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Dere S, Ayvaz S. Prediction of Drug-Drug Interactions by Using Profile Fingerprint Vectors and Protein Similarities. Healthc Inform Res 2020; 26:42-49. [PMID: 32082699 PMCID: PMC7010946 DOI: 10.4258/hir.2020.26.1.42] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 12/24/2019] [Accepted: 12/25/2019] [Indexed: 12/21/2022] Open
Abstract
Objectives Drug-drug interaction (DDI) is a vital problem that threatens people's health. However, the prediction of DDIs through in-vivo experiments is not only extremely costly but also difficult as many serious side effects are hard to detect in in-vivo and in-vitro settings. The aim of this study was to assess the effectiveness of similarity-based in-silico computational DDI prediction approaches and to provide a cost effective and scalable solution to predict potential DDIs. Methods In this study, widely known similarity-based computational DDI prediction methods were utilized to discover novel potential DDIs. More specifically, known interactions, drug targets, adverse effects, and protein similarities of drug pairs were used to construct drug fingerprints for the prediction of DDIs. Results Using the drug interaction profile, our approach achieved an area under the curve (AUC) of 0.975 in the prediction of a potential DDI. The drug adverse effect profile and protein profile similarity-based methods resulted in AUC values of 0.685 and 0.895, respectively, in the prediction of DDIs. Conclusions In this study, we developed a computational approach to the prediction of potential drug interactions. The performance of the similarity-based computational methods was comparatively evaluated using a comprehensive real-world DDI dataset. The evaluations showed that the drug interaction profile information is a better predictor of DDIs compared to drug adverse effects and protein similarities among DDI pairs.
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Affiliation(s)
- Selma Dere
- Department of Computer Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey
| | - Serkan Ayvaz
- Department of Software Engineering, Bahcesehir University, Besiktas, Istanbul, Turkey
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2
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Dmitriev AV, Lagunin AA, Karasev DА, Rudik AV, Pogodin PV, Filimonov DA, Poroikov VV. Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes. Curr Top Med Chem 2019; 19:319-336. [PMID: 30674264 DOI: 10.2174/1568026619666190123160406] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 01/02/2019] [Accepted: 01/07/2019] [Indexed: 02/07/2023]
Abstract
Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.
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Affiliation(s)
| | - Alexey A Lagunin
- Institute of Biomedical Chemistry, Moscow, Russian Federation.,Pirogov Russian National Research Medical University, Moscow, RussiaN Federation
| | | | | | - Pavel V Pogodin
- Institute of Biomedical Chemistry, Moscow, Russian Federation
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3
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Leelananda SP, Lindert S. Computational methods in drug discovery. Beilstein J Org Chem 2016; 12:2694-2718. [PMID: 28144341 PMCID: PMC5238551 DOI: 10.3762/bjoc.12.267] [Citation(s) in RCA: 280] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 11/22/2016] [Indexed: 12/11/2022] Open
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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Affiliation(s)
- Sumudu P Leelananda
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
| | - Steffen Lindert
- Department of Chemistry and Biochemistry, Ohio State University, Columbus, OH 43210, USA
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4
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Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti NP. Similarity-based modeling in large-scale prediction of drug-drug interactions. Nat Protoc 2014; 9:2147-63. [PMID: 25122524 DOI: 10.1038/nprot.2014.151] [Citation(s) in RCA: 116] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.
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Affiliation(s)
- Santiago Vilar
- 1] Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA. [2] Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Eugenio Uriarte
- Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Lourdes Santana
- Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela, Santiago de Compostela, Spain
| | - Tal Lorberbaum
- 1] Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA. [2] Department of Physiology and Cellular Biophysics, Columbia University Medical Center, New York, New York, USA. [3] Department of Systems Biology, Columbia University Medical Center, New York, New York, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
| | - Carol Friedman
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA
| | - Nicholas P Tatonetti
- 1] Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA. [2] Department of Systems Biology, Columbia University Medical Center, New York, New York, USA. [3] Department of Medicine, Columbia University Medical Center, New York, New York, USA
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5
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Cami A, Manzi S, Arnold A, Reis BY. Pharmacointeraction network models predict unknown drug-drug interactions. PLoS One 2013; 8:e61468. [PMID: 23620757 PMCID: PMC3631217 DOI: 10.1371/journal.pone.0061468] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2012] [Accepted: 03/11/2013] [Indexed: 12/20/2022] Open
Abstract
Drug-drug interactions (DDIs) can lead to serious and potentially lethal adverse events. In recent years, several drugs have been withdrawn from the market due to interaction-related adverse events (AEs). Current methods for detecting DDIs rely on the accumulation of sufficient clinical evidence in the post-market stage – a lengthy process that often takes years, during which time numerous patients may suffer from the adverse effects of the DDI. Detection methods are further hindered by the extremely large combinatoric space of possible drug-drug-AE combinations. There is therefore a practical need for predictive tools that can identify potential DDIs years in advance, enabling drug safety professionals to better prioritize their limited investigative resources and take appropriate regulatory action. To meet this need, we describe Predictive Pharmacointeraction Networks (PPINs) – a novel approach that predicts unknown DDIs by exploiting the network structure of all known DDIs, together with other intrinsic and taxonomic properties of drugs and AEs. We constructed an 856-drug DDI network from a 2009 snapshot of a widely-used drug safety database, and used it to develop PPIN models for predicting future DDIs. We compared the DDIs predicted based solely on these 2009 data, with newly reported DDIs that appeared in a 2012 snapshot of the same database. Using a standard multivariate approach to combine predictors, the PPIN model achieved an AUROC (area under the receiver operating characteristic curve) of 0.81 with a sensitivity of 48% given a specificity of 90%. An analysis of DDIs by severity level revealed that the model was most effective for predicting “contraindicated” DDIs (AUROC = 0.92) and less effective for “minor” DDIs (AUROC = 0.63). These results indicate that network based methods can be useful for predicting unknown drug-drug interactions.
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Affiliation(s)
- Aurel Cami
- Division of Emergency Medicine, Boston Children's Hospital, Boston, Massachusetts, USA.
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6
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Pordel M, Abdollahi A, Razavi B. Synthesis and biological evaluation of novel isoxazolo[4,3-e]indoles as antibacterial agents. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2013. [DOI: 10.1134/s1068162013020106] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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7
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Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C. Detection of drug-drug interactions by modeling interaction profile fingerprints. PLoS One 2013; 8:e58321. [PMID: 23520498 PMCID: PMC3592896 DOI: 10.1371/journal.pone.0058321] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2012] [Accepted: 02/01/2013] [Indexed: 11/19/2022] Open
Abstract
Drug-drug interactions (DDIs) constitute an important problem in postmarketing pharmacovigilance and in the development of new drugs. The effectiveness or toxicity of a medication could be affected by the co-administration of other drugs that share pharmacokinetic or pharmacodynamic pathways. For this reason, a great effort is being made to develop new methodologies to detect and assess DDIs. In this article, we present a novel method based on drug interaction profile fingerprints (IPFs) with successful application to DDI detection. IPFs were generated based on the DrugBank database, which provided 9,454 well-established DDIs as a primary source of interaction data. The model uses IPFs to measure the similarity of pairs of drugs and generates new putative DDIs from the non-intersecting interactions of a pair. We described as part of our analysis the pharmacological and biological effects associated with the putative interactions; for example, the interaction between haloperidol and dicyclomine can cause increased risk of psychosis and tardive dyskinesia. First, we evaluated the method through hold-out validation and then by using four independent test sets that did not overlap with DrugBank. Precision for the test sets ranged from 0.4–0.5 with more than two fold enrichment factor enhancement. In conclusion, we demonstrated the usefulness of the method in pharmacovigilance as a DDI predictor, and created a dataset of potential DDIs, highlighting the etiology or pharmacological effect of the DDI, and providing an exploratory tool to facilitate decision support in DDI detection and patient safety.
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Affiliation(s)
- Santiago Vilar
- Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, United States of America.
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8
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Pérez-Castillo Y, Lazar C, Taminau J, Froeyen M, Cabrera-Pérez MÁ, Nowé A. GA(M)E-QSAR: A Novel, Fully Automatic Genetic-Algorithm-(Meta)-Ensembles Approach for Binary Classification in Ligand-Based Drug Design. J Chem Inf Model 2012; 52:2366-86. [DOI: 10.1021/ci300146h] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Affiliation(s)
- Yunierkis Pérez-Castillo
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
- Molecular Simulations and Drug
Design Group, Centro de Bioactivos Químicos, Universidad Central “Marta Abreu” de Las Villas, Santa
Clara, Cuba
- Laboratory for
Medicinal Chemistry,
Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, B-3000 Leuven, Belgium
| | - Cosmin Lazar
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
| | - Jonatan Taminau
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
| | - Mathy Froeyen
- Laboratory for
Medicinal Chemistry,
Rega Institute for Medical Research, Katholieke Universiteit Leuven, Minderbroedersstraat 10, B-3000 Leuven, Belgium
| | - Miguel Ángel Cabrera-Pérez
- Molecular Simulations and Drug
Design Group, Centro de Bioactivos Químicos, Universidad Central “Marta Abreu” de Las Villas, Santa
Clara, Cuba
- Engineering
Department, Pharmacy and Pharmaceutical Technology Area,
Faculty of Pharmacy, University Miguel Hernandez, Alicante 03550, Spain
| | - Ann Nowé
- Computational Modeling Lab (CoMo), Department
of Computer Sciences, Faculty
of Sciences, Vrije Universiteit Brussel, Pleinlaan 2, B-1050 Brussel, Belgium
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9
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Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. INDI: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol 2012; 8:592. [PMID: 22806140 PMCID: PMC3421442 DOI: 10.1038/msb.2012.26] [Citation(s) in RCA: 137] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2012] [Accepted: 06/04/2012] [Indexed: 11/21/2022] Open
Abstract
INDI is a similarity-based drug–drug interaction prediction method that can infer both pharmacokinetic and pharmacodynamic interactions, as well as their severity levels. Both known and predicted drug interactions are found to be prevalent in clinical practice. ![]()
INDI is a similarity-based drug–drug interaction prediction method, capable of handling both pharmacokinetic and pharmacodynamic interactions. INDI predicts the severity of the interaction and the Cytochrome P450 isozyme involved in pharmacokinetic interactions. We show the prevalence of known and predicted drug interactions in drug adverse reports and in chronic medications taken by hospitalized patients.
Inferring drug–drug interactions (DDIs) is an essential step in drug development and drug administration. Most computational inference methods focus on modeling drug pharmacokinetics, aiming at interactions that result from a common metabolizing enzyme (CYP). Here, we introduce a novel prediction method, INDI (INferring Drug Interactions), allowing the inference of both pharmacokinetic, CYP-related DDIs (along with their associated CYPs) and pharmacodynamic, non-CYP associated ones. On cross validation, it obtains high specificity and sensitivity levels (AUC (area under the receiver-operating characteristic curve)⩾0.93). In application to the FDA adverse event reporting system, 53% of the drug events could potentially be connected to known (41%) or predicted (12%) DDIs. Additionally, INDI predicts the severity level of each DDI upon co-administration of the involved drugs, suggesting that severe interactions are abundant in the clinical practice. Examining regularly taken medications by hospitalized patients, 18% of the patients receive known or predicted severely interacting drugs and are hospitalized more frequently. Access to INDI and its predictions is provided via a web tool at http://www.cs.tau.ac.il/∼bnet/software/INDI, facilitating the inference and exploration of drug interactions and providing important leads for physicians and pharmaceutical companies alike.
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Affiliation(s)
- Assaf Gottlieb
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.
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10
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Su Z, Zhang B, Zhu W, Du Z. In silico and in vivo evaluation of flavonoid extracts on CYP2D6-mediated herb-drug interaction. J Mol Model 2012; 18:4657-63. [DOI: 10.1007/s00894-012-1472-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2011] [Accepted: 05/15/2012] [Indexed: 12/01/2022]
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Jones JP, Joswig-Jones CA, Hebner M, Chu Y, Koop DR. The effects of nitrogen-heme-iron coordination on substrate affinities for cytochrome P450 2E1. Chem Biol Interact 2011; 193:50-6. [PMID: 21600194 DOI: 10.1016/j.cbi.2011.05.001] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2011] [Revised: 05/02/2011] [Accepted: 05/03/2011] [Indexed: 11/29/2022]
Abstract
A descriptor based computational model was developed for cytochrome P450 2E1 (CYP2E1) based on inhibition constants determined for inhibition of chlorzoxazone, or 4-nitrophenol, metabolism. An empirical descriptor for type II binding was developed and tested for a series of CYP2E1 inhibitors. Inhibition constants where measured for 51 different compounds. A fast 2-dimensional predictive model was developed based on 40 compounds, and tested on 8 compounds of diverse structure. The trained model (n=40) had an r(2) value of 0.76 and an RMSE of 0.48. The correlation between the predicted and actual pK(i) values of the test set of compounds not included in the model gives an r(2) value of 0.78. The features that described binding include heme coordination (type II binding), molecular volume, octanol/water partition coefficient, solvent accessible surface area, and the sum of the atomic polarizabilities. The heme coordination parameter assigns an integer between 0 and 6 depending on structure, and is a new descriptor, based on simple quantum chemical calculations with correction for steric effects. The type II binding parameter was found to be important in obtaining a good correlation between predicted and experimental inhibition constants increasing the r(2) value from 0.38 to 0.77.
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Affiliation(s)
- Jeffrey P Jones
- Department of Chemistry, Washington State University, Pullman, WA 99164-4630, USA.
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12
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Xu C, Mager DE. Quantitative structure–pharmacokinetic relationships. Expert Opin Drug Metab Toxicol 2010; 7:63-77. [DOI: 10.1517/17425255.2011.537257] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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Peng CC, Pearson JT, Rock DA, Joswig-Jones CA, Jones JP. The effects of type II binding on metabolic stability and binding affinity in cytochrome P450 CYP3A4. Arch Biochem Biophys 2010; 497:68-81. [PMID: 20346909 PMCID: PMC2864005 DOI: 10.1016/j.abb.2010.03.011] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2010] [Revised: 03/16/2010] [Accepted: 03/21/2010] [Indexed: 11/20/2022]
Abstract
One goal in drug design is to decrease clearance due to metabolism. It has been suggested that a compound's metabolic stability can be increased by incorporation of a sp(2) nitrogen into an aromatic ring. Nitrogen incorporation is hypothesized to increase metabolic stability by coordination of nitrogen to the heme-iron (termed type II binding). However, questions regarding binding affinity, metabolic stability, and how metabolism of type II binders occurs remain unanswered. Herein, we use pyridinyl quinoline-4-carboxamide analogs to answer these questions. We show that type II binding can have a profound influence on binding affinity for CYP3A4, and the difference in binding affinity can be as high as 1200-fold. We also find that type II binding compounds can be extensively metabolized, which is not consistent with the dead-end complex kinetic model assumed for type II binders. Two alternate kinetic mechanisms are presented to explain the results. The first involves a rapid equilibrium between the type II bound substrate and a metabolically oriented binding mode. The second involves direct reduction of the nitrogen-coordinated heme followed by oxygen binding.
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Affiliation(s)
- Chi-Chi Peng
- Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630
| | - Josh T. Pearson
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc., 1201 Amgen Court West, Seattle, Washington 98119
| | - Dan A. Rock
- Department of Pharmacokinetics and Drug Metabolism, Amgen Inc., 1201 Amgen Court West, Seattle, Washington 98119
| | - Carolyn A. Joswig-Jones
- Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630
| | - Jeffrey P. Jones
- Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630
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Peng CC, Cape JL, Rushmore T, Crouch GJ, Jones JP. Cytochrome P450 2C9 type II binding studies on quinoline-4-carboxamide analogues. J Med Chem 2009; 51:8000-11. [PMID: 19053752 DOI: 10.1021/jm8011257] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
CYP2C9 is a significant P450 protein responsible for drug metabolism. With the increased use of heterocyclic compounds in drug design, a rapid and efficient predrug screening of these potential type II binding compounds is essential to avoid adverse drug reactions. To understand binding modes, we use quinoline-4-carboxamide analogues to study the factors that determine the structure-activity relationships. The results of this study suggest that the more accessible pyridine with the nitrogen para to the linkage can coordinate directly with the ferric heme iron, but this is not seen for the meta or ortho isomers. The pi-cation interaction of the naphthalene moiety and Arg 108 residue may also assist in stabilizing substrate binding within the active-site cavity. The type II substrate binding affinity is determined by the combination of steric, electrostatic, and hydrophobicity factors; meanwhile, it is enhanced by the strength of lone pair electrons coordination with the heme iron.
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Affiliation(s)
- Chi-Chi Peng
- Department of Chemistry, Washington State University, P.O. Box 644630, Pullman, Washington 99164-4630, USA
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Czodrowski P, Kriegl JM, Scheuerer S, Fox T. Computational approaches to predict drug metabolism. Expert Opin Drug Metab Toxicol 2009; 5:15-27. [DOI: 10.1517/17425250802568009] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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16
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Stjernschantz E, Vermeulen NPE, Oostenbrink C. Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450. Expert Opin Drug Metab Toxicol 2008; 4:513-27. [PMID: 18484912 DOI: 10.1517/17425255.4.5.513] [Citation(s) in RCA: 58] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
BACKGROUND Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s). OBJECTIVE This review discusses the recent advances of these methods and the implications that are specific for P450s. METHODS Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions. CONCLUSION P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.
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Affiliation(s)
- Eva Stjernschantz
- Vrije Universiteit Amsterdam, Leiden/Amsterdam Centre for Drug Research, Division of Molecular Toxicology, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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