Laminin mimetic angiogenic and collagen peptide hydrogel for enhance dermal wound healing.

Laminins are essential in basement membrane architecture and critical in re-epithelialization and angiogenesis. These processes and collagen deposition are vital in skin wound healing. The role of angiogenic peptides in accelerating the wound-healing process has been known. The bioactive peptides could be a potential approach due to their similar effects as growth factors and inherent biocompatible and biodegradable nature with lower cost. They can also recognize ligand-receptor interaction and mimic the extracellular matrix. Here, we report novel angiogenic DYVRLAI, CDYVRLAI, angiogenic-collagen PGPIKVAV, and Ac-PGPIKVAV peptides conjugated sodium carboxymethyl cellulose hydrogel, which was designed from laminin. The designed peptide exhibits a better binding with the α3ß1, αvß3, and α5ß1 integrins and CXCR2 receptor, indicating their angiogenic and collagen binding efficiency. The peptides were evaluated to stimulate wound healing in full-thickness excision wounds in normal and diabetic mice (type II). They demonstrated their efficacy in terms of angiogenesis (CD31), re-epithelialization through regeneration of the epidermis (H&E), and collagen deposition (MT). The synthesized peptide hydrogel (DYVRLAI and CDYVRLAI) showed enhanced wound contraction up to 10.1 % and 12.3 % on day 7th compared to standard becaplermin gel (49 %) in a normal wound model. The encouraging results were also observed with the diabetic model, where these peptides showed a significant decrease of 5.20 and 5.17 % in wound size on day 10th compared to the commercial gel (9.27 %). These outcomes signify that the modified angiogenic peptide is a cost effective, novel peptide motif to promote dermal wound healing in both models.

Design, synthesis, biological evaluations and in silico studies of N-substituted 2,4-thiazolidinedione derivatives as potential a-glucosidase inhibitors.

Diabetes mellitus is considered as one of the principal global health urgencies of the twenty first century. In the present investigation, novel N-substituted 2,4-thiazolidinedione derivatives were designed, synthesized, and characterized by spectral techniques. All the newly synthesized N-substituted 2,4-thiazolidinedione derivatives were tested for in vitro α-glucosidase inhibitory activities and compounds A-12 and A-14 were found to be the most potent which were further subjected to in-vivo disaccharide loading test. The most potent compound was also found to be non-toxic in cytotoxicity studies. Further, docking studies were carried out to investigate the binding mode and key interactions with amino acid residues of α-glucosidase. Molecular dynamic simulations studies for the compounds acarbose, A2, A12, and A14 were done with α-glucosidase protein. Further, ΔG was calculated for acarbose, A2, A12, and A14. In silico studies and absorption, distribution, metabolism, excretion (ADME) prediction studies were also executed to establish the 'druggable' pharmacokinetic profiles. Here, we have developed novel N-substituted TZD analogues with different alkyl groups as α-glucosidase inhibitors.Communicated by Ramaswamy H. Sarma.

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.

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.

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.

Urea-thiazole/benzothiazole hybrids with a triazole linker: synthesis, antimicrobial potential, pharmacokinetic profile and in silico mechanistic studies.

Some urea-thiazole/benzothiazole hybrids with a triazole linker were synthesized via Cu(I)-catalysed click reaction. After successfully analysed by various spectral techniques including FTIR, NMR and HRMS, antimicrobial screening of the synthesized hybrids along with their precursors was carried out against two Gram (+) bacteria (Staphylococcus aureus and Bacillus endophyticus), two Gram (-) bacteria (Escherichia coli and Pseudomonas fluorescens) and two fungi (Candida albicans and Rhizopus oryzae). All the synthesized compounds (4a-4l) displayed better biological response than the standard fluconazole against both of the tested fungi. Compounds 4h and 4j were found to be the most active compounds against R. oryzae and C. albicans, respectively. Molecular docking of hybrid 4j and its alkyne precursor 1b in the active site of C. albicans target sterol 14-α demethylase was also performed and was also supported by molecular dynamics studies. In silico ADME prediction of synthesized urea-thiazole/benzothiazole hybrids with a triazole linker and their alkyne precursors was also predicted.

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.

Identification of structural fingerprints for in vivo toxicity by using Monte Carlo based QSTR modeling of nitroaromatics.

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their in vivo toxicity, represented by 50% lethal dose concentration for rats (LD50). Both classification and regression-based QSTR models were developed to get an idea about different fingerprints for promoters and hinderers of nitroaromatics toxicity. The best classification model was obtained by using SMILES and graph-based (GAO) descriptor with 1ECK connectivity (sensitivity = 0.7143, specificity = 1.0000, accuracy = 0.8889, and MCC = 0.7774). The best regression model calculated by using SMILES and hydrogen-suppressed graph descriptors with 0ECk connectivity (R2 = 0.7386, Q2 = 0.6315, S = 0.467, and MAE = 0.340). Finally, a consensus QSTR model was generated to predict efficiently the toxicity of new compounds. The study highlighted that the comparative QSTR models by using the Monte Carlo method can also be generated and will be a useful tool for structural fingerprint analysis in case of nitroaromatics for preliminary evaluation of its toxicity to mammals.