Triple reuptake inhibitors (TRIs): do they promise us a rose garden?

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

Therapeutic Effect of Novel Antidepressant Drugs Acting at Specific Receptors of Neurotransmitters and Neuropeptides

BACKGROUND

Major depression is a frequent psychiatric disease. One- third of the depressive patients remain treatment-resistant; thus, it is urgent to find novel antidepressant drugs.

OBJECTIVE

In major depression, in several brain areas the neural networks involved and the alterations of neurotransmitters and neuropeptides are updated. According to these networks, new pharmacological agents and effective combinations of antidepressant drugs achieving a more efficacious antidepressant treatment are suggested.

RESULTS

In the neural networks, the prefrontal cortex has been included. In this brain area, glutamatergic neurons, which receive an activating potential from D2 dopaminergic neurons, presynaptically inhibit M1 muscarinic cholinergic neurons via NMDA receptors. Medium spiny GABAergic/somatostatin neurons, which receive projections from M1 muscarinic cholinergic neurons, presynaptically inhibit D2 dopaminergic neurons via GABAA/somatostatin1 receptors. The combination of an NMDA receptor antagonist with an M1 muscarinic cholinergic receptor antagonist can achive a rapid, long-lasting antidepressant effect.

CONCLUSION

In preclinical studies, the antidepressant effect of orvepitant, an NK1 receptor antagonist, has been demonstrated: this antagonist reaches a complete blockade of NK1 receptors. In clinical studies, the combination of an NMDA receptor antagonist with an M1 muscarinic cholinergic receptor antagonist should be investigated indepth as well as the therapeutic effect of orvepitant. In clinical studies, the antidepressant effect of a triple reuptake inhibitor should be examined and compared to current antidepressant drugs.

The Druggability of Solute Carriers

The transport of materials across membranes is a vital process for all aspects of cellular function, including growth, metabolism, and communication. Protein transporters are the molecular gates that control this movement and serve as key points of regulation for these processes, thus representing an attractive class of therapeutic targets. With more than 400 members, the solute carrier (SLC) membrane transport proteins are the largest family of transporters, yet, they are pharmacologically underexploited relative to other protein families and many of the available chemical tools possess suboptimal selectivity and efficacy. Fortuitously, there is increased interest in elucidating the physiological roles of SLCs as well as growing recognition of their therapeutic potential. This Perspective provides an overview of the SLC superfamily, including their biochemical and functional features, as well as their roles in various human diseases. In particular, we explore efforts and associated challenges toward drugging SLCs, as well as highlight opportunities for future drug discovery.