Computational screening of natural MtbDXR inhibitors for novel anti-tuberculosis compound discovery

DXR (1-deoxy-d-xylulose-5-phosphate reductoisomerase) is an essential enzyme in the Methylerythritol 4-phosphate (MEP) pathway, which is used by M. tuberculosis and a few other pathogens. This essential enzyme in the isoprenoid synthesis pathway has been previously reported as an important target for antibiotic drug design. However, till now, there is no record of any drug-like safe molecule to inhibit MtbDXR. Numerous plant species have been traditionally used for tuberculosis therapies. In this study, we selected six plant species with anti-tubercular properties. The chemoinformatic screening was performed on 352 phytochemicals from those plants against the MtbDXR protein. After molecular docking analysis, we filtered the top five compounds, CID: 5280443 (Apigenin), CID: 3220 (Emodin), CID: 5280863 (Kaempferol), CID: 5280445 (Luteolin), and CID: 6101979 (beta-Hydroxychalcone), based on binding affinity. Molecular dynamics simulations disclosed the stability of the compounds at the active site of the proteins. Finally, in silico ADME and toxicity evaluations confirmed the compounds to be effective and safe for oral administration. Thus, our findings identified three drug-like safe molecules- Apigenin, Kaempferol, and beta-Hydroxychalcone, that showed good stability in the protein's active site. The results of this computational approach may act as an initial instruction for future in vitro and in vivo testing to identify natural drug-like compounds to treat tuberculosis.Communicated by Ramaswamy H. Sarma.

Pharmacoinformatic screening of phytoconstituent and evaluation of its anti-PDAC effect using in vitro studies

With no prominent treatment for pancreatic ductal adenocarcinoma (PDAC) in conventional chemotherapy, recent studies have focused on uniting conventional and traditional medicines including plant phytoconstituents. Herein, we used pharmacoinformatic studies to identify potent phytoconstituent as ligand having inhibition activities against canonical anticancer targets, and evaluated its effect on PDAC cell lines. SwissTargetPrediction and SuperPred tools were utilized to segregate protein targets of ligand in humans, following which FunRich was applied to garner its targets in PDAC. STRING analysis predicted protein-protein interactions and dynamic simulation studies confirmed stability of ligand-protein complex. For in vitro cytotoxic potential, ligand treatment at different concentrations was given to PDAC cell lines both alone and combined with gemcitabine, followed by evaluation of effects on migration. Differential gene expression was checked using PCR for evaluating mechanism of cytotoxicity. Results showed pentagalloylglucose (PGG) with highest docking and MMGBSA scores for Cyclooxygenase 2 (Cox2) inhibition site. SwissTargetPrediction and SuperPred analysis detected 40 targets of PGG in PDAC. Simulation data showed stability of protein-ligand complex. In in vitro experiments Mia-PaCa-2 was more sensitive to PGG than Panc-1. PGG successfully inhibited migration both alone and in combination with gemcitabine. Additionally, PGG treatment induced apoptosis in both the cell lines; but showed antagonism when combined with gemcitabine. In conclusion, our report demonstrates PGG has good binding with Cox2 and showed anti-PDAC activity by inhibiting migration and inducing apoptosis, thus it can be used as a therapy option. But further studies are required to confirm its behaviour as a combination therapy drug.Communicated by Ramaswamy H. Sarma.

Identification and screening of potential inhibitors obtained from Plumeria rubra L. compounds against type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is the inability of the body's cells to retaliate to insulin, which can periodically culminate into absolute insulin deficiency. Hyperinsulinemia can be alleviated by administering oral medications or insulin. Prevailing medicaments engender repercussions with prolonged use, as they transmute to an inefficacious form. Hence, it will be advantageous to design plant-derived antihyperglycemic drugs with remarkable efficacy and safety quotients to address T2DM and associated comorbidities. Based on prior research, we have identified 7 novel phytocompounds from Plumeria rubra L. and 5 co-crystals that serve as an important residence for T2DM. The compounds are assessed for their inhibitory activity and dynamic stability against five major receptors which are responsible for T2DM. Additionally, in silico ADMET assessment followed by GPU-enabled GROMACS was performed on the selected compounds. The results demonstrated that β-d-Hexaglucoside had the highest binding affinity, hydrophobicity and bond length in contrast to all the targeted receptors. β-d-Hexaglucoside was subjected to dynamic simulation to analyze the root mean square deviation and root mean square fluctuation graph rates using the GROMOS force field in GROMACS software. Furthermore, β-d-Hexaglucoside exhibited inhibitory activity against diabetic receptors with a docking score of -9.5 kcal/mol. The current study proposes β-d-Hexaglucoside as a potential candidate for in-vitro or pre-clinical investigations to ameliorate T2DM management.Communicated by Ramaswamy H. Sarma.

fruits as potential inhibitors for MAPK14 protein

Plants and phytocompounds gained more attention because of their unrivalled variety of chemical diversity. In this view, the present study was executed to predict the anticancer potential of Solanum torvum Swartz. fruits derived phytocompounds against one of the breast cancer target proteins (MAPK14, PDB ID: 5ETA, resolution: 2.80 Å) through pharmacoinformatics-based screening and molecular dynamics simulation tools. Initially, a graph theoretical network approach was used to visualize the genes, enzymes, and proteins involved in the signalling pathway of breast cancer and identify the significant target protein (MAPK14). A total of thirty-three active compounds were selected from S. torvum sw. through the IMPPAT database, and their structures were drawn by Chemsketch software. The drug-like behaviours of the compounds were assessed through pharmacokinetics and physicochemical characterization studies. Five compounds, namely chlorogenin (-10.90 kcal × mol-1), corosolic acid (-10.80 kcal × mol-1), solaspigenin (-10.80 kcal × mol-1), paniculogenin (-10.70 kcal × mol-1), spirostane-3,6-dione (-10.70 kcal × mol-1) exhibited top binding score against MAPK14, these are higher than that of the standard drug (Doxorubicin) (-8.60 kcal × mol-1). Additionally, the five top-binding compounds revealed better drug-likeness traits and the lowest toxicity profiles. MD simulation studies confirmed the stability of the top five scored compounds with the MAPK14 binding pockets. According to these findings, the selected five compounds might be used as significant MAPK14 inhibitors and can be used as new medicines for the treatment of breast cancer.Communicated by Ramaswamy H. Sarma.

The computationally predicted drug-likeness, pharmacokinetics properties, medicinal chemistry parameters, and toxicity properties of Cucurbita maxima compounds

Natural compounds are increasingly becoming an important source of drug leads for computer-aided drug design approaches. Cucurbita maxima has been observed to have medicinal properties and can, therefore, be a potential source of novel drug leads. However, before compounds can be synthesized in the lab for tests, modern approaches require that the candidate compounds be screened for drug-likeness characteristics and toxicity, among others. In this work, the computational tools, SwissADME and DataWarrior were used to screen C. maxima compounds for their potential consideration as drug leads. A total of 130 compounds, downloaded from the LOTUS natural products database, were computationally analysed. The data set presented in this work will be useful to researchers searching for novel drug leads based on natural compounds.

New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools

Neurodegenerative diseases (NDD) have been of great interest to scientists for a long time due to their multifactorial character. Among these pathologies, Alzheimer’s disease (AD) is of special relevance, and despite the existence of approved drugs for its treatment, there is still no efficient pharmacological therapy to stop, slow, or repair neurodegeneration. Existing drugs have certain disadvantages, such as lack of efficacy and side effects. Therefore, there is a real need to discover new drugs that can deal with this problem. However, as AD is multifactorial in nature with so many physiological pathways involved, the most effective approach to modulate more than one of them in a relevant manner and without undesirable consequences is through polypharmacology. In this field, there has been significant progress in recent years in terms of pharmacoinformatics tools that allow the discovery of bioactive molecules with polypharmacological profiles without the need to spend a long time and excessive resources on complex experimental designs, making the drug design and development pipeline more efficient. In this review, we present from different perspectives how pharmacoinformatics tools can be useful when drug design programs are designed to tackle complex diseases such as AD, highlighting essential concepts, showing the relevance of artificial intelligence and new trends, as well as different databases and software with their main results, emphasizing the importance of coupling wet and dry approaches in drug design and development processes.

Metabolomics-based phenotypic screens for evaluation of drug synergy via direct-infusion mass spectrometry

Drugs used in combination can synergize to increase efficacy, decrease toxicity, and prevent drug resistance. While conventional high-throughput screens that rely on univariate data are incredibly valuable to identify promising drug candidates, phenotypic screening methodologies could be beneficial to provide deep insight into the molecular response of drug combination with a likelihood of improved clinical outcomes. We developed a high-content metabolomics drug screening platform using stable isotope-tracer direct-infusion mass spectrometry that informs an algorithm to determine synergy from multivariate phenomics data. Using a cancer drug library, we validated the drug screening, integrating isotope-enriched metabolomics data and computational data mining, on a panel of prostate cell lines and verified the synergy between CB-839 and docetaxel both in vitro (three-dimensional model) and in vivo. The proposed unbiased metabolomics screening platform can be used to rapidly generate phenotype-informed datasets and quantify synergy for combinatorial drug discovery.

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

[Choice of neuroprotective therapy regimens in patients with chronic cerebral ischemia, taking into account the synergy of drug interactions]

OBJECTIVE

Optimization of the choice of neuroprotective treatment regimens in patients with chronic cerebral ischemia that takes into account the synergy of drug interactions gives the doctor an opportunity for personalized approach that increases the effectiveness of treatment.

MATERIAL AND METHODS

Differential chemoreactomic analysis of the synergism of ethyl methyl hydroxypyridine succinate (EMHPS) and a number of monocomponent neuroprotective agents (piracetam, vinpocetine, citicoline, choline alfoscerate); proteomic analysis of polypeptide neuroprotectors (cerebrolysin, etc.); an expert analysis of multicomponent neuroprotector Cytoflavin.

RESULTS

Piracetam, citicoline (Neupilept) and choline alfoscerate (Cereton) effectively enhance the pharmacological properties of EMHPS and vice versa. Expert assessments of the synergism between the properties of EMHPS, polypeptide neuroprotectors (cerebrolysin) and other multicomponent drugs (cytoflavin), which are also used in adjuvant therapy with EMHPS, are presented.

CONCLUSION

In real clinical practice, of particular interest is the objectification of the appointment of combined therapy regimens. This study indicates that EMHPS can provide a favorable background for maximizing the effectiveness of therapy when used with other drugs.

In silico Discovery of Resveratrol Analogues as Potential Agents in Treatment of Metabolic Disorders

BACKGROUND

Resveratrol was demonstrated to act as partial agonist of PPAR-γ receptor, which opens up the possibility for its use in the treatment of metabolic disorders. Considering the poor bioavailability of resveratrol, particularly due to its low aqueous solubility, we aimed to identify analogues of resveratrol with improved pharmacokinetic properties and higher binding affinities towards PPAR-γ.

METHODS

3D structures of resveratrol and its analogues were retrieved from ZINC database, while PPAR-γ structure was obtained from Protein Data Bank. Docking studies were performed using Molegro Virtual Docker software. Molecular descriptors relevant to pharmacokinetics were calculated from ligand structures using VolSurf+ software.

RESULTS

Using structural similarity search method, 56 analogues of resveratrol were identified and subjected to docking analyses. Binding energies were ranged from -136.69 to -90.89 kcal/mol, with 16 analogues having higher affinities towards PPAR-γ in comparison to resveratrol. From the calculated values of SOLY descriptor, 23 studied compounds were shown to be more soluble in water than resveratrol. However, only two tetrahydroxy stilbene derivatives, piceatannol and oxyresveratrol, had both better solubility and affinity towards PPAR-γ. These compounds also had more favorable ADME profile, since they were shown to be more metabolically stable and wider distributed in body than resveratrol.

CONCLUSION

Piceatannol and oxyresveratrol should be considered as potential lead compounds for further drug development. Although experimental validation of obtained in silico results is required, this work can be considered as a step toward the discovery of new natural and safe drugs in treatment of metabolic disorders.

A super-combo-drug test to detect adverse drug events and drug interactions from electronic health records in the era of polypharmacy

Pharmacoinformatics research has experienced a great deal of successes in detecting drug-induced adverse events (AEs) using large-scale health record databases. In the era of polypharmacy, pharmacoinformatics faces many new challenges, and two significant challenges are to detect high-order drug interactions and to handle strongly correlated drugs. In this article, we propose a super-combo-drug test (SupCD-T) to address the aforementioned two challenges. SupCD-T detects drug interactions by identifying optimal drug combinations with increased AE risks. In addition, SupCD-T increases the statistical powers to detect single-drug effects by combining strongly correlated drugs. Although SupCD-T does not distinguish single-drug effects from their combination effects, it is noticeably more powerful in selecting an individual drug effect in the multiple regression analysis, where confounding justification between two correlated drugs reduces the power in testing the individual drug effects on AEs. Our simulation studies demonstrate that SupCD-T has generally better power comparing with the multiple regression analysis. In addition, SupCD-T is able to select meaningful drug combinations (eg, highly coprescribed drugs). Using electronic health record database, we illustrate the utility of SupCD-T and discover a number of drug combinations that have increased risk in myopathy. Some novel drug combinations have not yet been investigated and reported in the pharmacology research.

Insight into the screening of potential beta-lactamase inhibitors as anti-bacterial chemical agents through pharmacoinformatics study

Drug resistance is an unsolved and major concern in the bacterial infection. Continuous development of drug-resistance to the antibiotics exponentially rises the danger of bacterial infections. Chemical components from the plants are becoming a major resource of potentially effective therapeutic chemical agents for the wide range of diseases including bacterial infections. In the current study, pharmacoinformatics methodologies were implemented on more than two hundred known phytochemicals to find promising beta-lactamase inhibitors for therapeutically effective anti-bacterial agents. Initially, the molecular docking-based score was used to reduce the chemical space of the selected dataset. Fourteen molecules were found to have more affinity towards the beta-lactamase in compared to the well-known anti-bacterial agent, Avibactam. Binding interactions analysis revealed the strong binding interactions between phytochemicals and catalytic amino residues. For further analysis, molecular dynamics (MD) simulations, density functional theory (DFT) and in silico pharmacokinetics studies were performed. Parameters from MD simulations studies suggested that selected molecules are strong enough to retain in the active site in different orientations of the beta-lactamase. The orbital energies obtained from the DFT study was undoubtedly explained the potentiality of the selected compounds for being effective beta-lactamase inhibitors. The drug-likeness and acceptable pharmacokinetics parameters were observed using in silico ADME analysis. Therefore, observations from the multiple pharmacoinformatics approach explained without any doubt that selected molecules are potential enough being promising anti-bacterial compounds. [Formula: see text] Communicated by Ramaswamy H. Sarma.

Could we expect new praziquantel derivatives? A meta pharmacometrics/pharmacoinformatics analysis of all antischistosomal praziquantel derivatives found in the literature

Praziquantel (PZQ) is the first line drug for the treatment of human Schistosoma spp. worm infections. However, it suffers from low activity towards immature stages of the worm, and its prolonged use induces resistance/tolerance. During the last 40 years, 263 PZQ analogues have been synthesized and tested against Schistosoma spp. worms, but less than 10% of them showed significant activity. Here, we propose a rationalization of the chemical space of the PZQ derivatives by a ligand-based approach. First, we constructed an in-house database with all PZQ derivatives available in the literature. This analysis shows a high heterogeneity in the data. Fortunately, all studies include PZQ as a reference, permitting the classification of compounds into three classes according to their activities. Models involving ligand-based pharmacophore and logistic regression were performed. Five physicochemical parameters were identified as the best to explain the biological activity. In the end, we proposed new PZQ derivatives with modifications at positions 1 and 7, we analysed them with our models, and we observed that they can be more active than the previously synthesized derivatives. The main goal of this work was to conduct the most valuable meta-pharmacometrics/pharmacoinformatics analysis with all Praziquantel medicinal chemistry data available in the literature.

Pharmacoinformatics-based identification of chemically active molecules against Ebola virus

Ebola is a dangerous virus transmitted by animals and humans and to date there is no curable agent for such a deadly infectious disease. In this study, pharmacoinformatics-based methods were adopted to find effective novel chemical entities against Ebola virus. A well predictive and statistical robust pharmacophore model was developed from known Ebola virus inhibitors collected from the literature. The model explained the significance of each of hydrogen bond acceptor and donor, and two hydrophobic regions for activity. The National Cancer Institute and Asinex (Antiviral library) databases were screened using the final validated pharmacophore model. Initial hits were further screened with a set of criteria and finally eight molecules from both databases were proposed as promising anti Ebola agents. Further molecular docking and molecular dynamics studies were carried out and it was found that the proposed molecules possessed capability to interact with amino residues of Ebola protein as well as retaining equilibrium of protein-ligand systems. Finally, the binding energies were calculated using molecular mechanics Poisson-Boltzmann surface area approach and all proposed molecules showed strong binding affinity towards the Ebola protein receptor. Communicated by Ramaswamy H. Sarma.

Pharmacoinformatics and molecular docking reveal potential drug candidates against Schizophrenia to target TAAR6

Schizophrenia (SZ) is a complex disabling disorder that leads to the mental disability and afflicts 1% of the world's total population and placed in top ten medical disorders. In current work, bioinformatics analyses were carried out on Trace amine (TA)-associated receptor 6 (TAAR6) to recognize the potential drugs and compounds against SZ. Comparative modeling and threading-based approaches were utilized for the structure prediction of TAAR6. Fifty-nine predicted structures were evaluated by various model assessment techniques and final model having only eight amino acids in the outlier region and 98.5% overall quality factor was chosen for further pharmacoinformatics and molecular docking analyses. From an extensive literature review, 11 Food and Drug Administration (FDA) approved drugs were analyzed by computational techniques and Aripiprazole was found as the most effective drug against SZ by targeting TAAR6. Here, we report five novel molecules which exhibited the highest binding affinity, effective drug properties, and interestingly, observed better results than the approved selected drugs against SZ by targeting TAAR6. The docking analyses revealed that Arg-92, Trp-98, Gln-191, Thr-192, Ala-290, Cys-291, Tyr-293, and Glu-294 residues were observed as critical interacting residues in receptor-ligand interactions. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, Lipinski rule of five, highest binding affinity coupled with virtual screening (VS), and pharmacophore modeling approach illustrated that aripiprazole (-8.6 kcal/mol) and TAAR6_0094 (-9.3 kcal/mol) are potential inhibitors for targeting TAAR6. It is suggested that schizophrenic patients have to use Aripiprazole for the medication of SZ by targeting TAAR6 and develop effective therapies by utilizing scrutinized novel compound.

An Immunopharmacoinformatics Approach in Development of Vaccine and Drug Candidates for West Nile Virus

An outbreak of West Nile Virus (WNV) like the recent Ebola can be more epidemic and fatal to public health throughout the world. WNV possesses utmost threat as no vaccine or drug is currently available for its treatment except mosquito control. The current study applied the combined approach of immunoinformatics and pharmacoinformatics to design potential epitope-based vaccines and drug candidates against WNV. By analyzing the whole proteome of 2994 proteins, the WNV envelope glycoprotein was selected as a therapeutic target based on its highest antigenicity. After proper assessment “KSFLVHREW” and “ITPSAPSYT” were found to be the most potential T and B-cell epitopes, respectively. Besides, we have designed and validated four novel drugs from a known WNV inhibitor, AP30451 by adopting computational approaches. Toxicity assessment and drug score confirmed the effectiveness of these drug candidates. This in silico research might greatly facilitate the wet lab experiments to develop vaccine and drug against WNV.

Therapeutics Insight with Inclusive Immunopharmacology Explication of Human Rotavirus A for the Treatment of Diarrhea

Rotavirus is the most common cause of severe infant and childhood diarrhea worldwide, and the morbidity and mortality rate is going to be outnumbered in developing countries like Bangladesh. To mitigate this substantial burden of disease, new therapeutics such as vaccine and drug are swiftly required against rotavirus. The present therapeutics insight study was performed with comprehensive immunoinformatics and pharmacoinformatics approach. T and B-cell epitopes were assessed in the conserved region of outer capsid protein VP4 among the highly reviewed strains from different countries including Bangladesh. The results suggest that epitope SU1 (TLKNLNDNY) could be an ideal candidate among the predicted five epitopes for both T and B-cell epitopes for the development of universal vaccine against rotavirus. This research also suggests five novel drug compounds from medicinal plant Rhizophora mucronata Lamk. for better therapeutics strategies against rotavirus diarrhea based on 3D structure building, pharmacophore, ADMET, and QSAR properties. The exact mode of action between drug compounds and target protein VP4 were revealed by molecular docking analysis. Drug likeness and oral bioavailability further confirmed the effectiveness of the proposed drugs against rotavirus diarrhea. This study might be implemented for experimental validation to facilitate the novel vaccine and drug design.

Pharmacoinformatic and molecular docking studies reveal potential novel antidepressants against neurodegenerative disorders by targeting HSPB8

Charcot-Marie-Tooth (CMT) disease is an inherited peripheral neuromuscular disorder characterized by length-dependent and progressive degeneration of peripheral nerves, leading to muscular weakness. Research has shown that mutated HSPB8 may be responsible for depression, neurodegenerative disorders, and improper functioning of peripheral nerves, resulting in neuromuscular disorders like CMT. In the current work, a hybrid approach of virtual screening and molecular docking studies was followed by homology modeling and pharmacophore identification. Detailed screening analyses were carried out by 2-D similarity search against prescribed antidepressant drugs with physicochemical properties. LigandScout was employed to ascertain novel molecules and pharmacophore properties. In this study, we report three novel compounds that showed maximum binding affinity with HSPB8. Docking analysis elucidated that Met37, Ser57, Ser58, Trp60, Thr63, Thr114, Lys115, Asp116, Gly117, Val152, Val154, Leu186, Asp189, Ser190, Gln191, and Glu192 are critical residues for ligand-receptor interactions. Our analyses suggested paroxetine as a potent compound for targeting HSPB8. Selected compounds have more effective energy scores than the selected drug analogs. Additionally, site-directed mutagenesis could be significant for further analysis of the binding pocket. The novel findings based on an in silico approach may be momentous for potent drug design against depression and CMT.

Pharmacoinformatics elucidation of potential drug targets against migraine to target ion channel protein KCNK18

Migraine, a complex debilitating neurological disorder is strongly associated with potassium channel subfamily K member 18 (KCNK18). Research has emphasized that high levels of KCNK18 may be responsible for improper functioning of neurotransmitters, resulting in neurological disorders like migraine. In the present study, a hybrid approach of molecular docking and virtual screening were followed by pharmacophore identification and structure modeling. Screening was performed using a two-dimensional similarity search against recommended migraine drugs, keeping in view the physicochemical properties of drugs. LigandScout tool was used for exploring pharmacophore properties and designing novel molecules. Here, we report the screening of four novel compounds that have showed maximum binding affinity against KCNK18, obtained through the ZINC database, and Drug and Drug-Like libraries. Docking studies revealed that Asp-46, Ile-324, Ile-44, Gly-118, Leu-338, Val-113, and Phe-41 are critical residues for receptor–ligand interaction. A virtual screening approach coupled with docking energies and druglikeness rules illustrated that ergotamine and PB-414901692 are potential inhibitor compounds for targeting KCNK18. We propose that selected compounds may be more potent than the previously listed drug analogs based on the binding energy values. Further analysis of these inhibitors through site-directed mutagenesis could be helpful for exploring the details of ligand-binding pockets. Overall, the findings of this study may be helpful for designing novel therapeutic targets to cure migraine.