(±)-2-(N-tert-Butylamino)-3'-[(125)I]-iodo-4'-azidopropiophenone: a dopamine transporter and nicotinic acetylcholine receptor photoaffinity ligand based on bupropion (Wellbutrin, Zyban).

Towards addressing the knowledge gap of how bupropion interacts with the dopamine transporter (DAT) and nicotinic acetylcholine receptors (nAChRs), a ligand was synthesized in which the chlorine of bupropion was isosterically replaced with an iodine and a photoreactive azide was added to the 4'-position of the aromatic ring. Analog (±)-3 (SADU-3-72) demonstrated modest DAT and α4ß2 nAChR affinity. A radioiodinated version was shown to bind covalently to hDAT expressed in cultured cells and affinity-purified, lipid-reincorporated human α4ß2 neuronal nAChRs. Co-incubation of (±)-[(125)I]-3 with non-radioactive (±)-bupropion or (-)-cocaine blocked labeling of these proteins. Compound (±)-[(125)I]-3 represents the first successful example of a DAT and nAChR photoaffinity ligand based on the bupropion scaffold. Such ligands are expected to assist in mapping bupropion-binding pockets within plasma membrane monoamine transporters and ligand-gated nAChR ion channels.

Discovery of novel-scaffold monoamine transporter ligands via in silico screening with the S1 pocket of the serotonin transporter.

Discovery of new inhibitors of the plasmalemmal monoamine transporters (MATs) continues to provide pharmacotherapeutic options for depression, addiction, attention deficit disorders, psychosis, narcolepsy, and Parkinson's disease. The windfall of high-resolution MAT structural information afforded by X-ray crystallography has enabled the construction of credible computational models. Elucidation of lead compounds, creation of compound structure-activity series, and pharmacologic testing are staggering expenses that could be reduced by using a MAT computational model for virtual screening (VS) of structural libraries containing millions of compounds. Here, VS of the PubChem small molecule structural database using the S1 (primary substrate) ligand pocket of a serotonin transporter homology model yielded 19 prominent "hit" compounds. In vitro pharmacology of these VS hits revealed four structurally unique MAT substrate uptake inhibitors with high nanomolar affinity at one or more of the three MATs. In vivo characterization of three of these hits revealed significant activity in a mouse model of acute depression at doses that did not elicit untoward locomotor effects. This constitutes the first report of MAT inhibitor discovery using exclusively the primary substrate pocket as a VS tool. Novel-scaffold MAT inhibitors offer hope of new medications that lack the many classic adverse effects of existing antidepressant drugs.

Monoamine transporter structure, function, dynamics, and drug discovery: a computational perspective.

With the breakthrough crystallization of the bacterial leucine transporter protein LeuT, the first available X-ray structure for the neurotransmitter/sodium symporter family, development of 3-D computational models is suddenly essential for structure-function studies on the plasmalemmal monoamine transporters (MATs). LeuT-based MAT models have been used to guide elucidation of substrate and inhibitor binding pockets, and molecular dynamics simulations using these models are providing insight into conformations involved in the substrate translocation cycle. With credible MAT models finally in hand, structure-based virtual screening for novel ligands is yielding lead compounds toward the development of new medications for psychostimulant dependence, attention deficit hyperactivity, depression, anxiety, schizophrenia, and other disorders associated with dopamine, norepinephrine, or serotonin dysregulation.

Evolution of a Compact Photoprobe for the Dopamine Transporter Based on (±)-threo-Methylphenidate.

The development of photoaffinity ligands for determining covalent points of attachment to the dopamine transporter (DAT) has predominantly focused on tropane-based compounds bearing variable-length linkers between the photoreactive group and inhibitor pharmacophore. In order to expand the array of photoprobes useful for mapping inhibitor-binding pockets within the DAT, a compact non-tropane ligand was synthesized featuring a photoreactive azide and iodine tag directly attached to the aromatic ring of (±)-threo-methylphenidate. (±)-threo-4-Azido-3-iodomethylphenidate ((±)-6); K(i) = 4.0 ± 0.8 nM) displayed high affinity for hDAT. Moreover, a radioiodinated analog of (±)-6 demonstrated covalent ligation to the DAT in cultured cells and rat striatal membranes, thus suggesting the potential utility of this photoprobe in DAT structure-function studies.

Identification of a novel selective serotonin reuptake inhibitor by coupling monoamine transporter-based virtual screening and rational molecular hybridization.

Ligand virtual screening (VS) using the vestibular binding pocket of a 3-D monoamine transporter (MAT) computational model followed by in vitro pharmacology led to the identification of a human serotonin transporter (hSERT) inhibitor with modest affinity (hSERT K(i) = 284 nM). Structural comparison of this VS-elucidated compound, denoted MI-17, to known SERT ligands led to the rational design and synthesis of DJLDU-3-79, a molecular hybrid of MI-17 and dual SERT/5-HT(1A) receptor antagonist SSA-426. Relative to MI-17, DJLDU-3-79 displayed 7-fold improvement in hSERT binding affinity and a 3-fold increase in [(3)H]-serotonin uptake inhibition potency at hSERT/HEK cells. This hybrid compound displayed a hSERT:hDAT selectivity ratio of 50:1, and a hSERT:hNET (human norepinephrine transporter) ratio of >200:1. In mice, DJLDU-3-79 decreased immobility in the tail suspension test comparable to the SSRI fluvoxamine, suggesting that DJLDU-3-79 may possess antidepressant properties. This proof of concept study highlights MAT virtual screening as a powerful tool for identifying novel inhibitor chemotypes and chemical fragments for rational inhibitor design.

Chemical function-based pharmacophore development for novel, selective kappa opioid receptor agonists.

In an effort to reduce or eliminate the centrally associated side effects produced by opioid analgesics there has been an interest in the preparation of peripherally acting opioid receptor agonists. These compounds would have very limited or no access to the central nervous system. As a first step towards developing peripheral kappa opioid receptor (KOP) agonists, we have developed a quantitatively predictive chemical function-based pharmacophore model of selective kappa opioid receptor agonists by using the HypoGen algorithm implemented in the Catalyst software. The input for HypoGen was a training set of 26 KOP agonists exhibiting K(i) values ranging between 0.015nM and 2300nM. The best output hypothesis consists of four features: one hydrophobic (HYD), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one positive ionizable (PI) function. The predictive power of the model could be demonstrated by internal and external validation of the generated hypothesis. The resulting Catalyst pharmacophore can be used concurrently for rapid virtual screening of chemical databases to identify novel, selective KOP agonists that may be easily restricted to target tissues by synthetic modification. It is anticipated that such an approach will lead to the generation of novel selective KOP agonists that are clinically useful for the treatment of pain through peripheral mechanisms.