New design strategies for antidepressant drugs

By Kalyan Immadisetty, Laura M. Geffert, C. K. Surratt, Jeffry D. Madura. 2013

23 citations recorded by the source corpus.

Introduction: In spite of research efforts spanning six decades, the most prominent antidepressant drugs to date still carry several adverse effects, often serious enough to warrant discontinuation of the drug. Molecular mechanisms of depression are now better understood such that some of the specific receptors responsible can be targeted for activation or inhibition. This advance, coupled with the recent availability of crystal structures of relevant drug targets or their homologs, has opened the door for new antidepressant therapeutic compounds. Areas covered: The authors review the evolution of monoamine-based antidepressant drugs, up to the selective serotonin reuptake inhibitors (SSRIs). The authors discuss classic and contemporary antidepressant drug design strategies, with a focus on virtual screening and fragment-based drug design methods. Furthermore, they discuss the recent advancements in the understanding of the serotonin transporter (SERT) structure/function relationship in the context of recognition of SSRIs and outline a strategy for the use of computational approaches in producing new SSRI lead compounds. Expert opinion: The authors suggest that given the long-awaited availability of credible three-dimensional structures for the SERT and related monoamine transporter proteins, cutting-edge computational methods should be the linchpin of future drug discovery efforts regarding monoamine-based antidepressant lead compounds. Because these transporter inhibitors cause a ubiquitous increase in extraneuronal neurotransmitter levels leading to side and adverse therapeutic effects, the drug discovery should extend to appropriate manipulation of the ‘downstream' receptors affected by the neurotransmitter boost. Efficient use of new computational strategies will accelerate the drug discovery process and reduce its economic burden.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology, Computational Chemistry & Drug Discovery, Medical & Health AI

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In Molecular Biology

1. C. K. Surratt
2. Jeffry D. Madura
3. Michael J. Wasko
4. Kendy A. Pellegrene

In Computational Chemistry & Drug Discovery

1. C. K. Surratt
2. Jeffry D. Madura
3. Michael J. Wasko
4. Kendy A. Pellegrene

Citations in the corpus

• A Role for Fragment-Based Drug Design in Developing Novel Lead Compounds for Central Nervous System Targets (2015, background): “When a 3D receptor structure is available, structure-based drug design (SBDD) is an attractive alternative (5).”