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Introduction:Spondylocostal dysostosis (SCD) is characterized by multiple vertebral abnormalities associated with abnormalities of the ribs. Five genes causative for the disease have been identified. These include DLL3(OMIM *602768), MESP2(OMIM #608681), LFNG(OMIM #609813), TBX6(OMIM *602427), and HES7(OMIM *608059). Methods:In the current study, we investigated a Pakistani consanguineous family segregating spondylocostal dysotosis. Whole-exome sequencing (WES) followed by Sanger sequencing was performed using DNA of affected and unaffected individuals to identify pathogenic variant(s). The identified variant was interpreted using ACMG classification. Literature review was performed to summarize currently known mutated alleles of DLL3and the underlying clinical phenotypes. Results:Clinical examination using anthropometric measurements and radiographs diagnosed the patients to be afflicted with SCD. Pedigree analysis of the affected family showed an autosomal recessive inheritance pattern of the disease. WES followed by Sanger sequencing identified a novel homozygous nonsense variant (DLL3(NM_016941.4): c.535G>T; p.Glu179Ter) in the DLL3gene located on chromosome 19q13.2. Conclusion:The study will be helpful in carrier testing and genetic counseling to prevent segregation of the disease to the next generations within this family. It also provides knowledge for clinicians and researchers in search of a better understanding of SCD anomalies.

The global nonproliferation regime faces a major challenge in South Asia. India and Pakistan, two nuclear-armed states locked in an intense and enduring rivalry are investing heavily in their respective [...]

Pakistan is passing through an extremely delicate phase in its history. Recent instability in Pakistan, including the Taliban's advance into settled areas, prompted the Pakistani military to undertake large-scale military [...]

Purpose: The coronavirus disease 2019 (COVID-19) pandemic has subjected a considerable strain on the healthcare (HC) systems around the world. The most affected countries are developing countries because of their weak HC infrastructure and meagre resources. Hence, building the resilience of the HC system of such countries becomes essential. Therefore, this study aims to build a resilience-based model on the HC sector of Pakistan to combat the COVID-19 and future pandemics in the country. Design/methodology/approach: The study uses a novel hybrid approach to formulate a model based on resilient attributes (RAs) and resilient strategies (RSs). In the first step, the multi-criteria decision-making (MCDM) technique, i.e. full consistency method (FUCOM) is used to prioritize the RAs. Whereas, the fuzzy quality function deployment (QFD) is used to rank the RSs. Findings: The findings suggest "leadership and governance capacity" to be the topmost RA. Whereas "building the operational capacity of the management", "resilience education" and "Strengthening laboratories and diagnostic systems" are ranked to be the top three RSs, respectively. Practical implications: The model developed in this study and the prioritization RAs and RSs will help build resilience in the HC sector of Pakistan. The policymakers and the government can take help from the prioritized RAs and RSs developed in this study to help make the current HC system more resilient towards the current COVID-19 and future pandemics in the country. Originality/value: A new model has been developed to present a sound mathematical model for building resilience in the HC sector consisting of FUCOM and fuzzy QFD methods. The main contribution of the paper is the presentation of a comprehensive and more robust model that will help to make the current HC system of Pakistan more resilient. [ABSTRACT FROM AUTHOR]

Has a new era of detente and stability emerged in South Asia five years after India and Pakistan first openly tested nuclear weapons? In the process, have India and Pakistan [...]

International disaster databases and catalogs provide a baseline for researchers, governments, communities, and organizations to understand the risk of a particular place, analyze broader trends in disaster risk, and justify investments in mitigation. Perhaps because Singapore is routinely identified as one of the safest countries in the world, Singapore’s past disasters have not been studied extensively with few events captured in major global databases such as EM-DAT. In this article, we fill the disaster data gap for postwar Singapore (1950–2020) using specified metrics through an archival search, review of literature, and analysis of secondary sources. We present four key lessons from cataloging these events. First, we expand Singapore’s disaster catalog to 39 events in this time period and quantify the extent of this data gap. Second, we identify the mitigating actions that have followed past events that contribute to Singapore’s present-day safety. Third, we discuss how these past events uncover continuities among vulnerability bearers in Singapore. Last, we identify limitations of a disaster catalog when considering future risks. In expanding the disaster catalog, this case study of Singapore supports the need for comprehensive understanding of past disasters in order to examine current and future disaster resilience.

Brevifoliol is an abeo-taxane isolated from the Taxus wallichiana needles; eighteen semisynthetic esters derivatives of brevifoliol were prepared by Steglich esterification and screened for their anti-tubercular potential against Mycobacterium tuberculosis H37Ra avirulent strain. The 3- [chloro (7)] and 3, 5-[dinitro (8)] benzoic acid ester derivatives were most active (MIC 25 ug/ml) against the pathogen. Further, in silico docking studies of the active derivative 7 with mycobacterium enzyme inhA (enoyl-ACP reductase) gave the LibDock score of 152.68 and binding energy of -208.62 and formed three hydrogen bonds with SER94, MET98, and SER94. Similarly, when derivative 8 docked with inhA, it gave the LibDock score of 113.55 and binding energy of -175.46 and formed a single hydrogen bond with GLN100 and Pi-interaction with PHE97. On the other hand, the known standard drug isoniazid (INH) gave the LibDock score of 61.63, binding energy of -81.25 and formed one hydrogen bond with ASP148. These molecular docking results and the way of binding pattern indicated that compounds 7 and 8 bound well within the binding pocket of inhA and showed a higher binding affinity than the known drug isoniazid. Additionally, both the derivatives (7 and 8) showed no cytotoxicity, with CC50 195.10 and 111.36, respectively towards the mouse bone marrow-derived macrophages.

Background: In the present study, we have explored the utility of QSAR modelling, in silico ADMET, docking, chemical semi-synthesis, and in vitro evaluation studies for the identification of active camptothecin (CPT) derivatives against cancer-targeting human liver (HepG2) and lung (A549) cancer cell lines. Methods: Two QSAR models were developed as screenings tools using the multiple linear regression (MLR) method followed by ADMET and docking studies. The regression coefficient (r2) and cross-validation regression coefficients (rCV2T) of the QSAR model for the HepG2 cell line was 0.95 and 0.90, respectively, and for the A549 cell line, it was 0.93 and 0.81, respectively. Results: In silico studies show that CPT derivatives (CPT-1 and CPT-6) possess drug-like properties. Docking performed on DNA Topoisomerase-I showed significant binding affinity. Finally, predicted active derivatives were chemically semi synthesized, spectroscopically characterized, and evaluated in-vitro for cytotoxic/anticancer activity against HepG2 and A549 cell lines. Conclusion: The experimental results are consistent with the predicted results. These findings may be of immense importance in the anticancer drug development from an inexpensive and widely available natural product, camptothecin.

The aim of this article is to probe the convergence analysis of an efficient scheme, developed by Jentzen et al. (2011), for the stochastic Burgers’ equation (SBE) with term of additive noise. Although, the same scheme was used by Blomker et al. (2013) to carry out the full discretization of the SBE. But therein, Taylor series was not applied. In this work, Taylor series in integral form with remainder after one term is applied. As a consequence, minimum convergence order in time is updated to 3θfrom θ, where θ∈(0,12). Although, minimum temporal convergence order is proved to be as 2θby Khan (2021) using the higher order scheme. But the proposed scheme is simple in a manner that former uses two linear functionals of noise, whereas later employs single linear functional of noise. Finally, run time of the existing and the proposed scheme are compared to justify the analytical outcomes.

Background: Liver cancer is ranked as the fifth most prevalent and third most lethal cancer worldwide. The incidence rates of this cancer are on the rise, and only limited treatment options are available. Methods: To identify and optimize the inhibitors of liver cancer cell-lines, a QSAR model was developed by using multiple linear regression methods. The robustness of the model was validated through statistical methods and wet-lab experiments. Results: The developed QSAR models yielded high activity descriptor relationship accuracy of 91%, referred to by regression coefficient (r²= 0.91), and a high activity prediction accuracy of 89%. The external predicted (pred_r²) ability of the model was found to be 90%. Conclusion: The QSAR study indicates that chemical descriptors such as to measure of electronegative atom count (Epsilon3), atom type count descriptors (MMFF_10), number of a carbon atom connected with four single bonds (SssssCE- index), molecular weight and, number of oxygen atom connected with two aromatic bonds (SaaOE-index) are significantly correlated with anticancer activity. The model, which was validated statistically and through wet-lab experiments, was further used in the virtual screening of potential inhibitors against the liver cancer cell line WRL68. ADMET risk screening, synthetic accessibility, and Lipinski's rule of five are used to filter false positive hits. AfterwardS, to achieve a set of aligned ligand poses and rank the predicted active compounds, docking studies were carried out. The studied compounds and their metabolites were also analyzed for different pharmacokinetics parameters. Finally, a series of compounds was proposed as anticancer agents.

Auto Industry under WTO Regime simply implies as to how to protect our Automobile Manufacturing Industry including Parts Manufacturing after the elimination of Trade Related Investment Measures (TRIMS)? An question [...]

While unprecedented amounts of building damage data are now produced after earthquakes, stakeholders do not have a systematic method to synthesize and evaluate damage information, thus leaving many datasets unused. We propose a Geospatial Data Integration Framework (G-DIF) that employs regression kriging to combine a sparse sample of accurate field surveys with spatially exhaustive, though uncertain, damage data from forecasts or remote sensing. The framework can be implemented after an earthquake to produce a spatially distributed estimate of damage and, importantly, its uncertainty. An example application with real data collected after the 2015 Nepal earthquake illustrates how regression kriging can combine a diversity of datasets—and downweight uninformative sources—reflecting its ability to accommodate context-specific variations in data type and quality. Through a sensitivity analysis on the number of field surveys, we demonstrate that with only a few surveys, this method can provide more accurate results than a standard engineering forecast.

AbstractThe abnormal ubiquitin-proteasome is found as an important target in various human diseases, especially in cancer, and recently it has received prevalent attention as a challenging therapeutic target. The current work is designed to derive a predictive two-dimensional quantitative structure-activity relationship model for anticancer human proteasome target of NF-κB signaling pathway. The established 2 D-QSAR is dependent on multiple linear regression approach and validated through leave-One-Out and external test set prediction method. The robust QSAR model showed the r2of 0.83 and q2of 0.80 and pred_r2of 0.77. Three chemical properties, electronegativity count, average potential, and T_2_N_6 were identified as significant descriptors to predict the anticancer activities of the proteasome antagonists. Besides, the predicted top hit compounds were considered to check out the compliance with Rule of five and pharmacokinetic parameters for oral bioavailability in the human body. The molecular docking was accomplished to unravel the molecular mode of action of best-predicted compounds which was compatible with the standard drug. Following this approach, lastly two compounds NP and AP were recognized as the best candidates since these top compounds follow all the standard limit point of entire filters and indicated effective and decent docking score. The outcomes of the study sturdily suggested that the developed model and top hit compound’s binding conformation are rational in the exploration of unknown antagonist’s anticancer activity. The research would be of great support and is supposed to be of immense significance in the development and designing of drug-like candidates in preliminary drug discovery. Communicated by Ramaswamy H. Sarma

AbstractTriple-negative breast cancers (TNBCs) are one of the most aggressive and complex forms of cancers in women. TNBCs are commonly known for their complex heterogeneity and poor prognosis. The present work aimed to develop a predictive 2D and 3D quantitative structure–activity relationship (QSAR) models against metastatic TNBC cell line. The 2D-QSAR was based on multiple linear regression analysis and validated by Leave-One-Out (LOO) and external test set prediction approach. QSAR model presented regression coefficient values for training set (r2), LOO-based internal regression (q2) and external test set regression (pred_r2) which are 0.84, 0.82 and 0.75, respectively. Five properties, Epsilon4 (electronegativity), ChiV3cluster (valence molecular connectivity index), chi3chain (retention index for three-membered ring), TNN5 (nitrogen atoms separated through 5 bond distance) and nitrogen counts, were identified as important structural features responsible for anticancer activity of MDA-MB-231 inhibitors. Five novel derivatives of glycyrrhetinic acid (GA) named GA-1, GA-2, GA-3, GA-4 and GA-5 were semi-synthesised and screened through the QSAR model. Further, in vitroactivities of the derivatives were analysed against human TNBC cell line, MDA-MB-231. The result showed that GA-1 exhibits improved cytotoxic activity to that of parent compound (GA). Further, atomic property field (APF)-based 3D-QSAR and scoring recognise C-30 carboxylic group of GA-1 as major influential factor for its anticancer activity. The significance of C-30 carboxylic group in GA derivatives was also confirmed by molecular docking study against cancer target glyoxalase-I. Finally, the oral bioavailability and toxicity of GA-1 were assessed by computational ADMET studies.Communicated by Ramaswamy H. Sarma

Background: Human infections associated with skin and mucosal surfaces, mainly in tropical and sub-tropical parts of the world. During the last decade, there have been an increasing numbers of cases of fungal infections in immunocompromised patients, coupled with an increase in the number of incidences of drug resistance and toxicity to anti fungal agents. Hence, there is a dire need for safe, potent and affordable new antifungal drugs for the efficient management of candidal infections with minimum or no side effects. Introduction: Candidiasis represents a critical problem to human health and a serious concern worldwide. Due to the development of drug resistance, there is a need for new antifungal agents. Therefore, we reviewed the different medicinal plants as sources of novel anticandidal drugs. Methods: The comprehensive and detailed literature on medicinal plants was carried out using different databases, such as Google Scholar, PubMed, and Science Direct and all the relevant information from the articles were analyzed and included. Results: Relevant Publications up to the end of November 2018, reporting anticandidal activity of medicinal plants has been included in the present review. In the present study, we have reviewed in the light of SAR and mechanisms of action of those plants whose extracts or phytomolecules are active against candida strains. Conclusion: This article reviewed natural anticandidal drugs of plant origin and also summarized the potent antifungal bioactivity against fungal strains. Besides, mechanism of action of these potent active plant molecules was also explored for a comparative study. We concluded that the studied active plant molecules exhibit potential antifungal activity against resistant fungal strains.

AbstractBladder cancer is the common reason for mortality worldwide, and its increasing rate announces as a significant area of research in drug designing. The side effects and toxicity of existing drugs and the consequence of gradual cancer cell resistance against the available therapy make the treatment poor. Globally, there is a continuous high demand to develop new, more potent, and easily affordable drugs against cancer. The current research article illustrates the application of developed three-dimensional quantitative structure-activity relationship (3D-QSAR) based on human bladder cancer cell line T24 in vitroanticancer activity. The derived QSAR model has been used for prediction of natural compounds and analogs with 80% similarity of the most active compound of the dataset. The developed model describes the structure-activity relationship for terpenes and their derivatives at the molecular level. The developed comparative molecular field analysis (CoMFA) model shows a satisfactory cross-validation correlation coefficient (q2) of 0.54 and a regression correlation coefficient (r2) of 0.86. In order to evaluate the compliance with electronic pharmacokinetic parameters, Lipinski’s rule of five filter, absorption, distribution, metabolism, and excretion (ADME) and toxicity of predicted compounds have been calculated. Furthermore, molecular-docking study has been performed to prioritize these predicted compounds based on their docking score and binding pocket similarity through the identified potential anticancer targets. Finally, two compounds T9 and B42 have been identified as the best hit because these two fall within the standard limits of all filters and show a good binding affinity. Conclusively, all satisfactory results strongly suggest that the derived 3D-QSAR model and obtained candidate’s binding structures are reasonable in the prediction of a new antagonist’s activity. The strategy adopted in the present research is expected to be of immense importance and a great support in the identification and optimization of lead in the early and advance drug discovery.

Multitarget pharmacology of small-molecule cancer drugs significantly contributes to their mechanism of action, side effects, and emergence of drug resistance and opens ways to repurpose, combine, or customize drug therapy. In most cases, the set of targets affected at therapeutic concentrations is not fully characterized and/or the interaction efficacy values are not accurately quantified. We collected information about multiple targets for each cancer drug along with their experimental effective concentrations or binding activities from multiple sources. All multitarget activity values for each drug then were used to build two proximity network pharmacology maps of anticancer drugs and targets of those drugs, respectively. Together with the network map, we showed that the majority of the cancer drugs had substantial multitarget pharmacology based on our current knowledge. In addition, most of the cancer drugs simultaneously affect macromolecular targets from different classes and types. The target subset can further be accentuated and personalized by patient sample-specific expression data. The network maps of cancer drugs and targets as well as all quantified activity data were integrated into a freely available database, CancerDrugMap (http://ruben.ucsd.edu/dnet/maps/drugnet.html). The identified multitarget pharmacology of cancer drugs is essential for improving the efficacy of individually prescribed drugs and drug combinations and minimization of adverse effects.

Background: Lymphatic filariasis is one of the chronic diseases in many parts of the tropics and sub-tropics of the world despite the use of standard drugs diethylcarbamazine and ivermectin because they kill microfilaries and not the adult parasites. Therefore, new leads with activity on adult parasites are highly desirable. Objective: Anti-filarial lead optimization by semi-synthetic modification of glycyrrhetinic acid (GA). Methods: The GA was first converted into 3-O-acyl derivative, which was further converted into 12 amide derivatives. All these derivatives were assessed for their antifilarial potential by parasite motility assay. The binding affinity of active GA derivatives on trehalose-6-phosphate phosphatase (Bm-TPP) was assessed by molecular docking studies. Results: Among 15 GA derivatives, GAD-2, GAD-3, and GAD-4 were found more potent than the GA and standard drug DEC. These derivatives reduced the motility of Brugia malayi adult worms by up to 74% while the GA and DEC reduced only up to 49%. Further, GA and most of its derivatives exhibited two times more reduction in MTT assay when compared to the standard drug DEC. These derivatives also showed 100% reduction of microfilariae and good interactions with Bm-TPP protein. Conclusion: The present study suggests that 3-O-acyl and linear chain amide derivatives of glycyrrhetinic acid may be potent leads against B. malayi microfilariae and adult worms. These results might be helpful in developing QSAR model for optimizing a new class of antifilarial lead from a very common, inexpensive, and non toxic natural product.

Background: Due to the limited availability of antibiotics, Gram-negative bacteria (GNB) acquire different levels of drug resistance. It raised an urgent need to identify such agents, which can reverse the phenomenon of drug resistance. Objective: To understand the mechanism of drug resistance reversal of glycosides; niaziridin and niazirin isolated from the pods of Moringa oleifera and ouabain (control) against the clinical isolates of multidrug-resistant Escherichia coli. Methods: The MICs were determined following the CLSI guidelines for broth micro-dilution. In-vitro combination studies were performed by broth checkerboard method followed by Time-Kill studies, the efflux pump inhibition assay, ATPase inhibitory activity, mutation prevention concentration and in-silico studies. Results: The results showed that both glycosides did not possess antibacterial activity of their own, but in combination, they reduced the MIC of tetracycline up to 16 folds. Both were found to inhibit efflux pumps, but niaziridin was the best. In real time expression pattern analysis, niaziridin was also found responsible for the down expression of the two important efflux pump acrB & yojI genes alone as well as in combination. Niaziridin was also able to over express the porin forming genes (ompA & ompX). These glycosides decreased the mutation prevention concentration of tetracycline. Conclusion: This is the first ever report on glycosides, niazirin and niaziridin acting as drug resistance reversal agent through efflux pump inhibition and modulation of expression pattern drug resistant genes. This study may be helpful in preparing an effective antibacterial combination against the drug-resistant GNB from a widely growing Moringa oleifera.

The emergence of multi drug resistance (MDR) in Gram-negative bacteria (GNB) and lack of novel classes of antibacterial agents have raised an immediate need to identify antibacterial agents, which can reverse the phenomenon of MDR. The purpose of present study was to evaluate synergy potential and understanding the drug resistance reversal mechanism of chanoclavine isolated from Ipomoea muricataagainst the multi-drug-resistant clinical isolate of Escherichia coli(MDREC). Although chanoclavine did not show antibacterial activity of its own, but in combination, it could reduce the minimum inhibitory concentration (MIC) of tetracycline (TET) up to 16-folds. Chanoclavine was found to inhibit the efflux pumps which seem to be ATPase-dependent. In real-time expression analysis, chanoclavine showed down-regulation of different efflux pump genes and decreased the mutation prevention concentration of tetracycline. Further, in silicodocking studies revealed significant binding affinity of chanoclavine with different proteins known to be involved in drug resistance. In in silicoADME/toxicity studies, chanoclavine was found safe with good intestinal absorption, aqueous solubility, medium blood–brain barrier (BBB), no CYP 2D6 inhibition, no hepatotoxicity, no skin irritancy, and non-mutagenic indicating towards drug likeliness of this molecule. Based on these observations, it is hypothesized that chanoclavine might be inhibiting the efflux of tetracycline from MDREC and thus enabling the more availability of tetracycline inside the cell for its action.

Introduction: As a part of our drug discovery program for anti-tubercular agents, dihydroartemisinin (DHA-1) was screened against Mtb H37Rv, which showed moderate anti-tubercular activity (>25.0 μg/mL). These results prompted us to carry out the chemical transformation of DHA-1 into various derivatives and study their antitubercular potential. Materials and Methods: DHA-1 was semi-synthetically converted into four new acyl derivatives (DHA-1A – DHA-1D) and in-vitro evaluated for their anti-tubercular potential against Mycobacterium tuberculosis H37Rv virulent strain. The derivatives, DHA-1C (12-O-(4-nitro) benzoyl; MIC 12.5 μg/mL) and DHA-1D (12-O-chloro acetyl; MIC 3.12μg/mL) showed significant activity against the pathogen. Results: In silico studies of the most active derivative (DHA-1D) showed interaction with ARG448 inhibiting the mycobacterium enzymes. Additionally, it showed no cytotoxicity towards the Vero C1008 cells and Mouse bone marrow derived macrophages. Conclusion: DHA-1D killed 62% intracellular M. tuberculosis in Mouse bone marrow macrophage infection model. To the best of our knowledge, this is the first-ever report on the antitubercular potential of dihydroartemisinin and its derivatives. Since dihydroartemisinin is widely used as an antimalarial drug; these results may be of great help in anti-tubercular drug development from a very common, inexpensive, and non-toxic natural product.

To explore the anticancer compounds from tormentic acid derivatives, a quantitative structure–activity relationship (QSAR) model was developed by the multiple linear regression methods. The developed QSAR model yielded a high activity–descriptors relationship accuracy of 94% referred by regression coefficient (r2 = .94) and a high activity prediction accuracy of 91%. The QSAR study indicates that chemical descriptors, chiV5, T_T_Cl_7, T_2_T_4, SsCH3count, and Epsilon3 are significantly correlated with anticancer activity. This validated model was further been used for virtual screening and thus identification of new potential breast cancer inhibitors. Lipinski’s rule of five, ADMET risk and synthetic accessibility are used to filter false positive hits. Filtered compounds were then docked to identify the possible target binding pocket, to obtain a set of aligned ligand poses and to prioritize the predicted active compounds. The scrutinized compounds, as well as their metabolites, were predicted and analyzed for different pharmacokinetics parameters such as absorption, distribution, metabolism, excretion, and toxicity. Finally, the top-ranked compound NB-12 was evaluated by system pharmacology approach. Later studied the metabolic networks, disease biomarker networks, pathway maps, drug-target networks and generate significant gene networks. The strategy applied in this research work may act as a framework for rational design of potential anticancer drugs.

To determine a more precise and reliable method between Greulich–Pyle (GP) and MacKay’s (MK) method for the determination of skeletal age in an Indian pediatric population. We carried out a cross sectional study for the assessment of skeletal age on the basis of examination of hand and wrist radiographs of 106 patients (1–15 years of age) who presented with soft tissue injury to hand by the GP and the MK method. These radiographs were evaluated by a radiologist and an orthopedic surgeon independently. In girls, the mean age difference between chronological and skeletal age was 8 months by the GP method, whereas it was 17 months by MK method. For boys, the mean age difference was 10 months by the GP method and by MKs method, it was 20 months. By the GP method, 44% of the boys showed a more reliable estimate of age whereas it was 10% by the MK method. However, the same for girls was 25% by the GP method and 16% by the MK method. The inter-rater reliability for the raters was found to be κ=0.68 and this was statistically significant (P<0.001), 95% confidence interval (0.504–0.848). We concluded that the GP method appeared to be more reliable in the determination of skeletal age in the Indian pediatric population.

Background: Natural products showed anticancer activity and often induce apoptosis or autophagy in cancer cells through the PI3K/Akt/mTOR signaling pathways. The potential of natural products as PI3Ks inhibitors has been reported, which suggest PI3Ks a promising anticancer target. Phosphoinositide 3-kinase is a family of related intracellular signal transducer enzymes or lipid kinases that regulate different cellular processes involved in cancer. Objective: To identify the molecular reason behind the similar target based activity of selected shikimate pathway metabolites on PI3Kγ, a detail structure-activity relationship study was performed. Method: In the studied work, anticancer potential of plant molecules gallic acid and serpentine was evaluated against PI3Kγ isoform and compared with wortmannin, a steroid metabolite of the fungi and a non-specific covalent known inhibitor of PI3Ks by using in-silico QSAR, docking, ADMET, chemical isolation from plant, NMR and in-vitro activity. Results: A predictive QSAR model was developed by applying multiple linear regression which revealed identification of key structural properties regulating the inhibitory activity of serpentine and gallic acid on PI3Kγ. The model exhibited acceptable statistical parameters such as r2 0.76, r2CV 0.72, and q2 0.55. Structural elucidation was done through NMR studies. Predicted activities were further evaluated through in-vitro testing of gallic acid and serpentine targeting PI3Kγ. Conclusion: The identified chemical features modulating the activity were amide, amine, and secondary amine groups counts, highest occupied molecule orbital (HOMO) energy and valence connectivity index (order 2). In-silico ADME and toxicity risk assessment was done for pharmacokinetic and bioavailability compliance evaluation.

O. sanctum L. (O. tenuiflorum) is an important sacred medicinal plant of India known as Holy Basil or Tulsi. The chemical composition of volatile oil is highly complex and comprises high ratio of phenylpropanoids and terpenes, and some phenolic compound or flavonoids such as orientin and vicenin. These minor flavonoids are known to be antioxidant and anticancer in nature. Orientin reported as potential anticancer agent due to anti-proliferative activity on human liver cancer cell line HepG2, but its mechanism of action is not fully explored. In the present work an in-silico structure-activity relationship study on orientin was performed and built a pharmacophore mapping and QSAR model to screen out the potential structurally similar analogues from chemical database of Discovery Studio (DSv3.5, Accelrys, USA) as potential anticancer agent. Analogue fenofibryl glucuronide was selected for in vitro cytotoxic/anticancer activity evaluation through MTT assay. Binding affinity and mode of action of orientin and its analogue were explored through molecular docking studies on quinone oxidoreductase, a potential target of flavonoids. Contrary to the assumption, in vitro results showed only 41% cell death at 202.389 μM concentration (at 96 hrs). Therefore, we concluded that the selected orientin analogue fenofibryl glucuronide was non-cytotoxic/non-anti-carcinogenic up to 100 μg/ml (202.389 μM) concentrations for a long term exposure i.e., till 96 hrs in human cancer cells of HepG2. We concluded that orientin and its analogue fenofibryl glucuronide as pure compound showed no activity or less cytotoxicity activity on liver cancer cell line HepG2.

Andrographolide and neoandrographolide are major bioactive molecules of Andrographis paniculata, a well‐known medicinal plant. These molecules exhibited varying degrees of anti‐inflammatory and anticancer activities in‐vitroand in‐vivo. Role of begomovirus protein C2/TrAP in biosynthesis of andrographolide was identified through molecular modeling, docking and predicted results were substantiated by in vitrostudies. Homology molecular modeling and molecular docking were performed to study the binding conformations and different bonding behaviors, in order to reveal the possible mechanism of action behind higher accumulation of andrographolide. It was concluded that C2/TrAP inhibit the activation of SNF1‐Related Protein Kinase‐1 (SnRK1) in terpenoid pathway and removes the negative regulation of 3‐hydroxy‐3‐methylglutaryl‐CoA reductase (HMGR) by SnRK1, leading to higher accumulation of andrographolide and neoandrographolide in begomovirus infected plants. The binding site residues of SnRK1 docked with C2/TrAP were found to be associated with ATP binding site, substrate binding site and activation loop. Predicted results were also validated by HPTLC. This study provides important insights into understanding the role of viral protein in altering the regulation of biosynthesis of andrographolide and could be used in future research to develop biomimetic methods for increasing the production of such phytometabolites having anti‐cancerous and anti‐inflammatory properties.

The PI3K pathway is a signal transduction process including oncogenes and receptor tyrosine kinase regulating cellular functions i.e., survival, protein synthesis, and metabolism. In the present work, we have investigated the role of water molecules on inhibitor’s binding orientation in crystal structures of PI3K pathway targets using molecular docking approach. AutoDock v4.2 docking software was employed to dock PI3Kγ and its known inhibitors viz., wortmannin, quercetin, myricetin and pyridyl-triazine. Besides, serpentine was also docked on the same binding pocket, subsequently its anticancer activity was evaluated through in vitro experiment. Docking studies have been performed in the presence as well as in absence of water molecules at the binding pocket, and results were compared with crystallographic structural data. The comparison was done on the basis of binding energy, RMSD, inhibition constant (Ki), conserved and bridging water molecules, and found that, while considering water molecules during docking experiments, it increases the binding affinity of PI3K inhibitors.

A series of twenty one chalcone derivatives having indole moiety were synthesized and were evaluated against four human cancer cell lines. Indolyl chalcones 1a, 1b, 1d, 1f-1j, 2c, 2e, 2i showed good anticancer activity. Chalcones 1b and 1d were the most active and selective anticancer agents with IC50 values <1μg/ml and 1.51μg/ml, against WRL-68 cell line, respectively. Molecular mechanism was explored through in silico docking & ADMET studies.

Bioactivity guided isolation of Glycyrrhiza glabra (Leguminosae / Fabaceae) roots resulted in the characterization of 18β-glycyrrhetinic acid as a major anti-tubercular agent. Further, GA-1 was semi-synthetically converted into its nine derivatives, which were in-vitro evaluated for their antitubercular potential against Mycobacterium tuberculosis H37Rv using BACTEC-460 radiometric susceptibility assay. All the derivatives were active, but the benzylamide (GA-8, MIC 12.5μg/ml) and ethyl oxylate (GA-3, MIC 25.0 μg/ml) derivatives were significantly active against the pathogen. This was further supported by the molecular docking studies, which showed adequate docking (LibDock) scores for GA-3 (120.3) and GA-8 (112.6) with respect to the standard anti-tubercular drug, rifampicin (92.94) on the DNA-directed RNA polymerase subunit beta (rpoB) target site. Finally, the in silico pharmacokinetic and drug-likeness studies showed that GA-3 and GA- 8 possesses drug-like properties. This is the first ever report on the anti-tubercular potential of GA and its derivatives. These results may be of great help in anti-tubercular drug development from a very common, inexpensive, and non toxic natural product.

Diabetes accounts for high mortality rate worldwide affecting million of lives annually. Global prevalence of diabetes and its rising frequency makes it a key area of research in drug discovery programs. The research article describes the development of quantitative structure activity relationship model against PPARγ, a promising drug target for diabetes. Multiple linear regression approach was adopted for statistical model development and the QSAR relationship suggested the regression coefficient (r2) of 0.84 and the cross validation coefficient (rCV2) of 0.77. Further, the study suggested that chemical descriptors viz., dipole moment, electron affinity, dielectric energy, secondary amine group count and LogP correlated well with the activity. The docking studies showed that most active gymnemic acid analogues viz., gymnemasin D and gymnemic acid VII possess higher binding affinity to PPARγ. QSAR and ADMET studies based other predicted active gymnemc acid analogues were gymnemic acid I, gymnemic acid II, gymnemic acid III, gymnemic acid VIII, gymnemic acid X, gymnemic acid XII, gymnemic acid XIV, gymnemic acid XVIII and gymnemoside W2. Predicted activity results of three query compounds were found comparable to experimental in vivo data. Oral bioavailability of these active analogues is still a limiting factor and therefore further lead optimization required. Also, such study would be of great help in active pharmacophore discovery and lead optimization, and offering new insights into therapeutics for diabetes mellitus.

To screen the active antimalarial novel artemisinin derivatives, a QSAR modeling approach was used. QSAR model showed high correlation (r2= 0.83 and rCV2= 0.81) and indicated that Connectivity Index (order 1, standard), Connectivity Index (order 2, standard), Dipole Moment (debye), Dipole Vector X (debye) and LUMO Energy (eV) well correlate with activity. High binding likeness on antimalarial target plasmepsin was detected through molecular docking. Active artemisinin derivatives showed significant activity and indicated compliance with standard parameters of oral bioavailability and ADMET. The active artemisinin derivatives namely, -Artecyclopropylmether HMCP (A3), – Artepipernoylether (PIP-1) (A4) and 9-(-Dihydroartemisinoxy)methyl anthracene (A5) were semi-synthesized and characterized based on its 1H and 13C NMR spectroscopic data and later activity tested in vivo on mice infected with multidrug resistant strain of P. yoelii nigeriensis. Predicted results were successfully validated by in vivo experiments.

Phyllanthus amarus is a medicinal herb used in traditional Indian medicine for liver disorders. Several researches also show that it acts primarily in the liver, but the molecules were unidentified for liver protective activity. This study was to determine whether the lignans isolated from P. amarus attenuates the D-galactosamine (GalN) / Lipopolysaccharide (LPS)- induced acute hepatitis in mice. Standardize mixture of lignans (slPA) isolated from leaves of P. amarus using automated chromatographic technique was used for experiments. Experimental mice were orally pre-treated with slPA (10, 30 and100mg/kg) for 7 days before intra-peritoneal injection of GalN/LPS. Acute hepatitis in mice was confirmed by significant increase of pro-inflammatory cytokines, and hepatotoxic markers. Pre-treatment of slPA exhibit significant liver protection in dose dependant mannaer. In-silico molecular docking studies also suggests that lignans are preferentially more active due to strong binding affinity against pro-inflammatory cytokines; IL-1, IL-6, and TNF-. The electronic parameters of lignans for bioavailability, drug likeness and toxicity were within the acceptable limit. In-vivo and in-silico results suggest that pretreatment of slPA exhibit potent hepatoprotection against GalN/LPS-induced hepatitis in mice and the liver protective effects may be due to the inhibition of inflammatory mediators.

As a part of our anticancer drug discovery programme, QSAR models were developed for the prediction of anticancer activities of ursolic acid derivatives against the human hepatocellular carcinoma HepG2, breast carcinoma MDA-MB-231 and the human ductal breast epithelial T47D cancer cell lines followed by wet lab semi-synthesis of virtually active derivatives, their in-vitro biological evaluation and apoptosis. The development of QSAR models was carried out by forward stepwise multiple linear regression method using a leave-one-out approach. Virtually active derivatives were semi synthesized, spectroscopically characterized and then in-vitro tested against human cancer cell lines. Active derivatives were checked via DNA fragmentation assay. The results exhibited regression coefficients (r2) and the cross-validation regression coefficients (rCV2) for the human HepG2, MDA-MB-231 and T47D cancer cell lines as .95 and .90; .92 and .87; .89 and .83 respectively showing the prediction accuracy of the models against biological activities. Computational molecular modeling is a valid modern approach, widely used in the identification of potential drug leads. The most active virtual derivatives of UA were semi- synthesized and their in-vitro and ex-vivo evaluation showed similar results with the predicted one, validating our QSAR models. Out of several active derivatives, the three UA2, UA7 and UA10 were potentially active against the above human cancer cell lines. These findings may be of immense importance in the anticancer drug development of an inexpensive and widely available natural product, ursolic acid.

Because of mutation and natural selection, development of drug resistance to the existing antimalarial is the major problem in malaria treatment. This problem has created an urgent need of novel antimalarial drug targets as well as lead compounds. The important characteristic of malaria is that it shows the phenomenon of balanced polymorphisms. Several traits have been selected in response to disease pressure. Therefore such factors must be explored to understand the pathogenesis of malaria infection in human host. Apicoplast, hub of metabolism is present in Plasmodium falciparum (causative agent of falciparum malaria) having similarities with plant plastid. Among several pathways in apicoplast, Dolichol metabolic pathway is one of the most important pathway and has been known to play role in parasite survival in the human host. In P.falciparum, a phosphorylated derivative of Dolichol participates in biosynthesis of glycoproteins. Several proteins of this pathway play role in post translational modifications of proteins involved in the signal transduction pathways, regulation of DNA replication and cell cycle. This pathway can be used as antimalarial drug target. This report has explored progress towards the study of proteins and inhibitors of Dolichol metabolic pathway. For more comprehensive analysis, the host genetic factors and drug-protein interaction have been covered.

In the present work, QSAR model was derived by multiple linear regression method for the prediction of anticancer activity of 18-glycyrrhetinic acid derivatives against the human breast cancer cell line MCF-7. The QSAR model for anti-proliferative activity against MCF-7 showed high correlation (r2=0.90 and rCV2=0.83) and indicated that chemical descriptors namely, dipole moment (debye), steric energy (kcal/mole), heat of formation (kcal/mole), ionization potential (eV), LogP, LUMO energy (eV) and shape index (basic kappa, order 3) correlate well with activity. The QSAR virtually predicted that active derivatives were first semi-synthesized and characterized on the basis of their 1H and 13C NMR spectroscopic data and then were in-vitro tested against MCF-7 cancer cell line. In particular, octylamide derivative of glycyrrhetinic acid GA-12 has marked cytotoxic activity against MCF-7 similar to that of standard anticancer drug paclitaxel. The biological assays of active derivative selected by virtual screening showed significant experimental activity.

Despite of modern antifungal therapy, the mortality rates of invasive infection with human fungal pathogen Candida albicans are up to 40%. Studies suggest that drug resistance in the three most common species of human fungal pathogens viz., C. albicans, Aspergillus fumigatus (causing mortality rate up to 90%) and Cryptococcus neoformans (causing mortality rate up to 70%) is due to mutations in the target enzymes or high expression of drug transporter genes. Drug resistance in human fungal pathogens has led to an imperative need for the identification of new targets unique to fungal pathogens. In the present study, we have used a comparative genomics approach to find out potential target proteins unique to C. albicans, an opportunistic fungus responsible for severe infection in immune-compromised human. Interestingly, many target proteins of existing antifungal agents showed orthologs in human cells. To identify unique proteins, we have compared proteome of C. albicans [SC5314] i.e., 14,633 total proteins retrieved from the RefSeq database of NCBI, USA with proteome of human and non-pathogenic yeast Saccharomyces cerevisiae. Results showed that 4,568 proteins were identified unique to C. albicans as compared to those of human and later when these unique proteins were compared with S. cerevisiae proteome, finally 2,161 proteins were identified as unique proteins and after removing repeats total 1,618 unique proteins (42 functionally known, 1,566 hypothetical and 10 unknown) were selected as potential antifungal drug targets unique to C. albicans.

For the prediction of anticancer activity of glycyrrhetinic acid (GA-1) analogs against the human lung cancer cell line (A-549), a QSAR model was developed by forward stepwise multiple linear regression methodology. The regression coefficient (r2) and prediction accuracy (rCV2) of the QSAR model were taken 0.94 and 0.82, respectively in terms of correlation. The QSAR study indicates that the dipole moments, size of smallest ring, amine counts, hydroxyl and nitro functional groups are correlated well with cytotoxic activity. The docking studies showed high binding affinity of the predicted active compounds against the lung cancer target EGFR. These active glycyrrhetinic acid derivatives were then semi-synthesized, characterized and in-vitro tested for anticancer activity. The experimental results were in agreement with the predicted values and the ethyl oxalyl derivative of GA-1 (GA-3) showed equal cytotoxic activity to that of standard anticancer drug paclitaxel.

Aquilaria agallocha Roxb. family, Thymelaeaceae, is an evergreen plant of South-East Asia, commonly described as aloe wood or agarwood. Traditionally, the bark, root and heartwood are used for their medicinal properties as a folk medicine for hundreds of years. Chemical analyses revealed that the bulk of the oil is constituted by agarospirol (12.5), jinkoh-eremol (11.8) and hinesol (8.9) as major contributor. In the present work, a QSAR model for antiinflammatory activity of 10-epi--Eudesmol, jinkoh-eremol, agarospirol and other compounds has been developed by multiple linear regression method. The r2 and rCV2 of a model were 0.89 and 0.81 respectively. In silico molecular docking study suggests that compound 10-epi--Eudesmol, jinkoh-eremol and agarospirol are preferentially more active than other identified compounds with strong binding affinity to major anti-inflammatory and immunomodulatory receptors. The oil displayed a significant and dose dependent reduction of 12-O-tetradecanoylphorobol-13 acetate (TPA)- induced ear edema and MDA activity when compared with vehicle treated mice. Pro-inflammatory cytokines (IL-1, IL-6 and TNF-) were also reduced significantly in a dose dependent manner in all the TPA treated groups as compared to control. The present study indicates that agarwood oil significantly reduced the skin thickness, ear weight, oxidative stress and pro-inflammatory cytokines production in TPA-induced mouse ear inflammation model and contributed towards validation of its traditional use to treat inflammation related ailments.

Bioassay record of High Throughput Screening (HTS) contains compounds with high diversity. This high diversity in molecules causes an intense non-linearity into the molecular descriptors set. So to build a QSAR model covering the diversity in molecular structure is a tedious task. In the present work, a method has been proposed to extract information about pharmacophores covering a larger area in the HTS record and development of Support Vector Regression (SVR) QSAR model considering extracted pharmacophores specified to the cell line or target. A probabilistic approach has also been proposed to evaluate the authenticity of predictions made by QSAR model. The developed method has been used for virtual screening of library molecules. The advantage of this protocol is that, it is beneficial for a very large dataset. The proposed method has the capability to extract pharmacophore information from any HTS data. Additionally, this will be advantageous for the development of precised virtual screening model on the basis of high throughput screening data.

Present work deals with generation of virtual samples as mathematical modeling of empirical data on the basis of empirical data. The generated samples were used for development of QSAR model. The method deals with extrapolation of sample vector in such a manner that there is conservation of the empirical data distribution. The data distribution has been judged with statistical parameters. The method was implemented with anticancer activity of Gossypol acetic acid against BCL2 target for colorectal cancer. Considering the virtual samples only for model development, model training showed a regression coefficient for leave one out cross validation as 0.996 with 66 virtual samples, and a regression coefficient with external test set data (51 samples) as 0.993. External test set data which were never used in the virtual sample generation showed predicted regression coefficient value of >0.61. On the basis of QSAR model, nine compounds were suggested as anti-BCL2 active compounds. The suggested compounds were further validated by docking study with Gossypol acetic acid and ‘Tetrahydroisoquinoline amide substituted phenyl pyrazole’ cocrystallized with chimeric BCL2-XL (PDBID: 2W3L) protein.

The present study deals with the development of an artificial neural network based quantitative structure activity relationship (QSAR) model for virtual screening of active compounds which contain androstenedione carbonskeleton or their similar skeleton at the core. An empirical data modeling (with fitted data mapping) has been performed on the basis of bioassay record for human breast cancer cell line MCF7. The whole experimental data set was considered as test set. Standard feed-forward back-propagation neural network technique was applied to build the model. Leave-One- Out (LOO) cross-validation was performed to evaluate the performance of the model. The mapped model became the basis for selection best mapped compounds followed by development of Pharmacophore specific secondary QSAR model. In the present study, two best mapped molecules ‘4beta-hydroxy Withanolide-E‘ and ‘7, 8-Dehydrocalotropin‘ were used for development of the secondary QSAR model. These secondary-QSAR models were resulted with R2 LOOCV value 0.9845 and 0.9666 respectively. Docking studies, in silico phamacokinetic and toxicity analysis was also done for selected compounds. The screened compounds CID_73621, CID_16757497, CID_301751, CID_390666 and CID_46830222 were found with promising binding affinity value with aromatase with reference to the co-crystallized control compound androstenedione. Due to excellent extent of variance coverage in ANN based QSAR map model, it can be used as a robust non-linear QSAR model for androstenedione carbon-skeleton containing molecules and the protocol can be used to derive secondary QSAR models for other compounds set.

This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r2) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV2) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

Traditionally, the process of drug development has revolved around a screening approach and trial-and-error method, as no body knew which compound or approach could serve as a drug or therapy. This discovery process was very time consuming and laborious and discovery of a new drug used to take around 8-14 years and costs about US 1.8 billion. In order to minimize the time and cost in this drug discovery process, scientists around the world contributed tremendously and come up with a modern drug-designing program. The beauty of this modern drug designing is that now we can tailor the drug with desired combinations computationally before going for experimental laboratory work. In this review, traditional to modern methods of drug designing & discovery have been discussed. It covers the available web tools/databases and in silico techniques used in virtual screening and drug discovery processes to reduce the wet lab economy and time. Studies suggest that the best method for molecular docking based target identification is probably a hybrid of various types of algorithm encompassing novel search and scoring strategies e.g., PMF score, Dock score, Gold score etc. However, apart from in vitro assays and in vivo experiments, application of in silico QSAR & ADMET in the prediction of biological activity & bioavailability are proving beneficial in drug discovery process.

Urban sprawl has turn out to be an issue of worldwide interest, mainly because of its alleged negative impacts on the social, economic and environmental aspects of the city. Many studies indicate that it is the pattern, density, and rate of new urban growth that leads to sprawl. The proportion of the total population in a region to the total built-up area of the region is a measure of quantifying sprawl. This paper presents a quantitative method to analyse urban sprawl based on computation of indicators and GIS techniques. It was found out that for the study area the percentage change in the built-up area (1980-2005) was 14 times higher than the percentage change in the population growth, and also with the analysis capabilities in GIS, it determined the patterns and densities of new growth thereby classifying some of those areas as sprawl patterns.