In Vitro and In Vivo Investigations of Chromone Derivatives as Potential Multitarget-Directed Ligands: Cognitive Amelioration Utilizing a Scopolamine-Induced Zebrafish Model.

Alzheimer's disease is a complex neurological disorder linked with multiple pathological hallmarks. The interrelation of therapeutic targets assists in the enhancement of cognitive decline through interference with overall neuronal transmission. We have synthesized and screened various chromone derivatives as potential multitarget-directed ligands for the effective treatment of Alzheimer's disease. The synthesized compounds exhibited multipotent activity against AChE, BuChE, MAO-B, and amyloid ß aggregation. Three potent compounds, i.e., VN-3, VN-14, and VN-19 were identified that displayed remarkable activities against different targets. These compounds displayed IC50 values of 80 nM, 2.52 µM, and 140 nM against the AChE enzyme, respectively, and IC50 values of 2.07 µM, 70 nM, and 450 nM against the MAO-B isoform, respectively. VN-3 displayed potent activity against self-induced Aß1-42 aggregation with inhibition of 58.3%. In the ROS inhibition studies, the most potent compounds reduced the intracellular ROS levels up to 80% in SH-SY5Y cells at 25 µM concentration. The compounds were found to be neuroprotective and noncytotoxic even at a concentration of 25 µM against SH-SY5Y cells. In silico studies showed that the compounds were nicely accommodated in the active sites of the receptors along with thermodynamically stable orientations. Compound VN-19 exhibited a balanced multitargeting profile against AChE, BuChE, MAO-B, and Aß1-42 enzymes and was further evaluated for in vivo activities on the scopolamine-induced zebrafish model. VN-19 was found to ameliorate the cognitive decline in zebrafish brains by protecting them against scopolamine-induced neurodegeneration. Thus, VN-3, VN-14, and VN-19 were identified as potent multitarget-directed ligands with a balanced activity profile against different targets and can be developed as therapeutics for AD.

Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease.

Alzheimer's disease (AD) is a complex neurological disorder and no effective drug is available for its treatment. Numerous pathological conditions are believed to be responsible for the initiation and development of AD including c-Jun N-terminal kinases (JNKs). The JNKs are one of the enzymes from the mitogen-activated protein kinase (MAPK) family that controls the phosphorylation of various transcription factors on serine and threonine residues, and hold significant responsibilities in tasks like gene expression, cell proliferation, differentiation, and apoptosis. Since, JNK3 is primarily expressed in the brain hence its increased levels in the brain are associated with the AD pathology promoting neurofibrillary tangles, senile plaques, neuroinflammation, and nerve cell apoptosis. The current research work is focused on the development of novel JNK inhibitors as therapeutics for AD employing a structure-based virtual screening (SBVS) approach. The ZINC database (14634052 compounds) was investigated after employing pan assay interference (PAINs), drug-likeness, and diversity picking filter to distinguish molecules interacting with JNK3 by following three docking precision criteria: High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP) & MMGBSA. Five lead molecules showed a better docking score in the range of -13.091 to -14.051 kcal/mol better than the reference compound (- 11.828 kcal/mol). The lead compounds displayed acceptable pharmacokinetic properties and were subjected to molecular dynamic simulations of 100 ns and binding free energy calculations. All the lead molecules showed stable RMSD and hydrogen bond interactions throughout the trajectory. The ∆GMM/PBSA_total score for the lead compounds ZINC220382956, ZINC147071339, ZINC207081127, ZINC205151456, ZINC1228819126, and CC-930 was calculated and found to be - 31.39, - 42.8, - 37.04, - 39.01, - 36.5, - 34.16 kcal/mol, respectively. Thus, it was concluded that the lead molecules identified in these studies have the potential to be explored as potent JNK3 inhibitors.

Iodine-PEG as a unique combination for the metal-free synthesis of flavonoids through iodonium-triiodide ion-pair complexation.

An efficient metal-free single-step protocol has been developed for the direct synthesis of flavones from 2-hydroxyacetophenone and substituted benzaldehydes. This chemical transformation is exclusively promoted by the iodonium-triiodide ion couple formed through iodine and PEG-400 complexation. The triiodide anion not only helps in the abstraction of a proton from the acetophenone but also promotes the cyclization of intermediate chalcone to the corresponding flavones. The flavones were obtained in very high yields without using any toxic metal catalysts or harsh reaction conditions. The reaction mechanism was established through a series of test reactions and entrapping of reaction intermediates. The developed protocol provides direct access to flavones in high yields under milder reaction conditions with great substrate compatibility, including hydroxylated derivatives.

Virtual screening and molecular dynamics simulation approach for the identification of potential multi-target directed ligands for the treatment of Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurological disorder characterized by memory loss and cognitive impairment. The currently available single-targeting drugs have miserably failed in the treatment of AD, and multi-target directed ligands (MTDLs) are being explored as an alternative treatment strategy. Cholinesterase and monoamine oxidase enzymes are reported to play a crucial role in the pathology of AD, and multipotent ligands targeting these two enzymes simultaneously are under various phases of design and development. Recent studies have revealed that computational approaches are robust and trusted tools for identifying novel therapeutics. The current research work is focused on the development of potential multi-target directed ligands that simultaneously inhibit acetylcholinesterase (AChE) and monoamine oxidase B (MAO-B) enzymes employing a structure-based virtual screening (SBVS) approach. The ASINEX database was screened after applying pan assay interference and drug-likeness filter to identify novel molecules using three docking precision criteria; High Throughput Virtual Screening (HTVS), Standard Precision (SP), and Extra Precision (XP). Additionally, binding free energy calculations, ADME, and molecular dynamic simulations were employed to get structural insights into the mechanism of protein-ligand binding and pharmacokinetic properties. Three lead molecules viz. AOP19078710, BAS00314308 and BDD26909696 were successfully identified with binding scores of -10.565, -10.543 & -8.066 kcal/mol against AChE and -11.019, -12.357 & -10.068 kcal/mol against MAO-B, better score as compared to the standard inhibitors. In the near future, these molecules will be synthesized and evaluated through in vitro and in vivo assays for their inhibition potential against AChE and MAO-B enzymes.

Catalyzing a Cure: Discovery and development of LRRK2 inhibitors for the treatment of Parkinson's disease.

Parkinson's disease (PD) is an age-related second most common progressive neurodegenerative disorder that affects millions of people worldwide. Despite decades of research, no effective disease modifying therapeutics have reached clinics for treatment/management of PD. Leucine-rich repeat kinase 2 (LRRK2) which controls membrane trafficking and lysosomal function and its variant LRRK2-G2019S are involved in the development of both familial and sporadic PD. LRRK2, is therefore considered as a legitimate target for the development of therapeutics against PD. During the last decade, efforts have been made to develop effective, safe and selective LRRK2 inhibitors and also our understanding about LRRK2 has progressed. However, there is an urge to learn from the previously designed and reported LRRK2 inhibitors in order to effectively approach designing of new LRRK2 inhibitors. In this review, we have aimed to cover the pre-clinical studies undertaken to develop small molecule LRRK2 inhibitors by screening the patents and other available literature in the last decade. We have highlighted LRRK2 as targets in the progress of PD and subsequently covered detailed design, synthesis and development of diverse scaffolds as LRRK2 inhibitors. Moreover, LRRK2 inhibitors under clinical development has also been discussed. LRRK2 inhibitors seem to be potential targets for future therapeutic interventions in the treatment and management of PD and this review can act as a cynosure for guiding discovery, design, and development of selective and non-toxic LRRK2 inhibitors. Although, there might be challenges in developing effective LRRK2 inhibitors, the opportunity to successfully develop novel therapeutics targeting LRRK2 against PD has never been greater.

Screening of potential inhibitors against structural proteins from Monkeypox and related viruses of Poxviridae family via docking and molecular dynamics simulation.

Monkeypox virus (MPXV) is an orthopoxvirus which causes zoonotic infection in humans. Even though sporadic cases of this infection are limited to the African continent, but if the infection continues to increase unabated, it can be a cause of serious concern for the human populace. Smallpox vaccination has been in use against monkeypox infection but it only provides mild protection. In the current study, we have screened novel small molecules (estrone fused heterocycles (EH1-EH7)) exhibiting good binding with monkeypox virus protein and related proteins from Poxviridae family of viruses via computational approaches. EH1-7 series of small molecules selected for the work have been synthesized via cycloaddition methodology. Docking and Molecular Dynamics (MD) results highlight EH4 compound to have strong binding affinity towards monkeypox and other related viral proteins selected for the study. Thus, computational outcomes suggest EH4 as a good candidate against monkeypox. Currently, no antiviral medication has been approved against monkeypox and the treatment is only via therapeutics available for smallpox and related conditions that may be helpful against monkeypox. Our study is thus an attempt to screen novel compounds against monkeypox infection, which would, in turn, facilitate development of novel therapeutics against Poxviridae family. HIGHLIGHTSMonkeypox infection is a public health emergency and necessitates immediate drug discovery.Molecular docking study to screen estrone-fused heterocycles compounds against Monkeypox and other orthopoxviruses.Molecular dynamics simulations revealed interaction/high binding affinities among EH4 heterocyclic compound and profilin-like protein from the monkeypox virus.Estrone-fused heterocycles compounds are promising anti-viral agents as per our in silico analysis.Our study provides evidence for investigating estrone-fused heterocycles compounds for further pharmacological interventions.Communicated by Ramaswamy H. Sarma.

Monkeypox: This orthopoxvirus leads to mpox (monkeypox) disease which shows symptoms similar to that smallpox, however to less severe extent.Poxviridae family: This is commonly a family of double-stranded DNA viruses. The natural hosts for these viruses are arthropods and Vertebrates.Molecular Dynamic simulation: MD simulation is crucial for determining the ligand's stability and revealing the duration of its interaction with the respective macromolecular structure.Molecular Docking: Molecular docking aids in determining specific sites where the ligand binds with the macromolecule as well as its binding affinity. Bioinformatics tools such as docking have been widely employed for aiding drug discovery efforts.Protein binding energy: On docking protein with the ligand, the binding energy shows the free energy change during binding process between protein-ligand.

Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major pathological hallmarks of AD and has gained the interest of researchers as a potential drug target to treat AD. Lately, Artificial Intelligence (AI) has revolutionized the drug discovery process by speeding it up and reducing the overall cost. As a part of our continuous effort to identify potential tau aggregation inhibitors, and leveraging the power of AI, in this study, we used a fully automated AI-assisted ligand-based virtual screening tool, PyRMD to screen a library of 12 million compounds from the ZINC database to identify potential tau aggregation inhibitors. The preliminary hits from virtual screening were filtered for similar compounds and pan-assay interference compounds (the compounds containing reactive functional groups which can interfere with the assays) using RDKit. Further, the selected compounds were prioritized based on their molecular docking score with the binding pocket of tau where the binding pockets were identified using replica exchange molecular dynamics simulation. Thirty-three compounds showing good docking scores for all the tau clusters were selected and were further subjected to in silico pharmacokinetic prediction. Finally, top 10 compounds were selected for molecular dynamics simulation and MMPBSA binding free energy calculations resulting in the identification of UNK_175, UNK_1027, UNK_1172, UNK_1173, UNK_1237, UNK_1518, and UNK_2181 as potential tau aggregation inhibitors.

Integrated use of ligand and structure-based virtual screening, molecular dynamics, free energy calculation and ADME prediction for the identification of potential PTP1B inhibitors.

Protein tyrosine phosphatases (PTPs) are the group of enzymes that control both cellular activity and the dephosphorylation of tyrosine (Tyr)-phosphorylated proteins. Dysregulation of PTP1B has contributed to numerous diseases including Diabetes Mellitus, Alzheimer's disease, and obesity rendering PTP1B as a legitimate target for therapeutic applications. It is highly challenging to target this enzyme because of its highly conserved and positively charged active-site pocket motivating researchers to find novel lead compounds against it. The present work makes use of an integrated approach combining ligand-based and structure-based virtual screening to find hit compounds targeting PTP1B. Initially, pharmacophore modeling was performed to find common features like two hydrogen bond acceptors, an aromatic ring and one hydrogen bond donor from the potent PTP1B inhibitors. The dataset of compounds matching with the common pharmacophoric features was filtered to remove Pan-Assay Interference substructure and to match the Lipinski criteria. Then, compounds were further prioritized using molecular docking and top fifty compounds with good binding affinity were selected for absorption, distribution, metabolism, and excretion (ADME) predictions. The top five compounds with high solubility, absorption and permeability holding score of - 10 to - 9.3 kcal/mol along with Ertiprotafib were submitted to all-atom molecular dynamic (MD) studies. The MD studies and binding free energy calculations showed that compound M4, M5 and M8 were having better binding affinity for PTP1B enzyme with ∆Gtotal score of - 24.25, - 31.47 and - 33.81 kcal/mol respectively than other compounds indicating that compound M8 could be a suitable lead compound as PTP1B inhibitor.

Synthesis, Biological Evaluation and in Silico Study of N-(2- and 3-Pyridinyl)benzamide Derivatives as Quorum Sensing Inhibitors against Pseudomonas aeruginosa.

The effectiveness of treating bacterial infections is seriously threatened by the emergence of bacterial resistance to chemical treatment. Growth of microbes in biofilm is one of the main causes of resistance to antimicrobial drugs. Quorum sensing (QS) inhibition, which targets the QS signalling system by obstructing cell-cell communication, was developed as an alternative treatment by creating innovative anti-biofilm drugs. Therefore, the goal of this study is to develop novel antimicrobial drugs that are effective against Pseudomonas aeruginosa by inhibiting QS and acting as anti-biofilm agents. In this study, N-(2- and 3-pyridinyl)benzamide derivatives were selected to design and syntheses. Antibiofilm activity was revealed by all the synthesized compounds and the biofilm was visibly impaired, and the OD595nm readings of solubilized biofilm cells presented a momentous difference between the treated and untreated biofilms. The best anti-QS zone was observed for compound 5d and found to be 4.96 mm. Through in silico research, the physicochemical characteristics and binding manner of these produced compounds were examined. For the purpose of understanding the stability of the protein and ligand complex, molecular dynamic simulation was also carried out. The overall findings showed that N-(2- and 3-pyridinyl)benzamide derivatives could be the key to creating effective newer anti-quorum sensing drugs that are effective against different bacteria.

Mechanistic Insight into the Inhibition of Choline Acetyltransferase by Proton Pump Inhibitors.

Various pharmacoepidemiological investigational studies have indicated that Proton Pump Inhibitors (PPIs) may increase the likelihood of developing Alzheimer's disease (AD) and non-AD related dementias. Previously, we have reported the inhibition of the acetylcholine biosynthesizing enzyme choline acetyltransferase (ChAT) by PPIs, for which omeprazole, lansoprazole, and pantoprazole exhibited IC50 values of 0.1, 1.5, and 5.3 µM, respectively. In this study we utilize a battery of computational tools to perceive a mechanistic insight into the molecular interaction of PPIs with the ChAT binding pocket that may further help in designing novel ChAT ligands. Various in-silico tools make it possible for us to elucidate the binding interaction, conformational stability, and dynamics of the protein-ligand complexes within a 200 ns time frame. Further, the binding free energies for the PPI-ChAT complexes were explored. The results suggest that the PPIs exhibit equal or higher binding affinity toward the ChAT catalytic tunnel and are stable throughout the simulated time and that the pyridine ring of the PPIs interacts primarily with the catalytic residue His324. A free energy landscape analysis showed that the folding process was linear, and the residue interaction network analysis can provide insight into the roles of various amino acid residues in stabilization of the PPIs in the ChAT binding pocket. As a major factor for the onset of Alzheimer's disease is linked to cholinergic dysfunction, our previous and the present findings give clear insight into the PPI interaction with ChAT. The scaffold can be further simplified to develop novel ChAT ligands, which can also be used as ChAT tracer probes for the diagnosis of cholinergic dysfunction and to initiate timely therapeutic interventions to prevent or delay the progression of AD.

Molecular characterization of glutor-GLUT interaction and prediction of glutor's drug-likeness: implications for its utility as an antineoplastic agent.

Recent experimental evidence from our and other laboratories has strongly indicated that glutor, a piperazine-2-one derivative, which is a pan-GLUT inhibitor, displays a promising antineoplastic action by hampering glucose uptake owing to its ability to inhibit GLUT1 and GLUT3, which are overexpressed in neoplastic cells. However, the molecular mechanism(s) of the inhibiting action of glutor has remained elusive. Thus, for optimal utilization of the antineoplastic potential of glutor, it is essential to decipher the precise mechanism(s) of its interaction with GLUTs. Therefore, the present investigation was carried out to understand the molecular mechanism(s) of the binding of glutor to GLUT1 and GLUT3 in silico. This study suggests that glutor can effectively bind to GLUTs at the reported binding site. Moreover, the docking of glutor to GLUT was stabilised by several contacts between these two partners as shown by the 200 ns long molecular dynamic simulation carried out using Gromacs, indicating the formation of a stable complex. Moreover, glutor was found to possess all characteristics conducive to its drug-likeness. Hence, these observations suggest that glutor has the potential to be used in antineoplastic therapeutic applications.Communicated by Ramaswamy H. Sarma.

N-glycosylation induced changes in tau protein dynamics reveal its role in tau misfolding and aggregation: A microsecond long molecular dynamics study.

Various posttranslational modifications like hyperphosphorylation, O-GlcNAcylation, and acetylation have been attributed to induce the abnormal folding in tau protein. Recent in vitro studies revealed the possible involvement of N-glycosylation of tau protein in the abnormal folding and tau aggregation. Hence, in this study, we performed a microsecond long all atom molecular dynamics simulation to gain insights into the effects of N-glycosylation on Asn-359 residue which forms part of the microtubule binding region. Trajectory analysis of the stimulations coupled with essential dynamics and free energy landscape analysis suggested that tau, in its N-glycosylated form tends to exist in a largely folded conformation having high beta sheet propensity as compared to unmodified tau which exists in a large extended form with very less beta sheet propensity. Residue interaction network analysis of the lowest energy conformations further revealed that Phe378 and Lys353 are the functionally important residues in the peptide which helped in initiating the folding process and Phe378, Lys347, and Lys370 helped to maintain the stability of the protein in the folded state.

Identification of potential inhibitors of brain-specific CYP46A1 from phytoconstituents in Indian traditional medicinal plants.

Cytochrome P450 46A1 (CYP46A1) is a crucial enzyme in brain that converts cholesterol to 24 (S) hydroxy cholesterol thereby increasing its polarity to facilitate removal of excess cholesterol from the CNS. The inhibition of CYP46A1 with several synthetic molecules has been investigated extensively for treatment of Alzheimer's disease, Huntington's disease, glaucoma, and in hippocampal neurons from aged mice. However, phytochemicals have received far little attention in studies involving development of potential CYP46A1 inhibitors. Thus, in the present study phytoconstituents from Indian traditional medicinal plants; Bacopa monnieri, Piper longum, and Withania somnifera, were virtually screened for interaction with CYP46A1 using computational tools. Out of three plants, six molecules from P. longum and three molecules from W. somnifera were shortlisted to study interactions with CYP46A1 based on the physio-chemical parameters. Fargesin, piperolactam A and coumaperine from P. longum showed the higher binding affinity and the values were - 10.3, - 9.5, - 9.0 kcal/moles respectively, whereas, withaferin A from W. somnifera had a binding affinity of - 12.9 kcal/mol. These were selected as potential modulators as they exhibited suitable interactions with active site residues; Tyr109, Leu112, Trp368, Gly369, and Ala474. The selected molecules were further subjected to molecular dynamics simulation. Further, the pharmacological properties of molecules were also predicted using ADMET calculator and the data revealed that all the selected compounds had good absorption as well as solubility characteristics. In addition, sesamin, fargesin, piperolactam A, and coumaperine had minimal or no toxic effects. Thus, the study successfully identified compounds from Indian medicinal plants that may serve as potential inhibitors of CYP46A1 or base structures to design novel CYP46A1 inhibitors, which may be effective in treating neurological conditions involving perturbed cholesterol homeostasis. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-022-00098-x.

Deep learning tools for advancing drug discovery and development.

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug-target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.