Designing of dual inhibitors for GSK-3β and CDK5: Virtual screening and in vitro biological activities study

Innovative pathological network-based multitarget approaches for Alzheimer's disease treatment

Alzheimer's disease (AD) is the most prevalent neurodegenerative disease and is a major health threat globally. Its prevalence is forecasted to exponentially increase during the next 30 years due to the global aging population. Currently, approved drugs are merely symptomatic, being ineffective in delaying or blocking the relentless disease advance. Intensive AD research describes this disease as a highly complex multifactorial disease. Disclosure of novel pathological pathways and their interconnections has had a major impact on medicinal chemistry drug development for AD over the last two decades. The complex network of pathological events involved in the onset of the disease has prompted the development of multitarget drugs. These chemical entities combine pharmacological activities toward two or more drug targets of interest. These multitarget-directed ligands are proposed to modify different nodes in the pathological network aiming to delay or even stop disease progression. Here, we review the multitarget drug development strategy for AD during the last decade.

Molecular docking and dynamics simulations revealed the potential inhibitory activity of honey-iQfood ingredients against GSK-3β and CDK5 protein targets for brain health

Honey-iQfood is an herbal supplement made of a mixture of polyherbal extracts and wild honey. The mixture is traditionally claimed to improve various conditions related to brain cells and functions including dementia and Alzheimer's disease. Glycogen synthase kinase-3 beta (GSK-3β) and cyclin-dependent kinase 5 (CDK5) have been identified as being involved in the pathological hyperphosphorylation of tau proteins, which leads to the formation of neurofibrillary tangles and causes Alzheimer's disease. Therefore, this study was conducted to confirm the traditional claims by detection of active compounds, namely curcumin, gallic acid, catechin, rosmarinic acid, and andrographolide in the raw materials of Honey-iQfood through HPLC analysis, molecular docking, and dynamic simulations. Two potential compounds, andrographolide, and rosmarinic acid, produced the best binding affinities following the molecular docking of the active compounds against the GSK-3β and CDK5 targets. Andrographolide binds with GSK-3β at -8.2 kcal/mol, whereas rosmarinic acid binds to CDK5 targets at -8.6 kcal/mol. Molecular dynamics was further carried out to confirm the docking results and clarify their dynamic properties such as RMSD, RMSF, rGyr, SASA, PSA, and binding free energy. CDK5-andrographolide complexes had the best MM-GBSA score (-83.63 kcal/mol) compared to other complexes, indicating the better interaction profile and stability of the complex. These findings warrant further research into andrographolide and rosmarinic acid as efficient inhibitors of tau protein hyperphosphorylation to verify their therapeutic potential in brain-related illnesses.Communicated by Ramaswamy H. Sarma.

The neurobiology and therapeutic potential of multi-targeting β-secretase, glycogen synthase kinase 3β and acetylcholinesterase in Alzheimer's disease

Alzheimer's Disease (AD) is the most common form of dementia, affecting almost 50 million of people around the world, characterized by a complex and age-related progressive pathology with projections to duplicate its incidence by the end of 2050. AD pathology has two major hallmarks, the amyloid beta (Aβ) peptides accumulation and tau hyperphosphorylation, alongside with several sub pathologies including neuroinflammation, oxidative stress, loss of neurogenesis and synaptic dysfunction. In recent years, extensive research pointed out several therapeutic targets which have shown promising effects on modifying the course of the disease in preclinical models of AD but with substantial failure when transposed to clinic trials, suggesting that modulating just an isolated feature of the pathology might not be sufficient to improve brain function and enhance cognition. In line with this, there is a growing consensus that an ideal disease modifying drug should address more than one feature of the pathology. Considering these evidence, β-secretase (BACE1), Glycogen synthase kinase 3β (GSK-3β) and acetylcholinesterase (AChE) has emerged as interesting therapeutic targets. BACE1 is the rate-limiting step in the Aβ production, GSK-3β is considered the main kinase responsible for Tau hyperphosphorylation, and AChE play an important role in modulating memory formation and learning. However, the effects underlying the modulation of these enzymes are not limited by its primarily functions, showing interesting effects in a wide range of impaired events secondary to AD pathology. In this sense, this review will summarize the involvement of BACE1, GSK-3β and AChE on synaptic function, neuroplasticity, neuroinflammation and oxidative stress. Additionally, we will present and discuss new perspectives on the modulation of these pathways on AD pathology and future directions on the development of drugs that concomitantly target these enzymes.

Review of Alzheimer's disease drugs and their relationship with neuron-glia interaction

Alzheimer's disease (AD) is the most common cause of dementia worldwide. Because Alzheimer's disease has no known treatment, sufferers and their caregivers must concentrate on symptom management. Astrocytes and microglia are now known to play distinct physiological roles in synaptic function, the blood-brain barrier, and neurovascular coupling. Consequently, the search for drugs that can slow the degenerative process in dementia sufferers continues because existing drugs are designed to alleviate the symptoms of Alzheimer's disease. Drugs that address pathological changes without interfering with the normal function of glia, such as eliminating amyloid-beta deposits, are prospective treatments for neuroinflammatory illnesses. Because neuron-astrocytes-microglia interactions are so complex, developing effective, preventive, and therapeutic medications for AD will necessitate novel methodologies and strategic targets. This review focused on existing medications used in treating AD amongst which include Donepezil, Choline Alphoscerate, Galantamine, Dextromethorphan, palmitoylethanolamide, citalopram, resveratrol, and solanezumab. This review summarizes the effects of these drugs on neurons, astrocytes, and microglia interactions based on their pharmacokinetic properties, mechanism of action, dosing, and clinical presentations.

Therapeutic potential of quinazoline derivatives for Alzheimer's disease: A comprehensive review

Quinazolines are considered as a promising class of bioactive heterocyclic compounds with broad properties. Particularly, the quinazoline scaffold has an impressive role in the design and synthesis of new CNS-active drugs. The drug-like properties and pharmacological characteristics of quinazoline could lead to different drugs with various targets. Among CNS disorders, Alzheimer's disease (AD) is a progressive neurodegenerative disorder with memory loss, cognitive decline and language dysfunction. AD is a complex and multifactorial disease therefore, the need for finding multi-target drugs against this devastative disease is urgent. A literature survey revealed that quinazoline derivatives have diverse therapeutic potential for AD as modulators/inhibitors of β-amyloid, tau protein, cholinesterases, monoamine oxidases, and phosphodiesterases as well as other protective effects. Thus, we describe here the most relevant and recent studies about anti-AD agents with quinazoline structure which can further aid the development and discovery of new anti-AD agents.

High-throughput screening of natural compounds and inhibition of a major therapeutic target HsGSK-3β for Alzheimer's disease using computational approaches

BACKGROUND

Alzheimer's disease is a leading neurodegenerative disease worldwide and is the 6th leading cause of death in the USA. AD is a very complex disease and the drugs available in the market cannot fully cure it. The glycogen synthase kinase 3 beta plays a major role in the hyperphosphorylation of tau protein which forms the neurofibrillary tangles which is a major hallmark of AD. In this study, we have used a series of computational approaches to find novel inhibitors against GSK-3β to reduce the TAU hyperphosphorylation.

RESULTS

We have retrieved a set of compounds (n=167,741) and screened against GSK-3β in four sequential steps. The resulting analysis of virtual screening suggested that 404 compounds show good binding affinity and can be employed for pharmacokinetic analysis. From here, we have selected 20 compounds those were good in terms of pharmacokinetic parameters. All these compounds were re-docked by using Autodock Vina followed by Autodock. Four best compounds were employed for MDS and here predicted RMSD, RMSF, Rg, hydrogen bonds, SASA, PCA, and binding-free energy. From all these analyses, we have concluded that out of 167,741 compounds, the ZINC15968620, ZINC15968622, and ZINC70707119 can act as lead compounds against HsGSK-3β to reduce the hyperphosphorylation.

CONCLUSION

The study suggested three compounds (ZINC15968620, ZINC15968622, and ZINC70707119) have great potential to be a drug candidate and can be tested using in vitro and in vivo experiments for further characterization and applications.

Insight into glycogen synthase kinase-3β inhibitory activity of phyto-constituents from Melissa officinalis: in silico studies

Over activity of Glycogen synthase kinase-3β (GSK-3β), a serine/threonine-protein kinase has been implicated in a number of diseases including stroke, type II diabetes and Alzheimer disease (AD). This study aimed to find novel inhibitors of GSK-3β from phyto-constituents of Melissa officinalis with the aid of computational analysis. Molecular docking, induced-fit docking (IFD), calculation of binding free energy via the MM-GBSA approach and Lipinski's rule of five (RO5) were employed to filter the compounds and determine their druggability. Most importantly, the compounds pIC50 were predicted by machine learning-based model generated by AutoQSAR algorithm. The generated model was validated to affirm its predictive model. The best model obtained was Model kpls_desc_38 (R2 = 0.8467 and Q2 = 0.8069), and this external validated model was utilized to predict the bioactivities of the lead compounds. While a number of characterized compounds from Melissa officinalis showed better docking score, binding free energy alongside adherence to RO5 than co-cystallized ligand, only three compounds (salvianolic acid C, ellagic acid and naringenin) showed more satisfactory pIC50. The results obtained in this study can be useful to design potent inhibitors of GSK-3β.

A Pilot Study of Multi-Input Recurrent Neural Networks for Drug-Kinase Binding Prediction

The use of virtual drug screening can be beneficial to research teams, enabling them to narrow down potentially useful compounds for further study. A variety of virtual screening methods have been developed, typically with machine learning classifiers at the center of their design. In the present study, we created a virtual screener for protein kinase inhibitors. Experimental compound-target interaction data were obtained from the IDG-DREAM Drug-Kinase Binding Prediction Challenge. These data were converted and fed as inputs into two multi-input recurrent neural networks (RNNs). The first network utilized data encoded in one-hot representation, while the other incorporated embedding layers. The models were developed in Python, and were designed to output the IC50 of the target compounds. The performance of the models was assessed primarily through analysis of the Q2 values produced from runs of differing sample and epoch size; recorded loss values were also reported and graphed. The performance of the models was limited, though multiple changes are proposed for potential improvement of a multi-input recurrent neural network-based screening tool.

Physiological and Pathological Roles of Cdk5: Potential Directions for Therapeutic Targeting in Neurodegenerative Disease

Cyclin-dependent kinase 5 (Cdk5) is a proline-directed serine (ser)/threonine (Thr) kinase that has been demonstrated to be one of the most functionally diverse kinases within neurons. Cdk5 is regulated via binding with its neuron-specific regulatory subunits, p35 or p39. Cdk5-p35 activity is critical for a variety of developmental and cellular processes in the brain, including neuron migration, memory formation, microtubule regulation, and cell cycle suppression. Aberrant activation of Cdk5 via the truncated p35 byproduct, p25, is implicated in the pathogenesis of several neurodegenerative diseases. The present review highlights the importance of Cdk5 activity and function in the brain and demonstrates how deregulation of Cdk5 can contribute to the development of neurodegenerative conditions such as Alzheimer's and Parkinson's disease. Additionally, we cover past drug discovery attempts at inhibiting Cdk5-p25 activity and discuss which types of targeting strategies may prove to be the most successful moving forward.

GSK3β and Tau Protein in Alzheimer's Disease and Epilepsy

Alzheimer's disease (AD) is the most common form of dementia present in older adults; its etiology involves genetic and environmental factors. In recent years, epidemiological studies have shown a correlation between AD and chronic epilepsy since a considerable number of patients with AD may present seizures later on. Although the pathophysiology of seizures in AD is not completely understood, it could represent the result of several molecular mechanisms linked to amyloid beta-peptide (Aβ) accumulation and the hyperphosphorylation of tau protein, which may induce an imbalance in the release and recapture of excitatory and inhibitory neurotransmitters, structural alterations of the neuronal cytoskeleton, synaptic loss, and neuroinflammation. These changes could favor the recurrent development of hypersynchronous discharges and epileptogenesis, which, in a chronic state, favor the neurodegenerative process and influence the cognitive decline observed in AD. Supporting this correlation, histopathological studies in the brain tissue of temporal lobe epilepsy (TLE) patients have revealed the presence of Aβ deposits and the accumulation of tau protein in the neurofibrillary tangles (NFTs), accompanied by an increase of glycogen synthase kinase-3 beta (GSK3β) activity that may lead to an imminent alteration in posttranslational modifications of some microtubule-associated proteins (MAPs), mainly tau. The present review is focused on understanding the pathological aspects of GSK3β and tau in the development of TLE and AD.

The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions

Inflammasomes are intracellular multiprotein complexes in the cytoplasm that regulate inflammation activation in the innate immune system in response to pathogens and to host self-derived molecules. Recent advances greatly improved our understanding of the activation of nucleotide-binding oligomerization domain-like receptor (NLR) family pyrin domain containing 3 (NLRP3) inflammasomes at the molecular level. The NLRP3 belongs to the subfamily of NLRP which activates caspase 1, thus causing the production of proinflammatory cytokines (interleukin 1β and interleukin 18) and pyroptosis. This inflammasome is involved in multiple neurodegenerative and metabolic disorders including Alzheimer's disease, multiple sclerosis, type 2 diabetes mellitus, and gout. Therefore, therapeutic targeting to the NLRP3 inflammasome complex is a promising way to treat these diseases. Recent research advances paved the way toward drug research and development using a variety of machine learning-based and artificial intelligence-based approaches. These state-of-the-art approaches will lead to the discovery of better drugs after the training of such a system.

Virtual screening, pharmacokinetics, molecular dynamics and binding free energy analysis for small natural molecules against cyclin-dependent kinase 5 for Alzheimer's disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and characterized by brain cell death, memory loss and is the most common form of dementia. Although AD has devastating effects, however, drugs which can treat the AD remain limited. The cyclin-dependent kinase 5 (CDK5) has been recognized as being involved in the pathological hyperphosphorylation of tau protein, which leads to the formation of neurofibrillary tangles (NFTs). We utilized the structure-based virtual screening (SBVS) approach to find the potential inhibitors against HsCDK5. The natural compound subset from the ZINC database (n = 167,741) was retrieved and screened by using SBVS method. From here, we have predicted 297 potent inhibitors. These 297 compounds were evaluated through their pharmacokinetic properties by ADMET (absorption, distribution, metabolism, elimination/excretion and toxicity) descriptors. Finally, 17 compounds were selected and used for re-docking. After the refinement by molecular docking and by using drug-likeness analysis, we have identified four potential inhibitors (ZINC85877721, ZINC96114862, ZINC96115616 and ZINC96116231). All these four ligands were employed for 100 ns MDS study. From the root mean square deviation (RMSD), root mean square fluctuation (RMSF), Rg, number of hydrogen bonds, solvent accessible surface area (SASA), principal component analysis (PCA) and binding free energy analysis we have found that out of four inhibitors ZINC85877721 and ZINC96116231 showed good binding free energy of -198.84 and -159.32 kJ.mol^-1, respectively, and also good in other structural analyses. Both compounds displayed excellent pharmacological and structural properties to be the drug candidates. Collectively, these findings recommend that two compounds have great potential to be a promising agent against AD to reduce the CDK5 induced hyperphosphorylation and could be considered as therapeutic agents for the AD.Communicated by Ramaswamy H. Sarma.

Discovering Proangiogenic Drugs in Ischemic Stroke Based on the Relationship between Protein Domain and Drug Substructure

As an important protective mechanism against cerebral ischemia, angiogenesis has become a topic of interest in the treatment of ischemic stroke with the challenge that few drugs promote angiogenesis. Previous studies of the identification of drug-target interactions mainly focused on the overall structures of drugs and proteins, which limited the discovery of novel structure drugs. In this article, we proposed a new strategy for discovering proangiogenic drugs based on the assumption that drug-protein interaction is mediated by substructure and domain. First, we identified substructure-domain relationships according to the known drug-protein interactions and established the drug-substructure-domain-protein relationships of genes that are proangiogenic in brain tissue and expressed significantly during ischemic stroke. Then we quantified the intensity of interaction between each drug and each protein. Finally, we obtained 540 interactive relationships between 238 drugs and 54 genes, establishing a drug-gene network with two patterns of independent and complex drug-gene interactions. Both of the patterns facilitated finding not only drugs with the same overall structure but also drugs with a different overall structure based on the same or a similar protein spectrum. In addition, we analyzed the mechanism of action of each predicted drug and extracted drugs with similar mechanisms. In vitro, our results showed that azelnidipine, azilsartan, lercanidipine, nafcillin, and vortioxetine enhanced bEnd.3 cell proliferation, migration, tube formation, and the expression of angiogenic marker PCNA. Azelnidipine, azilsartan, lercanidipine, and nafcillin increased the level of expression of proangiogenic factor VEGF. Unlike previous studies focusing on the overall structures of drugs, our research highlighted local structural similarity, which has great potential in the search for more proangiogenic drugs with novel structures.

Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review

BACKGROUND

Virtual Screening (VS) has emerged as an important tool in the drug development process, as it conducts efficient in silico searches over millions of compounds, ultimately increasing yields of potential drug leads. As a subset of Artificial Intelligence (AI), Machine Learning (ML) is a powerful way of conducting VS for drug leads. ML for VS generally involves assembling a filtered training set of compounds, comprised of known actives and inactives. After training the model, it is validated and, if sufficiently accurate, used on previously unseen databases to screen for novel compounds with desired drug target binding activity.

OBJECTIVE

The study aims to review ML-based methods used for VS and applications to Alzheimer's Disease (AD) drug discovery.

METHODS

To update the current knowledge on ML for VS, we review thorough backgrounds, explanations, and VS applications of the following ML techniques: Naïve Bayes (NB), k-Nearest Neighbors (kNN), Support Vector Machines (SVM), Random Forests (RF), and Artificial Neural Networks (ANN).

RESULTS

All techniques have found success in VS, but the future of VS is likely to lean more largely toward the use of neural networks - and more specifically, Convolutional Neural Networks (CNN), which are a subset of ANN that utilize convolution. We additionally conceptualize a work flow for conducting ML-based VS for potential therapeutics for AD, a complex neurodegenerative disease with no known cure and prevention. This both serves as an example of how to apply the concepts introduced earlier in the review and as a potential workflow for future implementation.

CONCLUSION

Different ML techniques are powerful tools for VS, and they have advantages and disadvantages albeit. ML-based VS can be applied to AD drug development.

Past and current perspective on new therapeutic targets for Type-II diabetes

Loss of pancreatic β-cell function is a hallmark of Type-II diabetes mellitus (DM). It is a chronic metabolic disorder that results from defects in both insulin secretion and insulin action. Recently, United Kingdom Prospective Diabetes Study reported that Type-II DM is a progressive disorder. Although, DM can be treated initially by monotherapy with oral agent; eventually, it may require multiple drugs. Additionally, insulin therapy is needed in many patients to achieve glycemic control. Pharmacological approaches are unsatisfactory in improving the consequences of insulin resistance. Single therapeutic approach in the treatment of Type-II DM is unsuccessful and usually a combination therapy is adopted. Increased understanding of biochemical, cellular and pathological alterations in Type-II DM has provided new insight in the management of Type-II DM. Knowledge of underlying mechanisms of Type-II DM development is essential for the exploration of novel therapeutic targets. Present review provides an insight into therapeutic targets of Type-II DM and their role in the development of insulin resistance. An overview of important signaling pathways and mechanisms in Type-II DM is provided for the better understanding of disease pathology. This review includes case studies of drugs that are withdrawn from the market. The experience gathered from previous studies and knowledge of Type-II DM pathways can guide the anti-diabetic drug development toward the discovery of clinically viable drugs that are useful in Type-II DM.