Synthesis and biological evaluation of Halogen-Substituted novel α-Ketoamides as potential protein aggregation modulators in Alzheimer's disease.

The escalating prevalence of Alzheimer's disease (AD) has prompted extensive research into potential therapeutic interventions, with a specific focus on molecular targets such as amyloid beta (Aß) and tau protein aggregation. In this study, a series of α-ketoamide derivatives was synthesized from ß,γ-unsaturated α-keto thioesters, achieving high purity and good yield. Thioflavin T based Aß aggregation assay identified four promising compounds (BD19, BD23, BD24, and BD27) that demonstrated significant inhibitory effects on Aß aggregation. BD23, selected for its better solubility (0.045 ± 0.0012 mg/ml), was further subjected to in vitro Parallel Artificial Membrane Permeability Assay to determine the Blood-Brain-Barrier permeability and emerged as BBB permeable with permeability rate (Pe) of 10.66 ± 8.11 × 10-6 cm/s. In addition to its Aß inhibitory properties, BD23 exhibited significant inhibition of heparin-induced tau aggregation and demonstrated non-toxicity in SHSY5Y cell lines. Subsequent in vivo assays were conducted, administering compound BD23 to an Aß induced mouse model of AD at various doses (1, 2, & 5 mg/kg). The results revealed a noteworthy enhancement in cognitive functions, particularly when BD23 was administered at a dosage of 5 mg/kg, comparable to the effects observed with the standard dose of Donepezil (DNP). In silico investigations, including molecular docking, molecular dynamics simulations, and Density Functional Theory calculations provided insights into BD23's interactions with the targets and electronic properties. These analyses contribute to the understanding of the therapeutic potential of the lead compounds BD23 which further pave the way for further exploration of its therapeutic potential in the context of AD.

Virtual screening and molecular dynamics investigations using natural compounds against autotaxin for the treatment of chronic pain.

Chronic pain is a common and debilitating condition with a huge social and economic burden worldwide. Currently, available drugs in clinics are not adequately effective and possess a variety of severe side effects leading to treatment withdrawal and poor quality of life. Recent findings highlight the potential role of autotaxin (ATX) as a promising novel target for chronic pain management, extending beyond its previously established involvement in arthritis and other neurological disorders, such as Alzheimer's disease. In the present study, we used a virtual screening strategy by targeting ATX against commercially available natural compounds (enamine- phenotypic screening library) to identify the potential inhibitors for the treatment of chronic pain. After initial identification using molecular docking based virtual screening, molecular mechanics (MM/GBSA), ADMET profiling and molecular dynamics simulation were performed to verify top hits. The computational screening resulted in the identification of fifteen top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -25.792 (for compound Enamine_1850) to -74.722 Kcal/mol (for compound Enamine_1687). Moreover, the top-scoring hits have favourable ADME properties as calculated using in-silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about amino acid residues involved in binding. This study led to the identification of potential autotaxin inhibitors with favourable pharmacokinetic properties. Identified hits may further be investigated for their safety and efficacy potential using in-vitro and in-vivo models of chronic pain.Communicated by Ramaswamy H. Sarma.

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.

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

The development of bacterial resistance to chemical therapy poses a severe danger to efficacy of treating bacterial infections. One of the key factors for resistance to antimicrobial medications is growth of bacteria in biofilm. Quorum sensing (QS) inhibition was created as an alternative treatment by developing novel anti-biofilm medicines. Cell-cell communication is impeded by QS inhibition, which targets QS signaling pathway. The goal of this work is to develop newer drugs that are effective against Pseudomonas aeruginosa by decreasing QS and acting as anti-biofilm agents. In this investigation, N-(benzo[d]thiazol-2-yl)benzamide/N-(thiazol-2-yl)benzamide derivatives 3a-h were designed and synthesized in good yields. Further, molecular docking analyses revealed that binding affinity values were founded -11.2 to -7.6 kcal/mol that were moderate to good. The physicochemical properties of these prepared compounds were investigated through in-silico method. Molecular dynamic simulation was also used to know better understanding of stability of the protein and ligand complex. Comparing N-(benzo[d]thiazol-2-yl)benzamide 3a to salicylic acid (4.40±0.10) that was utilised as standard for quorum sensing inhibitor, the anti-QS action was found greater for N-(benzo[d]thiazol-2-yl)benzamide 3a (4.67±0.45) than salicylic acid (4.40±0.10). Overall, research results suggested that N-(benzo[d]thiazol-2-yl)benzamide/N-(thiazol-2-yl)benzamide derivatives 3a-h may hold to develop new quorum sensing inhibitors.

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.

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.

Synthesis, biological evaluation and in silico studies of 2-aminoquinolines and 1-aminoisoquinolines as antimicrobial agents.

The current study reports synthesis of 2-aminoquinolines and 1-aminoisoquinolines derivatives and their characterization. Further, in vitro studies were conducted to determine antimicrobial activities. Compound 3 h showed maximum activity against B. subtilis (IC50: 0.10±0.02 µM) and E. coli (IC50: 0.13±0.01 µM) whereas compound 3i showed higher antimicrobial activity against E. coli (IC50: 0.11±0.01) and C. viswanathii (IC50: 0.10±0.05 µM). Safety profiles of the most potent derivatives were evaluated utilizing cell viability assay using RAW 264.7 and HeLa cell lines and in vitro hemolytic assay was carried out freshly isolated RBC from healthy rat. Furthermore, in silico studies, like molecular docking, binding free energy calculations and ADME predictions were done to get the best lead candidates. Additionally, molecular dynamic simulation for 100 ns was performed to know stability of protein and ligand complex. The active compounds were found to be non-toxic and non-hemolytic and hold great promise to become newer antimicrobial agents.

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.

Free Fatty Acid Receptors (FFARs): Emerging Therapeutic Targets for the Management of Diabetes Mellitus.

Free fatty acids (FFAs) present in our dietary fats not only act as vital nutrients but also function as signalling molecules and modulate key biological functions through their active involvement in a multitude of energy metabolism pathways. However, it has been reported that excessive intake of dietary fat contributes to the development of different types of Diabetes mellitus. Free fatty acid receptors are the key regulators of most metabolic disorders. Among them, diabetes mellitus is a severe growing disorder and found in every corner of the world. For various metabolic disorders, particularly type 2 diabetes mellitus, these different free fatty acid receptors are being explored as drug targets. In the present review, various FFAs sensing G-protein coupled receptors (GPR) like GPR40 (FFAR1), GPR43 (FFAR2), GPR41 (FFAR3), GPR120 (FFAR4), and GPR84 are being explored as emerging novel therapeutic targets for antidiabetic drugs. Additionally, this review has covered pre-clinical discovery and development of different selective ligands targeted to these receptors starting from hit identification to lead optimization via chemical modification and the challenges and tactics selected by different medicinal chemists to improve potency, physicochemical properties, safety profiles, and pharmacokinetics of different FFAR agonists for making a potential drug candidate. Several molecules have been withdrawn in the clinical trials without reporting any reasons. We believe that this review will help the researchers to find a new direction in the discovery of new antidiabetic drugs.

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.

Structural modification aimed for improving solubility of lead compounds in early phase drug discovery.

Many lead compounds fail to reach clinical trials despite being potent because of low bioavailability attributed to their insufficient solubility making solubility a primary and crucial factor in early phase drug discovery. Solubility improvement of poorly soluble lead compounds without losing potency is a challenging task for the medicinal chemist in a drug discovery setup. Solubility is an important factor not only to dissipate or liquefy a substance but also to attain an optimal concentration of drug in systemic circulation required for the desired therapeutic effect. It has been estimated that more than forty percent of newly developed molecules are practically insoluble in water. Molecules with poor solubility not only cause difficulty for in vitro and in vivo assays but also add significant burdens to drug development in the form of longer time taken and increased cost to optimize the solubility. To tackle this problem, different techniques are being used such as physical, chemical, and miscellaneous methods to enhance solubility. Among them, the medicinal chemistry approach focussed on structural modification is a versatile and unique approach in way that it can also improve other pharmacokinetic/physicochemical parameters simultaneously. In this review, we have begun with brief introduction of solubility and its role followed by recent successful examples of different structural modification tactics reported in the literature including synthesis of prodrugs, hydrophilic and ionizable group insertion, addition & removal of hydrogen bonding, bioisosterism, disruption of molecular symmetry and planarity. Moreover, we have included a section on the obstacles in the solubility optimization and also summarised different in silico tools with potential application in solubility prediction. Overall, this review encompasses various successfully used solubility optimization examples using structure modification.

Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics.

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain.Communicated by Ramaswamy H. Sarma.