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Flowchart for the computational drug design used in this study. Abbreviations: Pls, partial least square; Qsar, quantitative structure–activity relationship; 2D, two-dimensional; 3D, three-dimensional; mTOr, mammalian target of rapamycin; FDa, Us Food and Drug administration.
Source publication
Wiame Lakhlili,1 Abdelaziz Yasri,2 Azeddine Ibrahimi1 1Biotechnology Laboratory (Medbiotech), Rabat Medical and Pharmacy School, Mohammed V University in Rabat, Rabat, Morroco; 2OribasePharma, Montpellier, France Abstract: The discovery of clinically relevant inhibitors of mammalian target of rapamycin (mTOR) for anticancer therapy has proved to be...
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Citations
... z = -45.349. These coordinates were determined using the potential substrate binding residues as centroids (in the hinge region and the activation loop) [19]. ...
Introduction
The mechanistic target of rapamycin (mTOR) coordinates the growth and metabolism of eukaryotic cells with a central role in the regulation of many fundamental cellular processes. It is strongly connected to phosphatidylinositol 3-kinase (PI3K) and AKT signaling. Activation of the PI3K/AKT/mTOR pathway leads to a profound disruption in the control of cell growth and survival, which ultimately leads to competitive growth advantage, metastatic competence, angiogenesis and therapeutic resistance.
Material and methods
To explore the common competitive adenosine triphosphate (ATP) inhibitors PI3K/AKT and PI3K/mTOR, we built a 2D mTOR-SAR model that predicted the bioactivity of AKT and PI3K inhibitors towards mTOR. The interaction of the best inhibitors was evaluated by docking analysis and compared to that of the standard AZ8055 and XL388 inhibitors.
Results
A mechanistic target of rapamycin-quantitative structure-activity relationship (mTOR-QSAR) model with a correlation coefficient (R²) of 0.80813 and a root mean square error of 0.17756 was obtained, validated and evaluated by a cross-validation leave-one-out method. The best predicted AKT and PI3K inhibitor pIC50 activities were 9.36–9.95 and 9.23–9.87 respectively.
Conclusions
After docking and several comparisons, the inhibitors with better predictions showed better affinity and interaction with mTOR compared to AZ8055 and XL388, so we have found that 2 AKT inhibitors and 9 mTOR inhibitors met the Lipinski and Veber criteria and could be future drugs.
... z = -45.349. These coordinates were determined using the potential substrate binding residues as centroids (in the hinge region and the activation loop) [19]. ...
Introduction: The mechanistic target of rapamycin (mTOR) coordinates the growth and metabolism of eu-karyotic cells with a central role in the regulation of many fundamental cellular processes. It is strongly connected to phosphatidylinositol 3-kinase (PI3K) and AKT signaling. Activation of the PI3K/AKT/mTOR pathway leads to a profound disruption in the control of cell growth and survival, which ultimately leads to competitive growth advantage, met-astatic competence, angiogenesis and therapeutic resistance. Material and methods: To explore the common competitive adenosine triphosphate (ATP) inhibitors PI3K/AKT and PI3K/mTOR, we built a 2D mTOR-SAR model that predicted the bioactiv-ity of AKT and PI3K inhibitors towards mTOR. The interaction of the best inhib-itors was evaluated by docking analysis and compared to that of the standard AZ8055 and XL388 inhibitors. Results: A mechanistic target of rapa-mycin-quantitative structure-activity relationship (mTOR-QSAR) model with a correlation coefficient (R 2) of 0.80813 and a root mean square error of 0.17756 was obtained, validated and evaluated by a cross-validation leave-one-out method. The best predicted AKT and PI3K inhibitor pIC50 activities were 9.36-9.95 and 9.23-9.87 respectively. Conclusions: After docking and several comparisons, the inhibitors with better predictions showed better affinity and interaction with mTOR compared to AZ8055 and XL388, so we have found that 2 AKT inhibitors and 9 mTOR inhibitors met the Lipinski and Veber criteria and could be future drugs.
... Other than for potential drug candidates, global in silico methods to assess the ability of chemicals to induce cell death, cell proliferation, and alteration of nutrient supply have not been developed. However, local QSARs (i.e., those dedicated to a specific group or class of chemicals) have been developed to: (1) facilitate the discovery of novel inhibitors of enzymes and signaling molecules associated with cellular processes, whose aberration might lead to cancer (e.g., [231][232][233][234][235][236][237]); (2) predict the antiproliferative activity of specific classes of candidate anticancer agents [238,239]; and (3) predict the ability of structurally-related compounds such as perfluorinated fatty acids, dialkyl phthalates, polycyclic aromatic hydrocarbons, and polychlorinated biphenyls, to alter GJIC function [102,[240][241][242]. ...
Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.
... QSAR modeling is the most used and powerful approach that allows correlating chemical modifications in a molecule with its biological activity [69]. This approach has been applied to find new treatments for Alzheimer's disease [70], malaria [71,72], diabetes [73,74], cancer [75][76][77], and HIV [78]. Based on the anti-inflammatory activity of pyrazolo[1,5-a]pyrimidine, 2,5diarylpyrazolo[1,5-a]pyrimidin-7-amines, new compounds were synthesized. ...
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... For the development of the model from a total of 56 molecules, 50 molecules were selected and the remaining six molecules, 2, 5, 43, 50, 51, and 52, were eliminated as statistical outliers because of the non-optimum Z score. 23 The 50 molecules were divided manually into two sets, a training set of 38 molecules and a test set of 12 molecules. The QSAR model was developed using the partial least-squares regression (PLSR) technique by the forward variable selection process with pK a activity fields as dependent variables and the calculated 116 physicochemical descriptors having a cross-correlation limit of 0.5 as independent variables. ...
The application of lipid-based drug delivery technologies for bioavailability enhancement of drugs has led to many successful products in the market for clinical use. Recent studies on amine-containing heterolipid-based synthetic vectors for delivery of siRNA have witnessed the United States Food and Drug Administration (USFDA) approval of the first siRNA drug in the year 2018. The studies on various synthetic lipids investigated for delivery of such nucleic acid therapeutics have revealed that the surface pK a of the constructed nanoparticles plays an important role. The nanoparticles showing pK a values within the range of 6−7 have performed very well. The development of high-performing lipid vectors with structural diversity and falling within the desired surface pK a is by no means trivial and requires tedious trial and error efforts; therefore, a practical solution is called for. Herein, an attempt to is made provide a solution by predicting the statistically significant pK a through a predictive quantitative structure−activity relationship (QSAR) model. The QSAR model has been constructed using a series of 56 amine-containing heterolipids having measured pK a values as a data set and employing a partial least-squares regression coupled with stepwise (SW-PLSR) forward algorithm technique. The model was tested using statistical parameters such as r 2 , q 2 , and pred_r 2 , and the model equation explains 97.2% (r 2 = 0.972) of the total variance in the training set and it has an internal (q 2) and an external (pred_r 2) predictive ability of ∼83 and ∼63%, respectively. The model was validated by synthesizing a series of designed heterolipids and comparing measured surface pK a values of their nanoparticle assembly using a 2-(p-toluidino)-6-napthalenesulfonic acid (TNS) assay. Predicted and measured surface pK a values of the synthesized heterolipids were in good agreement with a correlation coefficient of 93.3%, demonstrating the effectiveness of this QSAR model. Therefore, we foresee that our developed model would be useful as a tool to cut short tedious trial and error processes in designing new amine-containing heterolipid vectors for delivery of nucleic acid therapeutics, especially siRNA.
... y=0.806, z=29.623the coordinates were determined using the potential substrate binding residues as centroids (in the hinge region and the activation loop) [15]. ...
... In this study, to search the molecules with enhances properties and stability, a 2D quantitative structureactivity relationship (2D-QSAR) was employed to screen out 400 molecules curcumin derivatives with the variations on carbonyl moiety and active methylene group. The QSAR model, developed by partial least square (PLS) regression (Lakhlili et al., 2016) from 128 selected AXL inhibitors, was used to analyze the inhibitory AXL activity of compounds and identify molecular properties that influence activity with a view to provide a better rational design of some more curcumin analogs in the next studies. The molecular docking studies were performed to highlight the binding mode of the most active compounds with our three-dimensional (3D) structure obtained by homology modeling (Fatima et al., 2017). ...
... Test set is composed by the compounds that were not used in the model development. In several searches, the estimation of the true predictive power correlation in the developed QSAR model was done only by comparison of predicted and observed activities of an external test set (Lakhlili et al., 2016). ...
... Molecular docking was carried out to get better insight about interaction between ligands and target. Among the 400 analogs, 63 having the predict pIC50 exceeding a threshold set (Lakhlili et al., 2016) were docked with the crystal structure of AXL kinase obtained by homology modeling approach using Auto-Dock Vina (Trott and Olson, 2010). For all ligands, Gasteiger charges were designated and the torsions, allowing to turn during the docking procedure, are added using AutoDock tools v.1.5.6 (Morris et al., 2009). ...
AXL is an important drug target for cancers. Two-dimensional quantitative structure-activity relationship (2D-QSAR) tests were performed to elucidate a relationship between molecular structures and the activity of a series of 400 curcumin derivatives subjected to AXL kinase by ATP competition in the catalytic site. The partial least square regression method implanted in molecular operating environment software was applied to develop QSAR models, which were further validated for statistical significance by internal and external validation. The best model has proven to be statistically robust with a good predictive correlation of
R
2
= 0.996 and a significant cross-validation correlation coefficient of
q
2
= 0.707. Docking analysis reveled that three curcumin derivatives have the best affinity for AXL and formed a hydrogen bond with the important amino acid residues in the binding pocket. As treated in this article, the docking studies and 2D-QSAR approach will pave the way for the development of new drugs while highlighting curcumin and its derivatives.
... To reduce the number of descriptors considered for the study, both descriptors-contingency (a statistical application designed to assist in the selection of descriptors for QSAR) and correlation matrix were performed to limit the number of descriptors. A final set of three 2D-descriptors and nine i3Ddescriptors were identified to be significantly affecting pIC50 and was used in the construction of our QSAR model (Lakhlili, et al., 2016). The QuaSAR-module in MOE was used to generate the PLS QSAR model (MOE, 2017). ...
Tankyrases (TNKSs) have been implicated in many biological processes and have been proposed as a drug target for cancer therapy. The human genome encodes two homologous isoforms, TNKS-1 and TNKS-2. This study reported the first theoretical study of three-dimensional, two-dimensional quantitative structure activity relationships and a docking analysis of a series of 2-arylquinazolin-4-one, 3-arylisoquinolin-1-one, arylnaphthyridinone and aryltetrahydronaphthyridinone derivatives with remarkable TNKS-2 inhibiting activities reported recently in literatures. The predictive ability of the QSAR models was assessed using internal and external validation. 3D-QSAR model showed that the substituents R1 and R2 in studied compounds are key modulators to enhance the TNKS-2 inhibition. The 2D-QSAR model, was based mainly on three 2D descriptors and nine 3D descriptors. This suggested that TNKS-2 inhibition is predominantly controlled by steric properties of the inhibitors. Docking study was carried out by using ligands docking on the active site of three different crystal structures of TNKS-2 to understand the binding mode of these compounds as TNKS-2 inhibitors. We discussed the structural requirements for selective and potent TNKS-2 inhibitors. Four potent inhibitors were designed to be synthesized in the future.
... One report has shown that 10-day administrations of ATP-competitive mTOR inhibitor (AZD8055) successfully prolonged murine MHC-mismatched and vascularized heart allograft survival (Rosborough et al., 2014). Two-dimensional quantitative structure-activity relationship (QSAR) tests and molecular docking validation tests have been performed to elucidate their structural properties associated with their activity of mTOR ATPcompetitive inhibitors (Lakhlili, Yasri, & Ibrahimi, 2016). Ligand-based drug design strategies, such as virtual screening methodology, computational determination of ADME/Tox properties of selected molecules, and molecular dynamics are employed to select molecules with the potential becoming non-ATP-competitive inhibitors of the mTOR enzymatic complex (Kist & Caceres, 2016). ...
The mammalian target of rapamycin (mTOR), an atypical serine/threonine kinase, plays a central role in the regulation of cell proliferation, growth, differentiation, migration, and survival. In this study, the 3D structure of the mTOR (PDB ID: 2FAP) was used for the docking of 47 natural compounds and compared with pharmacophore model of 14 known mTOR inhibitors to identify the novel and specific natural inhibitor. The top four compounds, rutin, curcumin, antroquinonol, and benzyl cinnamate, have been selected based on their PLP score and further validated with hepatic stellate cells NHSC and THSC. Curcumin and antroquinonol significantly inhibited NHSC and THSC cells proliferation in a dose-dependent manner whereas rutin and benzyl cinnamate showed less alteration of cell viability. Rutin inhibited the phosphorylation of mTOR (p-mTOR) and p-p70 S6K in NHSC and THSC cells by Western blotting. Additionally, p-p70 S6K protein was significantly decreased by incubation with benzyl cinnamate and curcumin in THSC cells. Taken together, this result suggests that rutin is a potential mTOR inhibitor in screen hits of molecular docking to hamper the activation of HSC and further applications in the treatment of liver fibrosis.
... mTOR inhibitors have shown positive effects in mice models of colitis [109,110]. Lakhlili et al. applied partial least squares to infer linear models from a data set of 364 molecules with inhibitory activity against mTOR in competition with ATP, extracted from PubChem Compound [111]. The data set was split into training (70%) and test (30%) sets. ...
http://www.tandfonline.com/eprint/6weeYaBxaDykuiPKYUY7/full (free download)
Introduction: Although the therapeutic arsenal against ulcerative colitis has greatly expanded (including the revolutionary advent of biologics), there remain patients who are refractory to current medications while the safety of the available therapeutics could also be improved. Molecular topology provides a theoretic framework for the discovery of new therapeutic agents in a very efficient manner, and its applications in the field of ulcerative colitis have slowly begun to flourish.
Areas covered: After discussing the basics of molecular topology, the authors review QSAR models focusing on validated targets for the treatment of ulcerative colitis, entirely or partially based on topological descriptors.
Expert opinion: The application of molecular topology to ulcerative colitis drug discovery is still very limited, and many of the existing reports seem to be strictly theoretic, with no experimental validation or practical applications. Interestingly, mechanism-independent models based on phenotypic responses have recently been reported. Such models are in agreement with the recent interest raised by network pharmacology as a potential solution for complex disorders. These and other similar studies applying molecular topology suggest that some therapeutic categories may present a ‘topological pattern’ that goes beyond a specific mechanism of action.
