utility of using hierarchical clustering for the classification of conformers of highly flexible molecules in terms of the three-dimensional spatial orientation of key pharmacophore elements.

Singular value decomposition of torsional angles of analogs of the dopamine reuptake inhibitor GBR 12909

Analysis of large, flexible molecules, such as the dopamine reuptake inhibitor GBR 12909 (1), is complicated by the fact that they can take on a wide range of closely related conformations. The first step in the analysis is to classify the conformers into groups. Here, Singular Value Decomposition (SVD) was used to group conformations of GBR 12909 analogs by the similarity of their nonring torsional angles. The significance of the present work, the first application of SVD to the analysis of very flexible molecules, lies in the development of a novel scaling technique for circular data and in the grouping of molecular conformations using a technique that is independent of molecular alignment. Over 700 conformers each of a piperazine (2) and piperidine (3) analog of 1 were studied. Analysis of the score and loading plots showed that the conformers of 2 separate into three large groups due to torsional angles on the naphthalene side of the molecule, whereas those of 3 separate into nine groups due to torsional angles on the bisphenyl side of the molecule. These differences are due to nitrogen inversion at the unprotonated piperazinyl nitrogen of 2, which results in a different ensemble of conformers than those of 3, where no inversion is possible at the corresponding piperidinyl carbon.

Biological evaluation of a technetium-99m-labeled integrated tropane-BAT and its piperidine congener as potential dopamine transporter imaging agents

INTRODUCTION

Recently, we have reported modification of (99m)Tc-TRODAT-1 by integrating the N2S2 metal chelating unit and the tropane skeleton. Results of a preliminary biodistribution study in rats were promising with respect to brain uptake. The present report deals with the further biological characterization of the (99m)Tc-labelled integrated TRODAT derivatives ((99m)Tc-TropaBAT and (99m)Tc-norchloro-TropaBAT) and with the synthesis and biological evaluation of a novel (99m)Tc-labelled piperidine-based derivative ((99m)Tc-PipBAT).

METHODS

Biodistribution of all radiolabelled complexes was studied in normal mice. A more detailed ex vivo intracerebral distribution study of the two (99m)Tc-TropaBAT complexes was additionally performed in normal rats. Autoradiography of brain sections of normal mice (with or without pretreatment with FP-beta-CIT or haloperidol) and rats was performed. Affinity for the dopamine transporter (DAT) was also assessed in vitro in the presence or absence of cocaine.

RESULTS

Both (99m)Tc-TropaBAT complexes show a slightly higher brain uptake than (99m)Tc-TRODAT-1, but the striatum/cerebellum activity ratio is less favourable. Nevertheless, significant striatal uptake was detected after ex vivo autoradiography, but this uptake was also observed after pretreatment with FP-beta-CIT. Unexpectedly, no striatal uptake was detected after in vitro incubation of mouse brain sections with the tracer agents. For (99m)Tc-PipBAT, neither brain uptake nor in vitro striatal uptake was found.

CONCLUSION

Both (99m)Tc-TropaBAT complexes exhibit similar diffusion into brain as (99m)Tc-TRODAT-1, and ex vivo autoradiography shows significant striatal uptake. However, the inferior striatum/cerebellum activity ratio, the striatal uptake in mice pretreated with FP-beta-CIT or haloperidol, and the lack of striatal uptake during in vitro incubation prove that the DAT is not targeted. Brain uptake disappears when the tropane skeleton is replaced by a piperidine ring, and also in this case no striatal uptake is found in vitro.

Further structure-activity relationship studies of piperidine-based monoamine transporter inhibitors: effects of piperidine ring stereochemistry on potency. Identification of norepinephrine transporter selective ligands and broad-spectrum transporter inhibitors

4-(4-Chlorophenyl)piperidine analogues each bearing a thioacetamide side chain appendage similar to that found in the wake-promoting drug modafinil have been synthesized. The transporter inhibitory activity of both the cis and trans isomers of these 3,4-disubstituted piperidines in both their (+)- and (-)-enantiomeric forms was determined. These studies reveal that the (-)-cis analogues exhibit dopamine transporter/norepinephrine transporter (DAT/NET) selectivity as was previously reported for the (+)-trans analogues. On the other hand, the (-)-trans and the (+)-cis isomers show serotonin transporter (SERT) or SERT/NET selectivity. Among them, (+)-cis-5b shows a low nanomolar Ki for the NET with 39-fold and 321-fold lower potency at the DAT and SERT, respectively, thus making it a useful pharmacological research tool for exploring NET-associated behavioral signatures. On the other hand, several of the compounds described herein, such as (+)-trans-5c, show comparable activity at all three transporters. Because broad-spectrum transporter inhibitors have been hypothesized to exhibit a more rapid onset of action and/or a greater efficacy as antidepressant agents than those selective for SERT or SERT + NET, some of the present compounds will be valuable to study in animal models of depression.

Computational approaches to modeling drug transporters

Computational modeling has advanced our understanding of drug absorption, tissue distribution, excretion and toxicity profiles by providing both direct and indirect knowledge of drug-transporter interactions that would otherwise be unavailable using experimental methods. Currently, two complementary approaches are available in modeling transporters: substrate-based and transporter-based methods. The transporter-based approach directly predicts the transporter's three-dimensional structure to assist in understanding the drug transport process, whereas substrate-based models infer such information by studying a group of substrates or inhibitors with measured activities. In this review, the available strategies in both transporter-based and substrate-based approaches are explained and illustrated with applications and case studies. With increasing computational power and continuously improving modeling algorithms, computational techniques can assist in further understanding transporter-substrate interactions as well as, the optimization of transporter-directed drug design.

Conformational analysis of methylphenidate: comparison of molecular orbital and molecular mechanics methods

Methylphenidate (MP) binds to the cocaine binding site on the dopamine transporter and inhibits reuptake of dopamine, but does not appear to have the same abuse potential as cocaine. This study, part of a comprehensive effort to identify a drug treatment for cocaine abuse, investigates the effect of choice of calculation technique and of solvent model on the conformational potential energy surface (PES) of MP and a rigid methylphenidate (RMP) analogue which exhibits the same dopamine transporter binding affinity as MP. Conformational analysis was carried out by the AM1 and AM1/SM5.4 semiempirical molecular orbital methods, a molecular mechanics method (Tripos force field with the dielectric set equal to that of vacuum or water) and the HF/6-31G* molecular orbital method in vacuum phase. Although all three methods differ somewhat in the local details of the PES, the general trends are the same for neutral and protonated MP. In vacuum phase, protonation has a distinctive effect in decreasing the regions of space available to the local conformational minima. Solvent has little effect on the PES of the neutral molecule and tends to stabilize the protonated species. The random search (RS) conformational analysis technique using the Tripos force field was found to be capable of locating the minima found by the molecular orbital methods using systematic grid search. This suggests that the RS/Tripos force field/vacuum phase protocol is a reasonable choice for locating the local minima of MP. However, the Tripos force field gave significantly larger phenyl ring rotational barriers than the molecular orbital methods for MP and RMP. For both the neutral and protonated cases, all three methods found the phenyl ring rotational barriers for the RMP conformers/invertamers (denoted as cte, tte, and cta) to be: cte, tte > MP > cta. Solvation has negligible effect on the phenyl ring rotational barrier of RMP. The B3LYP/6-31G* density functional method was used to calculate the phenyl ring rotational barrier for neutral MP and gave results very similar to those of the HF/6-31G* method.