The Experts below are selected from a list of 213 Experts worldwide ranked by ideXlab platform
Vladimir Poroikov - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.
Frontiers in pharmacology, 2018Co-Authors: A A Lagunin, Maria A. Romanova, Anton D. Zadorozhny, Natalia S. Kurilenko, Boris V. Shilov, Pavel V. Pogodin, Sergey Ivanov, Dmitry Filimonov, Vladimir PoroikovAbstract:Estimation of interaction of drug-like compounds with Antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with Antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 Antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each Antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighbourhoods of atoms (QNA), multilevel neighbourhoods of atoms (MNA) descriptors and self-consistent regression. The accuracy of (Q)SAR models was validated by the 5-fold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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Table_2_Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.DOCX
2018Co-Authors: Alexey A. Lagunin, Maria A. Romanova, Anton D. Zadorozhny, Natalia S. Kurilenko, Boris V. Shilov, Pavel V. Pogodin, Sergey M. Ivanov, Dmitry A. Filimonov, Vladimir PoroikovAbstract:Estimation of interaction of drug-like compounds with Antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with Antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 Antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each Antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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Data_Sheet_3_Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.ZIP
2018Co-Authors: Alexey A. Lagunin, Maria A. Romanova, Anton D. Zadorozhny, Natalia S. Kurilenko, Boris V. Shilov, Pavel V. Pogodin, Sergey M. Ivanov, Dmitry A. Filimonov, Vladimir PoroikovAbstract:Estimation of interaction of drug-like compounds with Antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with Antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 Antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each Antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighborhoods of atoms (QNA), multilevel neighborhoods of atoms (MNA) descriptors, and self-consistent regression. The accuracy of (Q)SAR models was validated by the fivefold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases, sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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quantitative prediction of Antitarget interaction profiles for chemical compounds
Chemical Research in Toxicology, 2012Co-Authors: Alexey V Zakharov, A A Lagunin, D A Filimonov, Vladimir PoroikovAbstract:The evaluation of possible interactions between chemical compounds and Antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of Antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 Antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC50, Ki, and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R2test = 0.6–0.9; three tests sets h...
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quantitative prediction of Antitarget interaction profiles for chemical compounds
Chemical Research in Toxicology, 2012Co-Authors: Alexey V Zakharov, A A Lagunin, D A Filimonov, Vladimir PoroikovAbstract:The evaluation of possible interactions between chemical compounds and Antitarget proteins is an important task of research and development process. Here we describe the development and validation of QSAR models for the prediction of Antitarget end-points, created on the basis of Multilevel and Quantitative Neighborhoods of Atoms descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 Antitarget proteins (13 receptors, 2 enzymes and 3 transporters) were used to model thirty two sets of end-points (IC50, Ki and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95% and for half of the test sets it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets was typically in the range of R2test = 0.6–0.9; three tests sets had a lower R2test values, specifically 0.55 – 0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the Antitarget end-points and high coverage for all external test sets. On the basis of the created models we have developed a freely available on-line service for in silico prediction of 32 Antitarget end-points: http://www.pharmaexpert.ru/GUSAR/Antitargets.html.
Mei Lan Tan - One of the best experts on this subject based on the ideXlab platform.
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the effects of deoxyelephantopin on the cardiac delayed rectifier potassium channel current ikr and human ether a go go related gene herg expression
Food and Chemical Toxicology, 2017Co-Authors: Yi Fan Teah, Muhammad Asyraf Abduraman, Azimah Amanah, Mohd Ilham Adenan, Shaida Fariza Sulaiman, Mei Lan TanAbstract:Abstract Elephantopus scaber Linn and its major bioactive component, deoxyelephantopin are known for their medicinal properties and are often reported to have various cytotoxic and antitumor activities. This plant is widely used as folk medicine for a plethora of indications although its safety profile remains unknown. Human ether-a-go-go-related gene (hERG) encodes the cardiac IKr current which is a determinant of the duration of ventricular action potentials and QT interval. The hERG potassium channel is an important Antitarget in cardiotoxicity evaluation. This study investigated the effects of deoxyelephantopin on the current, mRNA and protein expression of hERG channel in hERG-transfected HEK293 cells. The hERG tail currents following depolarization pulses were insignificantly affected by deoxyelephantopin in the transfected cell line. Current reduction was less than 40% as compared with baseline at the highest concentration of 50 μM. The results were consistent with the molecular docking simulation and hERG surface protein expression. Interestingly, it does not affect the hERG expression at both transcriptional and translational level at most concentrations, although higher concentration at 10 μM caused protein accumulation. In conclusion, deoxyelephantopin is unlikely a clinically significant hERG channel and Ikr blocker.
Tudor I Oprea - One of the best experts on this subject based on the ideXlab platform.
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linking pharmacology to clinical reports cyclobenzaprine and its possible association with serotonin syndrome
Clinical Pharmacology & Therapeutics, 2011Co-Authors: Jordi Mestres, Steven A Seifert, Tudor I OpreaAbstract:Any therapeutic agent can produce adverse effects.1 Side effects that occur only rarely, have a long latency, or manifest exclusively in specific subpopulations are likely to remain undetected during clinical trials and are discovered only once the drug is on the market, through pharmacovigilance2 and postmarketing analyses.3 These adverse effects may be due to idiosyncratic or otherwise uncharacterized pharmacology that was not identified during the research and development process.4,5 The recognition of direct links between (i) the interaction of a drug with a particular protein, pathway, organelle, or other in vivo component and (ii) certain side effects has motivated the systematic testing of molecules across groups of safety-relevant targets during the early phases of drug discovery.5 This has contributed significantly to the reduction in the risk of failure late in the drug-optimization process. However, knowledge in this area is still very incomplete and varies widely among individual drugs,6 which further hampers our understanding of how drugs act on “targets” and “Antitargets.”7 Serotonin syndrome (SS) is a rare, potentially lethal event resulting from excessive central and peripheral serotonergic activity.8 SS is characterized by altered mental status, autonomic instability, and neuromuscular abnormalities. Many drugs have been linked to SS,9 which typically develops in patients within hours of initiating treatment, after dosage increase or overdose, or when combining two or more serotonergic drugs. Substances associated with SS include monoamine oxidase inhibitors, tricyclic antidepressants, selective serotonin reuptake inhibitors, opioids, and antibiotics.8 In this context, the role of cyclobenzaprine (Flexeril) as a causal agent of SS has been subject to debate.10–12 Despite its wide use as a skeletal muscle relaxant, little is known about its pharmacological profile. Cyclobenzaprine is a 5-HT2 receptor antagonist;13 moreover, it moderately inhibits the Toll-like receptor 4 (ref. 14) and aldehyde oxidase15 and is a substrate of the cytochrome P450 isoforms 1A2 and 3A4.16 Several case reports have suggested a causal relationship between cyclobenzaprine and SS.10,11 Therefore, we aimed to identify additional potential SS-relevant targets for which cyclobenzaprine might have affinity and that may provide pharmacological support to the link between cyclobenzaprine and SS.
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transporter mediated efflux influences cns side effects abcb1 from Antitarget to target
Molecular Informatics, 2010Co-Authors: Fabio Broccatelli, Emanuele Carosati, Gabriele Cruciani, Tudor I OpreaAbstract:We examined the relationship between sedation and orthostatic hypotension, two central side effects and ABCB1 transporter-mediated efflux for a set of 64 launched drugs that are documented as histamine H1 receptor antagonists. This relationship was placed in the context of passive diffusion (estimated using LogP, the octanol/water partition coefficient), receptor affinity, and the adjusted therapeutic daily dose, in order to account for side effect variability. Within this set, CNS permeability was not dependent on passive diffusion, as no significant differences were found for LogP and its pH-corrected equivalent, LogD(74). Sedation and orthostatic hypotension can be explained within the framework of ABCB1-mediated efflux and adjusted dose, while target potency has less influence. ABCB1, an Antitarget for anti-cancer agents, acts in fact as a drug target for non-sedating antihistamines. An empirical set of rules, based on the incidence of these two side-effects, target affinity and dose was used to predict efflux effects for a number of drugs. Among them, azelastine and mizolastine are predicted to be effluxed via ABCB1-mediated transport, whereas aripiprazole, clozapine, cyproheptadine, iloperidone, olanzapine, and ziprasidone are likely to be non-effluxed.
A A Lagunin - One of the best experts on this subject based on the ideXlab platform.
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Comparison of Quantitative and Qualitative (Q)SAR Models Created for the Prediction of Ki and IC50 Values of Antitarget Inhibitors.
Frontiers in pharmacology, 2018Co-Authors: A A Lagunin, Maria A. Romanova, Anton D. Zadorozhny, Natalia S. Kurilenko, Boris V. Shilov, Pavel V. Pogodin, Sergey Ivanov, Dmitry Filimonov, Vladimir PoroikovAbstract:Estimation of interaction of drug-like compounds with Antitargets is important for the assessment of possible toxic effects during drug development. Publicly available online databases provide data on the experimental results of chemical interactions with Antitargets, which can be used for the creation of (Q)SAR models. The structures and experimental Ki and IC50 values for compounds tested on the inhibition of 30 Antitargets from the ChEMBL 20 database were used. Data sets with Ki and IC50 values including more than 100 compounds were created for each Antitarget. The (Q)SAR models were created by GUSAR software using quantitative neighbourhoods of atoms (QNA), multilevel neighbourhoods of atoms (MNA) descriptors and self-consistent regression. The accuracy of (Q)SAR models was validated by the 5-fold cross-validation procedure. The balanced accuracy was higher for qualitative SAR models (0.80 and 0.81 for Ki and IC50 values, respectively) than for quantitative QSAR models (0.73 and 0.76 for Ki and IC50 values, respectively). In most cases sensitivity was higher for SAR models than for QSAR models, but specificity was higher for QSAR models. The mean R2 and RMSE were 0.64 and 0.77 for Ki values and 0.59 and 0.73 for IC50 values, respectively. The number of compounds falling within the applicability domain was higher for SAR models than for the test sets.
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quantitative prediction of Antitarget interaction profiles for chemical compounds
Chemical Research in Toxicology, 2012Co-Authors: Alexey V Zakharov, A A Lagunin, D A Filimonov, Vladimir PoroikovAbstract:The evaluation of possible interactions between chemical compounds and Antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of Antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 Antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC50, Ki, and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R2test = 0.6–0.9; three tests sets h...
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quantitative prediction of Antitarget interaction profiles for chemical compounds
Chemical Research in Toxicology, 2012Co-Authors: Alexey V Zakharov, A A Lagunin, D A Filimonov, Vladimir PoroikovAbstract:The evaluation of possible interactions between chemical compounds and Antitarget proteins is an important task of research and development process. Here we describe the development and validation of QSAR models for the prediction of Antitarget end-points, created on the basis of Multilevel and Quantitative Neighborhoods of Atoms descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 Antitarget proteins (13 receptors, 2 enzymes and 3 transporters) were used to model thirty two sets of end-points (IC50, Ki and Kact). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95% and for half of the test sets it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets was typically in the range of R2test = 0.6–0.9; three tests sets had a lower R2test values, specifically 0.55 – 0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the Antitarget end-points and high coverage for all external test sets. On the basis of the created models we have developed a freely available on-line service for in silico prediction of 32 Antitarget end-points: http://www.pharmaexpert.ru/GUSAR/Antitargets.html.
C M Overall - One of the best experts on this subject based on the ideXlab platform.
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missing the target matrix metalloproteinase Antitargets in inflammation and cancer
Trends in Pharmacological Sciences, 2013Co-Authors: Antoine Dufour, C M OverallAbstract:Matrix metalloproteinases (MMPs) are reputed to cause the inflammatory tissue destruction characterizing chronic inflammatory diseases and to degrade basement membrane collagen, thereby facilitating cancer cell metastasis. However, following the disappointing MMP drug cancer trials, recent studies using mouse models of disease coupled with high-throughput methods for substrate discovery have revealed surprising and unexpected new biological roles of MMPs in inflammatory diseases and cancer in vivo. Thus, MMPs modify signaling pathways and regulate the activity of whole families of cytokines of the immune response by precise proteolytic processing. By cleaving and inactivating cytokine-binding proteins and protease inhibitors, cytokine activities are unmasked and activities of diverse proteases are increased in an interconnected protease web. With new substrates come new roles, and 10 of 24 murine MMPs have antitumorigenic and anti-inflammatory roles making them drug Antitargets; that is, their beneficial actions should not be inhibited. Here, we examine whether the discovery that MMPs are drug Antitargets for one disease might pave the way for their use for other indications or whether this is a serious threat to the development of MMP inhibitors.
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Towards third generation matrix metalloproteinase inhibitors for cancer therapy
British Journal of Cancer, 2006Co-Authors: C M Overall, O KleifeldAbstract:The failure of matrix metalloproteinase (MMP) inhibitor drug clinical trials in cancer was partly due to the inadvertent inhibition of MMP Antitargets that counterbalanced the benefits of MMP target inhibition. We explore how MMP inhibitor drugs might be developed to achieve potent selectivity for validated MMP targets yet therapeutically spare MMP Antitargets that are critical in host protection.