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Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use

Huybrecht T'jollyn UGent, Koen Boussery UGent, RJ Mortishire-Smith, K Coe, B De Boeck, Jan Van Bocxlaer UGent and G Mannens (2011) DRUG METABOLISM AND DISPOSITION. 39(11). p.2066-2075
abstract
The aim of this study was to evaluate three different metabolite prediction software packages (Meteor, MetaSite, and StarDrop) with respect to their ability to predict loci of metabolism and suggest relative proportions of metabolites. A chemically diverse test set of 22 compounds, for which in vivo human mass balance studies and metabolic schemes were available, was used as basis for the evaluation. Each software package was provided with structures of the parent compounds, and predicted metabolites were compared with experimentally determined human metabolites. The evaluation consisted of two parts. First, different settings within each software package were investigated and the software was evaluated using those settings determined to give the best prediction. Second, the three different packages were combined using the optimized settings to see whether a synergistic effect concerning the overall metabolism prediction could be established. The performance of the software was scored for both sensitivity and precision, taking into account the capabilities/limitations of the particular software. Varying results were obtained for the individual packages. Meteor showed a general tendency toward overprediction, and this led to a relatively low precision (similar to 35%) but high sensitivity (similar to 70%). MetaSite and StarDrop both exhibited a sensitivity and precision of similar to 50%. By combining predictions obtained with the different packages, we found that increased precision can be obtained. We conclude that the state-of-the-art individual metabolite prediction software has many advantageous features but needs refinement to obtain acceptable prediction profiles. Synergistic use of different software packages could prove useful.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
computational models, soft spot identification, MASS-SPECTROMETRY, DRUG-METABOLISM, Cytochrome P450, catalytic site, OPTIMIZATION, CHALLENGES, DISCOVERY, STABILITY, SITE, in silico, metabolism
journal title
DRUG METABOLISM AND DISPOSITION
Drug Metab. Dispos.
volume
39
issue
11
pages
2066 - 2075
Web of Science type
Article
Web of Science id
000296539300011
JCR category
PHARMACOLOGY & PHARMACY
JCR impact factor
3.733 (2011)
JCR rank
50/259 (2011)
JCR quartile
1 (2011)
ISSN
0090-9556
DOI
10.1124/dmd.111.039982
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
2103430
handle
http://hdl.handle.net/1854/LU-2103430
date created
2012-05-10 10:46:44
date last changed
2012-05-11 10:07:36
@article{2103430,
  abstract     = {The aim of this study was to evaluate three different metabolite prediction software packages (Meteor, MetaSite, and StarDrop) with respect to their ability to predict loci of metabolism and suggest relative proportions of metabolites. A chemically diverse test set of 22 compounds, for which in vivo human mass balance studies and metabolic schemes were available, was used as basis for the evaluation. Each software package was provided with structures of the parent compounds, and predicted metabolites were compared with experimentally determined human metabolites. The evaluation consisted of two parts. First, different settings within each software package were investigated and the software was evaluated using those settings determined to give the best prediction. Second, the three different packages were combined using the optimized settings to see whether a synergistic effect concerning the overall metabolism prediction could be established. The performance of the software was scored for both sensitivity and precision, taking into account the capabilities/limitations of the particular software. Varying results were obtained for the individual packages. Meteor showed a general tendency toward overprediction, and this led to a relatively low precision (similar to 35\%) but high sensitivity (similar to 70\%). MetaSite and StarDrop both exhibited a sensitivity and precision of similar to 50\%. By combining predictions obtained with the different packages, we found that increased precision can be obtained. We conclude that the state-of-the-art individual metabolite prediction software has many advantageous features but needs refinement to obtain acceptable prediction profiles. Synergistic use of different software packages could prove useful.},
  author       = {T'jollyn, Huybrecht and Boussery, Koen and Mortishire-Smith, RJ and Coe, K and De Boeck, B and Van Bocxlaer, Jan and Mannens, G},
  issn         = {0090-9556},
  journal      = {DRUG METABOLISM AND DISPOSITION},
  keyword      = {computational models,soft spot identification,MASS-SPECTROMETRY,DRUG-METABOLISM,Cytochrome P450,catalytic site,OPTIMIZATION,CHALLENGES,DISCOVERY,STABILITY,SITE,in silico,metabolism},
  language     = {eng},
  number       = {11},
  pages        = {2066--2075},
  title        = {Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use},
  url          = {http://dx.doi.org/10.1124/dmd.111.039982},
  volume       = {39},
  year         = {2011},
}

Chicago
T’jollyn, Huybrecht, Koen Boussery, RJ Mortishire-Smith, K Coe, B De Boeck, Jan Van Bocxlaer, and G Mannens. 2011. “Evaluation of Three State-of-the-art Metabolite Prediction Software Packages (Meteor, MetaSite, and StarDrop) Through Independent and Synergistic Use.” Drug Metabolism and Disposition 39 (11): 2066–2075.
APA
T’jollyn, H., Boussery, K., Mortishire-Smith, R., Coe, K., De Boeck, B., Van Bocxlaer, J., & Mannens, G. (2011). Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use. DRUG METABOLISM AND DISPOSITION, 39(11), 2066–2075.
Vancouver
1.
T’jollyn H, Boussery K, Mortishire-Smith R, Coe K, De Boeck B, Van Bocxlaer J, et al. Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use. DRUG METABOLISM AND DISPOSITION. 2011;39(11):2066–75.
MLA
T’jollyn, Huybrecht, Koen Boussery, RJ Mortishire-Smith, et al. “Evaluation of Three State-of-the-art Metabolite Prediction Software Packages (Meteor, MetaSite, and StarDrop) Through Independent and Synergistic Use.” DRUG METABOLISM AND DISPOSITION 39.11 (2011): 2066–2075. Print.