Displaying publications 1 - 20 of 59 in total

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  1. QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain
    Jawarkar RD, Zaki MEA, Al-Hussain SA, Al-Mutairi AA, Samad A, Mukerjee N, et al.
    J Biomol Struct Dyn, 2024 Mar;42(5):2550-2569.
    PMID: 37144753 DOI: 10.1080/07391102.2023.2205948
    Due to the high rates of drug development failure and the massive expenses associated with drug discovery, repurposing existing drugs has become more popular. As a result, we have used QSAR modelling on a large and varied dataset of 657 compounds in an effort to discover both explicit and subtle structural features requisite for ACE2 inhibitory activity, with the goal of identifying novel hit molecules. The QSAR modelling yielded a statistically robust QSAR model with high predictivity (R2tr=0.84, R2ex=0.79), previously undisclosed features, and novel mechanistic interpretations. The developed QSAR model predicted the ACE2 inhibitory activity (PIC50) of 1615 ZINC FDA compounds. This led to the detection of a PIC50 of 8.604 M for the hit molecule (ZINC000027990463). The hit molecule's docking score is -9.67 kcal/mol (RMSD 1.4). The hit molecule revealed 25 interactions with the residue ASP40, which defines the N and C termini of the ectodomain of ACE2. The HIT molecule conducted more than thirty contacts with water molecules and exhibited polar interaction with the ARG522 residue coupled with the second chloride ion, which is 10.4 nm away from the zinc ion. Both molecular docking and QSAR produced comparable findings. Moreover, MD simulation and MMGBSA studies verified docking analysis. The MD simulation showed that the hit molecule-ACE2 receptor complex is stable for 400 ns, suggesting that repurposed hit molecule 3 is a viable ACE2 inhibitor.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  2. Combination of QSAR Modeling and Hybrid-Based Consensus Scoring to Identify Dual-Targeting Inhibitors of PLK1 and p38γ
    Cheng Z, Hwang SS, Bhave M, Rahman T, Chee Wezen X
    J Chem Inf Model, 2023 Nov 13;63(21):6912-6924.
    PMID: 37883148 DOI: 10.1021/acs.jcim.3c01252
    Polo-like kinase 1 (PLK1) and p38γ mitogen-activated protein kinase (p38γ) play important roles in cancer pathogenesis by controlling cell cycle progression and are therefore attractive cancer targets. The design of multitarget inhibitors may offer synergistic inhibition of distinct targets and reduce the risk of drug-drug interactions to improve the balance between therapeutic efficacy and safety. We combined deep-learning-based quantitative structure-activity relationship (QSAR) modeling and hybrid-based consensus scoring to screen for inhibitors with potential activity against the targeted proteins. Using this combination strategy, we identified a potent PLK1 inhibitor (compound 4) that inhibited PLK1 activity and liver cancer cell growth in the nanomolar range. Next, we deployed both our QSAR models for PLK1 and p38γ on the Enamine compound library to identify dual-targeting inhibitors against PLK1 and p38γ. Likewise, the identified hits were subsequently subjected to hybrid-based consensus scoring. Using this method, we identified a promising compound (compound 14) that could inhibit both PLK1 and p38γ activities. At nanomolar concentrations, compound 14 inhibited the growth of human hepatocellular carcinoma and hepatoblastoma cells in vitro. This study demonstrates the combined screening strategy to identify novel potential inhibitors for existing targets.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  3. 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
  4. Fatty Acid Conjugated Chalcones as Tubulin Polymerization Inhibitors: Design, Synthesis, QSAR, and Apoptotic and Antiproliferative Activity
    Mohamed SM, Abou-Ghadir OMF, El-Mokhtar MA, Aboraia AS, Abdel Aal AM
    J Nat Prod, 2023 May 26;86(5):1150-1158.
    PMID: 37098901 DOI: 10.1021/acs.jnatprod.2c00793
    Cancer is often associated with an aberrant increase in tubulin and microtubule activity required for cell migration, invasion, and metastasis. A new series of fatty acid conjugated chalcones have been designed as tubulin polymerization inhibitors and anticancer candidates. These conjugates were designed to harness the beneficial physicochemical properties, ease of synthesis, and tubulin inhibitory activity of two classes of natural components. New lipidated chalcones were synthesized from 4-aminoacetophenone via N-acylation followed by condensation with different aromatic aldehydes. All new compounds showed strong inhibition of tubulin polymerization and antiproliferative activity against breast and lung cancer cell lines (MCF-7 and A549) at low or sub-micromolar concentrations. A significant apoptotic effect was shown using a flow cytometry assay that corresponded to cytotoxicity against cancer cell lines, as indicated by a 3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide assay. Decanoic acid conjugates were more potent than longer lipid analogues, with the most active being more potent than the reference tubulin inhibitor, combretastatin-A4 and the anticancer drug, doxorubicin. None of the newly synthesized compounds caused any detectable cytotoxicity against the normal cell line (Wi-38) or hemolysis of red blood cells below 100 μM. It is unlikely that the new conjugates described would affect normal cells or interrupt with cell membranes due to their lipidic nature. A quantitative structure-activity relationship analysis was performed to determine the influence of 315 descriptors of the physicochemical properties of the new conjugates on their tubulin inhibitory activity. The obtained model revealed a strong correlation between the tubulin inhibitory activity of the investigated compounds and their dipole moment and degree of reactivity.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  5. QSAR classification model for diverse series of antifungal agents based on binary coyote optimization algorithm
    Al-Fakih AM, Qasim MK, Algamal ZY, Alharthi AM, Zainal-Abidin MH
    SAR QSAR Environ Res, 2023 Apr;34(4):285-298.
    PMID: 37157994 DOI: 10.1080/1062936X.2023.2208374
    One of the recently developed metaheuristic algorithms, the coyote optimization algorithm (COA), has shown to perform better in a number of difficult optimization tasks. The binary form, BCOA, is used in this study as a solution to the descriptor selection issue in classifying diverse antifungal series. Z-shape transfer functions (ZTF) are evaluated to verify their efficiency in improving BCOA performance in QSAR classification based on classification accuracy (CA), the geometric mean of sensitivity and specificity (G-mean), and the area under the curve (AUC). The Kruskal-Wallis test is also applied to show the statistical differences between the functions. The efficacy of the best suggested transfer function, ZTF4, is further assessed by comparing it to the most recent binary algorithms. The results prove that ZTF, especially ZTF4, significantly improves the performance of the original BCOA. The ZTF4 function yields the best CA and G-mean of 99.03% and 0.992%, respectively. It shows the fastest convergence behaviour compared to other binary algorithms. It takes the fewest iterations to reach high classification performance and selects the fewest descriptors. In conclusion, the obtained results indicate the ability of the ZTF4-based BCOA to find the smallest subset of descriptors while maintaining the best classification accuracy performance.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  6. Exploring the Traditional Chinese Medicine (TCM) database chemical space to target I7L protease from monkeypox virus using molecular screening and simulation approaches
    Khan A, Shahab M, Nasir F, Waheed Y, Alshammari A, Mohammad A, et al.
    SAR QSAR Environ Res, 2023;34(9):689-708.
    PMID: 37675795 DOI: 10.1080/1062936X.2023.2250723
    In the current study, we used molecular screening and simulation approaches to target I7L protease from monkeypox virus (mpox) from the Traditional Chinese Medicines (TCM) database. Using molecular screening, only four hits TCM27763, TCM33057, TCM34450 and TCM31564 demonstrated better pharmacological potential than TTP6171 (control). Binding of these molecules targeted Trp168, Asn171, Arg196, Cys237, Ser240, Trp242, Glu325, Ser326, and Cys328 residues and may affect the function of I7L protease in in vitro assay. Moreover, molecular simulation revealed stable dynamics, tighter structural packing and less flexible behaviour for all the complexes. We further reported that the average hydrogen bonds in TCM27763, TCM33057, TCM34450 and TCM31564I7L complexes remained higher than the control drug. Finally, the BF energy results revealed -62.60 ± 0.65 for the controlI7L complex, for the TCM27763I7L complex -71.92 ± 0.70 kcal/mol, for the TCM33057I7L complex the BF energy was -70.94 ± 0.70 kcal/mol, for the TCM34450I7L the BF energy was -69.94 ± 0.85 kcal/mol while for the TCM31564I7L complex the BF energy was calculated to be -69.16 ± 0.80 kcal/mol. Although, we used stateoftheart computational methods, these are theoretical insights that need further experimental validation.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  7. Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey
    Edros R, Feng TW, Dong RH
    SAR QSAR Environ Res, 2023;34(6):475-500.
    PMID: 37409842 DOI: 10.1080/1062936X.2023.2230868
    Current in silico modelling techniques, such as molecular dynamics, typically focus on compounds with the highest concentration from chromatographic analyses for bioactivity screening. Consequently, they reduce the need for labour-intensive in vitro studies but limit the utilization of extensive chromatographic data and molecular diversity for compound classification. Compound permeability across the blood-brain barrier (BBB) is a key concern in central nervous system (CNS) drug development, and this limitation can be addressed by applying cheminformatics with codeless machine learning (ML). Among the four models developed in this study, the Random Forest (RF) algorithm with the most robust performance in both internal and external validation was selected for model construction, with an accuracy (ACC) of 87.5% and 86.9% and area under the curve (AUC) of 0.907 and 0.726, respectively. The RF model was deployed to classify 285 compounds detected using liquid chromatography quadrupole time-of-flight mass spectrometry (LCQTOF-MS) in Kelulut honey; of which, 140 compounds were screened with 94 descriptors. Seventeen compounds were predicted to permeate the BBB, revealing their potential as drugs for treating neurodegenerative diseases. Our results highlight the importance of employing ML pattern recognition to identify compounds with neuroprotective potential from the entire pool of chromatographic data.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  8. 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*
  9. Synthesis, anti-diabetic and in silico QSAR analysis of flavone hydrazide Schiff base derivatives
    Jamil W, Shaikh J, Yousuf M, Taha M, Khan KM, Shah SAA
    J Biomol Struct Dyn, 2022;40(23):12723-12738.
    PMID: 34514955 DOI: 10.1080/07391102.2021.1975565
    This study reports synthesis of flavone hydrazide Schiff base derivatives with diverse functionalities for the cure of diabetic mellitus and their a-glucosidase inhibitor and in silico studies. In this regard, Flavone derivatives 1-20 has synthesized and characterized by various spectroscopic techniques. These compounds showed significant potential towards a-glucosidase enzyme inhibition activity and found to be many fold better active than the standard Acarbose (IC50 = 39.45 ± 0.11 µM). The IC50values ranges 1.02-38.1 µM. Among these, compounds 1(IC50 = 4.6 ± 0.23 µM), 2(IC50 = 1.02 ± 0.2 µM), 3(IC50 = 7.1 ± 0.11 µM), 4(IC50 = 8.3 ± 0.34 µM), 5(IC50 = 7.4 ± 0.15 µM), 6(IC50 = 8.5 ± 0.27 µM) and 18 (IC50 = 1.09 ± 0.26 µM) showed highest activity. It was revealed that the analogues having -OH substitution have higher activity than their look likes. The molecular docking analysis revealed that these molecules have high potential to interact with the protein molecule and have high ability to bind with the enzyme. Furthermore, in silico pharmacokinetics, physicochemical studies were also performed for these derivatives. The bioavailability radar analysis explored that of all these compounds have excellent bioavailability for five (5) descriptors, however, the sixth descriptor of instauration is slightly increased in all compounds.Communicated by Ramaswamy H. Sarma.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  10. Pd(II) complexes with chelating N-(1-alkylpyridin-4(1H)-ylidene)amide (PYA) ligands: Synthesis, characterization and evaluation of anticancer activity
    Zafar MN, Butt AM, Chaudhry GE, Perveen F, Nazar MF, Masood S, et al.
    J Inorg Biochem, 2021 11;224:111590.
    PMID: 34507110 DOI: 10.1016/j.jinorgbio.2021.111590
    The bidentate N-(1-Alkylpyridin-4(1H)-ylidene)amide (PYA) pro-ligands [H2LBn][Cl]2 (2), and [H2LMe][TfO]2 (3) were prepared by simple alkylation reactions of the known compound, N,N-di(pyridin-4-yl)oxalamide (H2L, 1). The Pd(II) complexes, [Pd(LBn)2][Cl]2 (4), [Pd(LMe)2][Cl][TfO] (5), Pd(LBn)Cl2 (6) and Pd(LMe)Cl2 (7) were synthesized through reactions between these pro-ligands and suitable Pd(II) substrates in the presence of base. The molecular structures of 3 and 6 were obtained by single crystal X-ray structure determinations. Studies of the experimental and computational DNA binding interactions of the compounds 1-7 revealed that overall 4 and 6 have the largest values for the binding parameters Kb and ΔGbo. The results showed a good correlation with the steric and electronic parameters obtained by quantitative structure activity relationship (QSAR) studies. In-vitro cytotoxicity studies against four different cell lines showed that the human breast cancer cell lines MCF-7, T47D and cervical cancer cell line HeLa had either higher or similar sensitivities towards 4, 6 and 2, respectively, compared to cisplatin. In general, the cytotoxicity of the compounds, represented by IC50 values, decreased in the order 4 > 6 > 2 > 5 > 3 > 1 > 7 in cancer cell lines. Apoptosis contributed significantly to the cytotoxic effects of these anticancer agents as evaluated by apoptosis studies.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  11. Fulltext Synthesis and Anti-Pancreatic Cancer Activity Studies of Novel 3-Amino-2-hydroxybenzofused 2-Phospha-γ-lactones
    Balam SK, Soora Harinath J, Krishnammagari SK, Gajjala RR, Polireddy K, Baki VB, et al.
    ACS Omega, 2021 May 04;6(17):11375-11388.
    PMID: 34056293 DOI: 10.1021/acsomega.1c00360
    A series of 3-amino-2-hydroxybenzofused 2-phosphalactones (4a-l) has been synthesized from the Kabachnik-Fields reaction via a facile route from a one-pot three-component reaction of diphenylphosphite with various 2-hydroxybenzaldehyes and heterocyclic amines in a new way of expansion. The in vitro anti-cell proliferation studies by MTT assay have revealed them as potential Panc-1, Miapaca-2, and BxPC-3 pancreatic cell growth inhibitors, and the same is supported by molecular docking, QSAR, and ADMET studies. The MTT assay of their SAHA derivatives against the same cell lines evidenced them as potential HDAC inhibitors and identified 4a, 4b, and 4k substituted with 1,3-thiazol, 1,3,4-thiadiazol, and 5-sulfanyl-1,3,4-thiadiazol moieties on phenyl and diethylamino phenyl rings as potential ones. Additionally, the flow cytometric analyses of 4a, 4b, and 4k against BxPC-3 cells revealed compound 4k as a lead compound that arrests the S phase cell cycle growth at low micromolar concentrations. The ADMET properties have ascertained their inherent pharmacokinetic potentiality, and the wholesome results prompted us to report it as the first study on anti-pancreatic cancer activity of cyclic α-aminophosphonates. Ultimately, this study serves as a good contribution to update the existing knowledge on the anticancer organophosphorus heterocyclic compounds and elevates the scope for generation of new anticancer drugs. Further, the studies like QSAR, drug properties, toxicity risks, and bioactivity scores predicted for them have ascertained the synthesized compounds as newer and potential drug candidates. Hence, this study had augmented the array of α-aminophosphonates by adding a new collection of 3-amino-2-hydroxybenzofused 2-phosphalactones, a class of cyclic α-aminophosphonates, to it, which proved them as potential anti-pancreatic cancer agents.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  12. 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
  13. Green Synthesis, Antioxidant, and Plant Growth Regulatory Activities of Novel α-Furfuryl-2-alkylaminophosphonates
    Nadiveedhi MR, Nuthalapati P, Gundluru M, Yanamula MR, Kallimakula SV, Pasupuleti VR, et al.
    ACS Omega, 2021 Feb 02;6(4):2934-2948.
    PMID: 33553912 DOI: 10.1021/acsomega.0c05302
    A series of novel α-furfuryl-2-alkylaminophosphonates have been efficiently synthesized from the one-pot three-component classical Kabachnik-Fields reaction in a green chemical approach by addition of an in situ generated dialkylphosphite to Schiff's base of aldehydes and amines by using environmental and eco-friendly silica gel supported iodine as a catalyst by microwave irradiation. The advantage of this protocol is simplicity in experimental procedures and products were resulted in high isolated yields. The synthesized α-furfuryl-2-alkylaminophosphonates were screened to in vitro antioxidant and plant growth regulatory activities and some are found to be potent with antioxidant and plant growth regulatory activities. These in vitro studies have been further supported by ADMET (absorption, distribution, metabolism, excretion, and toxicity), quantitative structure-activity relationship, molecular docking, and bioactivity studies and identified that they were potentially bound to the GLN340 amino acid residue in chain C of 1DNU protein and TYR597 amino acid residue in chain A of 4M7E protein, causing potential exhibition of antioxidant and plant growth regulatory activities. Eventually, title compounds are identified as good blood-brain barrier (BBB)-penetrable compounds and are considered as proficient central nervous system active and neuroprotective antioxidant agents as the neuroprotective property is determined with BBB penetration thresholds.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  14. 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
  15. 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
  16. 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
  17. Fulltext Synthesis of novel cytotoxic tetracyclic acridone derivatives and study of their molecular docking, ADMET, QSAR, bioactivity and protein binding properties
    Veligeti R, Madhu RB, Anireddy J, Pasupuleti VR, Avula VKR, Ethiraj KS, et al.
    Sci Rep, 2020 11 26;10(1):20720.
    PMID: 33244007 DOI: 10.1038/s41598-020-77590-1
    Acridone based synthetic and natural products with inherent anticancer activity advancing the research and generating a large number of structurally diversified compounds. In this sequence we have designed, synthesized a series of tetracyclic acridones with amide framework viz., 3-(alkyloyl/ aryloyl/ heteroaryloyl/ heteroaryl)-2,3-dihydropyrazino[3,2,1-de]acridin-7(1H)-ones and screened for their in vitro anti-cancer activity. The in vitro study revealed that compounds with cyclopropyl-acetyl, benzoyl, p-hydroxybenzoyl, p-(trifluoromethyl)benzoyl, p-fluorobenzoyl, m-fluorobenzoyl, picolinoyl, 6-methylpicolinoyl and 3-nicotinoyl groups are active against HT29, MDAMB231 and HEK293T cancer cell lines. The molecular docking studies performed for them against 4N5Y, HT29 and 2VWD revealed the potential ligand-protein binding interactions among the neutral aminoacid of the enzymes and carbonyl groups of the title compounds with a binding energy ranging from - 8.1394 to - 6.9915 kcal/mol. In addition, the BSA protein binding assay performed for them has confirmed their interaction with target proteins through strong binding to BSA macromolecule. The additional studies like ADMET, QSAR, bioactivity scores, drug properties and toxicity risks ascertained them as newer drug candidates. This study had added a new collection of piperazino fused acridone derivatives to the existing array of other nitrogen heterocyclic fused acridone derivatives as anticancer agents.
    Matched MeSH terms: Quantitative Structure-Activity Relationship
  18. QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm
    Algamal ZY, Qasim MK, Lee MH, Ali HTM
    SAR QSAR Environ Res, 2020 Nov;31(11):803-814.
    PMID: 32938208 DOI: 10.1080/1062936X.2020.1818616
    High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity relationship (QSAR) modelling. Obtaining a reliable QSAR model with few descriptors is an essential procedure in chemometrics. The binary grasshopper optimization algorithm (BGOA) is a new meta-heuristic optimization algorithm, which has been used successfully to perform feature selection. In this paper, four new transfer functions were adapted to improve the exploration and exploitation capability of the BGOA in QSAR modelling of influenza A viruses (H1N1). The QSAR model with these new quadratic transfer functions was internally and externally validated based on MSEtrain, Y-randomization test, MSEtest, and the applicability domain (AD). The validation results indicate that the model is robust and not due to chance correlation. In addition, the results indicate that the descriptor selection and prediction performance of the QSAR model for training dataset outperform the other S-shaped and V-shaped transfer functions. QSAR model using quadratic transfer function shows the lowest MSEtrain. For the test dataset, proposed QSAR model shows lower value of MSEtest compared with the other methods, indicating its higher predictive ability. In conclusion, the results reveal that the proposed QSAR model is an efficient approach for modelling high-dimensional QSAR models and it is useful for the estimation of IC50 values of neuraminidase inhibitors that have not been experimentally tested.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  19. Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression
    Alharthi AM, Lee MH, Algamal ZY, Al-Fakih AM
    SAR QSAR Environ Res, 2020 Aug;31(8):571-583.
    PMID: 32628042 DOI: 10.1080/1062936X.2020.1782467
    One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L1-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.
    Matched MeSH terms: Quantitative Structure-Activity Relationship*
  20. 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
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