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Miller RR, Madeira M, Wood HB, Geissler WM, Raab CE, Martin IJ. Integrating the Impact of Lipophilicity on Potency and Pharmacokinetic Parameters Enables the Use of Diverse Chemical Space during Small Molecule Drug Optimization. J Med Chem 2020; 63:12156-12170. [DOI: 10.1021/acs.jmedchem.9b01813] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Randy R. Miller
- Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., 126 E. Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Maria Madeira
- Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., 126 E. Lincoln Avenue, Rahway, New Jersey 07065, United States
| | - Harold B. Wood
- Chemistry, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Wayne M. Geissler
- Business Development & Licensing, Merck & Co., Inc., 2000 Galloping Hill Road, Kenilworth, New Jersey 07033, United States
| | - Conrad E. Raab
- Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., 770 Sumneytown Pike, West Point, Pennsylvania 19486, United States
| | - Iain J. Martin
- Pharmacokinetics, Pharmacodynamics and Drug Metabolism, Merck & Co., Inc., 33 Avenue Louis Pasteur, Boston, Massachusetts 02115, United States
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Danielson ML, Hu B, Shen J, Desai PV. In Silico ADME Techniques Used in Early-Phase Drug Discovery. TRANSLATING MOLECULES INTO MEDICINES 2017. [DOI: 10.1007/978-3-319-50042-3_4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Tyzack JD, Hunt PA, Segall MD. Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations. J Chem Inf Model 2016; 56:2180-2193. [PMID: 27753488 DOI: 10.1021/acs.jcim.6b00233] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
We describe methods for predicting cytochrome P450 (CYP) metabolism incorporating both pathway-specific reactivity and isoform-specific accessibility considerations. Semiempirical quantum mechanical (QM) simulations, parametrized using experimental data and ab initio calculations, estimate the reactivity of each potential site of metabolism (SOM) in the context of the whole molecule. Ligand-based models, trained using high-quality regioselectivity data, correct for orientation and steric effects of the different CYP isoform binding pockets. The resulting models identify a SOM in the top 2 predictions for between 82% and 91% of compounds in independent test sets across seven CYP isoforms. In addition to predicting the relative proportion of metabolite formation at each site, these methods estimate the activation energy at each site, from which additional information can be derived regarding their lability in absolute terms. We illustrate how this can guide the design of compounds to overcome issues with rapid CYP metabolism.
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Affiliation(s)
- Jonathan D Tyzack
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
| | - Peter A Hunt
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
| | - Matthew D Segall
- Optibrium Ltd. , 7221 Cambridge Research Park, Beach Drive, Cambridge CB25 9TL, U.K
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Scharkoi O, Becker R, Esslinger S, Weber M, Nehls I. Predicting sites of cytochrome P450-mediated hydroxylation applied to CYP3A4 and hexabromocyclododecane. MOLECULAR SIMULATION 2015. [DOI: 10.1080/08927022.2014.898845] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Wilk-Zasadna I, Bernasconi C, Pelkonen O, Coecke S. Biotransformation in vitro: An essential consideration in the quantitative in vitro-to-in vivo extrapolation (QIVIVE) of toxicity data. Toxicology 2014; 332:8-19. [PMID: 25456264 DOI: 10.1016/j.tox.2014.10.006] [Citation(s) in RCA: 55] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2014] [Revised: 06/11/2014] [Accepted: 10/11/2014] [Indexed: 12/14/2022]
Abstract
Early consideration of the multiplicity of factors that govern the biological fate of foreign compounds in living systems is a necessary prerequisite for the quantitative in vitro-in vivo extrapolation (QIVIVE) of toxicity data. Substantial technological advances in in vitro methodologies have facilitated the study of in vitro metabolism and the further use of such data for in vivo prediction. However, extrapolation to in vivo with a comfortable degree of confidence, requires continuous progress in the field to address challenges such as e.g., in vitro evaluation of chemical-chemical interactions, accounting for individual variability but also analytical challenges for ensuring sensitive measurement technologies. This paper discusses the current status of in vitro metabolism studies for QIVIVE extrapolation, serving today's hazard and risk assessment needs. A short overview of the methodologies for in vitro metabolism studies is given. Furthermore, recommendations for priority research and other activities are provided to ensure further widespread uptake of in vitro metabolism methods in 21st century toxicology. The need for more streamlined and explicitly described integrated approaches to reflect the physiology and the related dynamic and kinetic processes of the human body is highlighted i.e., using in vitro data in combination with in silico approaches.
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Affiliation(s)
- Iwona Wilk-Zasadna
- Systems Toxicology Unit/EURL ECVAM, Institute for Health and Consumer Protection, European Commission Joint Research Centre, Ispra, Varese I-21027, Italy
| | - Camilla Bernasconi
- Systems Toxicology Unit/EURL ECVAM, Institute for Health and Consumer Protection, European Commission Joint Research Centre, Ispra, Varese I-21027, Italy
| | - Olavi Pelkonen
- Department of Pharmacology and Toxicology, Institute of Biomedicine, University of Oulu, Oulu, Finland
| | - Sandra Coecke
- Systems Toxicology Unit/EURL ECVAM, Institute for Health and Consumer Protection, European Commission Joint Research Centre, Ispra, Varese I-21027, Italy.
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6
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Tyzack JD, Mussa HY, Williamson MJ, Kirchmair J, Glen RC. Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers. J Cheminform 2014; 6:29. [PMID: 24959208 PMCID: PMC4047555 DOI: 10.1186/1758-2946-6-29] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2014] [Accepted: 05/12/2014] [Indexed: 02/02/2023] Open
Abstract
Background The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository. Results It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively. Conclusions 2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.
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Affiliation(s)
- Jonathan D Tyzack
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Hamse Y Mussa
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Mark J Williamson
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
| | - Johannes Kirchmair
- ETH Zurich, Department of Chemistry and Applied Biosciences, Institute of Pharmaceutical Sciences, HCI G 474.2, Vladimir-Prelog-Weg 1-5/10, 8093 Zurich, Switzerland
| | - Robert C Glen
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW Cambridge, UK
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Cruciani G, Baroni M, Benedetti P, Goracci L, Fortuna CG. Exposition and reactivity optimization to predict sites of metabolism in chemicals. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e155-65. [PMID: 24050245 DOI: 10.1016/j.ddtec.2012.11.001] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug– drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.
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In silico mechanistic profiling to probe small molecule binding to sulfotransferases. PLoS One 2013; 8:e73587. [PMID: 24039991 PMCID: PMC3765257 DOI: 10.1371/journal.pone.0073587] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2013] [Accepted: 07/28/2013] [Indexed: 01/01/2023] Open
Abstract
Drug metabolizing enzymes play a key role in the metabolism, elimination and detoxification of xenobiotics, drugs and endogenous molecules. While their principal role is to detoxify organisms by modifying compounds, such as pollutants or drugs, for a rapid excretion, in some cases they render their substrates more toxic thereby inducing severe side effects and adverse drug reactions, or their inhibition can lead to drug–drug interactions. We focus on sulfotransferases (SULTs), a family of phase II metabolizing enzymes, acting on a large number of drugs and hormones and showing important structural flexibility. Here we report a novel in silico structure-based approach to probe ligand binding to SULTs. We explored the flexibility of SULTs by molecular dynamics (MD) simulations in order to identify the most suitable multiple receptor conformations for ligand binding prediction. Then, we employed structure-based docking-scoring approach to predict ligand binding and finally we combined the predicted interaction energies by using a QSAR methodology. The results showed that our protocol successfully prioritizes potent binders for the studied here SULT1 isoforms, and give new insights on specific molecular mechanisms for diverse ligands’ binding related to their binding sites plasticity. Our best QSAR models, introducing predicted protein-ligand interaction energy by using docking, showed accuracy of 67.28%, 78.00% and 75.46%, for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively. To the best of our knowledge our protocol is the first in silico structure-based approach consisting of a protein-ligand interaction analysis at atomic level that considers both ligand and enzyme flexibility, along with a QSAR approach, to identify small molecules that can interact with II phase dug metabolizing enzymes.
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Tyzack JD, Williamson MJ, Torella R, Glen RC. Prediction of cytochrome P450 xenobiotic metabolism: tethered docking and reactivity derived from ligand molecular orbital analysis. J Chem Inf Model 2013; 53:1294-305. [PMID: 23701380 DOI: 10.1021/ci400058s] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Metabolism of xenobiotic and endogenous compounds is frequently complex, not completely elucidated, and therefore often ambiguous. The prediction of sites of metabolism (SoM) can be particularly helpful as a first step toward the identification of metabolites, a process especially relevant to drug discovery. This paper describes a reactivity approach for predicting SoM whereby reactivity is derived directly from the ground state ligand molecular orbital analysis, calculated using Density Functional Theory, using a novel implementation of the average local ionization energy. Thus each potential SoM is sampled in the context of the whole ligand, in contrast to other popular approaches where activation energies are calculated for a predefined database of molecular fragments and assigned to matching moieties in a query ligand. In addition, one of the first descriptions of molecular dynamics of cytochrome P450 (CYP) isoforms 3A4, 2D6, and 2C9 in their Compound I state is reported, and, from the representative protein structures obtained, an analysis and evaluation of various docking approaches using GOLD is performed. In particular, a covalent docking approach is described coupled with the modeling of important electrostatic interactions between CYP and ligand using spherical constraints. Combining the docking and reactivity results, obtained using standard functionality from common docking and quantum chemical applications, enables a SoM to be identified in the top 2 predictions for 75%, 80%, and 78% of the data sets for 3A4, 2D6, and 2C9, respectively, results that are accessible and competitive with other recently published prediction tools.
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Affiliation(s)
- Jonathan D Tyzack
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, Lensfield Road, Cambridge, CB2 1EW, United Kingdom
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Pähler A, Brink A. Software aided approaches to structure-based metabolite identification in drug discovery and development. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 10:e207-e217. [PMID: 24050249 DOI: 10.1016/j.ddtec.2012.12.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Technological advances in mass spectrometry (MS) such as accurate mass high resolution instrumentation have fundamentally changed the approach to systematic metabolite identification over the past decade. Despite technological break-through on the instrumental side, metabolite identification still requires tedious manual data inspection and interpretation of huge analytical datasets. The process of metabolite identification has become largely facilitated and partly automated by cheminformatics approaches such as knowledge base metabolite prediction using, for example, Meteor, MetaDrug, MetaSite and StarDrop that are typically applied pre-acquisition. Likewise, emerging new technologies in postacquisition data analysis like mass defect filtering (MDF) have moved the technology driven analytical methodology to metabolite identification toward generic, structure-based workflows. The biggest challenge for automation however remains the structural assignment of drug metabolites. Software-guided approaches for the unsupervised metabolite identification still cannot compete with expert user manual data interpretation yet. Recently MassMetaSite has been introduced for the automated ranked output of metabolite structures based on the combination of metabolite prediction and interrogation of analytical mass spectrometric data. This approach and others are promising milestones toward an unsupervised process to metabolite identification and structural characterization moving away from a sample focused per-compound approach to a structure-driven generic workflow.
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Campagna-Slater V, Pottel J, Therrien E, Cantin LD, Moitessier N. Development of a computational tool to rival experts in the prediction of sites of metabolism of xenobiotics by p450s. J Chem Inf Model 2012; 52:2471-83. [PMID: 22916680 DOI: 10.1021/ci3003073] [Citation(s) in RCA: 44] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The metabolism of xenobiotics--and more specifically drugs--in the liver is a critical process controlling their half-life. Although there exist experimental methods, which measure the metabolic stability of xenobiotics and identify their metabolites, developing higher throughput predictive methods is an avenue of research. It is expected that predicting the chemical nature of the metabolites would be an asset for designing safer drugs and/or drugs with modulated half-lives. We have developed IMPACTS (In-silico Metabolism Prediction by Activated Cytochromes and Transition States), a computational tool combining docking to metabolic enzymes, transition state modeling, and rule-based substrate reactivity prediction to predict the site of metabolism (SoM) of xenobiotics. Its application to sets of CYP1A2, CYP2C9, CYP2D6, and CYP3A4 substrates and comparison to experts' predictions demonstrates its accuracy and significance. IMPACTS identified an experimentally observed SoM in the top 2 predicted sites for 77% of the substrates, while the accuracy of biotransformation experts' prediction was 65%. Application of IMPACTS to external sets and comparison of its accuracy to those of eleven other methods further validated the method implemented in IMPACTS.
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Affiliation(s)
- Valérie Campagna-Slater
- Department of Chemistry, McGill University, 801 Sherbrooke St W, Montreal, QC H3A 0B8, Canada
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Penner N, Xu L, Prakash C. Radiolabeled Absorption, Distribution, Metabolism, and Excretion Studies in Drug Development: Why, When, and How? Chem Res Toxicol 2012; 25:513-31. [DOI: 10.1021/tx300050f] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Natalia Penner
- Department
of Drug Metabolism and Preclinical Safety, Biogen Idec, Cambridge, Massachusetts 02142
| | - Lin Xu
- Department
of Drug Metabolism and Preclinical Safety, Biogen Idec, Cambridge, Massachusetts 02142
| | - Chandra Prakash
- Department
of Drug Metabolism and Preclinical Safety, Biogen Idec, Cambridge, Massachusetts 02142
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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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Jónsdóttir SÓ, Ringsted T, Nikolov NG, Dybdahl M, Wedebye EB, Niemelä JR. Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis. Bioorg Med Chem 2012; 20:2042-53. [PMID: 22364953 DOI: 10.1016/j.bmc.2012.01.049] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2011] [Revised: 01/21/2012] [Accepted: 01/25/2012] [Indexed: 12/29/2022]
Abstract
This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.
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Affiliation(s)
- Svava Ósk Jónsdóttir
- Department of Toxicology and Risk Assessment, National Food Institute, Technical University of Denmark, Mørkhøj Bygade 19, DK-2860 Søborg, Denmark.
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Nigsch F, Lounkine E, McCarren P, Cornett B, Glick M, Azzaoui K, Urban L, Marc P, Müller A, Hahne F, Heard DJ, Jenkins JL. Computational methods for early predictive safety assessment from biological and chemical data. Expert Opin Drug Metab Toxicol 2011; 7:1497-511. [DOI: 10.1517/17425255.2011.632632] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Pelkonen O, Turpeinen M, Raunio H. In vivo-in vitro-in silico pharmacokinetic modelling in drug development: current status and future directions. Clin Pharmacokinet 2011; 50:483-91. [PMID: 21740072 DOI: 10.2165/11592400-000000000-00000] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Although clinical drug trials are indispensable in providing an appropriate background for dosage recommendations, they can provide mechanistic pharmacokinetic information only indirectly with the help of certain biomarkers for pathological, physiological and pharmacological determinants. Thus, to provide such mechanistic information of clinical value, various in vitro and in silico tests and approaches are increasingly employed in drug discovery and development. Integration of the results of these primarily preclinical studies has been made possible by various computational models, such as in vitro-in vivo extrapolation of hepatic clearance or physiologically based pharmacokinetic modelling. In this article, the current status of these modelling approaches is surveyed and some examples are given, highlighting advantages and disadvantages in applying them at various phases of drug development. A new paradigm of model-based drug development is briefly described, and the importance of the approach of integrating all of the information coming from different investigations at all levels--be it in vivo, in vitro or in silico--is emphasized.
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Affiliation(s)
- Olavi Pelkonen
- Department of Pharmacology and Toxicology, Institute of Biomedicine, University of Oulu, Oulu, Finland.
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García-Sosa AT, Sild S, Takkis K, Maran U. Combined Approach Using Ligand Efficiency, Cross-Docking, and Antitarget Hits for Wild-Type and Drug-Resistant Y181C HIV-1 Reverse Transcriptase. J Chem Inf Model 2011; 51:2595-611. [DOI: 10.1021/ci200203h] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
| | - Sulev Sild
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Kalev Takkis
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
| | - Uko Maran
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu 50411, Estonia
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T'jollyn H, Boussery K, Mortishire-Smith RJ, Coe K, De Boeck B, Van Bocxlaer JF, Mannens G. Evaluation of Three State-of-the-Art Metabolite Prediction Software Packages (Meteor, MetaSite, and StarDrop) through Independent and Synergistic Use. Drug Metab Dispos 2011; 39:2066-75. [DOI: 10.1124/dmd.111.039982] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
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Mu F, Unkefer CJ, Unkefer PJ, Hlavacek WS. Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 2011; 27:1537-45. [PMID: 21478194 PMCID: PMC3102224 DOI: 10.1093/bioinformatics/btr177] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2010] [Revised: 02/23/2011] [Accepted: 03/25/2011] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION Our knowledge of the metabolites in cells and their reactions is far from complete as revealed by metabolomic measurements that detect many more small molecules than are documented in metabolic databases. Here, we develop an approach for predicting the reactivity of small-molecule metabolites in enzyme-catalyzed reactions that combines expert knowledge, computational chemistry and machine learning. RESULTS We classified 4843 reactions documented in the KEGG database, from all six Enzyme Commission classes (EC 1-6), into 80 reaction classes, each of which is marked by a characteristic functional group transformation. Reaction centers and surrounding local structures in substrates and products of these reactions were represented using SMARTS. We found that each of the SMARTS-defined chemical substructures is widely distributed among metabolites, but only a fraction of the functional groups in these substructures are reactive. Using atomic properties of atoms in a putative reaction center and molecular properties as features, we trained support vector machine (SVM) classifiers to discriminate between functional groups that are reactive and non-reactive. Classifier accuracy was assessed by cross-validation analysis. A typical sensitivity [TP/(TP+FN)] or specificity [TN/(TN+FP)] is ≈0.8. Our results suggest that metabolic reactivity of small-molecule compounds can be predicted with reasonable accuracy based on the presence of a potentially reactive functional group and the chemical features of its local environment. AVAILABILITY The classifiers presented here can be used to predict reactions via a web site (http://cellsignaling.lanl.gov/Reactivity/). The web site is freely available.
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Affiliation(s)
- Fangping Mu
- Theoretical Biology and Biophysics Group, Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA.
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Tarcsay Á, Keserű GM. In silicosite of metabolism prediction of cytochrome P450-mediated biotransformations. Expert Opin Drug Metab Toxicol 2011; 7:299-312. [DOI: 10.1517/17425255.2011.553599] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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An G, Bartels J, Vodovotz Y. In Silico Augmentation of the Drug Development Pipeline: Examples from the study of Acute Inflammation. Drug Dev Res 2010; 72:187-200. [PMID: 21552346 DOI: 10.1002/ddr.20415] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
The clinical translation of promising basic biomedical findings, whether derived from reductionist studies in academic laboratories or as the product of extensive high-throughput and -content screens in the biotechnology and pharmaceutical industries, has reached a period of stagnation in which ever higher research and development costs are yielding ever fewer new drugs. Systems biology and computational modeling have been touted as potential avenues by which to break through this logjam. However, few mechanistic computational approaches are utilized in a manner that is fully cognizant of the inherent clinical realities in which the drugs developed through this ostensibly rational process will be ultimately used. In this article, we present a Translational Systems Biology approach to inflammation. This approach is based on the use of mechanistic computational modeling centered on inherent clinical applicability, namely that a unified suite of models can be applied to generate in silico clinical trials, individualized computational models as tools for personalized medicine, and rational drug and device design based on disease mechanism.
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Affiliation(s)
- Gary An
- Department of Surgery, University of Chicago, Chicago, IL 60637
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