Pyrano[2,3-c]pyrazole fused spirooxindole-linked 1,2,3-triazoles as antioxidant agents: Exploring their utility in the development of antidiabetic drugs via inhibition of α-amylase and DPP4 activity.

Environment-benign, multicomponent synthetic methodologies are vital in modern pharmaceutical research and facilitates multi-targeted drug development via synergistic approach. Herein, we reported green and efficient synthesis of pyrano[2,3-c]pyrazole fused spirooxindole linked 1,2,3-triazoles using a tea waste supported copper catalyst (TWCu). The synthetic approach involves a one-pot, five-component reaction using N-propargylated isatin, hydrazine hydrate, ethyl acetoacetate, malononitrile/ethyl cyanoacetate and aryl azides as model substrates. Mechanistically, the reaction was found to proceed via in situ pyrazolone formation followed by Knoevenagel condensation, azide alkyne cycloaddition and Michael's addition reactions. The molecules were developed using structure-based drug design. The primary goal is to identifying anti-oxidant molecules with potential ability to modulate α-amylase and DPP4 (dipeptidyl-peptidase 4) activity. The anti-oxidant analysis, as determined via DPPH, suggested that the synthesized compounds, A6 and A10 possessed excellent anti-oxidant potential compared to butylated hydroxytoluene (BHT). In contrast, compounds A3, A5, A8, A9, A13, A15, and A18 were found to possess comparable anti-oxidant potential. Among these, A3 and A13 possessed potential α-amylase inhibitory activity compared to the acarbose, and A3 further emerged as dual inhibitors of both DPP4 and α-amylase with anti-oxidant potential. The relationship of functionalities on their anti-oxidant and enzymatic inhibition was explored in context to their SAR that was further corroborated using in silico techniques and enzyme kinetics.

In-silico activity prediction and docking studies of some flavonol derivatives as anti-prostate cancer agents based on Monte Carlo optimization.

The QSAR models are employed to predict the anti-proliferative activity of 81 derivatives of flavonol against prostate cancer using the Monte Carlo algorithm based on the index of ideality of correlation (IIC) criterion. CORAL software is employed to design the QSAR models. The molecular structures of flavonols are demonstrated using the simplified molecular input line entry system (SMILES) notation. The models are developed with the hybrid optimal descriptors i.e. using both SMILES and hydrogen-suppressed molecular graph (HSG). The QSAR model developed for split 3 is selected as a prominent model ([Formula: see text]= 0.727, [Formula: see text]= 0.628, [Formula: see text]= 0.642, and [Formula: see text]=0.615). The model is interpreted mechanistically by identifying the characteristics responsible for the promoter of the increase or decrease. The structural attributes as promoters of increase of pIC50 were aliphatic carbon atom connected to double-bound (C…=…, aliphatic oxygen atom connected to aliphatic carbon (O…C…), branching on aromatic ring (c…(…), and aliphatic nitrogen (N…). The pIC50 of eight natural flavonols with pIC50 more than 4.0, were predicted by the best model. The molecular docking is also performed for natural flavonols on the PC-3 cell line using the protein (PDB: 3RUK).

Electro-organic synthesis of C-5 sulfenylated amino uracils: Optimization and exploring topoisomerase-I based anti-cancer profile.

Cancer is spreading worldwide and is one of the leading causes of death. The use of existing chemotherapeutic agents is frequently limited due to side effects. As a result, it is critical to investigate new agents for cancer treatment. In this context, we developed an electrochemical method for the synthesis of a series of thiol-linked pyrimidine derivatives (3a-3p) and explored their anti-cancer potential. The biological profile of the synthesized compounds was evaluated against breast (MDAMB-231 and MCF-7) and colorectal (HCT-116) cancer cell lines. 3b and 3d emerged to be the most potent agents, with IC50 values ranging between 0.98 to 2.45 µM. Target delineation studies followed by secondary anticancer parameters were evaluated for most potent compounds, 3b and 3d. The analysis revealed compounds possess DNA intercalation potential and selective inhibition towards human topoisomerase (hTopo1). The analysis was further corroborated by DNA binding studies and in silico-based molecular modeling studies that validated the intercalating binding mode between the compounds and the DNA.

Development of thiazole-appended novel hydrazones as a new class of α-amylase inhibitors with anticancer assets: an in silico and in vitro approach.

Hyperamylasemia is reported to be associated with numerous chronic diseases, including diabetes and cancer. Considering this fact, we developed a series of thiazole-clubbed hydrazones. The derivatives were explored for their in vitro α-amylase inhibitory activity, which was further corroborated with their anticancer assets using a panel of cancer cells, including colon cancer (HCT-116), lung cancer (A549), and breast cancer (MDA-MB-231). To better understand pharmacokinetics, the synthetic derivatives were subjected to in silico ADMET prediction. The in vitro based biological investigation revealed that compared to the reference drug acarbose (IC50 = 0.21 ± 0.008 µM), all the synthesized compounds (5a-5aa) exhibited in vitro α-amylase inhibitory response in the range of IC50 values from 0.23 ± 0.003 to 0.5 ± 0.0 µM. Along with this, the proliferations of the HCT-116, A549 and MDA-MB-231 cells were inhibited when treated with the synthesized compounds. Notable cancer cell growth inhibition was observed for compounds 5e, 5f and 5y, which correlated with their α-amylase inhibition. Additionally, the kinetics investigation revealed that 5b, 5e, 5f and 5y exhibit uncompetitive inhibition. 5b was found to be the least cytotoxic and most potent α-amylase inhibitor and was further validated by absorption and fluorescence quenching technique.

Design and Development of COX-II Inhibitors: Current Scenario and Future Perspective.

Innate inflammation beyond a threshold is a significant problem involved in cardiovascular diseases, cancer, and many other chronic conditions. Cyclooxygenase (COX) enzymes are key inflammatory markers as they catalyze prostaglandins production and are crucial for inflammation processes. While COX-I is constitutively expressed and is generally involved in "housekeeping" roles, the expression of the COX-II isoform is induced by the stimulation of different inflammatory cytokines and also promotes the further generation of pro-inflammatory cytokines and chemokines, which affect the prognosis of various diseases. Hence, COX-II is considered an important therapeutic target for drug development against inflammation-related illnesses. Several selective COX-II inhibitors with safe gastric safety profiles features that do not cause gastrointestinal complications associated with classic anti-inflammatory drugs have been developed. Nevertheless, there is mounting evidence of cardiovascular side effects from COX-II inhibitors that resulted in the withdrawal of market-approved anti-COX-II drugs. This necessitates the development of COX-II inhibitors that not only exhibit inhibit potency but also are free of side effects. Probing the scaffold diversity of known inhibitors is vital to achieving this goal. A systematic review and discussion on the scaffold diversity of COX inhibitors are still limited. To address this gap, herein we present an overview of chemical structures and inhibitory activity of different scaffolds of known COX-II inhibitors. The insights from this article could be helpful in seeding the development of next-generation COX-II inhibitors.

Exploring the Multitarget Potential of Iridoids: Advances and Applications.

Iridoids are secondary plant metabolites that are multitarget compounds active against various diseases. Iridoids are structurally classified into iridoid glycosides and non-glycosidic iridoids according to the presence or absence of intramolecular glycosidic bonds; additionally, iridoid glycosides can be further subdivided into carbocyclic iridoids and secoiridoids. These monoterpenoids belong to the cyclopentan[c]-pyran system, which has a wide range of biological activities, including antiviral, anticancer, antiplasmodial, neuroprotective, anti-thrombolytic, antitrypanosomal, antidiabetic, hepatoprotective, anti-oxidant, antihyperlipidemic and anti-inflammatory properties. The basic chemical structure of iridoids in plants (the iridoid ring scaffold) is biosynthesized in plants by the enzyme iridoid synthase using 8-oxogeranial as a substrate. With advances in phytochemical research, many iridoid compounds with novel structure and outstanding activity have been identified in recent years. Biologically active iridoid derivatives have been found in a variety of plant families, including Plantaginaceae, Rubiaceae, Verbenaceae, and Scrophulariaceae. Iridoids have the potential of modulating many biological events in various diseases. This review highlights the multitarget potential of iridoids and includes a compilation of recent publications on the pharmacology of iridoids. Several in vitro and in vivo models used, along with the results, are also included in the paper. This paper's systematic summary was created by searching for relevant iridoid material on websites such as Google Scholar, PubMed, SciFinder Scholar, Science Direct, and others. The compilation will provide the researchers with a thorough understanding of iridoid and its congeners, which will further help in designing a large number of potential compounds with a strong impact on curing various diseases.

Development of reliable quantitative structure-toxicity relationship models for toxicity prediction of benzene derivatives using semiempirical descriptors.

The Health and environmental hazards of benzene and nitrobenzene (NB) derivatives have remained a topic of interest of researchers. In silico methods for prediction of toxicity of chemicals have proved their worth in accurate forecast of environmental as well as health toxicity and are strongly recommended by regulatory authorities. Two quantitative structure-toxicity relationship (QSTR) models explaining Scenedesmus obliquus toxicity trends among 39 benzene derivatives and Tetrahymena pyriformis toxicity of 103 NB and 392 benzene derivatives are developed using semiempirical quantum chemical parameters. The best constructed QSTR models have good fitting ability (R2 = 0.8053, 0.7591, and 0.8283) and robustness (Q2LOO = 0.7507, 0.7227, and 0.8194; Q2LMO = 0.7338, 0.7153, and 0.8172). The external predictivity of all the models are quite good (R2EXT = 0.8256, 0.9349, and 0.8698). Electronegativity, Cosmo volume, total energy, and molecular weight are responsible for the increase and decrease of toxicity of benzene derivatives against S. obliquus while electronegativity, electrophilicity index, the heat of formation, total energy, hydrophobicity, and cosmo volume are responsible for modulation of toxicity of NB and benzene derivatives toward T. pyriformis. These models fulfill the requirements of all the five OECD principles.

Quantitative structure-activity relationship modeling for predication of inhibition potencies of imatinib derivatives using SMILES attributes.

Chronic myelogenous leukemia (CML) which is resulted from the BCR-ABL tyrosine kinase (TK) chimeric oncoprotein, is a malignant clonal disorder of hematopoietic stem cells. Imatinib is used as an inhibitor of BCR-ABL TK in the treatment of CML patients. The main object of the present manuscript is focused on constructing quantitative activity relationships (QSARs) models for the prediction of inhibition potencies of a large series of imatinib derivatives against BCR-ABL TK. Herren, the inbuilt Monte Carlo algorithm of CORAL software is employed to develop QSAR models. The SMILES notations of chemical structures are used to compute the descriptor of correlation weights (CWs). QSAR models are established using the balance of correlation method with the index of ideality of correlation (IIC). The data set of 306 molecules is randomly divided into three splits. In QSAR modeling, the numerical value of R2, Q2, and IIC for the validation set of splits 1 to 3 are in the range of 0.7180-0.7755, 0.6891-0.7561, and 0.4431-0.8611 respectively. The numerical result of [Formula: see text] > 0.5 for all three constructed models in the Y-randomization test validate the reliability of established models. The promoters of increase/decrease for pIC50 are recognized and used for the mechanistic interpretation of structural attributes.

Deep learning and multi-omics approach to predict drug responses in cancer.

BACKGROUND: Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features. RESULTS: We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for [Formula: see text] values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC. CONCLUSION: The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.

Network-based integration of multi-omics data for clinical outcome prediction in neuroblastoma.

Multi-omics data are increasingly being gathered for investigations of complex diseases such as cancer. However, high dimensionality, small sample size, and heterogeneity of different omics types pose huge challenges to integrated analysis. In this paper, we evaluate two network-based approaches for integration of multi-omics data in an application of clinical outcome prediction of neuroblastoma. We derive Patient Similarity Networks (PSN) as the first step for individual omics data by computing distances among patients from omics features. The fusion of different omics can be investigated in two ways: the network-level fusion is achieved using Similarity Network Fusion algorithm for fusing the PSNs derived for individual omics types; and the feature-level fusion is achieved by fusing the network features obtained from individual PSNs. We demonstrate our methods on two high-risk neuroblastoma datasets from SEQC project and TARGET project. We propose Deep Neural Network and Machine Learning methods with Recursive Feature Elimination as the predictor of survival status of neuroblastoma patients. Our results indicate that network-level fusion outperformed feature-level fusion for integration of different omics data whereas feature-level fusion is more suitable incorporating different feature types derived from same omics type. We conclude that the network-based methods are capable of handling heterogeneity and high dimensionality well in the integration of multi-omics.

Physical exercise is associated with a reduction in plasma levels of fractalkine, TGF-ß1, eotaxin-1 and IL-6 in younger adults with mobility disability.

Mobility disability (MD) refers to substantial limitations in life activities that arise because of movement impairments. Although MD is most prevalent in older individuals, it can also affect younger adults. Increasing evidence suggests that inflammation can drive the development of MD and may need to be targeted for MD prevention. Physical exercise has anti-inflammatory properties and has been associated with MD prevention. However, no studies to date have examined whether exercise interventions affect the peripheral inflammatory status in younger adults with MD. To this end, we used blood samples from young and middle-aged adults with MD (N = 38; median age = 34 years) who participated in a 12-week intervention that included aerobic and resistance exercise training. A pre-post assessment of inflammatory biomarkers was conducted in plasma from two timepoints, i.e., before the exercise trial and at follow-up (3-7 days after the last exercise session). We successfully measured 15 inflammatory biomarkers and found that exercise was associated with a significant reduction in levels of soluble fractalkine, transforming growth factor beta 1 (TGF-ß1), eotaxin-1 and interleukin (IL) 6 (corrected α = 0.004). We also found significant male-specific effects of exercise on (i) increasing IL-16 and (ii) decreasing vascular endothelial growth factor-A (VEGF-A). In line with our results, previous studies have also found that exercise can reduce levels of TGF-ß1, eotaxin-1 and IL-6. However, our finding that exercise reduces plasma levels of fractalkine in younger adults with MD, as well as the sex-dependent findings, have not been previously reported and warrant replication in larger cohorts. Given the suggested role of inflammation in promoting MD development, our study provides additional support for the use of physical exercise as a treatment modality for MD.

Quantitative structure-toxicity relationship models for predication of toxicity of ionic liquids toward leukemia rat cell line IPC-81 based on index of ideality of correlation.

The application of ionic liquids (ILs) as green solvents has attracted the attention of the scientific community. However, ILs may play the role of toxins. Even though ionic liquids may assist to minimize air pollution, but their discharge into aquatic ecosystems might result in significant water pollution due to their potential toxicity and inaccessibility to biodegradation. Recently, more attention has been paid to the toxicity of ILs on plants, bacteria, and humans. Here, a quantitative structure-toxicity relationship study (QSTR) based on the Monte Carlo method of CORAL software has been applied to estimate the logarithm of the half-maximal effective concentration of toxicity of ILs against leukemia rat cell line IPC-81 (logEC50). A hybrid optimal descriptor is used to build QSTR models for a large set of 304 diverse ILs including ammonium, imidazolium, morpholinium, phosphonium, piperidinium, pyridinium, pyrrolidinium, quinolinium, sulfonium, and protic ILs. The SMILES notations of ILs are utilized to compute the descriptor correlation weight (DCW). Four splits are made from the whole dataset and each split is randomly divided into four sets (training subsets and validation set). The index of ideality of correlation (IIC) is applied to evaluate the authenticity and robustness of the QSTR models. A QSTR model with statistical parameters R2 = 0.85, CCC = 0.92, Q2 = 0.84, and MAE = 0.25 for the validation set of the best split is considered as a prime model. The outliers and promoters of increase/decrease of logEC50 are extracted and the mechanistic interpretation of effective descriptors for the model is also offered.HighlightsGlobal SMILES-based QSAR model was developed to predict the toxicity of ILs.The CORAL software is used to model the ILs toxicity on IPC-81 leukemia rat cell line.IIC is tested as a criterion of predictive potential.The toxicological effects of ILs are discussed based on the proposed model.

Unswerving modeling of hepatotoxicity of cadmium containing quantum dots using amalgamation of quasiSMILES, index of ideality of correlation, and consensus modeling.

Liver toxicity of quantum dots varies with size, concentration, and other structural as well as experimental parameters. For modeling hepatotoxicity, the eclectic data associated with cadmium containing quantum dots have been used in the creation of quasiSMILES for their representation. The core diameter is normalized for wider applicability and the index of the ideality of correlation is applied to construct the better quantitative features toxicity relationship models. Total eight splits are created and the best model is obtained through split 1 with better prediction criteria of validation set objects. The values of all statistical criteria used in the quality determination of a QSAR model are within the specified range for all the eight toxicity models developed here. Factors like TGA ligand and 0.6-0.7 nm diameter are favorable for liver toxicity while L-cysteine ligand and 0.5-0.6 nm core diameter are helpful in the reduction of toxicity. Further, the intelligent consensus modeling process forms a total of 40 individual and 20 consensus models and the best individual and consensus models are 'Good' in MAE-based criteria. The consensus modeling enhances the prediction ability as well as the accuracy of the developed models and increases the applicability space of the built models for hepatotoxicity prediction of quantum dots.

Monte Carlo Method Based QSAR Studies of Mer Kinase Inhibitors in Compliance with OECD Principles.

Monte Carlo method based QSAR studies for inhibitors of Mer kinase, a potential novel target for cancer treatment, has been carried out using balance of correlation technique. The data was divided into three random and dissimilar splits and hybrid optimal descriptors derived from SMILES and hydrogen filled graphs based notations were used for construction of QSAR models. The generated models have good fitting ability, robustness, generalizability and internal predictive ability. The external predictive ability has been tested using multiple criteria and described models exhibited good performance in all of these tests. The values of R2, Q2, R2test, Q2test, R2m and ∆R2m for the best model are 0.9502, 0.9388, 0.9469, 0.9083, 0.7534 and 0.0894 respectively. Also, the structural characteristics responsible for enhancement and reduction of activity have been extracted. Further, the agreement with the OECD rules for QSAR model has been discussed.

Design, synthesis, conformational and molecular docking study of some novel acyl hydrazone based molecular hybrids as antimalarial and antimicrobial agents.

BACKGROUND: Acyl hydrazones are an important class of heterocyclic compounds promising pharmacological characteristics. Malaria is a life-threatening mosquito-borne blood disease caused by a plasmodium parasite. In some places, malaria can be treated and controlled with early diagnosis. However, some countries lack the resources to do this effectively. RESULTS: The present work involves the design and synthesis of some novel acyl hydrazone based molecular hybrids of 1,4-dihydropyridine and pyrazole (5a-g). These molecular hybrids were synthesised by condensation of 1,4-dihydropyridin-4-yl-phenoxyacetohydrazides with differently substituted pyrazole carbaldehyde. The final compound (5) showed two conformations (the major, E, s-cis and the minor, E, s-trans) as revealed by NMR spectral data and further supported by the energy calculations (MOPAC2016 using PM7 method). All the synthesised compounds were screened for their in vitro antimalarial activities against chloroquine-sensitive malaria parasite Plasmodium falciparum (3D7) and antimicrobial activity against Gram positive bacteria i.e. Bacillus cereus, Gram negative bacteria i.e. Escherichia coli and antifungal activity against one fungus i.e. Aspergillus niger [corrected]. All these compounds were found more potent than chloroquine and clotrimazole, the standard drugs. CONCLUSIONS: In vitro antiplasmodial IC50 value of the most potent compound 5d was found to be 4.40 nM which is even less than all the three reference drugs chloroquine (18.7 nM), pyrimethamine (11 nM) and artimisinin (6 nM). In silico binding study of compound 5d with plasmodial cysteine protease falcipain-2 indicated the inhibition of falcipain-2 as the probable reason for the antimalarial potency of compound 5d. All the compounds had shown good to excellent antimicrobial and antifungal activities.

Plasma GDF15 level is elevated in psychosis and inversely correlated with severity.

Accumulating evidence suggests that GDF15 is a biomarker for ageing and morbidity of many somatic disorders such as cancer and inflammatory disorders. Recently, elevated serum GDF15 level was proposed as a marker for mood disorder. However, psychosis severity was not investigated in relation to plasma GDF15 levels. In the present study we measured GDF15 levels in plasma of 120 psychosis patients compared to 120 age and gender matched healthy controls. Within the patient cohort GDF15 levels were evaluated for association with age, gender, lifestyle factors, C-reactive protein levels, psychosis severity and metabolic disorder. Psychosis patients had elevated GDF15 levels compared to controls (medianPsychosis = 744 ng/mL, mediancontrols = 516 ng/mL, p < 0.001). Within the psychosis cohort, GDF15 levels, when corrected for age, metabolic health and lifestyle factors, were negatively correlated with psychosis severity (ß = -0.218, p = 0.012). While GDF15 levels were elevated in patients versus healthy controls, the negative correlation between psychosis severity and GDF15 suggests a loss of anti-inflammatory GDF15 mediated functionality in severe psychosis. Study replication in larger cohorts will be necessary to assess the potential of GDF15 as a prognostic biomarker in psychosis.

Plasma contact system activation drives anaphylaxis in severe mast cell-mediated allergic reactions.

BACKGROUND: Anaphylaxis is an acute, potentially lethal, multisystem syndrome resulting from the sudden release of mast cell-derived mediators into the circulation. OBJECTIVES AND METHODS: We report here that a plasma protease cascade, the factor XII-driven contact system, critically contributes to the pathogenesis of anaphylaxis in both murine models and human subjects. RESULTS: Deficiency in or pharmacologic inhibition of factor XII, plasma kallikrein, high-molecular-weight kininogen, or the bradykinin B2 receptor, but not the B1 receptor, largely attenuated allergen/IgE-mediated mast cell hyperresponsiveness in mice. Reconstitutions of factor XII null mice with human factor XII restored susceptibility for allergen/IgE-mediated hypotension. Activated mast cells systemically released heparin, which provided a negatively charged surface for factor XII autoactivation. Activated factor XII generates plasma kallikrein, which proteolyzes kininogen, leading to the liberation of bradykinin. We evaluated the contact system in patients with anaphylaxis. In all 10 plasma samples immunoblotting revealed activation of factor XII, plasma kallikrein, and kininogen during the acute phase of anaphylaxis but not at basal conditions or in healthy control subjects. The severity of anaphylaxis was associated with mast cell degranulation, increased plasma heparin levels, the intensity of contact system activation, and bradykinin formation. CONCLUSIONS: In summary, the data collectively show a role of the contact system in patients with anaphylaxis and support the hypothesis that targeting bradykinin generation and signaling provides a novel and alternative treatment strategy for anaphylactic attacks.