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Lovisa Afzelius,
Catrin Hasselgren Arnby,
Anders Broo,
Lars Carlsson,
Christine Isaksson,
Ulrik Jurva,
Britta Kjellander,
Karin Kolmodin,
Kristina Nilsson,
Florian Raubacher,
Lars Weidolf
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ABSTRACT: In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.
Drug Metabolism Reviews 02/2007; 39(1):61-86. · 6.40 Impact Factor
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ABSTRACT: The 16th International Symposium on Microsomes and Drug Oxidations (MDO2006) in Budapest, Hungary, had almost 400 attendees and was nicely organized by Laszlo Vereczkey and colleagues. The meeting had a very high standard in the field of drug metabolism, drug transport and related areas and in addition, the social events were much appreciated. At the meeting 70 invited lectures were presented in plenary sessions and in three parallel symposia sessions, and about 178 posters were shown, among them 26 posters in the young investigators workshop. The review herein is given of a majority (57) of the lectures presented at the Symposium.
Drug News & Perspectives 01/2007; 19(10):637-51. · 2.21 Impact Factor
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ABSTRACT: Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic liability. This information can now be obtained from in silico predictions, and therefore, a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging. In this study, metabolite identification using MetaSite and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and four-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor enabled achieved a similar prediction success (78%) using the MetaSite method. The docking method had a relatively lower prediction success (approximately 57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite methodology is automated, rapid, and has relatively accurate predictions compared with the docking methodology used in this study.
Drug Metabolism and Disposition 07/2006; 34(6):976-83. · 3.73 Impact Factor
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05/2006: pages 219 - 247; , ISBN: 9783527607679
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ABSTRACT: This work had two separate aims: to evaluate different modeling techniques and to make a detailed structural characterization of CYP2C9. To achieve these goals, the consensus principal component analysis (CPCA) technique and distance measurements were used to explore available crystal structures, newly built homology models, and repeated molecular dynamics simulations. The CPCA was based on molecular interaction fields focused on the active site regions of the proteins and include detailed amino acid analysis. The comparison of the CYP2C9 and CYP2C5 crystal structures revealed differences in the flexible regions such as the B-C and F-G loop and the N and C termini. Cross homology models of CYP2C9 and CYP2C5, using their respective crystal structures as templates, indicated that such models were more similar to their templates than to their target proteins. Inclusion of multiple templates slightly improved the similarity to the crystal target in some cases and could be recommended even though it requires a careful manual alignment process. The application of molecular dynamics simulations to highly flexible proteins such as cytochromes P450 is also explored and the information is extracted by the CPCA. Advantages and drawbacks are presented for the different modeling techniques. Despite the varying modeling success, the models give insight and understanding by the mutual forming and discarding of hypotheses. This is a dynamic process since the crystal structures are improving with time and, therefore, the answers to the models are also changing accordingly.
Drug Metabolism and Disposition 12/2004; 32(11):1218-29. · 3.73 Impact Factor
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ABSTRACT: A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignment-independent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r(2) of 0.81 and q(2) of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.
Journal of Medicinal Chemistry 03/2004; 47(4):907-14. · 5.25 Impact Factor
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ABSTRACT: The aim of the present study is to develop a method for predicting the site at which molecules will be metabolized by CYP 2C9 (cytochrome P450 2C9) using a previously reported protein homology model of the enzyme. Such a method would be of great help in designing new compounds with a better pharmacokinetic profile, or in designing prodrugs where the compound needs to be metabolized in order to become active. The methodology is based on a comparison between alignment-independent descriptors derived from GRID Molecular Interaction Fields for the CYP 2C9 active site, and a distance-based representation of the substrate. The predicted site of metabolism is reported as a ranking list of all the hydrogen atoms of each substrate molecule. Eighty-seven CYP 2C9-catalyzed oxidative reactions reported in the literature have been analyzed. In more than 90% of these cases, the hydrogen atom ranked at the first, second, or third position was the experimentally reported site of oxidation.
Journal of Medicinal Chemistry 07/2003; 46(12):2313-24. · 5.25 Impact Factor
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ABSTRACT: This study describes the use of alignment-independent descriptors for obtaining qualitative and quantitative predictions of the competitive inhibition of CYP2C9 on a serie of highly structurally diverse compounds. This was accomplished by calculating alignment independent descriptors in ALMOND. These GRid INdependent Descriptors (GRIND) represent the most important GRID-interactions as a function of the distance instead of the actual position of each grid-point. The experimental data was determined under uniform conditions. The inhibitor data set consists of 35 structurally diverse competitive stereospecific inhibitors of the cytochrome P450 2C9 and the non -inhibitor data set of 46 compounds. In a PLS discriminant analysis 21 inhibitors and 21 non-inhibitors (1 and 0 as activities) were analyzed using the ALMOND program obtaining a model with an r2 of 0.74 and a cross-validation value (q2) of 0.64. The model was externally validated with 39 compounds (14 inhibitors/25 non-inhibitors). 74% of the compounds were correctly predicted and an additional 13% was assigned to a borderline cluster. Thereafter, a model for quantitative predictions was generated by a PLS analysis of the GRIND descriptors using the experimental Ki-value for 21 of the competitive inhibitors (r2 = 0.77, q2 = 0.60). The model was externally validated using 12 compounds and predicted 11 out of 12 of the Ki-values within 0.5 log units. The discriminant model will be useful in screening for CYP2C9 inhibitors from large compound collections. The 3D-QSAR model will be used during lead optimization to avoid chemistry that result in inhibition of CYP2C9.
Journal of Computer-Aided Molecular Design 08/2002; 16(7):443-58. · 3.39 Impact Factor
