Document Type


Date of Award

Spring 5-31-2005

Degree Name

Doctor of Philosophy in Chemistry - (Ph.D.)


Chemistry and Environmental Science

First Advisor

Carol A. Venanzi

Second Advisor

Joseph W. Bozzelli

Third Advisor

Tamara M. Gund

Fourth Advisor

Sanjay V. Malhotra

Fifth Advisor

Christopher C. Van Dyke


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.

Included in

Chemistry Commons



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