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The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS)
[Image: see text] Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (R(s)) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GS...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American
Chemical Society
2016
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089532/ https://www.ncbi.nlm.nih.gov/pubmed/27363449 http://dx.doi.org/10.1021/acs.est.6b01407 |
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author | Miller, Thomas H. Baz-Lomba, Jose A. Harman, Christopher Reid, Malcolm J. Owen, Stewart F. Bury, Nicolas R. Thomas, Kevin V. Barron, Leon P. |
author_facet | Miller, Thomas H. Baz-Lomba, Jose A. Harman, Christopher Reid, Malcolm J. Owen, Stewart F. Bury, Nicolas R. Thomas, Kevin V. Barron, Leon P. |
author_sort | Miller, Thomas H. |
collection | PubMed |
description | [Image: see text] Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (R(s)) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for R(s) prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict R(s). Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(–1) (RTD-model) and 0.03 ± 0.03 L d(–1) (GSD-model) relative to experimentally determined R(s). Prediction variability in replicated models was the same or less than for measured R(s). Networks were externally validated using a measured R(s) data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(–1) and 0.0437 ± 0.02 L d(–1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for R(s) measurement. The use of in silico tools for R(s) determination represents a more economical approach than laboratory calibrations. |
format | Online Article Text |
id | pubmed-5089532 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | American
Chemical Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-50895322016-11-02 The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS) Miller, Thomas H. Baz-Lomba, Jose A. Harman, Christopher Reid, Malcolm J. Owen, Stewart F. Bury, Nicolas R. Thomas, Kevin V. Barron, Leon P. Environ Sci Technol [Image: see text] Modeling and prediction of polar organic chemical integrative sampler (POCIS) sampling rates (R(s)) for 73 compounds using artificial neural networks (ANNs) is presented for the first time. Two models were constructed: the first was developed ab initio using a genetic algorithm (GSD-model) to shortlist 24 descriptors covering constitutional, topological, geometrical and physicochemical properties and the second model was adapted for R(s) prediction from a previous chromatographic retention model (RTD-model). Mechanistic evaluation of descriptors showed that models did not require comprehensive a priori information to predict R(s). Average predicted errors for the verification and blind test sets were 0.03 ± 0.02 L d(–1) (RTD-model) and 0.03 ± 0.03 L d(–1) (GSD-model) relative to experimentally determined R(s). Prediction variability in replicated models was the same or less than for measured R(s). Networks were externally validated using a measured R(s) data set of six benzodiazepines. The RTD-model performed best in comparison to the GSD-model for these compounds (average absolute errors of 0.0145 ± 0.008 L d(–1) and 0.0437 ± 0.02 L d(–1), respectively). Improvements to generalizability of modeling approaches will be reliant on the need for standardized guidelines for R(s) measurement. The use of in silico tools for R(s) determination represents a more economical approach than laboratory calibrations. American Chemical Society 2016-07-01 2016-08-02 /pmc/articles/PMC5089532/ /pubmed/27363449 http://dx.doi.org/10.1021/acs.est.6b01407 Text en Copyright © 2016 American Chemical Society This is an open access article published under a Creative Commons Attribution (CC-BY) License (http://pubs.acs.org/page/policy/authorchoice_ccby_termsofuse.html) , which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. |
spellingShingle | Miller, Thomas H. Baz-Lomba, Jose A. Harman, Christopher Reid, Malcolm J. Owen, Stewart F. Bury, Nicolas R. Thomas, Kevin V. Barron, Leon P. The First Attempt at Non-Linear in Silico Prediction of Sampling Rates for Polar Organic Chemical Integrative Samplers (POCIS) |
title | The
First Attempt at Non-Linear in Silico Prediction
of Sampling Rates for Polar Organic Chemical Integrative Samplers
(POCIS) |
title_full | The
First Attempt at Non-Linear in Silico Prediction
of Sampling Rates for Polar Organic Chemical Integrative Samplers
(POCIS) |
title_fullStr | The
First Attempt at Non-Linear in Silico Prediction
of Sampling Rates for Polar Organic Chemical Integrative Samplers
(POCIS) |
title_full_unstemmed | The
First Attempt at Non-Linear in Silico Prediction
of Sampling Rates for Polar Organic Chemical Integrative Samplers
(POCIS) |
title_short | The
First Attempt at Non-Linear in Silico Prediction
of Sampling Rates for Polar Organic Chemical Integrative Samplers
(POCIS) |
title_sort | the
first attempt at non-linear in silico prediction
of sampling rates for polar organic chemical integrative samplers
(pocis) |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089532/ https://www.ncbi.nlm.nih.gov/pubmed/27363449 http://dx.doi.org/10.1021/acs.est.6b01407 |
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