A new protocol for predicting novel GSK-3β ATP competitive inhibitors

Impact of GSK-3β and CK-1δ on Wnt signaling pathway in alzheimer disease: A dual target approach

Alzheimer's disease (AD) is an enigmatic neurological illness that offers few treatment options. Recent exploration has highlighted the crucial connection of the Wnt signaling pathway in AD pathogenesis, shedding light on potential therapeutic targets. The present study focuses on the dual targeting of glycogen synthase kinase-3β (GSK-3β) and casein kinase-1δ (CK-1δ) within the framework of the Wnt signaling pathway as a possible technique for AD intervention. GSK-3β and CK-1δ are multifunctional kinases known for their roles in tau hyperphosphorylation, amyloid processing, and synaptic dysfunction, all of which are major hallmarks of Alzheimer's disease. They are intricately linked to Wnt signaling, which plays a pivotal part in sustaining neuronal function and synaptic plasticity. Dysregulation of the Wnt pathway in AD contributes to cognitive decline and neurodegeneration. This review delves into the molecular mechanisms by which GSK-3β and CK-1δ impact the Wnt signaling pathway, elucidating their roles in AD pathogenesis. We discuss the potential of small-molecule inhibitors along with their SAR studies along with the multi-targetd approach targeting GSK-3β and CK-1δ to modulate Wnt signaling and mitigate AD-related pathology. In summary, the dual targeting of GSK-3β and CK-1δ within the framework of the Wnt signaling pathway presents an innovative and promising avenue for future AD therapies, offering new hope for patients and caregivers in the quest to combat this challenging condition.

Developing a straightforward route toward the synthesis of arylaminomaleimides by palladium-catalyzed arylation of one-pot synthesized aminomaleimides

3-Aryl-4-aminomaleimides have well-demonstrated applications, such as being used as fluorophores and inhibitors. However, their previous synthesis methods have involved tedious multi-step procedures or methods that need pre-functionalized maleimides and toxic or unstable reagents. Here, a feasible method is developed to synthesize these useful compounds. This includes the one-pot preparation of 3-aminomaleimides, followed by their direct arylation through a palladium-catalyzed Heck reaction with various aryl iodides regioselectively at the β-position of their amine substituents. The results show that this method efficiently exhibits a broad scope.

3D-quantitative structure-activity relationship and antiviral effects of curcumin derivatives as potent inhibitors of influenza H1N1 neuraminidase

Curcumin derivatives have been shown to inhibit replication of human influenza A viruses (IAVs). However, it is not clear whether curcumin and its derivatives can inhibit neuraminidase (NA) of influenza virus. In this study, a meaningful 3D quantitative structure–activity relationship model (comparative molecular field analysis R² = 0.997, q² = 0.527, s = 0.064, F = 282.663) was built to understand the chemical–biological interactions between their activities and neuraminidase. Molecular docking was used to predict binding models between curcumin derivatives and neuraminidase. Real-time polymerase chain reactions showed that the five active curcumin derivatives might have direct effects on viral particle infectivity in H1N1-infected lung epithelial (MDCK) cells. Neuraminidase activation assay showed that five active curcumin derivatives decreased H1N1-induced neuraminidase activation in MDCK cells. Indirect immunofluorescence assay indicated that two active curcumin derivatives (tetramethylcurcumin and curcumin) down-regulated the nucleoprotein expression. Curcumin inhibited IAV in vivo. The therapeutic mechanism of curcumin in the treatment of influenza viral pneumonia is related to improving the immune function of infected mice and regulating secretion of tumor necrosis-α, interleukin-6, and interferon-γ. These results indicate that curcumin derivatives inhibit IAV by blocking neuraminidase in the cellular model and curcumin also has anti-IAV activity in the animal model.

Single enzyme nanoparticle, an effective tool for enzyme replacement therapy

The term "single enzyme nanoparticle" (SEN) refers to a chemically or biologically engineered single enzyme molecule. SENs are distinguished from conventional protein nanoparticles in that they can maintain their individual structure and enzymatic activity following modification. Furthermore, SENs exhibit enhanced properties as biopharmaceuticals, such as reduced antigenicity, and increased stability and targetability, which are attributed to the introduction of specific moieties, such as poly(ethylene glycol), carbohydrates, and antibodies. Enzyme replacement therapy (ERT) is a crucial therapeutic option for controlling enzyme-deficiency-related disorders. However, the unfavorable properties of enzymes, including immunogenicity, lack of targetability, and instability, can undermine the clinical significance of ERT. As shown in the cases of Adagen®, Revcovi®, Palynziq®, and Strensiq®, SEN can be an effective technology for overcoming these obstacles. Based on these four licensed products, we expect that additional SENs will be introduced for ERT in the near future. In this article, we review the concepts and features of SENs, as well as their preparation methods. Additionally, we summarize different types of enzyme deficiency disorders and the corresponding therapeutic enzymes. Finally, we focus on the current status of SENs in ERT by reviewing FDA-approved products.

Rosmarinic Acid Derivatives' Inhibition of Glycogen Synthase Kinase-3β Is the Pharmacological Basis of Kangen-Karyu in Alzheimer's Disease

Inhibition of glycogen synthase kinase 3β (GSK-3β) is considered to be the central therapeutic approach against Alzheimer's disease (AD). In the present study, boiled water extracts of the Kangen-karyu (KK) herbal mixture and its constituents were screened for GSK-3β inhibitory activity. KK is used in traditional Kampo and Chinese medicines for improving cognitive function. The GSK-3β inhibition potential was evaluated by using the Kinase-Glo luminescent kinase assay platform. Furthermore, enzyme kinetics and in silico modeling were performed by using AutoDockTools to demonstrate the mechanism of enzyme inhibition. KK extract significantly inhibited GSK-3β in a concentration-dependent manner (IC50: 17.05 ± 1.14 μg/mL) when compared with the reference drug luteolin (IC50: 2.18 ± 0.13 μM). Among the six components of KK, extracts of Cyperi Rhizoma and Salviae Miltiorrhizae Radix significantly inhibited GSK-3β with IC50 values of 20.68 ± 2.50 and 7.77 ± 1.38 μg/mL, respectively. Among the constituents of the roots of S. miltiorrhiza water extract, rosmarinic acid, magnesium lithospermate B, salvianolic acid A, salvianolic acid B, and salvianolic acid C inhibited GSK-3β with IC50 values ranging from 6.97 to 135.5 μM. Salvianolic acid B was found to be an ATP-competitive inhibitor of GSK-3β and showed the lowest IC50 value (6.97 ± 0.96 µM). In silico modeling suggested a mechanism of action by which the hydrophobic, π⁻cation, and hydrophilic interactions of salvianolic acid B at ATP and substrate sites are critical for the observed GSK-3β inhibition. Therefore, one of the mechanisms of action of KK against AD may be the inhibition of GSK-3β and one of the active components of KK is the root of S. miltiorrhiza and its constituents: rosmarinic acid, magnesium lithospermate B, and salvianolic acids A, B, and C. Our results demonstrate the pharmacological basis for the use of KK against AD.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

(Z)-2-(3-Chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide as GSK-3β inhibitor: Identification by virtual screening and its validation in enzyme- and cell-based assay

Glycogen synthase kinase 3β (GSK-3β) is a widely investigated molecular target for numerous diseases including Alzheimer's disease, cancer, and diabetes mellitus. The present study was aimed to discover new scaffolds for GSK-3β inhibition, through protein structure-guided virtual screening approach. With the availability of large number of GSK-3β crystal structures with varying degree of RMSD in protein backbone and RMSF in side chain geometry, herein appropriate crystal structures were selected based on the characteristic ROC curve and percentage enrichment of actives. The validated docking protocol was employed to screen a library of 50,000 small molecules using molecular docking and binding affinity calculations. Based on the GLIDE docking score, Prime MMGB/SA binding affinity, and interaction pattern analysis, the top 50 ligands were selected for GSK-3β inhibition. (Z)-2-(3-chlorobenzylidene)-3,4-dihydro-N-(2-methoxyethyl)-3-oxo-2H-benzo[b][1,4]oxazine-6-carboxamide (F389-0663, 7) was identified as a potent inhibitor of GSK-3β with an IC₅₀ value of 1.6 μm. Further, GSK-3β inhibition activity was then investigated in cell-based assay. The treatment of neuroblastoma N2a cells with 12.5 μm of F389-0663 resulted in the significant increase in GSK-3β Ser9 levels, which is indicative of the GSK-3β inhibitory activity of a compound. The molecular dynamic simulations were carried out to understand the interactions of F389-0663 with GSK-3β protein.

Getting the most out of PubChem for virtual screening

INTRODUCTION

With the emergence of the 'big data' era, the biomedical research community has great interest in exploiting publicly available chemical information for drug discovery. PubChem is an example of public databases that provide a large amount of chemical information free of charge.

AREAS COVERED

This article provides an overview of how PubChem's data, tools, and services can be used for virtual screening and reviews recent publications that discuss important aspects of exploiting PubChem for drug discovery.

EXPERT OPINION

PubChem offers comprehensive chemical information useful for drug discovery. It also provides multiple programmatic access routes, which are essential to build automated virtual screening pipelines that exploit PubChem data. In addition, PubChemRDF allows users to download PubChem data and load them into a local computing facility, facilitating data integration between PubChem and other resources. PubChem resources have been used in many studies for developing bioactivity and toxicity prediction models, discovering polypharmacologic (multi-target) ligands, and identifying new macromolecule targets of compounds (for drug-repurposing or off-target side effect prediction). These studies demonstrate the usefulness of PubChem as a key resource for computer-aided drug discovery and related area.

Discovery of novel PDE9 inhibitors capable of inhibiting Aβ aggregation as potential candidates for the treatment of Alzheimer's disease

Recently, phosphodiesterase-9 (PDE9) inhibitors and biometal-chelators have received much attention as potential therapeutics for the treatment of Alzheimer’s disease (AD). Here, we designed, synthesized, and evaluated a novel series of PDE9 inhibitors with the ability to chelate metal ions. The bioassay results showed that most of these molecules strongly inhibited PDE9 activity. Compound 16 showed an IC₅₀ of 34 nM against PDE9 and more than 55-fold selectivity against other PDEs. In addition, this compound displayed remarkable metal-chelating capacity and a considerable ability to halt copper redox cycling. Notably, in comparison to the reference compound clioquinol, it inhibited metal-induced Aβ_1-42 aggregation more effectively and promoted greater disassembly of the highly structured Aβ fibrils generated through Cu²⁺-induced Aβ aggregation. These activities of 16, together with its favorable blood-brain barrier permeability, suggest that 16 may be a promising compound for treatment of AD.

QSAR studies in the discovery of novel type-II diabetic therapies

INTRODUCTION

Type-II diabetes mellitus (T2DM) is a complex chronic disease that represents a major therapeutic challenge. Despite extensive efforts in T2DM drug development, therapies remain unsatisfactory. Currently, there are many novel and important antidiabetic drug targets under investigation by many research groups worldwide. One of the main challenges to develop effective orally active hypoglycemic agents is off-target effects. Computational tools have impacted drug discovery at many levels. One of the earliest methods is quantitative structure-activity relationship (QSAR) studies. QSAR strategies help medicinal chemists understand the relationship between hypoglycemic activity and molecular properties. Hence, QSAR may hold promise in guiding the synthesis of specifically designed novel ligands that demonstrate high potency and target selectivity.

AREAS COVERED

This review aims to provide an overview of the QSAR strategies used to model antidiabetic agents. In particular, this review focuses on drug targets that raised recent scientific interest and/or led to successful antidiabetic agents in the market. Special emphasis has been made on studies that led to the identification of novel antidiabetic scaffolds.

EXPERT OPINION

Computer-aided molecular design and discovery techniques like QSAR have a great potential in designing leads against complex diseases such as T2DM. Combined with other in silico techniques, QSAR can provide more useful and rational insights to facilitate the discovery of novel compounds. However, since T2DM is a complex disease that includes several faulty biological targets, multi-target QSAR studies are recommended in the future to achieve efficient antidiabetic therapies.

Mechanistic studies for tri-targeted inhibition of enzymes involved in cholesterol biosynthesis by green tea polyphenols

In the present study, we found that three enzymes, MVK, MDD and FPPS, in the mevalonate pathway (MVP) of cholesterol biosynthesis, can be simultaneously inhibited by two green tea polyphenols ((-)-epicatechin-3-gallate, ECG; (-)-epigallocatechin-3-gallate, EGCG). Molecular dynamics simulations and pharmacophore studies were carried out to elucidate the tri-targeted inhibition mechanisms. Our results indicate that similar triangular binding pockets exist in all three enzymes, which is essential for their binding with polyphenols. Two distinct binding poses for ECG and EGCG were observed in our MD simulations. These results shed light on the potential for further selective and multi-targeted inhibitor design for the treatment of hyperlipidemia.

Discovery of multitarget-directed ligands against Alzheimer's disease through systematic prediction of chemical-protein interactions

To determine chemical-protein interactions (CPI) is costly, time-consuming, and labor-intensive. In silico prediction of CPI can facilitate the target identification and drug discovery. Although many in silico target prediction tools have been developed, few of them could predict active molecules against multitarget for a single disease. In this investigation, naive Bayesian (NB) and recursive partitioning (RP) algorithms were applied to construct classifiers for predicting the active molecules against 25 key targets toward Alzheimer's disease (AD) using the multitarget-quantitative structure-activity relationships (mt-QSAR) method. Each molecule was initially represented with two kinds of fingerprint descriptors (ECFP6 and MACCS). One hundred classifiers were constructed, and their performance was evaluated and verified with internally 5-fold cross-validation and external test set validation. The range of the area under the receiver operating characteristic curve (ROC) for the test sets was from 0.741 to 1.0, with an average of 0.965. In addition, the important fragments for multitarget against AD given by NB classifiers were also analyzed. Finally, the validated models were employed to systematically predict the potential targets for six approved anti-AD drugs and 19 known active compounds related to AD. The prediction results were confirmed by reported bioactivity data and our in vitro experimental validation, resulting in several multitarget-directed ligands (MTDLs) against AD, including seven acetylcholinesterase (AChE) inhibitors ranging from 0.442 to 72.26 μM and four histamine receptor 3 (H3R) antagonists ranging from 0.308 to 58.6 μM. To be exciting, the best MTDL DL0410 was identified as an dual cholinesterase inhibitor with IC50 values of 0.442 μM (AChE) and 3.57 μM (BuChE) as well as a H3R antagonist with an IC50 of 0.308 μM. This investigation is the first report using mt-QASR approach to predict chemical-protein interaction for a single disease and discovering highly potent MTDLs. This protocol may be useful for in silico multitarget prediction of other diseases.

In vitro antiviral effects and 3D QSAR study of resveratrol derivatives as potent inhibitors of influenza H1N1 neuraminidase

The anti-influenza virus activities of 50 resveratrol (RV: 3, 5, 4'-trihydroxy-trans-stilbene) derivatives were evaluated using a neuraminidase (NA) activity assay. The results showed that 35 compounds exerted an inhibitory effect on the NA activity of the influenza virus strain A/PR/8/34 (H1N1) with 50% inhibitory concentration (IC50) values ranging from 3.56 to 186.1 μm. Next, the 35 RV derivatives were used to develop 3D quantitative structure-activity relationship (3D QSAR) models for understanding the chemical-biological interactions governing their activities against NA. The comparative molecular field analysis (CoMFA r2=0.973, q2=0.620, qtest2=0.661) and the comparative molecular similarity indices analysis (CoMSIA r2=0.956, q2=0.610, qtest2=0.531) were applied. Afterward, molecular docking was performed to study the molecular interactions between the RV derivatives and NA. Finally, a cytopathic effect (CPE) reduction assay was used to evaluate the antiviral effects of the RV derivatives in vitro. Time-of-addition studies demonstrated that the RV derivatives might have a direct effect on viral particle infectivity. Our results indicate that the RV derivatives are potentially useful antiviral compounds for new drug design and development for influenza treatment.

Insights into the interactions between maleimide derivates and GSK3β combining molecular docking and QSAR

Many protein kinase (PK) inhibitors have been reported in recent years, but only a few have been approved for clinical use. The understanding of the available molecular information using computational tools is an alternative to contribute to this process. With this in mind, we studied the binding modes of 77 maleimide derivates inside the PK glycogen synthase kinase 3 beta (GSK3β) using docking experiments. We found that the orientations that these compounds adopt inside GSK3β binding site prioritize the formation of hydrogen bond (HB) interactions between the maleimide group and the residues at the hinge region (residues Val135 and Asp133), and adopt propeller-like conformations (where the maleimide is the propeller axis and the heterocyclic substituents are two slanted blades). In addition, quantitative structure–activity relationship (QSAR) models using CoMSIA methodology were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. We found a model to explain the structure–activity relationship of non-cyclic maleimide (NCM) derivatives (54 compounds). The best CoMSIA model (training set included 44 compounds) included steric, hydrophobic, and HB donor fields and had a good Q² value of 0.539. It also predicted adequately the most active compounds contained in the test set. Furthermore, the analysis of the plots of the steric CoMSIA field describes the elements involved in the differential potency of the inhibitors that can be considered for the selection of suitable inhibitors.

Scaffold hopping of potential anti-tumor agents by WEGA: a shape-based approach

In this paper, we describe the first prospective application of the shape-comparison program, WEGA (weighted Gaussian algorithm), to find new scaffolds for anti-tumor agents.

Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

Discovery of new selective human aldose reductase inhibitors through virtual screening multiple binding pocket conformations

Aldose reductase reduces glucose to sorbitol. It plays a key role in many of the complications arising from diabetes. Thus, aldose reductase inhibitors (ARI) have been identified as promising therapeutic agents for treating such complications of diabetes, as neuropathy, nephropathy, retinopathy, and cataracts. In this paper, a virtual screening protocol applied to a library of compounds in house has been utilized to discover novel ARIs. IC50's were determined for 15 hits that inhibited ALR2 to greater than 50% at 50 μM, and ten of these have an IC50 of 10 μM or less, corresponding to a rather substantial hit rate of 14% at this level. The specificity of these compounds relative to their cross-reactivity with human ALR1 was also assessed by inhibition assays. This resulted in identification of novel inhibitors with IC50's comparable to the commercially available drug, epalrestat, and greater than an order of magnitude better selectivity.

CoMFA and CoMSIA studies of 1,2-dihydropyridine derivatives as anticancer agents

Taking advantage of our in-house experimental data on 3-cyano-2-imino-1, 2-dihydropyridine and 3-cyano-2- oxo-1,2-dihydropyridine derivatives as inhibitors of the growth of the human HT-29 colon adenocarcinoma tumor cell line, we have established a highly significant CoMFA and CoMSIA models (q2cv=0.70/0.639). The models were investigated to assure their stability and predictivity (r2pred=0.65/0.61) and successfully applied to design two new potential cell growth inhibitory agents with IC50s in the submicromolar range.

Structure-based discovery of highly selective phosphodiesterase-9A inhibitors and implications for inhibitor design

A new series of phosphodiesterase-9 (PDE9) inhibitors that contain a scaffold of 6-amino-pyrazolopyrimidinone have been discovered by a combination of structure-based design and computational docking. This procedure significantly saved the load of chemical synthesis and is an effective method for the discovery of inhibitors. The best compound 28 has an IC(50) of 21 nM and 3.3 μM, respectively, for PDE9 and PDE5 and about 3 orders of magnitude of selectivity against other PDE families. The crystal structure of the PDE9 catalytic domain in complex with 28 has been determined and shows a hydrogen bond between 28 and Tyr424. This hydrogen bond may account for the 860-fold selectivity of 28 against PDE1B, in comparison with about 30-fold selectivity of BAY73-6691. Thus, our studies suggest that Tyr424, a unique residue of PDE8 and PDE9, is a potential target for improvement of selectivity of PDE9 inhibitors.