Molecular Targets of Valeric Acid: A Bioactive Natural Product for Endocrine, Metabolic, and Immunological Disorders.

BACKGROUNDS: Postbiotics produced by gut microbiota have exhibited diverse pharmacological activities. Valeric acid, a postbiotic material produced by gut microbiota and some plant species like valerian, has been explored to have diverse pharmacological activities. METHODS: This narrative review aims to summarise the beneficial role of valeric acid for different health conditions along with its underlying mechanism. In order to get ample scientific evidence, various databases like Science Direct, PubMed, Scopus, Google Scholar and Google were exhaustively explored to collect relevant information. Collected data were arranged and analyzed to reach meaningful a conclusion regarding the bioactivity profiling of valeric acid, its mechanism, and future prospects. RESULTS: Valeric acid belongs to short-chain fatty acids (SCFAs) compounds like acetate, propionate, butyrate, pentanoic (valeric) acid, and hexanoic (caproic) acid. Valeric acid has been identified as one of the potent histone deacetylase (HDAC) inhibitors. In different preclinical in -vitro and in-vivo studies, valeric acid has been found to have anti-cancer, anti-diabetic, antihypertensive, anti-inflammatory, and immunomodulatory activity and affects molecular pathways of different diseases like Alzheimer's, Parkinson's, and epilepsy. CONCLUSION: These findings highlight the role of valeric acid as a potential novel therapeutic agent for endocrine, metabolic and immunity-related health conditions, and it must be tested under clinical conditions to develop as a promising drug.

Design, synthesis, molecular docking and pharmacological evaluation of some thiadiazole based nipecotic acid derivatives as a potential anticonvulsant and antidepressant agents.

In our continuous effort to develop novel antiepileptic drug, a new series of nipecotic acid derivatives having1,3,4-thiadiazole nucleus were designed and synthesized. This study aims to improve the lipophilicity of nipecotic acid by attaching some lipophilic anchors like thiadiazole and substituted aryl acid derivatives. In our previous study, we noticed that the N-substituted oxadiazole derivative of nipecotic acid exhibited significant antiepileptic activity in the rodent model. The synthesized compounds were characterized by FT-IR, 1H-NMR, 13C-NMR, Mass, and elemental analysis. The anticonvulsant activity was evaluated by using the maximal electroshock-induced seizure model in rats (MES) and the subcutaneous pentylenetetrazol (scPTZ) test in mice. None of the compounds were found to be active in the MES model whereas compounds (TN2, TN9, TN12, TN13, and TN15) produced significant protection against the scPTZ-induced seizures model. The compounds showing antiepileptic activity were additionally evaluated for antidepressant activity by using the forced swim test, 5-hydroxytryptophan (5-HTP)-induced head twitch test, and learned helplessness test. All the molecules that showed anticonvulsant activity (TN2, TN9, TN12, TN13, and TN15), also exerted significant antidepressant effects in the animal models. The selected compounds were subjected to different toxicity studies. Compounds were found to have no neurotoxicity in the rota-rod test and devoid of hepatic and renal toxicity in 30 days repeated oral toxicity test. Further, a homology model was developed to perform the in-silico molecular docking and dynamics studies which revealed the similar binding of compound TN9 within the active binding pocket and were found to be the most potent anti-epileptic agent. The market expectation for newly developed antiepileptic thiadiazole-based nipecotic acid derivatives is significant, driven by their potential to offer improved therapeutic outcomes and reduced side effects, addressing a critical need in epilepsy treatment. These innovative compounds hold promise for meeting the demand for more effective and safer antiepileptic medications. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03897-1.

Development of multi-targetable chalcone derivatives bearing N-aryl piperazine moiety for the treatment of Alzheimer's disease.

The multi-target directed ligand (MTDL) discovery has been gaining immense attention in the development of therapeutics for Alzheimer's disease (AD). The strategy has been evolved as an auspicious approach suitable to combat the heterogeneity and the multifactorial nature of AD. Therefore, multi-targetable chalcone derivatives bearing N-aryl piperazine moiety were designed, synthesized, and evaluated for the treatment of AD. All the synthesized compounds were screened for thein vitro activityagainst acetylcholinesterase (AChE), butylcholinesterase (BuChE), ß-secretase-1 (BACE-1), and inhibition of amyloid ß (Aß) aggregation. Amongst all the tested derivatives, compound 41bearing unsubstituted benzylpiperazine fragment and para-bromo substitution at the chalcone scaffold exhibited balanced inhibitory profile against the selected targets. Compound 41 elicited favourable permeation across the blood-brain barrier in the PAMPA assay. The molecular docking and dynamics simulation studies revealed the binding mode analysis and protein-ligand stability ofthe compound with AChE and BACE-1. Furthermore,itameliorated cognitive dysfunctions and signified memory improvement in thein-vivobehavioural studies (scopolamine-induced amnesia model). Theex vivobiochemical analysis of mice brain homogenates established the reduced AChE and increased ACh levels. The antioxidant activity of compound 41 was accessed with the determination of catalase (CAT) and malondialdehyde (MDA) levels. The findings suggested thatcompound 41, containing a privileged chalcone scaffold, can act as a lead molecule for developing AD therapeutics.

Recent Applications of Bioinformatics in Target Identification and Drug Discovery for Alzheimer's Disease.

Alzheimer's disease (AD) is a complex multifactorial neurodegenerative disease characterized by progressive memory loss. The main pathological features of the disease are extracellular deposition of amyloid ß (Aß) plaques and intracellular neurofibrillary tangles composed of hyperphosphorylated tau protein. Understanding factors contributing to AD progression, the number of molecular signatures, and the development of therapeutic agents played a significant role in the discovery of disease-modifying drugs to treat the disease. Bioinformatics has established its significance in many areas of biology. The role of bioinformatics in drug discovery, is emerging significantly and will continue to evolve. In recent years, different bioinformatics methodologies, viz. protein signaling pathway, molecular signature differences between different classes of drugs, interacting profiles of drugs and their potential therapeutic mechanisms, have been applied to identify potential therapeutic targets of AD. Bioinformatics tools were also found to contribute to the discovery of novel drugs, omics-based biomarkers, and drug repurposing for AD. The review aims to explore the applications of various advanced bioinformatics tools in the identification of targets, biomarkers, pathways, and potential therapeutics for the treatment of the disease.

Development of homology model, docking protocol and Machine-Learning based scoring functions for identification of Equus caballus's butyrylcholinesterase inhibitors.

Machine learning (ML), an emerging field in drug design, has the potential to predict in silico toxicity, shape-based analysis of inhibitors, scoring function (SF) etc. In the present study, a homology model, docking protocol, and a dedicated SF have been developed to identify the inhibitors of horse butyrylcholinesterase (BChE) enzyme. Horse BChE enzyme has homology with human BChE and is a substitute for the screening of in vitro inhibitors. The developed homology model was validated and the active site residues were identified from Cavityplus to generate grid box for docking. The validation of docking involved comparison of interactions of ligands co-crystallised with human BChE and the docked poses of the corresponding ligands with horse BChE. A high degree of similarity in the interaction profiles of generated poses validated the docking protocol. Scoring of ligands was further validated by docking with known BChE inhibitors. The binding energies obtained from SF was correlated with IC50 values of inhibitors through classification and regression-based methods, which indicated poor predictivity of native SF. Therefore, protein-ligand binding energy, interaction profile, and ligand descriptors were used to develop and validate the classification and regression-based models. The validated extra tree binary classifier, random forest and extra tree regression-based models were compiled as a protein-ligand SF and were made available to the users through web application and python library. ML models exhibited improved area under the curve for ROC and good correlation between the predicted and observed IC50 values, than the Autodock SF. Communicated by Ramaswamy H. Sarma.

Cellular and molecular influencers of neuroinflammation in Alzheimer's disease: Recent concepts & roles.

Alzheimer's disease (AD), an extremely common neurodegenerative disorder of the older generation, is one of the leading causes of death globally. Besides the conventional hallmarks i.e. Amyloid-ß (Aß) plaques and neurofibrillary tangles (NFTs), neuroinflammation also serves as a major contributing factor in the pathogenesis of AD. There are mounting evidences to support the fundamental role of cellular (microglia, astrocytes, mast cells, and T-cells) and molecular (cytokines, chemokines, caspases, and complement proteins) influencers of neuroinflammation in producing/promoting neurodegeneration and dementia in AD. Genome-wide association studies (GWAS) have revealed the involvement of various single nucleotide polymorphisms (SNPs) of genes related to neuroinflammation with the risk of developing AD. Modulating the release of the neuroinflammatory molecules and targeting their relevant mechanisms may have beneficial effects on the onset, progress and severity of the disease. Here, we review the distinct role of various mediators and modulators of neuroinflammation that impact the pathogenesis and progression of AD as well as incite further research efforts for the treatment of AD through a neuroinflammatory approach.

Classification of beta-site amyloid precursor protein cleaving enzyme 1 inhibitors by using machine learning methods.

The beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a transmembrane aspartyl-protease, that cleaves amyloid precursor protein (APP) at the ß-site. The sequential proteolytic cleavage of APP, first by ß-secretase and then by γ-secretase complex, leads to the production and release of amyloid-ß peptide, a pathological hallmark of Alzheimer's disease (AD). BACE1 inhibitors are reported to possess considerable potential in decreasing the level of amyloid-ß in brain and preventing the progression of AD. A classification study has been conducted on 3536 diverse BACE1 inhibitors, obtained from Binding DB database, by extracting two types of descriptors, that is molecular property (Mordred) and fingerprints (Pubchem, MACCS and KRFP). Furthermore, based on the descriptors, various machine learning algorithms such as Naïve Bayesian (NB), nearest known neighbours (kNN), support vector machine (SVM), random forest (RF) and gradient-boosted algorithms (XGB) were applied to develop classification models. The performance of models was evaluated by using accuracy, precision, recall and F1 score of test set. The best NB, kNN, SVM, RF and XGB classifiers had F1 score of 0.74, 0.85, 0.86, 0.87 and 0.87, respectively. The diverse 3536 BACE1 inhibitors were clustered into 11 subsets, and the structural features of each subset were evaluated. The important fragments present in active and inactive compounds were also identified. The model developed in the study would serve as a valuable tool for the designing of BACE1 inhibitors, and also in virtual screening of molecules to identify these.

Facile Synthesis, Characterization, and In Vitro Antimicrobial Screening of a New Series of 2,4,6-Trisubstituted-s-triazine Based Compounds.

A series of new 2,4,6-trisubstituted-s-triazine was synthesized, assessed for antimicrobial activity, and characterized by FTIR, (1)HNMR, (13)CNMR, and elemental analysis. The tested compounds, 4d, 4g, 4h, 4k, and 4n, have shown considerable in vitro antibacterial efficacy with reference to the standard drug ciprofloxacin (MIC 3.125 µgmL(-1) against B. subtilis, E. coli, and K. pneumoniae). It was observed that compounds 4d and 4h displayed equipotent antibacterial efficacy against B. subtilis (MIC 3.125 µgmL(-1)) and S. aureus (MIC 6.25 µgmL(-1)). The studies demonstrated that the para-fluorophenylpiperazine substituted s-triazine (4n) was potent and exhibited broad spectrum antibacterial activity against S. epidermidis, K. pneumoniae, and P. aeruginosa with MIC of 6.25 µgmL(-1) and for E. coli, it showed an MIC of 3.125 µgmL(-1) equipotent with reference to the standard drug. Among all the compounds under investigation, compound 4g also demonstrated significant antifungal activity (3.125 µgmL(-1)) against C. albicans.