Combined 3D-QSAR, molecular docking, and molecular dynamics study of tacrine derivatives as potential acetylcholinesterase (AChE) inhibitors of Alzheimer's disease

Comprehensive chemical profiling, bioactivity evaluation, and computational modelling of various extracts from cleome ornithopodioides L

In this study, various extracts of Cleome ornithopodioides L.: n-hexane, dichloromethane (DCM), ethyl acetate (EA), ethanol, 70% ethanol, and water (infusion) were used to examine the chemical profiles and biological activities of the plant. The characterisation of the compounds was carried out by HPLC coupled to ion trap mass spectrometry. Antioxidant activity was evaluated through six assays. Additionally, the enzyme inhibition activities of the extracts were tested against acetylcholinesterase (AChE), butyrylcholinesterase (BChE), tyrosinase, α-amylase, and α-glucosidase. The extracts showed varying levels of bioactive compounds, with the water (infused) (38.95 mg GAE/g) and the 70% ethanol (38.75 mg GAE/g) extracts having the highest total phenolics. The extracts primarily contained flavonoid glycosides (quercetin glycosides). The 70% ethanol extract exhibited the highest antioxidant activity as indicated by four assays (DPPH: 68.21 mg TE/g; ABTS: 193.48 mg TE/g; CUPRAC: 143.68; FRAP: 123.86 mg TE/g). The ethanol extract also showed significant inhibition of BChE (15.69 mg GALAE/g). This highlights their potential for future research into applications in health and medicine, where they could offer valuable therapeutic benefits.

Amino-7,8-dihydro-4H-chromenone derivatives as potential inhibitors of acetylcholinesterase and butyrylcholinesterase for Alzheimer's disease management; in vitro and in silico study

In this article, we present the design and synthesis of amino-7,8-dihydro-4H-chromenone derivatives as possible inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) for the management of Alzheimer's disease (AD). The target compounds were evaluated against AChE and BChE in vitro, and 4k exhibited good potency against BChE (IC50 = 0.65 ± 0.13 µM) compared with donepezil used as a positive control. Kinetic studies revealed that compound 4k exhibited a competitive-type inhibition with a Ki value of 0.55 µM. Molecular docking and molecular dynamics simulations further supported the rationality of our design strategy, as 4k showed promising binding interactions with the active sites of BChE. Overall, our findings highlight the potential of amino-7,8-dihydro-4H-chromenone derivatives as promising candidates for developing novel therapeutics targeting cholinesterase in managing AD.

Development of Activity Rules and Chemical Fragment Design for In Silico Discovery of AChE and BACE1 Dual Inhibitors against Alzheimer's Disease

Multi-target drug development has become an attractive strategy in the discovery of drugs to treat of Alzheimer's disease (AzD). In this study, for the first time, a rule-based machine learning (ML) approach with classification trees (CT) was applied for the rational design of novel dual-target acetylcholinesterase (AChE) and β-site amyloid-protein precursor cleaving enzyme 1 (BACE1) inhibitors. Updated data from 3524 compounds with AChE and BACE1 measurements were curated from the ChEMBL database. The best global accuracies of training/external validation for AChE and BACE1 were 0.85/0.80 and 0.83/0.81, respectively. The rules were then applied to screen dual inhibitors from the original databases. Based on the best rules obtained from each classification tree, a set of potential AChE and BACE1 inhibitors were identified, and active fragments were extracted using Murcko-type decomposition analysis. More than 250 novel inhibitors were designed in silico based on active fragments and predicted AChE and BACE1 inhibitory activity using consensus QSAR models and docking validations. The rule-based and ML approach applied in this study may be useful for the in silico design and screening of new AChE and BACE1 dual inhibitors against AzD.

In silico study of tacrine and acetylcholine binding profile with human acetylcholinesterase: docking and electronic structure

Alzheimer disease (AD) is a neurodegenerative process, one of the most common and incident dementia in the population over 60 years. AD manifests the presence of complex biochemical processes involved in neuronal degeneration, such as the formation of senile plaques containing amyloid-β peptides, the development of intracellular neurofibrillary tangles, and the suppression of the acetylcholine neurotransmitter. In this way, we performed a set of theoretical tests of tacrine ligand and acetylcholine neurotransmitter against the human acetylcholinesterase enzyme. Molecular docking was used to understand the most important interactions of these molecules with the enzyme. Computational chemistry calculation was carried out using MP2, DFT, and semi-empirical methods, starting from molecular docking structures. We have also performed studies regarding the non-covalent interactions, electron localization function, molecular electrostatic potential and explicit water molecule influence. For Trp86 residue, we show two main interactions in accordance to the results of the literature for TcAChE. First, intermolecular interactions of the cation-π and sigma-π type were found. Second, close stacking interactions were stablished between THA+ and Trp86 residue on one side and with Tyr337 residue on the other side.

Computational Studies on Acetylcholinesterase Inhibitors: From Biochemistry to Chemistry

Acetylcholinesterase inhibitors are the most promising therapeutics for Alzheimer's disease treatment as these prevent the loss of acetylcholine and slows the progression of the disease. The drugs approved for the management of Alzheimer's disease by the FDA are acetylcholinesterase inhibitors but are associated with side effects. Consistent and stringent efforts by the researchers with the help of computational methods opened new ways of developing novel molecules with good acetylcholinesterase inhibitory activity. In this manuscript, we reviewed the studies that identified the essential structural features of acetylcholinesterase inhibitors at the molecular level as well as the techniques like molecular docking, molecular dynamics, quantitative structure-activity relationship, virtual screening, and pharmacophore modelling that were used in designing these inhibitors.

A Comprehensive Review of Cholinesterase Modeling and Simulation

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Design, synthesis and evaluation of novel dimethylamino chalcone-O-alkylamines derivatives as potential multifunctional agents against Alzheimer's disease

A novel series of dimethylamino chalcone-O-alkylamines derivatives was designed and synthesized as multifunctional agents for the treatment of AD. All the target compounds exhibited significant abilities to inhibit and disaggregate Aβ aggregation, and acted as potential selective AChE inhibitors, biometal chelators and selective MAO-B inhibitors. Among these compounds, compound TM-6 showed the greatest inhibitory activity against self-induced Aβ aggregation (IC50 = 0.88 μM) and well disaggregation ability toward self-induced Aβ aggregation (95.1%, 25 μM), the TEM images, molecular docking study and molecular dynamics simulations provided reasonable explanation for its high efficiency, and it was also found to be a remarkable antioxidant (ORAC-FL values of 2.1eq.), the best AChE inhibitor (IC50 = 0.13 μM) and MAO-B inhibitor (IC50 = 1.0 μM), as well as a good neuroprotectant. UV-visual spectrometry and ThT fluorescence assay revealed that compound TM-6 was not only a good biometal chelator by inhibiting Cu2+-induced Aβ aggregation (95.3%, 25 μM) but also could disassemble the well-structured Aβ fibrils (88.1%, 25 μM). Further, TM-6 could cross the blood-brain barrier (BBB) in vitro. More importantly, compound TM-6 did not show any acute toxicity in mice at doses of up to 1000 mg/kg and improved scopolamine-induced memory impairment. Taken together, these data indicated that TM-6, an excellent balanced multifunctional inhibitor, was a potential lead compound for the treatment of AD.

Casuarictin: A new herbal drug molecule for Alzheimer's disease as inhibitor of presenilin stabilization factor like protein

Alzheimer's disease is a progressive neurodegenerative disorder. In this disease neurodegeneration occurs due to deposition of aggregated amyloid-beta plaques and neurofibrillary tangles (hyperphosphorylated tau proteins). Present study focuses on interaction of different phytochemicals with presenilin stabilization factor like protein (PSFL). PSFL protein is known to stabilize Presenilin, which is mainly involved in intramembrane hydrolysis of selected type- I membrane proteins, including amyloid-beta precursor protein, and produces amyloid-beta protein. Amyloid-beta are small peptides comprising of 36–43 amino acids, which play a significant role in senile plaques formation in the brains of Alzheimer patients. Virtual screening and docking of phytochemicals with PSFL protein was done to find the potential inhibitor. Based on binding affinity, docked energy and molecular dynamics simulations, three phytochemicals namely Saponin, Casuarictin, and Enoxolone, were identified as potential inhibitors for the target protein.

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.

Computational investigation of TGF-β receptor inhibitors for treatment of idiopathic pulmonary fibrosis: Field-based QSAR model and molecular dynamics simulation

The discovery of drugs relevant to transforming growth factor β (TGF-β) receptor inhibitors have been considered as a considerable challenge during therapy idiopathic pulmonary fibrosis diseases. For the first time, herein we illustrate a field-based quantitative structure-activity relationship (QSAR) model and molecular dynamics (MD) simulations for novel 7-substituted-pyrazolo [4, 3-b] pyridine derivatives with biological activity for the TGF-β receptor, with an attempt of elucidating the 3D structural features that are essential for the activity. Results demonstrate that the field-based model (Q² = 0.548, R²_training = 0.840, R²_test = 0.750) are acceptable with good predictive capabilities. In addition, MD studies were also carried out on the training set with the aim of exploring their binding modes in the active pocket of TGF-β receptor, resulting in some of the crucial structural fragments which are responsible for inhibitory activity. Therefore, we summarized the following features required for TGF-β receptor inhibition: electronegative in region1, bulky groups in region2 and smaller groups in region3. Based on the model and related information, we hope the above information provides an important insight for understanding the interactions of the inhibitors and TGF-β receptor, which may be useful in discovering novel potent inhibitors.

Synthesis and Acaricidal Activities of Scopoletin Phenolic Ether Derivatives: QSAR, Molecular Docking Study and in Silico ADME Predictions

Thirty phenolic ether derivatives of scopoletin modified at the 7-hydroxy position were synthesized, and their structures were confirmed by IR, ¹H-NMR, 13C-NMR, MS and elemental analysis. Preliminary acaricidal activities of these compounds against female adults of Tetranychus cinnabarinus (Boisduval) were evaluated using the slide-dip method. The results indicated that some of these compounds exhibit more pronounced acaricidal activity than scopoletin, especially compounds 32, 20, 28, 27 and 8 which exhibited about 8.41-, 7.32-, 7.23-, 6.76-, and 6.65-fold higher acaricidal potency. Compound 32 possessed the the most promising acaricidal activity and exhibited about 1.45-fold higher acaricidal potency against T. cinnabarinus than propargite. Statistically significant 2D-QSAR model supports the observed acaricidal activities and reveals that polarizability (HATS5p) was the most important parameter controlling bioactivity. 3D-QSAR (CoMFA: q² = 0.802, r² = 0.993; CoMSIA: q² = 0.735, r² = 0.965) results show that bulky substituents at R₄, R₁, R₂ and R₅ (C₆, C₃, C₄, and C₇) positions, electron positive groups at R₅ (C₇) position, hydrophobic groups at R₁ (C₃) and R₂ (C₄), H-bond donors groups at R₁ (C₃) and R₄ (C₆) will increase their acaricidal activity, which provide a good insight into the molecular features relevant to the acaricidal activity for further designing novel acaricidal agents. Molecular docking demonstrates that these selected derivatives display different bide modes with TcPMCA1 from lead compound and they interact with more key amino acid residues than scopoletin. In silico ADME properties of scopoletin and its phenolic ether derivatives were also analyzed and showed potential to develop as good acaricidal candidates.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreover, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulating single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharmacology, multi-modal therapies, multifaceted, and/or the more recently proposed term "multi-targeted designed drugs". Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challenges such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summarizing some of the most prominent and computationally explored single targets against AD and further, we discussed a successful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computational approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r² = 0.882 and r² = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.