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  1. Fulltext Green Biosynthesis, Antioxidant, Antibacterial, and Anticancer Activities of Silver Nanoparticles of Luffa acutangula Leaf Extract
    Nallappan D, Fauzi AN, Krishna BS, Kumar BP, Reddy AVK, Syed T, et al.
    Biomed Res Int, 2021;2021:5125681.
    PMID: 34631882 DOI: 10.1155/2021/5125681
    Studies on green biosynthesis of newly engineered nanoparticles for their prominent medicinal applications are being the torch-bearing concerns of the state-of-the-art research strategies. In this concern, we have engineered the biosynthesized Luffa acutangula silver nanoparticles of flavonoid O-glycosides in the anisotropic form isolated from aqueous leave extracts of Luffa acutangula, a popular traditional and ayurvedic plant in south-east Asian countries. These were structurally confirmed by Ultraviolet-visible (UV-Vis), Fourier transform infrared spectroscopy accessed with attenuated total reflection (FTIR-ATR) spectral analyses followed by the scanning electron microscopic (SEM) and the X-ray diffraction (XRD) crystallographic studies and found them with the face-centered cubic (fcc) structure. Medicinally, we have explored their significant antioxidant (DPPH and ABTS assays), antibacterial (disc diffusion assay on E. coli, S. aureus, B. subtilis, S. fecilis, and S. boydii), and anticancer (MTT assay on MCF-7, MDA-MB-231, U87, and DBTRG cell lines) potentialities which augmented the present investigation. The molecular docking analysis of title compounds against 3NM8 (DPPH) and 1DNU (ABTS) proteins for antioxidant activity; 5FGK (Gram-Positive Bacteria) and 1AB4 (Gram-Negative Bacteria) proteins for antibacterial activity; and 4GBD (MCF-7), 5FI2 (MDA-MB-231), 1D5R (U87), and 5TIJ (DBTRG) proteins for anticancer activity has affirmed the promising ligand-protein binding interactions among the hydroxy groups of the title compounds and aspartic acid of the concerned enzymatic proteins. The binding energy varying from -9.1645 to -7.7955 for Cosmosioside (1, Apigenin-7-glucoside) and from -9.2690 to -7.8306 for Cynaroside (2, Luteolin-7-glucoside) implies the isolated compounds as potential bioactive compounds. In addition, the performed studies like QSAR, ADMET, bioactivity properties, drug scores, and toxicity risks confirmed them as potential drug candidates and aspartic acid receptor antagonists. This research auxiliary augmented the existing array of phytological nanomedicines with new drug candidates that are credible with multiple bioactivities.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  2. Fulltext QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents
    Veerasamy R, Rajak H
    Turk J Pharm Sci, 2021 04 20;18(2):151-156.
    PMID: 33900700 DOI: 10.4274/tjps.galenos.2020.45556
    Objectives: The present study aimed to establish significant and validated quantitative structure-activity relationship (QSAR) models for neuraminidase inhibitors and correlate their physicochemical, steric, and electrostatic properties with their anti-influenza activity.

    Materials and Methods: We have developed and validated 2D and 3D QSAR models by using multiple linear regression, partial least square regression, and k-nearest neighbor-molecular field analysis methods.

    Results: 2D QSAR models had q2: 0.950 and pred_r2: 0.877 and 3D QSAR models had q2: 0.899 and pred_r2: 0.957. These results showed that the models werere predictive.

    Conclusion: Parameters such as hydrogen count and hydrophilicity were involved in 2D QSAR models. The 3D QSAR study revealed that steric and hydrophobic descriptors were negatively contributed to neuraminidase inhibitory activity. The results of this study could be used as platform for design of better anti-influenza drugs.

    Matched MeSH terms: Quantitative Structure-Activity Relationship
  3. Fulltext Validation of quantitative structure-activity relationship (QSAR) model for photosensitizer activity prediction
    Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA
    Int J Mol Sci, 2011;12(12):8626-44.
    PMID: 22272096 DOI: 10.3390/ijms12128626
    Photodynamic therapy is a relatively new treatment method for cancer which utilizes a combination of oxygen, a photosensitizer and light to generate reactive singlet oxygen that eradicates tumors via direct cell-killing, vasculature damage and engagement of the immune system. Most of photosensitizers that are in clinical and pre-clinical assessments, or those that are already approved for clinical use, are mainly based on cyclic tetrapyrroles. In an attempt to discover new effective photosensitizers, we report the use of the quantitative structure-activity relationship (QSAR) method to develop a model that could correlate the structural features of cyclic tetrapyrrole-based compounds with their photodynamic therapy (PDT) activity. In this study, a set of 36 porphyrin derivatives was used in the model development where 24 of these compounds were in the training set and the remaining 12 compounds were in the test set. The development of the QSAR model involved the use of the multiple linear regression analysis (MLRA) method. Based on the method, r(2) value, r(2) (CV) value and r(2) prediction value of 0.87, 0.71 and 0.70 were obtained. The QSAR model was also employed to predict the experimental compounds in an external test set. This external test set comprises 20 porphyrin-based compounds with experimental IC(50) values ranging from 0.39 μM to 7.04 μM. Thus the model showed good correlative and predictive ability, with a predictive correlation coefficient (r(2) prediction for external test set) of 0.52. The developed QSAR model was used to discover some compounds as new lead photosensitizers from this external test set.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  4. Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm
    Alharthi AM, Kadir DH, Al-Fakih AM, Algamal ZY, Al-Thanoon NA, Qasim MK
    SAR QSAR Environ Res, 2023;34(10):831-846.
    PMID: 37885432 DOI: 10.1080/1062936X.2023.2261855
    The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  5. Multiple molecular targets mediated antioxidant activity, molecular docking, ADMET, QSAR and bioactivity studies of halo substituted urea derivatives of α-Methyl-l-DOPA
    Vadabingi N, Avula VKR, Zyryanov GV, Vallela S, Anireddy JS, Pasupuleti VR, et al.
    Bioorg Chem, 2020 04;97:103708.
    PMID: 32146177 DOI: 10.1016/j.bioorg.2020.103708
    A series of novel α-methyl-l-DOPA urea derivatives viz., 3-(3,4-dihydroxyphenyl)-2-methyl-2-(3-halo/trifluoromethyl substituted phenyl ureido)propanoic acids (6a-e) have been synthesized from the reaction of α-methyl-l-DOPA (3) with various aryl isocyanates (4a-e) by using triethylamine (5, TEA) as a base catalyst in THF at reflux conditions. The synthesized compounds are structurally characterized by spectral (IR, 1H &13C NMR and MASS) and elemental analysis studies and screened for their in-vitro antioxidant activity against DPPH, NO and H2O2 free radical scavenging assays and identified compounds 6c &6d as potential antioxidants. The acquired in vitro results were correlated with the results of molecular docking, ADMET, QSAR and bioactivity studies performed for them and predicted that the recorded in silico binding affinities are in good correlation with the in vitro antioxidant activity results. The molecular docking analysis has comprehended the strong hydrogen bonding interactions of 6a-e with 1CB4, 1N8Q, 3MNG, 1OG5, 1DNU, 3NRZ, 2CDU, 1HD2 and 2HCK proteins of their respective SOD, LO, PRXS5, CP450, MP, XO, NO, PRY5 and HCK enzymes. This has sustained the effective binding of 6a-e and resulted in functional inhibition of selective aminoacid residues to be pronounced as multiple molecular targets mediated antioxidant potent compounds. In addition, the evaluated toxicology risks of 6a-e are identified with in the potential limits of drug candidates. The conformational analysis of 6c & 6d prominently infers that urea moiety uniting α-methyl-l-DOPA with halo substituted aryl units into a distinctive orientation to comply good structure-activity to inhibit the proliferation of reactive oxygen species in vivo.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  6. Quantitative Structure-Activity Relationship (QSAR) Studies for the Inhibition of MAOs
    Ramesh M, Muthuraman A
    Comb Chem High Throughput Screen, 2020;23(9):887-897.
    PMID: 32208114 DOI: 10.2174/1386207323666200324173231
    Monoamine oxidases are the crucial drug targets for the treatment of neurodegenerative disorders like depression, Parkinson's disease, and Alzheimer's disease. The enzymes catalyze the oxidative deamination of several monoamine containing neurotransmitters, i.e. serotonin (5-HT), melatonin, epinephrine, norepinephrine, phenylethylamine, benzylamine, dopamine, tyramine, etc. The oxidative reaction of monoamine oxidases results in the production of hydrogen peroxide that leads to the neurodegeneration process. Therefore, the inhibition of monoamine oxidases has shown a profound effect against neurodegenerative diseases. At present, the design and development of newer lead molecules for the inhibition of monoamine oxidases are under intensive research in the field of medicinal chemistry. Recently, the advancement in QSAR methodologies has shown considerable interest in the development of monoamine oxidase inhibitors. The present review describes the development of QSAR methodologies, and their role in the design of newer monoamine oxidase inhibitors. It will assist the medicinal chemist in the identification of selective and potent monoamine oxidase inhibitors from various chemical scaffolds.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  7. Models for skin and brain penetration of major components from essential oils used in aromatherapy for dementia patients
    Agatonovic-Kustrin S, Chan CKY, Gegechkori V, Morton DW
    J Biomol Struct Dyn, 2020 May;38(8):2402-2411.
    PMID: 31204906 DOI: 10.1080/07391102.2019.1633408
    Aromatherapy with essential oils (EOs) has been linked to improvement of cognitive function in patients with dementia. In order to act systemically, active EO components must be absorbed through the skin, enter the systemic circulation, and cross the blood brain barrier (BBB). Thus, the aim of this work was to develop quantitative structure activity relationships (QSARs), to predict skin and blood barrier penetrative abilities of 119 terpenoids from EOs used in aromatherapy. The first model was based on experimentally measured skin permeability for 162 molecules, and the second model on BBB permeability for 138 molecules. Each molecule was encoded with 63 calculated molecular descriptors and an artificial neural network was used to correlate molecular descriptors to permeabilities. Developed QSAR models confirm that EOs components penetrate through the skin and across the BBB. Some well-known descriptors, such as log P (lipophilicity), molecular size and shape, dominated the QSAR model for BBB permeability. Compounds with the highest predicted BBB penetration were hydrocarbon terpenes with the smallest molecular size and highest lipophilicity. Thus, molecular size is a limiting factor for penetration. Compounds with the highest skin permeability have slightly higher molecular size, high lipophilicity and low polarity. Our work shows that a major disadvantage of novel multitarget compounds developed for the treatment of Alzheimer's disease is the size of molecules, which cause problems in their delivery to the brain. Therefore, there is a need for smaller compounds, which possess more desirable physicochemical properties and pharmacokinetics, in addition to targeted biological effects.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  8. Xanthone: Potential Acetylcholinesterase Inhibitor for Alzheimer's Disease Treatment
    Vanessa VV, Mah SH
    Mini Rev Med Chem, 2021;21(17):2507-2529.
    PMID: 33583373 DOI: 10.2174/1389557521666210212152514
    Alzheimer's disease is a neurodegenerative disorder that results in progressive and irreversible central nervous system impairment, which has become one of the severe issues recently. The most successful approach of Alzheimer's treatment is the administration of cholinesterase inhibitors to prevent the hydrolysis of acetylcholine and subsequently improve cholinergic postsynaptic transmission. This review highlights a class of heterocycles, namely xanthone, and its remarkable acetylcholinesterase inhibitory activities. Naturally occurring xanthones, including oxygenated, prenylated, pyrano, and glycosylated xanthones, exhibited promising inhibition effects towards acetylcholinesterase. Interestingly, synthetic xanthone derivatives with complex substituents such as alkyl, pyrrolidine, piperidine, and morpholine have shown greater acetylcholinesterase inhibition activities. The structure-activity relationship of xanthones revealed that the type and position of the substituent(s) attached to the xanthone moiety influenced acetylcholinesterase inhibition activities where hydrophobic moiety will lead to an improved activity by contributing to the π-π interactions, as well as the hydroxy substituent(s) by forming hydrogen-bond interactions. Thus, further studies, including quantitative structure-activity relationship, in vivo and clinical validation studies are crucial for the development of xanthones into novel anti-Alzheimer's disease drugs.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  9. Synthesis of 2-(2-oxo-2H-chromen-4-yl)acetamides as potent acetylcholinesterase inhibitors and molecular insights into binding interactions
    Kara J, Suwanhom P, Wattanapiromsakul C, Nualnoi T, Puripattanavong J, Khongkow P, et al.
    Arch Pharm (Weinheim), 2019 Jul;352(7):e1800310.
    PMID: 31125474 DOI: 10.1002/ardp.201800310
    Sixteen novel coumarin-based compounds are reported as potent acetylcholinesterase (AChE) inhibitors. The most active compound in this series, 5a (IC50 0.04 ± 0.01 µM), noncompetitively inhibited AChE with a higher potency than tacrine and galantamine. Compounds 5d, 5j, and 5 m showed a moderate antilipid peroxidation activity. The compounds showed cytotoxicity in the same range as the standard drugs in HEK-293 cells. Molecular docking demonstrated that 5a acted as a dual binding site inhibitor. The coumarin moiety occupied the peripheral anionic site and showed π-π interaction with Trp278. The tertiary amino group displayed significant cation-π interaction with Phe329. The aromatic group showed π-π interaction with Trp83 at the catalytic anionic site. The long chain of methylene lay along the gorge interacting with Phe330 via hydrophobic interaction. Molecular docking was applied to postulate the selectivity toward AChE of 5a in comparison with donepezil and tacrine. Structural insights into the selectivity of the coumarin derivatives toward huAChE were explored by molecular docking and 3D QSAR and molecular dynamics simulation for 20 ns. ADMET analysis suggested that the 2-(2-oxo-2H-chromen-4-yl)acetamides showed a good pharmacokinetic profile and no hepatotoxicity. These coumarin derivatives showed high potential for further development as anti-Alzheimer agents.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  10. QSPR prediction of the hydroxyl radical rate constant of water contaminants
    Borhani TN, Saniedanesh M, Bagheri M, Lim JS
    Water Res, 2016 07 01;98:344-53.
    PMID: 27124124 DOI: 10.1016/j.watres.2016.04.038
    In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  11. Effect of imidazolium-based ionic liquids on bacterial growth inhibition investigated via experimental and QSAR modelling studies
    Ghanem OB, Mutalib MI, El-Harbawi M, Gonfa G, Kait CF, Alitheen NB, et al.
    J Hazard Mater, 2015 Oct 30;297:198-206.
    PMID: 25965417 DOI: 10.1016/j.jhazmat.2015.04.082
    Tuning the characteristics of solvents to fit industrial requirements has currently become a major interest in both academic and industrial communities, notably in the field of room temperature ionic liquids (RTILs), which are considered one of the most promising green alternatives to molecular organic solvents. In this work, several sets of imidazolium-based ionic liquids were synthesized, and their toxicities were assessed towards four human pathogens bacteria to investigate how tunability can affect this characteristic. Additionally, the toxicity of particular RTILs bearing an amino acid anion was introduced in this work. EC50 values (50% effective concentration) were established, and significant variations were observed; although all studied ILs displayed an imidazolium moiety, the toxicity values were found to vary between 0.05 mM for the most toxic to 85.57 mM for the least toxic. Linear quantitative structure activity relationship models were then developed using the charge density distribution (σ-profiles) as molecular descriptors, which can yield accuracies as high as 95%.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  12. A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives
    Algamal ZY, Lee MH
    SAR QSAR Environ Res, 2017 Jan;28(1):75-90.
    PMID: 28176549 DOI: 10.1080/1062936X.2017.1278618
    A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  13. Fulltext Synthesis and sar study of diarylpentanoid analogues as new anti-inflammatory agents
    Leong SW, Faudzi SM, Abas F, Aluwi MF, Rullah K, Wai LK, et al.
    Molecules, 2014 Oct 09;19(10):16058-81.
    PMID: 25302700 DOI: 10.3390/molecules191016058
    A series of ninety-seven diarylpentanoid derivatives were synthesized and evaluated for their anti-inflammatory activity through NO suppression assay using interferone gamma (IFN-γ)/lipopolysaccharide (LPS)-stimulated RAW264.7 macrophages. Twelve compounds (9, 25, 28, 43, 63, 64, 81, 83, 84, 86, 88 and 97) exhibited greater or similar NO inhibitory activity in comparison with curcumin (14.7 ± 0.2 µM), notably compounds 88 and 97, which demonstrated the most significant NO suppression activity with IC50 values of 4.9 ± 0.3 µM and 9.6 ± 0.5 µM, respectively. A structure-activity relationship (SAR) study revealed that the presence of a hydroxyl group in both aromatic rings is critical for bioactivity of these molecules. With the exception of the polyphenolic derivatives, low electron density in ring-A and high electron density in ring-B are important for enhancing NO inhibition. Meanwhile, pharmacophore mapping showed that hydroxyl substituents at both meta- and para-positions of ring-B could be the marker for highly active diarylpentanoid derivatives.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  14. Two dimensional-QSAR and molecular dynamics studies of a selected class of aldoxime- and hydroxy-functionalized chalcones as monoamine oxidase-B inhibitors
    Mathew B, Ravichandran V, Raghuraman S, Rangarajan TM, Abdelgawad MA, Ahmad I, et al.
    J Biomol Struct Dyn, 2023 Nov;41(19):9256-9266.
    PMID: 36411738 DOI: 10.1080/07391102.2022.2146198
    Candidates generated from unsaturated ketone (chalcone) demonstrated as strong, reversible and specific monoamine oxidase-B (MAO-B) inhibitory activity. For the research on MAO-B inhibition, our team has synthesized and evaluated a panel of aldoxime-chalcone ethers (ACE) and hydroxylchalcones (HC). The MAO-B inhibitory activity of several candidates is in the micro- to nanomolar range in these series. The purpose of this research was to develop predictive QSAR models and look into the relation between MAO-B inhibition by aldoxime and hydroxyl-functionalized chalcones. It was shown that the molecular descriptors ETA Shape P, MDEO-12, ETA dBetaP, SpMax1 Bhi and ETA EtaP B are significant in the inhibitory action of the MAO-B target. Using the current 2D QSAR models, potential chalcone-based MAO-B inhibitors might be created. The lead molecules were further analyzed by the detailed molecular dynamics study to establish the stability of the ligand-enzyme complex.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  15. Fulltext 3D-QSAR modelling dataset of bioflavonoids for predicting the potential modulatory effect on P-glycoprotein activity
    Wongrattanakamon P, Lee VS, Nimmanpipug P, Jiranusornkul S
    Data Brief, 2016 Dec;9:35-42.
    PMID: 27626051 DOI: 10.1016/j.dib.2016.08.004
    The data is obtained from exploring the modulatory activities of bioflavonoids on P-glycoprotein function by ligand-based approaches. Multivariate Linear-QSAR models for predicting the induced/inhibitory activities of the flavonoids were created. Molecular descriptors were initially used as independent variables and a dependent variable was expressed as pFAR. The variables were then used in MLR analysis by stepwise regression calculation to build the linear QSAR data. The entire dataset consisted of 23 bioflavonoids was used as a training set. Regarding the obtained MLR QSAR model, R of 0.963, R (2)=0.927, [Formula: see text], SEE=0.197, F=33.849 and q (2)=0.927 were achieved. The true predictabilities of QSAR model were justified by evaluation with the external dataset (Table 4). The pFARs of representative flavonoids were predicted by MLR QSAR modelling. The data showed that internal and external validations may generate the same conclusion.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  16. Multicriteria decision making attributes and estimation of physicochemical properties of kidney cancer drugs via topological descriptors
    Husin MN, Khan AR, Awan NUH, Campena FJH, Tchier F, Hussain S
    PLoS One, 2024;19(5):e0302276.
    PMID: 38713692 DOI: 10.1371/journal.pone.0302276
    Based on topological descriptors, QSPR analysis is an incredibly helpful statistical method for examining many physical and chemical properties of compounds without demanding costly and time-consuming laboratory tests. Firstly, we discuss and provide research on kidney cancer drugs using topological indices and done partition of the edges of kidney cancer drugs which are based on the degree. Secondly, we examine the attributes of nineteen drugs casodex, eligard, mitoxanrone, rubraca, and zoladex, etc and among others, using linear QSPR model. The study in the article not only demonstrates a good correlation between TIs and physical characteristics with the QSPR model being the most suitable for predicting complexity, enthalpy, molar refractivity, and other factors and a best-fit model is attained in this study. This theoretical approach might benefit chemists and professionals in the pharmaceutical industry to forecast the characteristics of kidney cancer therapies. This leads towards new opportunities to paved the way for drug discovery and the formation of efficient and suitable treatment options in therapeutic targeting. We also employed multicriteria decision making techniques like COPRAS and PROMETHEE-II for ranking of said disease treatment drugs and physicochemical characteristics.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  17. Designing the angiogenic inhibitor for brain tumor via disruption of VEGF and IL17A expression
    Khan MS, Majid AM, Iqbal MA, Majid AS, Al-Mansoub M, Haque RS
    Eur J Pharm Sci, 2016 Oct 10;93:304-18.
    PMID: 27552907 DOI: 10.1016/j.ejps.2016.08.032
    Glioblastoma multiforme is a highly malignant, heterogenic, and drug resistant tumor. The blood-brain barrier (BBB), systemic cytotoxicity, and limited specificity are the main obstacles in designing brain tumor drugs. In this study a computational approach was used to design brain tumor drugs that could downregulate VEGF and IL17A in glioblastoma multiforme type four. Computational screening tools were used to evaluate potential candidates for antiangiogenic activity, target binding, BBB permeability, and ADME physicochemical properties. Additionally, in vitro cytotoxicity, migration, invasion, tube formation, apoptosis, ROS and ELISA assays were conducted for molecule 6 that was deemed most likely to succeed. The efflux ratio of membrane permeability and calculated docking scores of permeability to glycoproteins (P-gps) were used to determine the BBB permeability of the molecules. The results showed BBB permeation for molecule 6, with the predicted efficiency of 0.55kcal/mol and binding affinity of -37kj/mol corresponding to an experimental efflux ratio of 0.625 and predicted -15kj/mol of binding affinity for P-gps. Molecule 6 significantly affected the angiogenesis pathways by 2-fold downregulation of IL17A and VEGF through inactivation of active sites of HSP90 (predicted binding: -37kj/mol, predicted efficiency: 0.55kcal/mol) and p23 (predicted binding: 12kj/mol, predicted efficiency: 0.17kcal/mol) chaperon proteins. Additionally, molecule 6 activated the 17.38% relative fold of ROS level at 18.3μg/mL and upregulated the caspase which lead the potential synergistic apoptosis through the antiangiogenic activity of molecule 6 and thereby the highly efficacious anticancer upshot. The results indicate that the binding of the molecules to the therapeutic target is not essential to produce a lethal effect on cancer cells of the brain and that antiangiogenic efficiency is much more important.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  18. Pharmacophore Modelling and Virtual Screening Studies for the Discovery of Potential Natural Products Based PDE1B Inhibitor Lead Compounds
    Shy TW, Gaurav A
    Cent Nerv Syst Agents Med Chem, 2021;21(3):195-204.
    PMID: 34970959 DOI: 10.2174/1871524922666211231115638
    AIM: The aim of the present study was to apply pharmacophore based virtual screening to a natural product database to identify potential PDE1B inhibitor lead compounds for neurodegenerative and neuropsychiatric disorders.

    BACKGROUND: Neurodegenerative and neuropsychiatric disorders are a major health burden globally. The existing therapies do not provide optimal relief and are associated with substantial adverse effects. This has resulted in a huge unmet medical need for newer and more effective therapies for these disorders. Phosphodiesterase (PDEs) enzymes have been identified as potential targets of drugs for neurodegenerative and neuropsychiatric disorders, and one of the subtypes, i.e., PDE1B, accounts for more than 90 % of total brain PDE activity associated with learning and memory process, making it an interesting drug target for the treatment of neurodegenerative disorders.

    OBJECTIVES: The present study has been conducted to identify potential PDE1B inhibitor lead compounds from the natural product database.

    METHODS: Ligand-based pharmacophore models were generated and validated; they were then employed for virtual screening of Universal Natural Products Database (UNPD) followed by docking with PDE1B to identify the best hit compound.

    RESULTS: Virtual screening led to the identification of 85 compounds which were then docked into the active site of PDE1B. Out of the 85 compounds, six showed a higher affinity for PDE1B than the standard PDE1B inhibitors. The top scoring compound was identified as Cedreprenone.

    CONCLUSION: Virtual screening of UNPD using Ligand based pharmacophore led to the identification of Cedreprenone, a potential new natural PDE1B inhibitor lead compound.

    Matched MeSH terms: Quantitative Structure-Activity Relationship
  19. Optimization of antiproliferative activity of substituted phenyl 4-(2-oxoimidazolidin-1-yl) benzenesulfonates: QSAR and CoMFA analyses
    Masand VH, Mahajan DT, Alafeefy AM, Bukhari SN, Elsayed NN
    Eur J Pharm Sci, 2015 Sep 18;77:230-7.
    PMID: 26066412 DOI: 10.1016/j.ejps.2015.06.001
    Multiple separate quantitative structure-activity relationships (QSARs) models were built for the antiproliferative activity of substituted Phenyl 4-(2-Oxoimidazolidin-1-yl)-benzenesulfonates (PIB-SOs). A variety of descriptors were considered for PIB-SOs through QSAR model building. Genetic algorithm (GA), available in QSARINS, was employed to select optimum number and set of descriptors to build the multi-linear regression equations for a dataset of PIB-SOs. The best three parametric models were subjected to thorough internal and external validation along with Y-randomization using QSARINS, according to the OECD principles for QSAR model validation. The models were found to be statistically robust with high external predictivity. The best three parametric model, based on steric, 3D- and finger print descriptors, was found to have R(2)=0.91, R(2)ex=0.89, and CCCex=0.94. The CoMFA model, which is based on a combination of steric and electrostatic effects and graphically inferred using contour plots, gave F=229.34, R(2)CV=0.71 and R(2)=0.94. Steric repulsion, frequency of occurrence of carbon and nitrogen at topological distance of seven, and internal electronic environment of the molecule were found to have correlation with the anti-tumor activity of PIB-SOs.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  20. Development of QSAR model to predict the ecotoxicity of Vibrio fischeri using COSMO-RS descriptors
    Ghanem OB, Mutalib MIA, Lévêque JM, El-Harbawi M
    Chemosphere, 2017 Mar;170:242-250.
    PMID: 28006757 DOI: 10.1016/j.chemosphere.2016.12.003
    Ionic liquids (ILs) are class of solvent whose properties can be modified and tuned to meet industrial requirements. However, a high number of potentially available cations and anions leads to an even increasing members of newly-synthesized ionic liquids, adding to the complexity of understanding on their impact on aquatic organisms. Quantitative structure activity∖property relationship (QSAR∖QSPR) technique has been proven to be a useful method for toxicity prediction. In this work,σ-profile descriptors were used to build linear and non-linear QSAR models to predict the ecotoxicities of a wide variety of ILs towards bioluminescent bacterium Vibrio fischeri. Linear model was constructed using five descriptors resulting in high accuracy prediction of 0.906. The model performance and stability were ascertained using k-fold cross validation method. The selected descriptors set from the linear model was then used in multilayer perceptron (MLP) technique to develop the non-linear model, the accuracy of the model was further enhanced achieving high correlation coefficient with the lowest value being 0.961 with the highest mean square error of 0.157.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
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