Identification of a Staphylococcus aureus amidase catalytic domain inhibitor to prevent biofilm formation by sequential virtual screening, molecular dynamics simulation and biological evaluation.

Staphylococcus aureus (S. aureus) is one of the common causes of implant associated biofilm infections and their biofilms are resistant to antibiotics. S. aureus amidase (AM) protein, a cell wall hydrolase that cleaves the amide bond between N-acetylmuramic acid and L-alanine residue of the stem peptide, is several fold over-expressed under biofilm conditions. Previous studies demonstrated an autolysin mutant in S. aureus that lacks the AM protein, is highly impaired in biofilm development. We carried out a structure-based small molecule design using the crystal structure of AM protein catalytic domain to identify inhibitors that can block amidase activity and therefore inhibits S. aureus biofilm formation. Sequential virtual screening followed by pharmacokinetic analysis and bioassay studies filtered 25 small molecules from different databases. Two compounds from the SPECS database, SPECS-1 and SPECS-2, were selected based on the best docking score and minimum biofilm inhibitory concentration towards S. aureus biofilms. SPECS-1 and SPECS-2 were further tested for their structural/energetic stability in complex with the AM protein using molecular dynamics simulation and MM-GBSA techniques. In vitro, biofilm inhibition studies on different surfaces confirmed that treatment with SPECS-1 and SPECS-2 at a concentration of 250 µg/ml exhibited significant prevention and disruption of S. aureus biofilms. Finally, the in vitro anti-biofilm activities of these two compounds were validated against Methicillin-resistant S. aureus clinical isolates. We concluded that the discovered compounds SPECS-1 and SPECS-2 are safe and exhibit biofilm preventive and disruption activity for inhibiting the S. aureus biofilms and hence can be used to treat implant-associated biofilm infections.

Identification of a PD1/PD-L1 inhibitor by structure-based pharmacophore modelling, virtual screening, molecular docking and biological evaluation.

PD-1/PD-L1 is a critical druggable target for immunotherapy against sepsis. Chemoinformatics techniques involved the structure-based 3D pharmacophore model development followed by virtual screening of small molecule databases to identify the small molecules against PD-L1 pathway inhibition. Raltitrexed and Safinamide act as potent repurposed drugs, and three other Specs database compounds using in silico methods. These compounds were screened based on the pharmacophore fit score and binding affinity towards the active site of the PD-L1 protein. In silico pharmacokinetic profiling of these screened compounds was done to test their biological activity. Next, experimental validation of the best four virtually screened hits was done in vitro for its hemocompatibility and cytotoxicity. Among these, Raltitrexed, Safinamide and Specs compound (AK-968/40642641) effectively increased the proliferation of immune cells and IFN-γ production. These compounds can act as potent PDL-1 inhibitors for adjuvant therapy against sepsis.

Doxycycline prevents blood-brain barrier dysfunction and microvascular hyperpermeability after traumatic brain injury.

The main objective of this study was to determine the cellular and molecular effects of doxycycline on the blood-brain barrier (BBB) and protection against secondary injuries following traumatic brain injury (TBI). Microvascular hyperpermeability and cerebral edema resulting from BBB dysfunction after TBI leads to elevation of intracranial pressure, secondary brain ischemia, herniation, and brain death. There are currently no effective therapies to modulate the underlying pathophysiology responsible for TBI-induced BBB dysfunction and hyperpermeability. The loss of BBB integrity by the proteolytic enzyme matrix metalloproteinase-9 (MMP-9) is critical to TBI-induced BBB hyperpermeability, and doxycycline possesses anti-MMP-9 effect. In this study, the effect of doxycycline on BBB hyperpermeability was studied utilizing molecular modeling (using Glide) in silico, cell culture-based models in vitro, and a mouse model of TBI in vivo. Brain microvascular endothelial cell assays of tight junction protein immunofluorescence and barrier permeability were performed. Adult C57BL/6 mice were subjected to sham versus TBI with or without doxycycline treatment and immediate intravital microscopic analysis for evaluating BBB integrity. Postmortem mouse brain tissue was collected to measure MMP-9 enzyme activity. It was found that doxycycline binding to the MMP-9 active sites have binding affinity of -7.07 kcal/mol. Doxycycline treated cell monolayers were protected from microvascular hyperpermeability and retained tight junction integrity (p < 0.05). Doxycycline treatment decreased BBB hyperpermeability following TBI in mice by 25% (p < 0.05). MMP-9 enzyme activity in brain tissue decreased with doxycycline treatment following TBI (p < 0.05). Doxycycline preserves BBB tight junction integrity following TBI via inhibiting MMP-9 activity. When established in human subjects, doxycycline, may provide readily accessible medical treatment after TBI to attenuate secondary injury.

Discovery of Anticancer Hybrid Molecules by Supervised Machine Learning Models and in Vitro Validation in Drug Resistant Chronic Myeloid Leukemia Cells.

The concept of hybrid drugs for targeting multiple aberrant pathways of cancer, by combining the key pharmacophores of clinically approved single-targeted drugs, has emerged as a promising approach for overcoming drug-resistance. Here, we report the design of unique hybrid molecules by combining the two pharmacophores of clinically approved BCR-ABL inhibitor (ponatinib) and HDAC inhibitor (vorinostat) and results of in vitro studies in drug-resistant CML cells. Robust 2D-QSAR and 3D-pharmacophore machine learning supervised models were developed for virtual screening of the hybrid molecules based on their predicted BCR-ABL and HDAC inhibitory activity. The developed 2D-QSAR model showed five information rich molecular descriptors while the 3D-pharmacophore model of BCR-ABL showed five different chemical features (hydrogen bond acceptor, donor, hydrophobic group, positive ion group, and aromatic rings) and the HDAC model showed four different chemical features (hydrogen bond acceptor, donor, positive ion group, and aromatic rings) for potent BCR-ABL and HDAC inhibition. Virtual screening of the 16 designed hybrid molecules identified FP7 and FP10 with better potential of inhibitory activity. FP7 was the most effective molecule with predicted IC50 using the BCR-ABL based 2D-QSAR model of 0.005 µM and that of the HDAC model of 0.153 µM, and that using the BCR-ABL based 3D-pharmacophore model was 0.02 µM and that with HDAC model was 0.014 µM. In vitro study (dose-response relationship) of FP7 in wild type and imatinib-resistant CML cell lines harboring Thr315Ile or Tyr253His mutations showed growth inhibitory IC50 values of 0.000 16, 0.0039, and 0.01 µM, respectively. This molecule also showed better biocompatibility when tested in whole blood and in PBMCs as compared to ponatinib or vorinostat.

Machine learning models for predicting the activity of AChE and BACE1 dual inhibitors for the treatment of Alzheimer's disease.

Multi-target directed ligand-based 2D-QSAR models were developed using different N-benzyl piperidine derivatives showing inhibitory activity toward acetylcholinesterase (AChE) and ß-Site amyloid precursor protein cleaving enzyme (BACE1). Five different classes of molecular descriptors belonging to spatial, structural, thermodynamics, electro-topological and E-state indices were used for machine learning by linear method, genetic function approximation (GFA) and nonlinear method, support vector machine (SVM) and artificial neural network (ANN). Dataset used for QSAR model development includes 57 AChE and 53 BACE1 inhibitors. Statistically significant models were developed for AChE (R2 = 0.8688, q2 = 0.8600) and BACE1 (R2 = 0.8177, q2 = 0.7888) enzyme inhibitors. Each model was generated with an optimum five significant molecular descriptors such as electro-topological (ES_Count_aaCH and ES_Count_dssC), structural (QED_HBD, Num_TerminalRotomers), spatial (JURS_FNSA_1) for AChE and structural (Cl_Count, Num_Terminal Rotomers), electro-topological (ES_Count_dO), electronic (Dipole_Z) and spatial (Shadow_nu) for BACE1 enzyme, determining the key role in its enzyme inhibitory activity. The predictive ability of the generated machine learning models was validated using the leave-one-out, Fischer (F) statistics and predictions based on the test set of 11 AChE (r2 = 0.8469, r2pred = 0.8138) and BACE1 (r2 = 0.7805, r2pred = 0.7128) inhibitors. Further, nonlinear machine learning methods such as ANN and SVM predicted better than the linear method GFA. These molecular descriptors are very important in describing the inhibitory activity of AChE and BACE1 enzymes and should be used further for the rational design of multi-targeted anti-Alzheimer's lead molecules.

A phytochemical-based medication search for the SARS-CoV-2 infection by molecular docking models towards spike glycoproteins and main proteases.

Identifying best bioactive phytochemicals from different medicinal plants using molecular docking techniques demonstrates a potential pre-clinical compound discovery against SARS-CoV-2 viral infection. The in silico screening of bioactive phytochemicals with the two druggable targets of SARS-CoV-2 by simple precision/extra precision molecular docking methods was used to compute binding affinity at its active sites. phyllaemblicin and cinnamtannin class of phytocompounds showed a better binding affinity range (-9.0 to -8.0 kcal mol-1) towards both these SARS-CoV-2 targets; the corresponding active site residues in the spike protein were predicted as: Y453, Q496, Q498, N501, Y449, Q493, G496, T500, Y505, L455, Q493, and K417; and Mpro: Q189, H164, H163, P168, H41, L167, Q192, M165, C145, Y54, M49, and Q189. Molecular dynamics simulation further established the structural and energetic stability of protein-phytocompound complexes and their interactions with their key residues supporting the molecular docking analysis. Protein-protein docking using ZDOCK and Prodigy server predicted the binding pose and affinity (-13.8 kcal mol-1) of the spike glycoprotein towards the human ACE2 enzyme and also showed significant structural variations in the ACE2 recognition site upon the binding of phyllaemblicin C compound at their binding interface. The phyllaemblicin and cinnamtannin class of phytochemicals can be potential inhibitors of both the spike and Mpro proteins of SARS-CoV-2; furthermore, its pharmacology and clinical optimization would lead towards novel COVID-19 small-molecule therapy.

Predicting the Molecular Mechanism of EGFR Domain II Dimer Binding Interface by Machine Learning to Identify Potent Small Molecule Inhibitor for Treatment of Cancer.

Epidermal growth factor receptor (EGFR) is a transmembrane druggable target controlling cellular differentiation, proliferation, migration, survival and invasion. EGFR activation mainly occurs by its homo/hetro dimerization molecular phenomenon leading to tumor development and invasion. Several tyrosine kinase based inhibitors were discovered as potent anti-cancer drugs. However, mutations in its kinase domain confer resistance to most of these drugs. To overcome this drug resistance, development of small molecule inhibitors disrupting the EGFR Domain II dimer binding by machine learning methods are promising. Based on this insight, a structure-based drug repurposing strategy was adopted to repurpose the existing FDA approved drugs in blocking the EGFR Domain II mediated dimerization. We identified five best repurposed drug molecules showing good binding affinity at its key arm-cavity dimer interface residues by different machine learning methods. The molecular mechanisms of action of these repurposed drugs were computationally validated by molecular electrostatics potential mapping, point mutations at the dimer arm-cavity binding interface, molecular docking and receptor interaction studies. The present machine learning strategy thus forms the basis of identifying potent and putative small molecule drugs for the treatment of different types of cancer.

Multiple e-Pharmacophore modeling to identify a single molecule that could target both streptomycin and paromomycin binding sites for 30S ribosomal subunit inhibition.

The bacterial ribosome is an established target for anti-bacterial therapy since decades. Several inhibitors have already been developed targeting both defined subunits (50S and 30S) of the ribosome. Aminoglycosides and tetracyclines are two classes of antibiotics that bind to the 30S ribosomal subunit. These inhibitors can target multiple active sites on ribosome that have a complex structure. To screen putative inhibitors against 30S subunit of the ribosome, the crystal structures in complex with various known inhibitors were analyzed using pharmacophore modeling approach. Multiple active sites were considered for building energy-based three-dimensional (3D) pharmacophore models. The generated models were validated using enrichment factor on decoy data-set. Virtual screening was performed using the developed 3D pharmacophore models and molecular interaction towards the 30S ribosomal unit was analyzed using the hits obtained for each pharmacophore model. The hits that were common to both streptomycin and paromomycin binding sites were identified. Further, to predict the activity of these hits a robust 2D-QSAR model with good predictive ability was developed using 16 streptomycin analogs. Hence, the developed models were able to identify novel inhibitors that are capable of binding to multiple active sites present on 30S ribosomal subunit.

Computational simulations and experimental validation of structure- physicochemical properties of pristine and functionalized graphene: Implications for adverse effects on p53 mediated DNA damage response.

Recent reports indicated DNA damaging potential of few-layer graphene in human cell systems. Here, we used computational technique to understand the interaction of both pristine (pG) or carboxyl functionalized graphene (fG) of different sizes (1, 6, and 10nm) with an important DNA repair protein p53. The molecular docking study revealed strong interaction between pG and DNA binding domains (DBD) of p53 with binding free energies (BE) varying from -12.0 (1nm) to -34 (6nm)kcal/mol, while fG showed relatively less interaction with BE varying from -6.7 (1nm) to -11.1 (6nm)kcal/mol. Most importantly, pG or fG bound p53-DBDs could not bind to DNA. Further, microarray analysis of human primary endothelial cells revealed graphene intervention on DNA damage and its structure-properties effect using comet assay studies. Thus, computational and experimental results revealed the structure-physicochemical property dependent adverse effects of graphene in DNA repair protein p53.

Mechanistic understanding of Phenyllactic acid mediated inhibition of quorum sensing and biofilm development in Pseudomonas aeruginosa.

Pseudomonas aeruginosa depends on its quorum sensing (QS) system for its virulence factors' production and biofilm formation. Biofilms of P. aeruginosa on the surface of indwelling catheters are often resistant to antibiotic therapy. Alternative approaches that employ QS inhibitors alone or in combination with antibiotics are being developed to tackle P. aeruginosa infections. Here, we have studied the mechanism of action of 3-Phenyllactic acid (PLA), a QS inhibitory compound produced by Lactobacillus species, against P. aeruginosa PAO1. Our study revealed that PLA inhibited the expression of virulence factors such as pyocyanin, protease, and rhamnolipids that are involved in the biofilm formation of P. aeruginosa PAO1. Swarming motility, another important criterion for biofilm formation of P. aeruginosa PAO1, was also inhibited by PLA. Gene expression, mass spectrometric, functional complementation assays, and in silico data indicated that the quorum quenching and biofilm inhibitory activities of PLA are attributed to its ability to interact with P. aeruginosa QS receptors. PLA antagonistically binds to QS receptors RhlR and PqsR with a higher affinity than its cognate ligands N-butyryl-L-homoserine lactone (C4-HSL) and 2-heptyl-3,4-dihydroxyquinoline (PQS; Pseudomonas quinolone signal). Using an in vivo intraperitoneal catheter-associated medaka fish infection model, we proved that PLA inhibited the initial attachment of P. aeruginosa PAO1 on implanted catheter tubes. Our in vitro and in vivo results revealed the potential of PLA as anti-biofilm compound against P. aeruginosa.

Preparation, characterization, drug release and computational modelling studies of antibiotics loaded amorphous chitin nanoparticles.

We present a computational investigation of binding affinity of different types of drugs with chitin nanocarriers. Understanding the chitn polymer-drug interaction is important to design and optimize the chitin based drug delivery systems. The binding affinity of three different types of anti-bacterial drugs Ethionamide (ETA) Methacycline (MET) and Rifampicin (RIF) with amorphous chitin nanoparticles (AC-NPs) were studied by integrating computational and experimental techniques. The binding energies (BE) of hydrophobic ETA, hydrophilic MET and hydrophobic RIF were -7.3kcal/mol, -5.1kcal/mol and -8.1kcal/mol respectively, with respect to AC-NPs, using molecular docking studies. This theoretical result was in good correlation with the experimental studies of AC-drug loading and drug entrapment efficiencies of MET (3.5±0.1 and 25± 2%), ETA (5.6±0.02 and 45±4%) and RIF (8.9±0.20 and 53±5%) drugs respectively. Stability studies of the drug encapsulated nanoparticles showed stable values of size, zeta and polydispersity index at 6°C temperature. The correlation between computational BE and experimental drug entrapment efficiencies of RIF, ETA and MET drugs with four AC-NPs strands were 0.999 respectively, while that of the drug loading efficiencies were 0.854 respectively. Further, the molecular docking results predict the atomic level details derived from the electrostatic, hydrogen bonding and hydrophobic interactions of the drug and nanoparticle for its encapsulation and loading in the chitin-based host-guest nanosystems. The present results thus revealed the drug loading and drug delivery insights and has the potential of reducing the time and cost of processing new antibiotic drug delivery nanosystem optimization, development and discovery.

Understanding the Structure-Function Relationship of Lysozyme Resistance in Staphylococcus aureus by Peptidoglycan O-Acetylation Using Molecular Docking, Dynamics, and Lysis Assay.

Lysozyme is an important component of the host innate defense system. It cleaves the ß-1,4 glycosidic bonds between N-acetylmuramic acid and N-acetylglucosamine of bacterial peptidoglycan and induce bacterial lysis. Staphylococcus aureus (S. aureus), an opportunistic commensal pathogen, is highly resistant to lysozyme, because of the O-acetylation of peptidoglycan by O-acetyl transferase (oatA). To understand the structure-function relationship of lysozyme resistance in S. aureus by peptidoglycan O-acetylation, we adapted an integrated approach to (i) understand the effect of lysozyme on the growth of S. aureus parental and the oatA mutant strain, (ii) study the lysozyme induced lysis of exponentially grown and stationary phase of both the S. aureus parental and oatA mutant strain, (iii) investigate the dynamic interaction mechanism between normal (de-O-acetylated) and O-acetylated peptidoglycan substrate in complex with lysozyme using molecular docking and molecular dynamics simulations, and (iv) quantify lysozyme resistance of S. aureus parental and the oatA mutant in different human biological fluids. The results indicated for the first time that the active site cleft of lysozyme binding with O-acetylated peptidoglycan in S. aureus was sterically hindered and the structural stability was higher for the lysozyme in complex with normal peptidoglycan. This could have conferred reduced survival of the S. aureus oatA mutant in different human biological fluids. Consistent with this computational analysis, the experimental data confirmed decrease in the growth, lysozyme induced lysis, and lysozyme resistance, due to peptidoglycan O-acetylation in S. aureus.

Dual binding site and selective acetylcholinesterase inhibitors derived from integrated pharmacophore models and sequential virtual screening.

In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

Synthesis, biological evaluation and molecular modeling study of pyrazole derivatives as selective COX-2 inhibitors and anti-inflammatory agents.

A novel series of pyrazole derivatives were synthesized and evaluated in vivo for their anti-inflammatory activity in carrageenan-induced rat paw edema model. Among all compounds, 5a, and 5b showed comparable anti-inflammatory activity to Nimesulide, the standard drug taken for the studies. In silico (docking) studies were carried out to investigate the theoretical binding mode of the compounds to target the cyclooxygenase (COX-2) using Autodock 4.2.

Chemical modifications of cinchona alkaloids lead to enhanced inhibition of human butyrylcholinesterase.

Butyrylcholinesterase (BChE) inhibitors were identified from a collection containing cinchonine, cinchonidine and synthetic derivatives, and further characterized using cytotoxicity and molecular docking studies. The most active ones were: (10 triple bond)-10,11-dibromo-10,11-dihydrocinchonidine (11), a competitive inhibitor with Ki = 3.45 +/- 0.39 microM, and IC50 BChE = 9.83 +/- 0.30 microM/human (h)BChE = 34.47 +/- 4.63 and O-(trimethylsilyl)cinchonine (15), a mixed inhibitor with Kiuc = 1.73 +/- 0.46 microM and Kic = 0.85 +/- 0.26 microM, and IC50 BChE = 0.56 +/- 0.14 microM/hBChE = 0.24 +/- 0.04. In cytotoxicity experiments, > or = 80% of the cells remained viable when exposed to concentrations of up to 80 microM of both inhibitors in four different cell lines, including neurons. Due to the bulkier trimethylsilyl side group of 15, it covered the active site of hBChE better than 11 with an OH-group while not being able to fit into the active site gorge of hAChE, thus explaining the selectivity of 15 towards hBChE.

Uridine monophosphate kinase as potential target for tuberculosis: from target to lead identification.

Mycobacterium tuberculosis (Mtb) is a causative agent of tuberculosis (TB) disease, which has affected approximately 2 billion people worldwide. Due to the emergence of resistance towards the existing drugs, discovery of new anti-TB drugs is an important global healthcare challenge. To address this problem, there is an urgent need to identify new drug targets in Mtb. In the present study, the subtractive genomics approach has been employed for the identification of new drug targets against TB. Screening the Mtb proteome using the Database of Essential Genes (DEG) and human proteome resulted in the identification of 60 key proteins which have no eukaryotic counterparts. Critical analysis of these proteins using Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways database revealed uridine monophosphate kinase (UMPK) enzyme as a potential drug target for developing novel anti-TB drugs. Homology model of Mtb-UMPK was constructed for the first time on the basis of the crystal structure of E. coli-UMPK, in order to understand its structure-function relationships, and which would in turn facilitate to perform structure-based inhibitor design. Furthermore, the structural similarity search was carried out using physiological inhibitor UTP of Mtb-UMPK to virtually screen ZINC database. Retrieved hits were further screened by implementing several filters like ADME and toxicity followed by molecular docking. Finally, on the basis of the Glide docking score and the mode of binding, 6 putative leads were identified as inhibitors of this enzyme which can potentially emerge as future drugs for the treatment of TB.

The exploration of thienothiazines as selective butyrylcholinesterase inhibitors.

The role of butyrylcholinesterase (BChE) in the progression of Alzheimer's disease (AD) has recently become more crucial. In the AD brain, selective BChE inhibitors have been demonstrated to have a beneficial effect in vivo, probably by recovering cholinergic activity and/or by restoring AChE:BChE activity ratios to the levels observed in the healthy brain. Thienothiazines are compounds sharing some structural features with phenothiazines, which are known to be potent BChE inhibitors. Thus, in this contribution 45 thienothiazines were investigated for their BChE inhibitory activity. Six of them were proven to be potent and selective inhibitors of equine BChE's hydrolase activity. Structure-activity relationships were laid out, and a tentative pharmacophore model for BChE inhibitors of the thienothiazine type was proposed. The most active compound, 3f, displayed a mixed type of inhibition and was also active against the human BChE (huBChE) with an IC(50) huBChE of 0.51 ± 0.07 µM. Computational studies suggested that 3f likely binds to the catalytic site and nearby to the peripheral site of the huBChE in an extended form. In addition, the chemical space occupied by the active thienothiazines, as opposed to phenothiazines and other representative chemical classes of BChE inhibitors, was explored with the aid of ChemGPS-NP, and the relevant chemical space regions were identified. This study shows for the first time that thienothiazines represent a new group of BChE inhibitors that can be used as molecular probes for studying the role of BChE in the brain or for developing newer drug leads for AD therapy.

Homology modeling, molecular dynamics and inhibitor binding study on MurD ligase of Mycobacterium tuberculosis.

The cell wall of mycobacterium offers well validated targets which can be exploited for discovery of new lead compounds. MurC-MurF ligases catalyze a series of irreversible steps in the biosynthesis of peptidoglycan precursor, i.e. MurD catalyzes the ligation of D-glutamate to the nucleotide precursor UMA. The three dimensional structure of Mtb-MurD is not known and was predicted by us for the first time using comparative homology modeling technique. The accuracy and stability of the predicted Mtb-MurD structure was validated using Procheck and molecular dynamics simulation. Key interactions in Mtb-MurD were studied using docking analysis of available transition state inhibitors of E.coli-MurD. The docking analysis revealed that analogues of both L and D forms of glutamic acid have similar interaction profiles with Mtb-MurD. Further, residues His192, Arg382, Ser463, and Tyr470 are proposed to be important for inhibitor-(Mtb-MurD) interactions. We also identified few pharmacophoric features essential for Mtb-MurD ligase inhibitory activity and which can further been utilized for the discovery of putative antitubercular chemotherapy.

Antiobesity and lipid lowering effects of Glycyrrhiza chalcones: experimental and computational studies.

Twelve flavonoids (1-12), isolated from Glycyrrhiza glabra roots were evaluated for their pancreatic lipase (PL) inhibitory activity in vitro. The structures of the isolated compounds were elucidated by spectroscopic methods. Amongst all the compounds 7, 8, 10 and 11 showed strong inhibition against PL with IC(50) values of 7.3 µM, 35.5 µM, 14.9 µM and 37.6 µM, respectively. Molecular docking studies on the most active compound 7 revealed that it binds with the key amino acid residues of the PL active site. In silico absorption, distribution, metabolism and excretion (ADME) parameters were also computed on the active compounds to determine their preliminary pharmacokinetic properties. Further, investigations were carried out to determine the antiobesity and lipid lowering effects of 7 and 10 in high fat diet (HFD) fed male SD rats. In the rats supplemented with compound 7 the body weight increase was only 23.2±3.6 g as compared to 64.2±0.5 g in the HFD control group while in the rats treated with compound 10 showed 23.2±3.6 g weight gain only. Compound 7 decreased the levels of plasma total cholesterol (TC) to 84.6±1.4 mg/dl and plasma total triglycerides (TG) to 128.8±6.0 mg/dl. Compound 10 also lowered the plasma TC and TG levels considerably. The results indicate the potential of the chalcone scaffold as a source of PL inhibitors for preventing obesity.