Singular value decomposition of analogs of GBR 12909

Analogs of GBR 12909 are drugs that could potentially be used to treat cocaine addiction. Singular Value Decomposition (SVD) is a multivariate analysis technique used to show relationships between the data and the variables associated with the data. The input data consists of the conformers of each analog (DM324, 728 conformers; TP250, 739 conformers) along with the eight torsional angles (Al, A2, B1-B6). A novel scaling technique was developed to address the problem of data circularity by subtracting the values of the torsional angles of the global energy minimum conformation from those of each conformer. In SVD the original data matrix X of dimensions r x c is decomposed into three matrices, U, 5, and V where X=USVT. The columns of U represent the principal component (PC) scores. The rows of SVT contain the PC loadings. Analysis of the score and loading plots shows that DM324 separates into three distinct groups along PC1 due to Al and six groups due to A2. TP250 separates into three groups along PC7 (due to B4) and three groups along PC8 (due to B3) resulting in nine clusters. The significance of this work is that it is the first application of SVD to the clustering of very flexible molecules. In the future, representative conformations of these analogs will be used in pharmacophore modeling with the ultimate goal of designing a drug useful in the treatment of cocaine abuse

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

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