Focus on Human Monoamine Transporter Selectivity. New Human DAT and NET Models, Experimental Validation, and SERT...

Area of Science:

• Computational chemistry
• Neuroscience
• Pharmacology

Background:

• Serotonin transporter (SERT) inhibitors are common antidepressants.
• Drug selectivity is influenced by transporter protein structure.
• Three human monoamine transporters (DAT, NET, SERT) are targets for drug development.

Purpose of the Study:

• To build computational models for human DAT, NET, and SERT.
• To predict inhibitor binding poses and develop 3D-QSAR models.
• To identify novel potent SERT inhibitors and understand selectivity determinants.

Main Methods:

• Homology modeling for hDAT and hNET.
• 3D-QSAR model development for DAT, NET, and SERT.
• Inhibitor binding pose prediction and validation with in-house compounds.

Main Results:

• Validated 3D structures for hDAT and hNET, compared to hSERT.
• Identified structural differences in binding sites correlated with inhibitor selectivity.
• Discovered potent SERT inhibitors (compounds 20a-d and 21a-d), with compound 20b showing superior affinity to paroxetine in human platelets.

Conclusions:

• Computational modeling provides insights into monoamine transporter structure-activity relationships.
• Novel potent SERT inhibitors were identified with potential therapeutic applications.
• Understanding transporter selectivity is crucial for developing effective antidepressant drugs.