Psychoplastogenic DYRK1A Inhibitors with Therapeutic Effects Relevant to Alzheimer's Disease.

Tauopathy, neuronal atrophy, and psychological impairments are hallmarks of neurodegenerative diseases, such as Alzheimer's disease, that currently lack efficacious clinical treatments capable of rectifying these issues. To address these unmet needs, we used rational drug design to combine the pharmacophores of DYRK1A inhibitors and isoDMTs to develop psychoplastogenic DYRK1A inhibitors. Using this approach, we discovered a nonhallucinogenic compound capable of promoting cortical neuron growth and suppressing tau hyperphosphorylation while also having the potential to mitigate the biological and psychological symptoms of dementia. Together, our results suggest that hybridization of the DYRK1A and psychoplastogen pharmacophores represents a promising strategy for identifying compounds that might address the cognitive as well as the behavioral and psychological symptoms of dementia.

Identification of Psychoplastogenic Tropanes Lacking Muscarinic Activity.

Tropane-containing small molecules like scopolamine are a promising class of psychoplastogens. However, their potent antagonism of all muscarinic receptor subtypes presents the potential for undesirable anticholinergic side effects. In an effort to decouple their neuroplasticity-promoting effects from their muscarinic activity, we performed phenotypic structure-activity relationship studies across a variety of structurally distinct subclasses of tropanes. We discovered several novel tropanes capable of significantly increasing cortical neuronal growth while exhibiting drastically reduced activity at all muscarinic receptor subtypes compared to scopolamine.

Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis.

Artificial intelligence and machine learning have demonstrated their potential role in predictive chemistry and synthetic planning of small molecules; there are at least a few reports of companies employing in silico synthetic planning into their overall approach to accessing target molecules. A data-driven synthesis planning program is one component being developed and evaluated by the Machine Learning for Pharmaceutical Discovery and Synthesis (MLPDS) consortium, comprising MIT and 13 chemical and pharmaceutical company members. Together, we wrote this perspective to share how we think predictive models can be integrated into medicinal chemistry synthesis workflows, how they are currently used within MLPDS member companies, and the outlook for this field.

Structural, kinetic, and pharmacodynamic mechanisms of D-amino acid oxidase inhibition by small molecules.

We characterized the mechanism and pharmacodynamics of five structurally distinct inhibitors of d-amino acid oxidase. All inhibitors bound the oxidized form of human enzyme with affinity slightly higher than that of benzoate (Kd ≈ 2-4 µM). Stopped-flow experiments showed that pyrrole-based inhibitors possessed high affinity (Kd ≈ 100-200 nM) and slow release kinetics (k < 0.01 s(-1)) in the presence of substrate, while inhibitors with pendent aromatic groups altered conformations of the active site lid, as evidenced by X-ray crystallography, and showed slower kinetics of association. Rigid bioisosteres of benzoic acid induced a closed-lid conformation, had slower release in the presence of substrate, and were more potent than benzoate. Steady-state d-serine concentrations were described in a PK/PD model, and competition for d-serine sites on NMDA receptors was demonstrated in vivo. DAAO inhibition increased the spatiotemporal influence of glial-derived d-serine, suggesting localized effects on neuronal circuits where DAAO can exert a neuromodulatory role.