Multi-Target Screening and Experimental Validation of Natural Products from Selaginella Plants against Alzheimer's Disease

Lathyrane and premyrsinane Euphorbia diterpenes against Alzheimer's disease: Bioinspired synthesis, anti-cholinesterase and neuroprotection bioactivity

The first systematic acylated diversification of naturally scarce premyrsinane diterpenes, together with their biosynthetic precursors lathyrane diterpene were carried out. Two new series of premyrsinane derivates (1a-32a) and lathyrane derivates (1-32) were synthesized from the naturally abundant lathyrane diterpene Euphorbia factor L3 through a bioinspired approach. The cholinesterase inhibitory and neuroprotective activities of these diterpenes were investigated to explore potential anti-Alzheimer's disease (AD) bioactive lead compounds. In general, the lathyrane diterpenes showed the better acetylcholinesterase (AChE) inhibitory activity than that of premyrsinanes. The lathyrane derivative 17 bearing a 3-dimethylaminobenzoyl moiety showed the best AChE inhibition effect with the IC50 value of 7.1 μM. Molecular docking demonstrated that 17 could bond with AChE well (-8 kal/mol). On the other hand, premyrsinanes showed a better neuroprotection profile against H2O2-induced injury in SH-SY5Y cells. Among them, the premyrsinane diterpene 16a had significant neuroprotective effect with the cell viability rate of 113.5 % at 12.5 μM (the model group with 51.2 %). The immunofluorescence, western blot and reactive oxygen species (ROS) analysis were conducted to demonstrate the mechanism of 16a. Furthermore, a preliminary SAR analysis of the two categories of diterpenes was performed to provide the insights for anti-AD drug development.

Molecular docking analysis of flavonoids with AChE and BACE-1

Flavonoids are promising therapeutics for the treatment of Alzheimer's disease (AD). Therefore, it is of interest to study the anti-AD potential of 35 flavonoids towards the inhibition of AchE and BACE-1. Hence, the physicochemical, pharmacokinetic parameters, toxicity risk and drug-likeliness of the selected 35 flavonoids were computed. Further, the molecular docking analysis of flavonoids with AChE and BACE-1 were completed. A binding energy of -10.42 kcal/mol Epicatechin gallate, -10.16 kcal/mol sterubin and -10.11 kcal/mol Fisetin was observed with AchE as potential inhibitors. Similarly, Biochainin-A -9.81kcal/mol, Sterubin -8.96 kcal/mol and Epicatechin gallate -7.4 7 kcal/mol showed with BACE-1. Thus, these flavonoids are potential leads for structure-based design of effective anti-Alzheimer's agents.

Automated machine learning approach for developing a quantitative structure-activity relationship model for cardiac steroid inhibition of Na+/K+-ATPase

BACKGROUND

Quantitative structure-activity relationship (QSAR) modeling is a method of characterizing the relationship between chemical structures and biological activity. Automated machine learning enables computers to learn from large datasets and can be used for chemoinformatics. Cardiac steroids (CSs) inhibit the activity of Na⁺/K⁺-ATPase (NKA) in several species, including humans, since the binding pocket in which NKA binds to CSs is highly conserved. CSs are used to treat heart disease and have been developed into anticancer drugs for use in clinical trials. Novel CSs are, therefore, frequently synthesized and their activities evaluated. The purpose of this study is to develop a QSAR model via automated machine learning to predict the potential inhibitory activity of compounds without performing experiments.

METHODS

The chemical structures and inhibitory activities of 215 CS derivatives were obtained from the scientific literature. Predictive QSAR models were constructed using molecular descriptors, fingerprints, and biological activities.

RESULTS

The best predictive QSAR models were selected based on the LogLoss value. Using these models, the Matthews correlation coefficient, F1 score, and area under the curve of the test dataset were 0.6729, 0.8813, and 0.8812, respectively. Next, we showed automated construction of the predictive models for CS derivatives, which may be useful for identifying novel CSs suitable for candidate drug development.

CONCLUSION

The automated machine learning-based QSAR method developed here should be applicable for the time-efficient construction of predictive models using only a small number of compounds.

Functionalised penetrating peptide-chondroitin sulphate‑gold nanoparticles: Synthesis, characterization, and applications as an anti-Alzheimer's disease drug

The purpose of this study was to construct a transmembrane peptide-chondroitin sulphate‑gold nanoparticle (TAT-CS@Au) delivery system and investigate its activity as an anti-Alzheimer's disease (AD) drug. We successfully prepared TAT-CS@Au nanoparticles, investigated their anti-AD effects, and explored the possible mechanisms in in vitro models. TAT-CS@Au exhibited excellent cellular uptake and transport capacity, effectively inhibited the accumulation of Aβ_1-40, and significantly reduced Aβ_1-40-induced apoptosis in SH-SY5Y cells. Furthermore, TAT-CS@Au significantly reduced oxidative stress damage and cholinergic injury induced by Aβ_1-40 by regulating intracellular concentrations of reactive oxygen species (ROS), malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and acetylcholine (ACh). Western blotting results demonstrated that TAT-CS@Au inhibited aberrant tau phosphorylation (Ser199, Thr205, Ser404, and Ser396) through GSK3β inactivation. TAT-CS@Au decreased the levels of inflammatory factors, specifically TNF-α, IL-6, and IL-1β, by inhibiting NF-κB nuclear translocation by activating MAPK signalling pathways. Overall, these results indicate that TAT-CS@Au exhibits excellent transmembrane ability, inhibits Aβ_1-40 accumulation, antagonises oxidative stress, reduces aberrant tau phosphorylation, and suppresses the expression of inflammatory factors. TAT-CS@Au may be a multi-target anti-AD drug with good cell permeability, providing new insights into the design and research of anti-AD therapeutics.

In Silico Identification of Anti-SARS-CoV-2 Medicinal Plants Using Cheminformatics and Machine Learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of COVID-19, is spreading rapidly and has caused hundreds of millions of infections and millions of deaths worldwide. Due to the lack of specific vaccines and effective treatments for COVID-19, there is an urgent need to identify effective drugs. Traditional Chinese medicine (TCM) is a valuable resource for identifying novel anti-SARS-CoV-2 drugs based on the important contribution of TCM and its potential benefits in COVID-19 treatment. Herein, we aimed to discover novel anti-SARS-CoV-2 compounds and medicinal plants from TCM by establishing a prediction method of anti-SARS-CoV-2 activity using machine learning methods. We first constructed a benchmark dataset from anti-SARS-CoV-2 bioactivity data collected from the ChEMBL database. Then, we established random forest (RF) and support vector machine (SVM) models that both achieved satisfactory predictive performance with AUC values of 0.90. By using this method, a total of 1011 active anti-SARS-CoV-2 compounds were predicted from the TCMSP database. Among these compounds, six compounds with highly potent activity were confirmed in the anti-SARS-CoV-2 experiments. The molecular fingerprint similarity analysis revealed that only 24 of the 1011 compounds have high similarity to the FDA-approved antiviral drugs, indicating that most of the compounds were structurally novel. Based on the predicted anti-SARS-CoV-2 compounds, we identified 74 anti-SARS-CoV-2 medicinal plants through enrichment analysis. The 74 plants are widely distributed in 68 genera and 43 families, 14 of which belong to antipyretic detoxicate plants. In summary, this study provided several medicinal plants with potential anti-SARS-CoV-2 activity, which offer an attractive starting point and a broader scope to mine for potentially novel anti-SARS-CoV-2 drugs.

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods

Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

Concatenation of molecular docking and molecular simulation of BACE-1, γ-secretase targeted ligands: in pursuit of Alzheimer's treatment

INTRODUCTION

Alzheimer's disease (AD), the most predominant cause of dementia, has evolved tremendously with an escalating frequency, mainly affecting the elderly population. An effective means of delaying, preventing, or treating AD is yet to be achieved. The failure rate of dementia drug trials has been relatively higher than in other disease-related clinical trials. Hence, multi-targeted therapeutic approaches are gaining attention in pharmacological developments.

AIMS

As an extension of our earlier reports, we have performed docking and molecular dynamic (MD) simulation studies for the same 13 potential ligands against beta-site APP cleaving enzyme 1 (BACE-1) and γ-secretase as a therapeutic target for AD. The In-silico screening of these ligands as potential inhibitors of BACE-1 and γ-secretase was performed using AutoDock enabled PyRx v-0.8. The protein-ligand interactions were analyzed in Discovery Studio 2020 (BIOVIA). The stability of the most promising ligand against BACE-1 and γ-secretase was evaluated by MD simulation using Desmond-2018 (Schrodinger, LLC, NY, USA).

RESULTS

The computational screening revealed that the docking energy values for each of the ligands against both the target enzymes were in the range of -7.0 to -10.1 kcal/mol. Among the 13 ligands, 8 (55E, 6Z2, 6Z5, BRW, F1B, GVP, IQ6, and X37) showed binding energies of ≤-8 kcal/mol against BACE-1 and γ-secretase. For the selected enzyme targets, BACE-1 and γ-secretase, 6Z5 displayed the lowest binding energy of -10.1 and -9.8 kcal/mol, respectively. The MD simulation study confirmed the stability of BACE-6Z5 and γ-secretase-6Z5 complexes and highlighted the formation of a stable complex between 6Z5 and target enzymes.

CONCLUSION

The virtual screening, molecular docking, and molecular dynamics simulation studies revealed the potential of these multi-enzyme targeted ligands. Among the studied ligands, 6Z5 seems to have the best binding potential and forms a stable complex with BACE-1 and γ-secretase. We recommend the synthesis of 6Z5 for future in-vitro and in-vivo studies.

Chemoinformatics and Machine Learning Approaches for Identifying Antiviral Compounds

Current pandemics propelled research efforts in unprecedented fashion, primarily triggering computational efforts towards new vaccine and drug development as well as drug repurposing. There is an urgent need to design novel drugs with targeted biological activity and minimum adverse reactions that may be useful to manage viral outbreaks. Hence an attempt has been made to develop Machine Learning based predictive models that can be used to assess whether a compound has the potency to be antiviral or not. To this end, a set of 2358 antiviral compounds were compiled from the CAS COVID-19 antiviral SAR dataset whose activity was reported based on IC₅₀ value. A total 1157 two-dimensional molecular descriptors were computed among which, the most highly correlated descriptors were selected using Tree-based, Correlation-based and Mutual information-based feature selection methods. Seven Machine Learning algorithms i. e., Random Forest, XGBoost, Support Vector Machine, KNN, Decision Tree, MLP Classifier and Logistic Regression were benchmarked. The best performance was achieved by the models developed using Random Forest and XGBoost algorithms in all the feature selection methods. The maximum predictive accuracy of both these models was 88 % with internal validation. Whereas, with an external dataset, a maximum accuracy of 93.10 % for XGBoost and 100 % for Random Forest based model was achievable. Furthermore, the study demonstrated scaffold analysis of the molecules as a pragmatic approach to explore the importance of structurally diverse compounds in data driven studies.

Identification of Butyrylcholinesterase and Monoamine Oxidase B Targeted Ligands and their Putative Application in Alzheimer's Treatment: A Computational Strategy

BACKGROUND

With the burgeoning worldwide aging population, the incidence of Alzheimer's disease (AD) and its associated disorders is continuously rising. To appraise other relevant drug targets that could lead to potent enzyme targeting, 13 previously predicted ligands (shown favorable binding with AChE (acetylcholinesterase) and GSK-3 (glycogen synthase kinase) were screened for targeting 3 different enzymes, namely butyrylcholinesterase (BChE), monoamine oxidase A (MAO-A), and monoamine oxidase B (MAO-B) to possibly meet the unmet medical need of better AD treatment.

MATERIALS AND METHODS

The study utilized in silico screening of 13 ligands against BChE, MAO-A and MAOB using PyRx-Python prescription 0.8. The visualization of the active interaction of studied compounds with targeted proteins was performed by Discovery Studio 2020 (BIOVIA).

RESULTS

The computational screening of studied ligands revealed the docking energies in the range of -2.4 to -11.3 kcal/mol for all the studied enzymes. Among the 13 ligands, 8 ligands (55E, 6Z2, 6Z5, BRW, F1B, GVP, IQ6, and X37) showed the binding energies of ≤ -8.0 kcal/mol towards BChE, MAO-A and MAO-B. The ligand 6Z5 was found to be the most potent inhibitor of BChE and MAO-B, with a binding energy of -9.7 and -10.4 kcal mol, respectively. Molecular dynamics simulation of BChE-6Z5 and MAO-B-6Z5 complex confirmed the formation of a stable complex.

CONCLUSION

Our computational screening, molecular docking, and molecular dynamics simulation studies revealed that the above-mentioned enzymes targeted ligands might expedite the future design of potent anti-AD drugs generated on this chemical scaffold.

A review on α-mangostin as a potential multi-target-directed ligand for Alzheimer's disease

Alzheimer's disease (AD) is an age-related neurodegenerative disease characterized by progressive memory loss, declining language skills and other cognitive disorders. AD has brought great mental and economic burden to patients, families and society. However due to the complexity of AD's pathology, drugs developed for the treatment of AD often fail in clinical or experimental trials. The main problems of current anti-AD drugs are low efficacy due to mono-target method or side effects, especially high hepatotoxicity. To tackle these two main problems, multi-target-directed ligand (MTDL) based on "one molecule, multiple targets" has been studied. MTDLs can regulate multiple biological targets at the same time, so it has shown higher efficacy, better safety. As a natural active small molecule, α-mangostin (α-M) has shown potential multi-factor anti-AD activities in a series of studies, furthermore it also has a certain hepatoprotective effect. The good availability of α-M also provides support for its application in clinical research. In this work, multiple activities of α-M related to AD therapy were reviewed, which included anti-cholinesterase, anti-amyloid-cascade, anti-inflammation, anti-oxidative stress, low toxicity, hepatoprotective effects and drug formulation. It shows that α-M is a promising candidate for the treatment of AD.

Therapeutic Potential of Natural Psychoactive Drugs for Central Nervous System Disorders: A Perspective from Polypharmacology

In the drug development, the formation of highly selective ligands has been unsuccessful in the treatment of central nervous system disorders. Multi-target ligands, from the polypharmacology paradigm, are being proposed as treatments for these complex disorders, since they offer enhanced efficacy and a strong safety profile. Natural products are the best examples of multi-target compounds, so they are of high interest within this paradigm. Additionally, recent research on psychoactive drugs of natural origin, such as ayahuasca and cannabis, has demonstrated the promising therapeutic potential for the treatment of some psychiatric and neurological disorders. In this text, we describe how research on psychoactive drugs can be effectively combined with the polypharmacology paradigm, providing ayahuasca and cannabis research as examples. The advantages and disadvantages are also discussed.

Seladelicatulasine A-G, C27 steroidal glycosides with cholinesterase inhibitory activities from Selaginella delicatula

Seven undescribed C₂₇ steroidal glycosides, Seladelicatulasine A-G, including six cholestanol glycosides and one spirostanol glycoside, were isolated from Selaginella delicatula. Their structures were elucidated by 1D/2D NMR spectra and HRESIMS analyses. The absolute configurations of the sugars were determined by enzymatic hydrolysis and GC/MS analyses. These cholestanol glycosides were isolated from the family Selaginellaceae for the first time. Seladelicatulasine F is characterized as a rare B-5,6-secosteroid. In addition, all the compounds were evaluated for their inhibitory activities against cholinesterase (AChE/BChE) and monoamine oxidase (MAO-A/MAO-B). These steroidal glycosides displayed selective inhibition activities on cholinesterase. Seladelicatulasine A, B and E inhibited the AChE activity with IC₅₀ values of 0.31, 0.09, and 0.04 μM, respectively. Seladelicatulasine A and F showed the strongest inhibition activity on BChE with IC₅₀ values of 0.37 and 0.65 μM, respectively.

Inhibition of BACE1, MAO-B, cholinesterase enzymes, and anti-amyloidogenic potential of selected natural phytoconstituents: Multi-target-directed ligand approach

Alzheimer's disease (AD) is a neurodegenerative disorder, and multiple factors are involved in disease progression. This is why there is an urgent need to develop novel molecules with multi-target-directed ligands (MTDLs) potential. The current study explores the active phytoconstituents from traditionally used medicinal spices, namely piperine, cinnamaldehyde, eugenol, cuminaldehyde, and alpha-terpinyl acetate for the inhibition of β-secretase, monoamine oxidase, cholinesterase enzymes, anti-aggregation of amyloid β (Aβ) fibrils, and their protective effect against hydrogen peroxide (H₂ O₂ ) and Aβ-induced toxicity. Eugenol showed inhibitory activity against MAO-B enzyme, free radical scavenging activity, and anti-aggregation activity against Aβ peptides than other phytoconstituents. It also demonstrated a significant cytoprotective effect against H₂ O₂ -induced oxidative stress and Aβ-induced cytotoxicity in pheochromocytoma (PC) 12 cells. A molecular docking study of eugenol showed interactions with active site residue of the target enzymes. The study successfully demonstrated that eugenol could have an MTDLs potential better than synthesized drugs used in the treatment of AD. PRACTICAL APPLICATIONS: The present study demonstrated multi-target-directed ligand potential of eugenol and can be developed to treat complex diseases like Alzheimer's. Eugenol can bind to different Alzheimer's targets such as β-secretase (BACE1), Monoamine oxidase B (MAO-B), Cholinesterase's, and amyloid β_1-42 fibrils and might have a disease-modifying potential. The other natural phytoconstituents such as piperine, cinnamaldehyde, cuminaldehyde, and alpha-terpinyl acetate also demonstrated MTDL potential could also be used for developing novel molecules for disease-modifying effect. It also protects against oxidative stress.

Network Pharmacology-Based and Experimental Identification of the Effects of Quercetin on Alzheimer's Disease

Alzheimer’s disease (AD) is one of the neurodegenerative brain disorders inducing nearly half of dementia cases, and the diagnosis and treatment of AD are the primary issues clinically. However, there is a lack of effective biomarkers and drugs for AD diagnosis and therapeutics so far. In this study, bioinformatics analysis combined with an experimental verification strategy was used to identify the biomarkers and the quercetin targets for AD diagnosis and treatment. First, differentially expressed genes in the AD brain were identified by microarray data analysis. Second, quercetin, a predominant flavonoid, was used to screen the target genes. Third, the drug–disease network was determined, and the target genes of quercetin treatment were obtained in AD-related HT-22 cell-based assay. Six genes, including MAPT, PIK3R1, CASP8, DAPK1, MAPK1, and CYCS, were validated by the system pharmacology analysis in the hippocampus samples of AD patients. The results suggested that MAPT, PIK3R1, CASP8, and DAPK1 were significantly increased, but MAPK1 and CYCS were significantly decreased in HT-22 cells after Aβ1-42 treatment. Moreover, MAPK1 and CYCS were markedly increased, but MAPT, PIK3R1, CASP8, and DAPK1 were markedly decreased after quercetin treatment in these HT-22 cells. Altogether, MAPT, PIK3R1, CASP8, DAPK1, MAPK1, and CYCS are all the biomarkers for AD diagnosis and the targets of quercetin treatment, and our findings may provide valuable biomarkers for AD diagnosis and treatment.

Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections

In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.

Recent advances in screening active components from natural products based on bioaffinity techniques

Natural products have provided numerous lead compounds for drug discovery. However, the traditional analytical methods cannot detect most of these active components, especially at their usual low concentrations, from complex natural products. Herein, we reviewed the recent technological advances (2015–2019) related to the separation and screening bioactive components from natural resources, especially the emerging screening methods based on the bioaffinity techniques, including biological chromatography, affinity electrophoresis, affinity mass spectroscopy, and the latest magnetic and optical methods. These screening methods are uniquely advanced compared to other traditional methods, and they can fish out the active components from complex natural products because of the affinity between target and components, without tedious separation works. Therefore, these new tools can reduce the time and cost of the drug discovery process and accelerate the development of more effective and better-targeted therapeutic agents.

Trilobatin Protects Against Aβ25-35-Induced Hippocampal HT22 Cells Apoptosis Through Mediating ROS/p38/Caspase 3-Dependent Pathway

Emerging evidence reveals that an aberrant accumulation of β-amyloid (Aβ) is the main reason of Alzheimer’s disease (AD) pathogenesis. Thus, inhibition of Aβ-induced neurotoxicity may be promising therapeutic tactics to mitigate AD onset and advance. The development of agent candidates by cultured neurons against Aβ-induced cytotoxicity is widely accepted to be an efficient strategy to explore the drug for AD patients. Previously, we have revealed that trilobatin (TLB), a small molecule monomer, derives from Lithocarpus polystachyus Rehd, possessed antioxidative activities on hydrogen peroxide-induced oxidative injury in PC12 cells. The present study was designed to investigate the effects and the underlying mechanism of TLB on Aβ-induced injury in hippocampal HT22 cells. The results demonstrated that TLB attenuated Aβ_25–35-induced HT22 cell death, as evidenced by MTT assay and LDH release. Furthermore, TLB dramatically mitigated cell death after Aβ_25–35 insulted via decreasing the intracellular and mitochondrial ROS overproduction and restoring antioxidant enzyme activities, as well as suppressing apoptosis. Of note, Aβ_25–35 triggered increase in ratio of Bax/Bcl-2, activation of caspase-3, phosphorylation of tau, JNK, p38 MAPK, and decrease in Sirt3 expression, whereas TLB reversed these changes. Intriguingly, TLB could directly bind to p38, as evidenced by molecular docking and p38 inhibitor. Taken together, the results reveal that TLB effectively protects against Aβ_25–35-induced neuronal cell death via activating ROS/p38/caspase 3-dependent pathway. Our findings afford evidence for the potential development of TLB to hinder neuronal death during AD.

Protective effects of chondroitin sulphate nano-selenium on a mouse model of Alzheimer's disease

In this study, the effect of chondroitin sulphate nano-selenium (CS@Se) on Alzheimer's disease (AD) in mice was investigated. CS@Se alleviated anxiety and improved the spatial learning and memory impairment in AD mice. CS@Se significantly reduced cell oedema and pyknosis, protected the mitochondria, and improved abnormal changes in the ultrastructure of hippocampal neuron synapses of AD mice. Moreover, CS@Se significantly increased the levels of superoxide dismutase(SOD), glutathione peroxidase (GSH-Px), Na⁺/K⁺-ATPase assay (Na⁺/K⁺-ATPase) and acetyltransferase (ChAT), and decreased the levels of malondialdehyde (MDA) and acetylcholinesterase (ChAE) in AD mice. Western blot results showed that CS@Se can attenuate excessive phosphorylation of tau (Ser396/Ser404) by regulating the expression of glycogen synthase kinase-3 beta (GSK-3β). In addition, CS@Se can activate the extracellular signal-regulated kinase 1/2 (ERK 1/2) and p38 mitogen-activated protein kinase (p38 MAPK) signalling pathways to inhibit nuclear transcription factor kappa B (NF-κB) nuclear translocation, thereby regulating the expression of pro-inflammatory cytokines. In summary, CS@Se can reduce oxidative stress damage, inhibit excessive tau phosphorylation, reduce inflammation to delay AD development, and increase the learning and memory capacities of AD mice.

Neuroprotective Effects of Quercetin in Alzheimer's Disease

Quercetin is a flavonoid with notable pharmacological effects and promising therapeutic potential. It is widely distributed among plants and found commonly in daily diets predominantly in fruits and vegetables. Neuroprotection by quercetin has been reported in several in vitro studies. It has been shown to protect neurons from oxidative damage while reducing lipid peroxidation. In addition to its antioxidant properties, it inhibits the fibril formation of amyloid-β proteins, counteracting cell lyses and inflammatory cascade pathways. In this review, we provide a synopsis of the recent literature exploring the relationship between quercetin and cognitive performance in Alzheimer's disease and its potential as a lead compound in clinical applications.

Preparation and in vitro evaluation of multi-target-directed selenium-chondroitin sulfate nanoparticles in protecting against the Alzheimer's disease

The purpose of this study was to ascertain the effect of selenium-chondroitin sulfate nanoparticles (CS@Se) on multi-target-directed therapy for the treatment of Alzheimer's disease (AD). CS@Se nanoparticles were successfully synthesized, and their therapeutic effects were studied in in vitro AD models. CS@Se effectively inhibited amyloid-β (Aβ) aggregation and protected SH-SY5Y cells from Aβ_1-42-induced cytotoxicity. Moreover, CS@Se significantly decreased okadaic acid-induced actin cytoskeleton instability in SH-SY5Y cells. In addition, CS@Se decreased the levels of reactive oxygen species (ROS) and malondialdehyde (MDA) and increased the levels of glutathione peroxidase (GSH-Px). The Western blot results indicated that CS@Se attenuated the hyperphosphorylation of tau (Ser396/Ser404) by regulating the expression of GSK-3β. In summary, this study demonstrated that CS@Se could inhibit the aggregation of Aβ, reduce damage to the cytoskeleton, mitigate oxidative stress and attenuate the hyperphosphorylation of tau protein. CS@Se might be a potent multi-functional agent for the treatment of AD and thus warrants further research and evaluation.

Strategies to target bioactive molecules to subcellular compartments. Focus on natural compounds

Many potential pharmacological targets are present in multiple subcellular compartments and have different pathophysiological roles depending on location. In these cases, selective targeting of a drug to the relevant subcellular domain(s) may help to sharpen its impact by providing topological specificity, thus limiting side effects, and to concentrate the compound where needed, thus increasing its effectiveness. We review here the state of the art in precision subcellular delivery. The major approaches confer "homing" properties to the active principle via permanent or reversible (in pro-drug fashion) modifications, or through the use of special-design nanoparticles or liposomes to ferry a drug(s) cargo to its desired destination. An assortment of peptides, substituents with delocalized positive charges, custom-blended lipid mixtures, pH- or enzyme-sensitive groups provide the main tools of the trade. Mitochondria, lysosomes and the cell membrane may be mentioned as the fronts on which the most significant advances have been made. Most of the examples presented here have to do with targeting natural compounds - in particular polyphenols, known as pleiotropic agents - to one or the other subcellular compartment.

Embelin, a Potent Molecule for Alzheimer's Disease: A Proof of Concept From Blood-Brain Barrier Permeability, Acetylcholinesterase Inhibition and Molecular Docking Studies

Embelin is well-known in ethnomedicine and reported to have central nervous system activities. However, there is no report on blood-brain barrier (BBB) permeability of embelin. Here the BBB permeability of embelin was evaluated using in vitro primary porcine brain endothelial cell (PBEC) model of the BBB. Embelin was also evaluated for acetylcholinesterase (AChE) inhibitory activity and docking prediction for interaction with AChE and amyloid beta (Aβ) binding sites. Embelin was found to be non-toxic to the PBECs and did not disturb the PBEC barrier function. The PBECs showed restrictive tight junctions with average transendothelial electrical resistance of 365.37 ± 113.00 Ω.cm², for monolayers used for permeability assays. Permeability assays were conducted from apical-to-basolateral direction (blood-to-brain side). Embelin showed apparent permeability (P_app) value of 35.46 ± 20.33 × 10⁻⁶ cm/s with 85.53% recovery. In vitro AChE inhibitory assay demonstrated that embelin could inhibit the enzyme. Molecular docking study showed that embelin binds well to active site of AChE with CDOCKER interaction energy of −65.75 kcal/mol which correlates with the in vitro results. Docking of embelin with Aβ peptides also revealed the promising binding with low CDOCKER interaction energy. Thus, findings from this study indicate that embelin could be a suitable molecule to be further developed as therapeutic molecule to treat neurological disorders particularly Alzheimer's disease.

Small Molecule Natural Products and Alzheimer's Disease

Alzheimer's disease (AD) is a progressive and deadly neurodegenerative disease that is characterized by memory loss, cognitive impairment and dementia. Several hypotheses have been proposed for the pathogenesis based on the pathological changes in the brain of AD patients during the last few decades. Unfortunately, there is no effective agents/therapies to prevent or control AD at present. Currently, only a few drugs, which function as acetylcholinesterase (AChE) inhibitors or N-methyl-Daspartate (NMDA) receptor antagonists, are available to alleviate symptoms. Since many small molecule natural products have shown their functions as agonists or antagonists of receptors, as well as inhibitors of enzymes and proteins in the brain during the development of central nervous system (CNS) drugs, it is likely that natural products will play an important role in anti-AD drug development. We review recent papers on using small molecule natural products as drug candidates for the treatment of AD. These natural products possess antioxidant, anti-inflammatory, anticholinesterase, anti-amyloidogenic and neuroprotective activities. Moreover, bioactive natural products intended to be used for preventing AD, reducing the symptoms of AD and the new targets for treatment of AD are summarized.

Safflower (Carthamus tinctorius L.) seed attenuates memory impairment induced by scopolamine in mice via regulation of cholinergic dysfunction and oxidative stress

Safflower seed extract containing serotonin and its derivatives improves scopolamine-induced memory impairment, it could be a promising herbal medicine for the treatment of dementia.

Uncaria rhynchophylla ameliorates amyloid beta deposition and amyloid beta-mediated pathology in 5XFAD mice

One of the pathological hallmarks of Alzheimer's disease (AD) is the abnormal aggregation of amyloid beta (Aβ) peptides. Uncaria rhynchophylla (UR), one of the Uncaria species, has long been used to treat neurodegenerative disease. In particular, it has been reported that UR inhibits aggregation of Aβ in vitro. However, little is known about the histological effects of UR treatment on Aβ pathology in AD animal models. In the present study, we investigated the effect of UR on Aβ aggregation, Aβ-mediated pathologies and adult hippocampal neurogenesis in the brain of 5XFAD mice. First, using the thioflavin T assay and amyloid staining, we demonstrated that UR treatment effectively inhibited Aβ aggregation and accumulation in the cortex and subiculum. Second, immunofluorescence staining showed that administration of UR attenuated gliosis and neurodegeneration in the subiculum and cortex. Third, UR treatment ameliorated impaired adult hippocampal neurogenesis. The present results indicate that UR significantly alleviates Aβ deposition and Aβ-mediated neuropathology in the brain in 5XFAD mice, suggesting the potency of UR as a preventive and therapeutic agent for AD.

Computational Study of the Aza-Michael Addition of the Flavonoid (+)-Taxifolin in the Inhibition of β-Amyloid Fibril Aggregation

Inhibition of abnormal protein self-aggregation is an attractive strategy against amyloidogenic diseases, but has found limited success due to the complexity of protein self-assembly, the absence of fully reproducible aggregation assays, and the scarce knowledge of the inhibition mechanisms by small molecules. In this context, catechol-containing compounds may lead to covalent adducts with amyloid fibrils that interfere with the aggregation process. In particular, the covalent adduct formed between the oxidized form of (+)-taxifolin and an β-amyloid (Aβ42) suggests the involvement of a specific recognition motif that enables the chemical reaction with Aβ42. In this study, we have examined the mechanisms implicated in the aza-Michael addition of the o-quinone species of (+)-taxifolin with Aβ42 fibrils. The results support the binding of (+)-taxifolin to the hydrophobic groove delimited by the edges defined by Lys16 and Glu22 residues in the fibril. The chemical reaction proceeds through the nucleophilic attack of the deprotonated amino group of a Lys16 residue in a process activated by the interaction between the o-quinone ring with a vicinal Lys16 residue, as well as by a water-assisted proton transfer, which is the rate-limiting step of the reaction. This specific inhibition mechanism, which may explain the enhanced anti-aggregating activity of oxidized flavonoids compared to fresh compounds, holds promise for developing disease-modifying therapies.

Analysing and Navigating Natural Products Space for Generating Small, Diverse, But Representative Chemical Libraries

Armed with the digital availability of two natural products libraries, amounting to some 195 885 molecular entities, we ask the question of how we can best sample from them to maximize their "representativeness" in smaller and more usable libraries of 96, 384, 1152, and 1920 molecules. The term "representativeness" is intended to include diversity, but for numerical reasons (and the likelihood of being able to perform a QSAR) it is necessary to focus on areas of chemical space that are more highly populated. Encoding chemical structures as fingerprints using the RDKit "patterned" algorithm, we first assess the granularity of the natural products space using a simple clustering algorithm, showing that there are major regions of "denseness" but also a great many very sparsely populated areas. We then apply a "hybrid" hierarchical K-means clustering algorithm to the data to produce more statistically robust clusters from which representative and appropriate numbers of samples may be chosen. There is necessarily again a trade-off between cluster size and cluster number, but within these constraints, libraries containing 384 or 1152 molecules can be found that come from clusters that represent some 18 and 30% of the whole chemical space, with cluster sizes of, respectively, 50 and 27 or above, just about sufficient to perform a QSAR. By using the online availability of molecules via the Molport system (www.molport.com), we are also able to construct (and, for the first time, provide the contents of) a small virtual library of available molecules that provided effective coverage of the chemical space described. Consistent with this, the average molecular similarities of the contents of the libraries developed is considerably smaller than is that of the original libraries. The suggested libraries may have use in molecular or phenotypic screening, including for determining possible transporter substrates.