Synthesis and biological evaluation of 3-benzisoxazolyl-4-indolylmaleimides as potent, selective inhibitors of glycogen synthase kinase-3β

Modeling structure-activity relationships with machine learning to identify GSK3-targeted small molecules as potential COVID-19 therapeutics

Coronaviruses induce severe upper respiratory tract infections, which can spread to the lungs. The nucleocapsid protein (N protein) plays an important role in genome replication, transcription, and virion assembly in SARS-CoV-2, the virus causing COVID-19, and in other coronaviruses. Glycogen synthase kinase 3 (GSK3) activation phosphorylates the viral N protein. To combat COVID-19 and future coronavirus outbreaks, interference with the dependence of N protein on GSK3 may be a viable strategy. Toward this end, this study aimed to construct robust machine learning models to identify GSK3 inhibitors from Food and Drug Administration-approved and investigational drug libraries using the quantitative structure-activity relationship approach. A non-redundant dataset consisting of 495 and 3070 compounds for GSK3α and GSK3β, respectively, was acquired from the ChEMBL database. Twelve sets of molecular descriptors were used to define these inhibitors, and machine learning algorithms were selected using the LazyPredict package. Histogram-based gradient boosting and light gradient boosting machine algorithms were used to develop predictive models that were evaluated based on the root mean square error and R-squared value. Finally, the top two drugs (selinexor and ruboxistaurin) were selected for molecular dynamics simulation based on the highest predicted activity (negative log of the half-maximal inhibitory concentration, pIC₅₀ value) to further investigate the structural stability of the protein-ligand complexes. This artificial intelligence-based virtual high-throughput screening approach is an effective strategy for accelerating drug discovery and finding novel pharmacological targets while reducing the cost and time.

Machine Learning for Discovery of New ADORA Modulators

Adenosine (ADO) is an extracellular signaling molecule generated locally under conditions that produce ischemia, hypoxia, or inflammation. It is involved in modulating a range of physiological functions throughout the brain and periphery through the membrane-bound G protein-coupled receptors, called adenosine receptors (ARs) A₁AR, A_2AAR, A_2BAR, and A₃AR. These are therefore important targets for neurological, cardiovascular, inflammatory, and autoimmune diseases and are the subject of drug development directed toward the cyclic adenosine monophosphate and other signaling pathways. Initially using public data for A₁AR agonists we generated and validated a Bayesian machine learning model (Receiver Operator Characteristic of 0.87) that we used to identify molecules for testing. Three selected molecules, crisaborole, febuxostat and paroxetine, showed initial activity in vitro using the HEK293 A₁AR Nomad cell line. However, radioligand binding, β-arrestin assay and calcium influx assay did not confirm this A₁AR activity. Nevertheless, several other AR activities were identified. Febuxostat and paroxetine both inhibited orthosteric radioligand binding in the µM range for A_2AAR and A₃AR. In HEK293 cells expressing the human A_2AAR, stimulation of cAMP was observed for crisaborole (EC₅₀ 2.8 µM) and paroxetine (EC₅₀ 14 µM), but not for febuxostat. Crisaborole also increased cAMP accumulation in A_2BAR-expressing HEK293 cells, but it was weaker than at the A_2AAR. At the human A₃AR, paroxetine did not show any agonist activity at 100 µM, although it displayed binding with a K_i value of 14.5 µM, suggesting antagonist activity. We have now identified novel modulators of A_2AAR, A_2BAR and A₃AR subtypes that are clinically used for other therapeutic indications, and which are structurally distinct from previously reported tool compounds or drugs.

Promising heterocycle-based scaffolds in recent (2019-2021) anti-Alzheimer's drug design and discovery

Alzheimer's disease (AD) is one of the neurodegenerative diseases that led to morbidity and mortality world-wide. It is a complex disease whose etiology is not completely known that leads to difficulty in prevent or cure of the AD. Also, there are only few approved drugs for AD treatment. Apart from deaths due to AD, expenditure of treatment and care of AD patients is higher than that of treatment of HIV and cancer diseases combined. Hence, it leads to an economic burden also. Although research is being carried out on designing drugs for AD, most of them have ended up in poor inhibitors with high toxicity. Hence, researchers should shoulder a great responsibility of discovery of efficient drugs for AD treatment. In the field of drug discovery, heterocycles played an important role. Also, most of the heterocyclic scaffolds have been used in design of potent anti-AD agents. In view of this, heterocyclic molecules reported recently are compiled and evaluated comprehensively. Especially, the molecules which exhibited pronounced activity are emphasized and described with respect to structure-activity relationship (SAR) in brief.

Machine Learning for Discovery of GSK3β Inhibitors

Alzheimer's disease (AD) is the most common cause of dementia, affecting approximately 35 million people worldwide. The current treatment options for people with AD consist of drugs designed to slow the rate of decline in memory and cognition, but these treatments are not curative, and patients eventually suffer complete cognitive injury. With the substantial amounts of published data on targets for this disease, we proposed that machine learning software could be used to find novel small-molecule treatments that can supplement the AD drugs currently on the market. In order to do this, we used publicly available data in ChEMBL to build and validate Bayesian machine learning models for AD target proteins. The first AD target that we have addressed with this method is the serine-threonine kinase glycogen synthase kinase 3 beta (GSK3β), which is a proline-directed serine-threonine kinase that phosphorylates the microtubule-stabilizing protein tau. This phosphorylation prompts tau to dissociate from the microtubule and form insoluble oligomers called paired helical filaments, which are one of the components of the neurofibrillary tangles found in AD brains. Using our Bayesian machine learning model for GSK3β consisting of 2368 molecules, this model produced a five-fold cross validation ROC of 0.905. This model was also used for virtual screening of large libraries of FDA-approved drugs and clinical candidates. Subsequent testing of selected compounds revealed a selective small-molecule inhibitor, ruboxistaurin, with activity against GSK3β (avg IC50 = 97.3 nM) and GSK3α (IC50 = 695.9 nM). Several other structurally diverse inhibitors were also identified. We are now applying this machine learning approach to additional AD targets to identify approved drugs or clinical trial candidates that can be repurposed as AD therapeutics. This represents a viable approach to accelerate drug discovery and do so at a fraction of the cost of traditional high throughput screening.

Drug repurposing studies of PARP inhibitors as a new therapy for inherited retinal degeneration

The enzyme poly-ADP-ribose-polymerase (PARP) has important roles for many forms of DNA repair and it also participates in transcription, chromatin remodeling and cell death signaling. Currently, some PARP inhibitors are approved for cancer therapy, by means of canceling DNA repair processes and cell division. Drug repurposing is a new and attractive aspect of therapy development that could offer low-cost and accelerated establishment of new treatment options. Excessive PARP activity is also involved in neurodegenerative diseases including the currently untreatable and blinding retinitis pigmentosa group of inherited retinal photoreceptor degenerations. Hence, repurposing of known PARP inhibitors for patients with non-oncological diseases might provide a facilitated route for a novel retinitis pigmentosa therapy. Here, we demonstrate and compare the efficacy of two different PARP inhibitors, BMN-673 and 3-aminobenzamide, by using a well-established retinitis pigmentosa model, the rd1 mouse. Moreover, the mechanistic aspects of the PARP inhibitor-induced protection were also investigated in the present study. Our results showed that rd1 rod photoreceptor cell death was decreased by about 25-40% together with the application of these two PARP inhibitors. The wealth of human clinical data available for BMN-673 highlights a strong potential for a rapid clinical translation into novel retinitis pigmentosa treatments. Remarkably, we have found that the efficacy of 3 aminobenzamide was able to decrease PARylation at the nanomolar level. Our data also provide a link between PARP activity with the Wnt/β-catenin pathway and the major intracellular antioxidant concentrations behind the PARP-dependent retinal degeneration. In addition, molecular modeling studies were integrated with experimental studies for better understanding of the role of PARP1 inhibitors in retinal degeneration.

3-(7-Azaindolyl)-4-indolylmaleimides as a novel class of mutant isocitrate dehydrogenase-1 inhibitors: Design, synthesis, and biological evaluation

A series of 3-(7-azainodyl)-4-indolylmaleimides was designed, synthesized, and evaluated for their isocitrate dehydrogenase 1 (IDH1)/R132H inhibitory activities. Many compounds such as 11a, 11c, 11e, 11g, and 11s exhibited favorable inhibitory effects on IDH1/R132H and were highly selective against the wild-type IDH1. Evaluation of the biological activities at the cellular level showed that compounds 11a, 11c, 11e, 11g, and 11s could effectively suppress the production of 2-hydroxyglutaric acid in U87MG cells expressing IDH1/R132H. Preliminary structure-activity relationship (SAR) and molecular modeling studies were discussed based on the experimental data obtained. These findings may provide new insights into the development of novel IDH1/R132H inhibitors.

Benzisoxazole: a privileged scaffold for medicinal chemistry

The benzisoxazole analogs represent one of the privileged structures in medicinal chemistry and there has been an increasing number of studies on benzisoxazole-containing compounds. The unique benzisoxazole scaffold also exhibits an impressive potential as antimicrobial, anticancer, anti-inflammatory, anti-glycation agents and so on. This review examines the state of the art in medicinal chemistry as it relates to the comprehensive and general summary of the different benzisoxazole analogs, their use as starting building blocks of multifarious architectures on scales sufficient to drive human drug trials. The number of reports describing benzisoxazole-containing highly active compounds leads to the expectation that this scaffold will further emerge as a potential candidate in the field of drug discovery.

Natural and synthetic bioactive inhibitors of glycogen synthase kinase

Glycogen synthase kinase-3 is a multi-functional serine-threonine kinase and is involved in diverse physiological processes, including metabolism, cell cycle, and gene expression by regulating a wide variety of known substrates like glycogen synthase, tau-protein and β-catenin. Aberrant GSK-3 has been involved in diabetes, inflammation, cancer, Alzheimer's and bipolar disorder. In this review, we present an overview of the involvement of GSK-3 in various signalling pathways, resulting in a number of adverse pathologies due to its dysregulation. In addition, a detailed description of the small molecule inhibitors of GSK-3 with different mode of action discovered or specifically developed for GSK-3 has been presented. Furthermore, some clues for the future optimization of these promising molecules to develop specific drugs inhibiting GSK-3, for the treatment of associated disease conditions have also been discussed.

Synthesis and biological evaluation of 3-([1,2,4]triazolo[4,3-a]pyridin-3-yl)-4-(indol-3-yl)-maleimides as potent, selective GSK-3β inhibitors and neuroprotective agents

A series of novel 3-([1,2,4]triazolo[4,3-a]pyridin-3-yl)-4-(indol-3-yl)-maleimides were designed, prepared and evaluated for their GSK-3β inhibitory activities. Most compounds showed high potency to GSK-3β inhibition with high selectivity. Among them, compounds 7c, 7f, 7h, 7l and 7m significantly reduced GSK-3β substrate Tau phosphorylation at Ser396 in primary neurons, showing the inhibition of cellular GSK-3β. In the in vitro neuronal injury models, compounds 7c, 7f, 7h, 7l and 7m prevented neuronal death against glutamate, oxygen-glucose deprivation and nutrient serum deprivation which are associated with cerebral ischemic stroke. In the in vivo cerebral ischemia animal model, compound 7f reduced infarct size by 15% and improved the neurological deficit following focal cerebral ischemia. These findings may provide new insights into the development of novel GSK-3β inhibitors with potential neuroprotective activity.