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Kato H. Computational prediction of cytochrome P450 inhibition and induction. Drug Metab Pharmacokinet 2019; 35:30-44. [PMID: 31902468 DOI: 10.1016/j.dmpk.2019.11.006] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/27/2019] [Accepted: 11/17/2019] [Indexed: 12/14/2022]
Abstract
Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.
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Affiliation(s)
- Harutoshi Kato
- DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Aoba-ku, Yokohama-shi, 227-0033, Japan.
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2
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Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools. Adv Drug Deliv Rev 2015; 86:83-100. [PMID: 26037068 DOI: 10.1016/j.addr.2015.03.014] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2014] [Revised: 03/18/2015] [Accepted: 03/22/2015] [Indexed: 02/05/2023]
Abstract
In-silico methods have been explored as potential tools for assessing ADME and ADME regulatory properties particularly in early drug discovery stages. Machine learning methods, with their ability in classifying diverse structures and complex mechanisms, are well suited for predicting ADME and ADME regulatory properties. Recent efforts have been directed at the broadening of application scopes and the improvement of predictive performance with particular focuses on the coverage of ADME properties, and exploration of more diversified training data, appropriate molecular features, and consensus modeling. Moreover, several online machine learning ADME prediction servers have emerged. Here we review these progresses and discuss the performances, application prospects and challenges of exploring machine learning methods as useful tools in predicting ADME and ADME regulatory properties.
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Belka M, Hewelt-Belka W, Sławiński J, Bączek T. Mass spectrometry based identification of geometric isomers during metabolic stability study of a new cytotoxic sulfonamide derivatives supported by quantitative structure-retention relationships. PLoS One 2014; 9:e98096. [PMID: 24893169 PMCID: PMC4043666 DOI: 10.1371/journal.pone.0098096] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 04/29/2014] [Indexed: 12/24/2022] Open
Abstract
A set of 15 new sulphonamide derivatives, presenting antitumor activity have been subjected to a metabolic stability study. The results showed that besides products of biotransformation, some additional peaks occurred in chromatograms. Tandem mass spectrometry revealed the same mass and fragmentation pathway, suggesting that geometric isomerization occurred. Thus, to support this hypothesis, quantitative structure-retention relationships were applied. Human liver microsomes were used as an in vitro model of metabolism. The biotransformation reactions were tracked by liquid chromatography assay and additionally, fragmentation mass spectra were recorded. In silico molecular modeling at a semi-empirical level was conducted as a starting point for molecular descriptor calculations. A quantitative structure-retention relationship model was built applying multiple linear regression based on selected three-dimensional descriptors. The studied compounds revealed high metabolic stability, with a tendency to form hydroxylated biotransformation products. However, significant chemical instability in conditions simulating human body fluids was noticed. According to literature and MS data geometrical isomerization was suggested. The developed in sillico model was able to describe the relationship between the geometry of isomer pairs and their chromatographic retention properties, thus it supported the hypothesis that the observed pairs of peaks are most likely geometric isomers. However, extensive structural investigations are needed to fully identify isomers' geometry. An effort to describe MS fragmentation pathways of novel chemical structures is often not enough to propose structures of potent metabolites and products of other chemical reactions that can be observed in compound solutions at early drug discovery studies. The results indicate that the relatively non-expensive and not time- and labor-consuming in sillico approach could be a good supportive tool assisting the identification of cis-trans isomers based on retention data. This methodology can be helpful during the structural identification of biotransformation and degradation products of new chemical entities--potential new drugs.
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Affiliation(s)
- Mariusz Belka
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Weronika Hewelt-Belka
- Department of Analytical Chemistry, Chemical Faculty, Gdańsk University of Technology, Gdańsk, Poland
- Mass Spectrometry and Chromatography Laboratory, Pomeranian Science and Technology Park, Gdynia, Poland
| | - Jarosław Sławiński
- Department of Organic Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
| | - Tomasz Bączek
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Medical University of Gdańsk, Gdańsk, Poland
- * E-mail:
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Wonganan P, Jonsson-Schmunk K, Callahan SM, Choi JH, Croyle MA. Evaluation of the HC-04 cell line as an in vitro model for mechanistic assessment of changes in hepatic cytochrome P450 3A during adenovirus infection. Drug Metab Dispos 2014; 42:1191-201. [PMID: 24764148 DOI: 10.1124/dmd.113.056663] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
HC-04 cells were evaluated as an in vitro model for mechanistic study of changes in the function of hepatic CYP3A during virus infection. Similar to in vivo observations, infection with a first generation recombinant adenovirus significantly inhibited CYP3A4 catalytic activity in an isoform-specific manner. Virus (MOI 100) significantly reduced expression of the retinoid X receptor (RXR) by 30% 96 hours after infection. Cytoplasmic concentrations of the pregnane X receptor (PXR) were reduced by 50%, whereas the amount of the constitutive androstane receptor (CAR) in the nuclear fraction doubled with respect to uninfected controls. Hepatocyte nuclear factor 4α (HNF-4α) and peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α) were also reduced by ∼70% during infection. Virus suppressed CYP3A4 activity in the presence of the PXR agonist rifampicin and did not affect CYP3A4 activity in the presence of the CAR agonist CITCO [6-(4-chlorophenyl) imidazo[2,1-b][1,3]thiazole-5-carbaldehyde-O-(3,4-dichlorobenzyl)oxime], suggesting that virus-induced modification of PXR may be responsible for observed changes in hepatic CYP3A4. The HC-04 cell line is easy to maintain, and CYP3A4 in these cells was responsive to known inducers and suppressors. Dexamethasone (200 μM) and phenobarbital (500 μM) increased activity by 230 and 124%, whereas ketoconazole (10 μM) and lipopolysaccharide (LPS) (10 μg/ml) reduced activity by 90 and 92%, respectively. This suggests that HC-04 cells can be a valuable tool for mechanistic study of drug metabolism during infection and for routine toxicological screening of novel compounds prior to use in the clinic.
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Affiliation(s)
- Piyanuch Wonganan
- Division of Pharmaceutics, College of Pharmacy (P.W., K. J.-S., S. M. C., J.-H. C. and M. A. C.) and the Institute of Cellular and Molecular Biology (M. A. C.), The University of Texas at Austin, Austin, Texas
| | - Kristina Jonsson-Schmunk
- Division of Pharmaceutics, College of Pharmacy (P.W., K. J.-S., S. M. C., J.-H. C. and M. A. C.) and the Institute of Cellular and Molecular Biology (M. A. C.), The University of Texas at Austin, Austin, Texas
| | - Shellie M Callahan
- Division of Pharmaceutics, College of Pharmacy (P.W., K. J.-S., S. M. C., J.-H. C. and M. A. C.) and the Institute of Cellular and Molecular Biology (M. A. C.), The University of Texas at Austin, Austin, Texas
| | - Jin Huk Choi
- Division of Pharmaceutics, College of Pharmacy (P.W., K. J.-S., S. M. C., J.-H. C. and M. A. C.) and the Institute of Cellular and Molecular Biology (M. A. C.), The University of Texas at Austin, Austin, Texas
| | - Maria A Croyle
- Division of Pharmaceutics, College of Pharmacy (P.W., K. J.-S., S. M. C., J.-H. C. and M. A. C.) and the Institute of Cellular and Molecular Biology (M. A. C.), The University of Texas at Austin, Austin, Texas
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Cui YL, Zhang JL, Zheng QC, Niu RJ, Xu Y, Zhang HX, Sun CC. Structural and Dynamic Basis of Human Cytochrome P450 7B1: A Survey of Substrate Selectivity and Major Active Site Access Channels. Chemistry 2012. [DOI: 10.1002/chem.201202627] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
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Hamon V, Horvath D, Gaudin C, Desrivot J, Junges C, Arrault A, Bertrand M, Vayer P. QSAR Modelling of CYP3A4 Inhibition as a Screening Tool in the Context of DrugDrug Interaction Studies. Mol Inform 2012; 31:669-77. [PMID: 27477817 DOI: 10.1002/minf.201200004] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2012] [Accepted: 07/03/2012] [Indexed: 11/12/2022]
Abstract
Drugdrug interaction potential (DDI), especially cytochrome P450 (CYP) 3A4 inhibition potential, is one of the most important parameters to be optimized before preclinical and clinical pharmaceutical development as regard to the number of marketed drug metabolized mainly by this CYP and potentially co-administered with the future drug. The present study aims to develop in silico models for CYP3A4 inhibition prediction to help medicinal chemists during the discovery phase and even before the synthesis of new chemical entities (NCEs), focusing on NCEs devoid of any inhibitory potential toward this CYP. In order to find a relevant relationship between CYP3A4 inhibition and chemical features of the screened compounds, we applied a genetic-algorithm-based QSAR exploratory tool SQS (Stochastic QSAR Sampler) in combination with different description approaches comprising alignment-independent Volsurf descriptors, ISIDA fragments and Topological Fuzzy Pharmacophore Triplets. The experimental data used to build models were extracted from an in-house database. We derived a model with good prediction ability that was confirmed on both newly synthesized compound and public dataset retrieved from Pubchem database. This model is a promising efficient tool for filtering out potentially problematic compounds.
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Affiliation(s)
- Véronique Hamon
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France
| | - Dragos Horvath
- Laboratoire d'Infochimie, UMR 7177, CNRS-Université Louis Pasteur, Strasbourg, France
| | - Cédric Gaudin
- Laboratoire d'Infochimie, UMR 7177, CNRS-Université Louis Pasteur, Strasbourg, France
| | - Julie Desrivot
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France
| | - Céline Junges
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France
| | - Alban Arrault
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France
| | - Marc Bertrand
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France
| | - Philippe Vayer
- Technologie Servier, 25-27 rue Eugène Vignat, 45000 Orléans, France.
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Tie Y, McPhail B, Hong H, Pearce BA, Schnackenberg LK, Ge W, Buzatu DA, Wilkes JG, Fuscoe JC, Tong W, Fowler BA, Beger RD, Demchuk E. Modeling chemical interaction profiles: II. Molecular docking, spectral data-activity relationship, and structure-activity relationship models for potent and weak inhibitors of cytochrome P450 CYP3A4 isozyme. Molecules 2012; 17:3407-60. [PMID: 22421793 PMCID: PMC6268819 DOI: 10.3390/molecules17033407] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2012] [Revised: 02/27/2012] [Accepted: 02/28/2012] [Indexed: 01/15/2023] Open
Abstract
Polypharmacy increasingly has become a topic of public health concern, particularly as the U.S. population ages. Drug labels often contain insufficient information to enable the clinician to safely use multiple drugs. Because many of the drugs are bio-transformed by cytochrome P450 (CYP) enzymes, inhibition of CYP activity has long been associated with potentially adverse health effects. In an attempt to reduce the uncertainty pertaining to CYP-mediated drug-drug/chemical interactions, an interagency collaborative group developed a consensus approach to prioritizing information concerning CYP inhibition. The consensus involved computational molecular docking, spectral data-activity relationship (SDAR), and structure-activity relationship (SAR) models that addressed the clinical potency of CYP inhibition. The models were built upon chemicals that were categorized as either potent or weak inhibitors of the CYP3A4 isozyme. The categorization was carried out using information from clinical trials because currently available in vitro high-throughput screening data were not fully representative of the in vivo potency of inhibition. During categorization it was found that compounds, which break the Lipinski rule of five by molecular weight, were about twice more likely to be inhibitors of CYP3A4 compared to those, which obey the rule. Similarly, among inhibitors that break the rule, potent inhibitors were 2–3 times more frequent. The molecular docking classification relied on logistic regression, by which the docking scores from different docking algorithms, CYP3A4 three-dimensional structures, and binding sites on them were combined in a unified probabilistic model. The SDAR models employed a multiple linear regression approach applied to binned 1D 13C-NMR and 1D 15N-NMR spectral descriptors. Structure-based and physical-chemical descriptors were used as the basis for developing SAR models by the decision forest method. Thirty-three potent inhibitors and 88 weak inhibitors of CYP3A4 were used to train the models. Using these models, a synthetic majority rules consensus classifier was implemented, while the confidence of estimation was assigned following the percent agreement strategy. The classifier was applied to a testing set of 120 inhibitors not included in the development of the models. Five compounds of the test set, including known strong inhibitors dalfopristin and tioconazole, were classified as probable potent inhibitors of CYP3A4. Other known strong inhibitors, such as lopinavir, oltipraz, quercetin, raloxifene, and troglitazone, were among 18 compounds classified as plausible potent inhibitors of CYP3A4. The consensus estimation of inhibition potency is expected to aid in the nomination of pharmaceuticals, dietary supplements, environmental pollutants, and occupational and other chemicals for in-depth evaluation of the CYP3A4 inhibitory activity. It may serve also as an estimate of chemical interactions via CYP3A4 metabolic pharmacokinetic pathways occurring through polypharmacy and nutritional and environmental exposures to chemical mixtures.
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Affiliation(s)
- Yunfeng Tie
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Brooks McPhail
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Huixiao Hong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Bruce A. Pearce
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Laura K. Schnackenberg
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Weigong Ge
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Dan A. Buzatu
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Jon G. Wilkes
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - James C. Fuscoe
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Weida Tong
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Bruce A. Fowler
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
| | - Richard D. Beger
- Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR 72079, USA; (H.H.); (B.A.P.); (L.K.S.); (W.G.); (D.A.B.); (J.G.W.); (J.C.F.); (W.T.); (R.D.B.)
| | - Eugene Demchuk
- Division of Toxicology and Environmental Medicine, Agency for Toxic Substances and Disease Registry, Atlanta, GA 30333, USA; (Y.T.); (B.M.); (B.A.F.)
- Department of Basic Pharmaceutical Sciences, West Virginia University, Morgantown, WV 26506-9530, USA
- Author to whom correspondence should be addressed; ; Tel.: +1-770-488-3327; Fax: +1-404-248-4142
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Kirchmair J, Williamson MJ, Tyzack JD, Tan L, Bond PJ, Bender A, Glen RC. Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms. J Chem Inf Model 2012; 52:617-48. [PMID: 22339582 PMCID: PMC3317594 DOI: 10.1021/ci200542m] [Citation(s) in RCA: 187] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
![]()
Metabolism of xenobiotics remains a central challenge
for the discovery
and development of drugs, cosmetics, nutritional supplements, and
agrochemicals. Metabolic transformations are frequently related to
the incidence of toxic effects that may result from the emergence
of reactive species, the systemic accumulation of metabolites, or
by induction of metabolic pathways. Experimental investigation of
the metabolism of small organic molecules is particularly resource
demanding; hence, computational methods are of considerable interest
to complement experimental approaches. This review provides a broad
overview of structure- and ligand-based computational methods for
the prediction of xenobiotic metabolism. Current computational approaches
to address xenobiotic metabolism are discussed from three major perspectives:
(i) prediction of sites of metabolism (SOMs), (ii) elucidation of
potential metabolites and their chemical structures, and (iii) prediction
of direct and indirect effects of xenobiotics on metabolizing enzymes,
where the focus is on the cytochrome P450 (CYP) superfamily of enzymes,
the cardinal xenobiotics metabolizing enzymes. For each of these domains,
a variety of approaches and their applications are systematically
reviewed, including expert systems, data mining approaches, quantitative
structure–activity relationships (QSARs), and machine learning-based
methods, pharmacophore-based algorithms, shape-focused techniques,
molecular interaction fields (MIFs), reactivity-focused techniques,
protein–ligand docking, molecular dynamics (MD) simulations,
and combinations of methods. Predictive metabolism is a developing
area, and there is still enormous potential for improvement. However,
it is clear that the combination of rapidly increasing amounts of
available ligand- and structure-related experimental data (in particular,
quantitative data) with novel and diverse simulation and modeling
approaches is accelerating the development of effective tools for
prediction of in vivo metabolism, which is reflected by the diverse
and comprehensive data sources and methods for metabolism prediction
reviewed here. This review attempts to survey the range and scope
of computational methods applied to metabolism prediction and also
to compare and contrast their applicability and performance.
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Affiliation(s)
- Johannes Kirchmair
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom
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Structure based discovery of small molecules to regulate the activity of human insulin degrading enzyme. PLoS One 2012; 7:e31787. [PMID: 22355395 PMCID: PMC3280214 DOI: 10.1371/journal.pone.0031787] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2011] [Accepted: 01/19/2012] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Insulin-degrading enzyme (IDE) is an allosteric Zn(+2) metalloprotease involved in the degradation of many peptides including amyloid-β, and insulin that play key roles in Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM), respectively. Therefore, the use of therapeutic agents that regulate the activity of IDE would be a viable approach towards generating pharmaceutical treatments for these diseases. Crystal structure of IDE revealed that N-terminal has an exosite which is ∼30 Å away from the catalytic region and serves as a regulation site by orientation of the substrates of IDE to the catalytic site. It is possible to find small molecules that bind to the exosite of IDE and enhance its proteolytic activity towards different substrates. METHODOLOGY/PRINCIPAL FINDINGS In this study, we applied structure based drug design method combined with experimental methods to discover four novel molecules that enhance the activity of human IDE. The novel compounds, designated as D3, D4, D6, and D10 enhanced IDE mediated proteolysis of substrate V, insulin and amyloid-β, while enhanced degradation profiles were obtained towards substrate V and insulin in the presence of D10 only. CONCLUSION/SIGNIFICANCE This paper describes the first examples of a computer-aided discovery of IDE regulators, showing that in vitro and in vivo activation of this important enzyme with small molecules is possible.
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Mishra NK. Computational modeling of P450s for toxicity prediction. Expert Opin Drug Metab Toxicol 2011; 7:1211-31. [DOI: 10.1517/17425255.2011.611501] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Novotarskyi S, Sushko I, Körner R, Pandey AK, Tetko IV. A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J Chem Inf Model 2011; 51:1271-80. [DOI: 10.1021/ci200091h] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Sergii Novotarskyi
- eADMET GmbH, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
| | - Iurii Sushko
- eADMET GmbH, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
| | - Robert Körner
- eADMET GmbH, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
| | - Anil Kumar Pandey
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
| | - Igor V. Tetko
- eADMET GmbH, Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health (GmbH), Ingolstädter Landstrasse 1, D-85764 Neuherberg, Germany
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Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, Lee PW, Tang Y. Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers. J Chem Inf Model 2011; 51:996-1011. [DOI: 10.1021/ci200028n] [Citation(s) in RCA: 133] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Feixiong Cheng
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Yue Yu
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jie Shen
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Lei Yang
- School of Information Science & Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Weihua Li
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guixia Liu
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Philip W. Lee
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
- Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan
| | - Yun Tang
- Department of Pharmaceutical Sciences, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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Dagliyan O, Uney-Yuksektepe F, Kavakli IH, Turkay M. Optimization based tumor classification from microarray gene expression data. PLoS One 2011; 6:e14579. [PMID: 21326602 PMCID: PMC3033885 DOI: 10.1371/journal.pone.0014579] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2010] [Accepted: 12/23/2010] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND An important use of data obtained from microarray measurements is the classification of tumor types with respect to genes that are either up or down regulated in specific cancer types. A number of algorithms have been proposed to obtain such classifications. These algorithms usually require parameter optimization to obtain accurate results depending on the type of data. Additionally, it is highly critical to find an optimal set of markers among those up or down regulated genes that can be clinically utilized to build assays for the diagnosis or to follow progression of specific cancer types. In this paper, we employ a mixed integer programming based classification algorithm named hyper-box enclosure method (HBE) for the classification of some cancer types with a minimal set of predictor genes. This optimization based method which is a user friendly and efficient classifier may allow the clinicians to diagnose and follow progression of certain cancer types. METHODOLOGY/PRINCIPAL FINDINGS We apply HBE algorithm to some well known data sets such as leukemia, prostate cancer, diffuse large B-cell lymphoma (DLBCL), small round blue cell tumors (SRBCT) to find some predictor genes that can be utilized for diagnosis and prognosis in a robust manner with a high accuracy. Our approach does not require any modification or parameter optimization for each data set. Additionally, information gain attribute evaluator, relief attribute evaluator and correlation-based feature selection methods are employed for the gene selection. The results are compared with those from other studies and biological roles of selected genes in corresponding cancer type are described. CONCLUSIONS/SIGNIFICANCE The performance of our algorithm overall was better than the other algorithms reported in the literature and classifiers found in WEKA data-mining package. Since it does not require a parameter optimization and it performs consistently very high prediction rate on different type of data sets, HBE method is an effective and consistent tool for cancer type prediction with a small number of gene markers.
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MESH Headings
- Algorithms
- Calibration
- Electronic Data Processing/standards
- Gene Expression Profiling/methods
- Gene Expression Profiling/standards
- Gene Expression Regulation, Neoplastic
- Humans
- Leukemia/classification
- Leukemia/diagnosis
- Leukemia/genetics
- Lymphoma, Large B-Cell, Diffuse/classification
- Lymphoma, Large B-Cell, Diffuse/diagnosis
- Lymphoma, Large B-Cell, Diffuse/genetics
- Male
- Microarray Analysis/methods
- Microarray Analysis/standards
- Models, Theoretical
- Neoplasms/classification
- Neoplasms/diagnosis
- Neoplasms/genetics
- Pattern Recognition, Automated/methods
- Pattern Recognition, Automated/standards
- Prognosis
- Prostatic Neoplasms/classification
- Prostatic Neoplasms/diagnosis
- Prostatic Neoplasms/genetics
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Affiliation(s)
- Onur Dagliyan
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | | | - I. Halil Kavakli
- Department of Chemical and Biological Engineering, Koc University, Istanbul, Turkey
| | - Metin Turkay
- Department of Industrial Engineering, Koc University, Istanbul, Turkey
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Michielan L, Moro S. Pharmaceutical Perspectives of Nonlinear QSAR Strategies. J Chem Inf Model 2010; 50:961-78. [DOI: 10.1021/ci100072z] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Lisa Michielan
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Dipartimento di Scienze Farmaceutiche, Università di Padova, via Marzolo 5, I-35131 Padova, Italy
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