Modeling of flexible drug-like molecules : qsar of GBR 12909 analog dat/sert selectivity

The dopamine reuptake inhibitor GBR 12909 and related dialkyl piperazine and piperidine analogs have been studied as agonist substitution therapies acting on the dopamine transporter (DAT) to treat cocaine addiction. Undesirable binding to the serotonin transporter (SERT) can vary greatly depending on the specific substituents on the molecule. This study uses Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices (CoMSIA) techniques to determine a stable and predictive model for DAT/SERT selectivity for a set of flexible GBR 12909 analogs. Families of analogs were constructed from six pairs of naphthyl-substituted piperazine and piperidine templates identified by hierarchical clustering as representative conformers. Three-dimensional quantitative structure-activity relationship (3D-QSAR) studies led to focused models that were stable to y-value scrambling. Test set correlation validation led to one acceptable model (q2=0.508, two components, r2=0.685, average residual = 0.00 for the training set, 0.22 for the extended test set). DAT/SERT selectivities higher than that of the most active compound in the QSAR series were predicted for nine novel compounds. This is the first CoMFA/CoMSIA study of the highly flexible GBR 12909 class of dopamine reuptake inhibitors. Previously, molecular modeling was based on more rigid dopamine reuptake inhibitors, and often only on global energy minimum (GEM) structures. Flexible molecules like GBR 12909 have multiple possible binding conformations, distributed across the potential energy surface in key torsional angle space, which can vary from the GEM by as much as 20 kcal/mol or more. The significance of this study lies in the combining of a clustering technique for identifying representative conformers from a set of low-energy (less than 20 kcal/mol from the GEM) conformers with an extensive 3D-QSAR analysis based on each representative conformer and analogs in a similar potential bioactive conformation

Ligand-based design of dopamine reuptake inhibitors : fuzzy relational clustering and 2-D and 3-D QSAR modleing

As the three-dimensional structure of the dopamine transporter (DAT) remains undiscovered, any attempt to model the binding of drug-like ligands to this protein must necessarily include strategies that use ligand information. For flexible ligands that bind to the DAT, the identification of the binding conformation becomes an important but challenging task. In the first part of this work, the selection of a few representative structures as putative binding conformations from a large collection of conformations of a flexible GBR 12909 analogue was demonstrated by cluster analysis. Novel structurebased features that can be easily generalized to other molecules were developed and used for clustering. Since the feature space may or may not be Euclidean, a recently-developed fuzzy relational clustering algorithm capable of handling such data was used. Both superposition-dependent and superposition-independent features were used along with region-specific clustering that focused on separate pharmacophore elements in the molecule. Separate sets of representative structures were identified for the superpositiondependent and superposition-independent analyses. In the second part of this work, several QSAR models were developed for a series of analogues of methylphenidate (MP), another potent dopamine reuptake inhibitor. In a novel method, the Electrotopological-state (B-state) indices for atoms of the scaffold common to all 80 compounds were used to develop an effective test set spanning both the structure space as well as the activity space. The utility of B-state indices in modeling a series of analogues with a common scaffold was demonstrated. Several models were developed using various combinations of 2-D and 3-D descriptors in the Molconn-Z and MOE descriptor sets. The models derived from CoMFA descriptors were found to be the most predictive and explanatory. Progressive scrambling of all models indicated several stable models. The best models were used to predict the activity of the test set analogues and were found to produce reasonable residuals. Substitutions in the phenyl ring of MP, especially at the 3- and 4-positions, were found to be the most important for DATbinding. It was predicted that for better DAT-binding the substituents at these positions should be relatively bulky, electron-rich atoms or groups

Ligand-based drug design : I. conformational studies of GBR 12909 analogs as cocaine antagonists; II. 3d-QSAR studies of salvinorin a analogs as kappa opioid agonists

Ligand-based drug design (LBDD) techniques are applied when the structure of the receptor is unknown but when a series of compounds or ligands have been identified that show the biological activity of the interest. Generally, availability of a series of compounds with high activity, with no activity, and also with a range of intermediate activities for the desired biological target is required. It is common that structures of membrane-bound proteins (for example, monoamine transporter proteins and opioid receptor proteins) are unknown as these proteins are notoriously difficult to crystallize. In Part I of this study, analogs of the flexible dopamine reuptake inhibitor, GBR 12909, may have potential usefulness in the treatment of cocaine abuse. As a first step in the 3D-QSAR modeling of the dopamine transporter (DAT)/serotonin transporter (SERT) selectivity of these compounds, conformational analysis of a piperazine and related piperidine analog of GBR12909 is performed. These analogs have eight rotatable bonds and are somewhat easier to deal with computationally than the parent compound. Ensembles of conformers consisting of local minima on the potential energy surface of the molecule were generated in the vacuum phase and implicit solvent (also known as continuum solvent) by random search conformational analysis using the molecular mechanics methods and the Tripos and MMFF94 force fields. These conformer populations were classified by relative energy, molecular shape, and their behavior in 2D torsional angle space in order to evaluate their sensitivity to the choice of charges and force field. Some differences were noted in the conformer populations due to differences in the treatment of the tertiary amine nitrogen and ether oxygen atom types by the force fields. In Part II of this study, 3D-QSAR studies of salvinorin A analogs as kappa opioid (K) receptor agonists were performed. Salvinorin A is a naturally-occurring diterpene from the plant Salvia divinorum which activates the kappa opioid receptor (KOR) selectively and potently. It is the only known natural non-nitrogenous agent active at the human KOR. Salvinorin A may represent a novel lead compound with possible potential in the treatment of addiction and pain. The primary aim of the current study was to develop Comparative Molecular Field Analysis (CoMFA) models to clarify the correlation between the molecular features of the 2-position analogs of salvinorin A and their KOR binding affinity. The final, stable CoMFA model has predictivity given by q2 of 0.62 and fit given by r2 of 0.86. The steric and electrostatic contributions were 47% and 53%, respectively. The CoMFA contour map indicated that the presence of a negative environment and steric region near the 2-position would lead to improved binding affinity at the KOR. Novel salvinorin A analogs with improved binding affinity were predicted based on the stable and predictive CoMFA model. Novel analogs were synthesized by Dr. Thomas Prisinzano of the University of Iowa and preliminary biological results are available from the Rothman laboratory at the National Institute on Drug Abuse. These novel analogs appear to be KOR selective

Scaffold Perception, ComPharmacophore Model Development, And Quantitative Structure-Affinity Relationships Of Sigma Site Ligands

Sigma receptors are endogenous proteins with potential utility in treating psychological disorders, ischemia, the psychological and convulsive effects of drugs of abuse, and as an imaging agent for cancerous tissues, among others. Drug design efforts targeting these receptors have been hindered by a lack of structural information of the receptors themselves. Traditional ligand-based approaches have succeeded in generating many compounds with high affinity, and quite a few with selectivity for σ-1 receptors. There are few selective ligands for use as pharmacological probes for the σ-2 receptor. Much effort has gone into exploring the structure activity relationships of ligands targeting these receptors. A critical review of the existing literature covering pharmacophore development for σ receptors was undertaken with the intent to develop computational models to assist in ligand-based drug design efforts. Inspired by the lack of pharmacophore models with general utility, and confronted by the obstacles of data heterogeneity, a database of σ ligands and their binding affinity data was collected. Cohorts of data collected under similar experimental methodologies were assembled and clustered by measures of scaffold dissimilarity. Multiple-Instance Learning techniques were used to train classification models that differentiated molecules as active or inactive, and to assist in the identification of relevant conformations of σ ligands at their macromolecular targets. Conformations of high-affinity ligands were then used to develop general pharmacophore models as part of a virtual screening approach. Structure-activity relationship models based on virtual screening alignments of known sigma ligands were developed in the search for selective σ-1 and σ-2 receptor probes