Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors
Alcohol researchers/clinicians have two ways to collect subject /patient field data, standard-drink self-report and the breath analyzer, neither of which is passive or accurate because active subject participation is required. Transdermal alcohol sensors have been developed to measure transdermal a...
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| Language: | English |
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AIMS Press
2016-06-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2016023 |
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| author | Zheng Dai I.G. Rosen Chuming Wang Nancy Barnett Susan E. Luczak |
| author_facet | Zheng Dai I.G. Rosen Chuming Wang Nancy Barnett Susan E. Luczak |
| author_sort | Zheng Dai |
| collection | DOAJ |
| description | Alcohol researchers/clinicians have two ways to collect subject /patient field data, standard-drink self-report and the breath analyzer, neither of which is passive or accurate because active subject participation is required. Transdermal alcohol sensors have been developed to measure transdermal alcohol concentration (TAC), but they are used primarily as abstinence monitors because converting TAC into more meaningful blood/breath alcohol concentration (BAC/BrAC) is difficult. In this paper, BAC/BrAC is estimated from TAC by first calibrating forward distributed parameter-based convolution models for ethanol transport from the blood through the skin using patient-collected drinking data for a single drinking episode and a nonlinear pharmacokinetic metabolic absorption/elimination model to estimate BAC. TAC and estimated BAC are then used to fit the forward convolution filter. Nonlinear least squares with adjoint-based gradient computation are used to fit both models. Calibration results are compared with those obtained using BAC/BrAC from alcohol challenges and from standard, linear, metabolic absorption, and zero order kinetics-based elimination models, by considering peak BAC, time of peak, and area under the BAC curve. Our models (with population parameters) could be included in a smart phone app that makes it convenient for the subject/patient to enter drinking data for a single episode in the field. |
| format | Article |
| id | doaj-art-3aad8e85926f40aaa5f89455666189e8 |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2016-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-3aad8e85926f40aaa5f89455666189e82025-01-24T02:36:57ZengAIMS PressMathematical Biosciences and Engineering1551-00182016-06-0113591193410.3934/mbe.2016023Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensorsZheng Dai0I.G. Rosen1Chuming Wang2Nancy Barnett3Susan E. Luczak4Department of Mathematics, University of Southern California, Los Angeles, CA 90089-2532Department of Mathematics, University of Southern California, Los Angeles, CA 90089-2532Department of Mathematics, University of Southern California, Los Angeles, CA 90089-2532School of Public Health, Brown University, Providence, RI 02912Department of Psychology, University of Southern California, Los Angeles, CA 90089-1061Alcohol researchers/clinicians have two ways to collect subject /patient field data, standard-drink self-report and the breath analyzer, neither of which is passive or accurate because active subject participation is required. Transdermal alcohol sensors have been developed to measure transdermal alcohol concentration (TAC), but they are used primarily as abstinence monitors because converting TAC into more meaningful blood/breath alcohol concentration (BAC/BrAC) is difficult. In this paper, BAC/BrAC is estimated from TAC by first calibrating forward distributed parameter-based convolution models for ethanol transport from the blood through the skin using patient-collected drinking data for a single drinking episode and a nonlinear pharmacokinetic metabolic absorption/elimination model to estimate BAC. TAC and estimated BAC are then used to fit the forward convolution filter. Nonlinear least squares with adjoint-based gradient computation are used to fit both models. Calibration results are compared with those obtained using BAC/BrAC from alcohol challenges and from standard, linear, metabolic absorption, and zero order kinetics-based elimination models, by considering peak BAC, time of peak, and area under the BAC curve. Our models (with population parameters) could be included in a smart phone app that makes it convenient for the subject/patient to enter drinking data for a single episode in the field.https://www.aimspress.com/article/doi/10.3934/mbe.2016023deconvolutionmichaelis-mentenboundary input and output.transdermal alcohol sensordistributed parameter systemdrinking diarypharmacokinetic modeling |
| spellingShingle | Zheng Dai I.G. Rosen Chuming Wang Nancy Barnett Susan E. Luczak Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors Mathematical Biosciences and Engineering deconvolution michaelis-menten boundary input and output. transdermal alcohol sensor distributed parameter system drinking diary pharmacokinetic modeling |
| title | Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| title_full | Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| title_fullStr | Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| title_full_unstemmed | Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| title_short | Using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| title_sort | using drinking data and pharmacokinetic modeling to calibrate transport model and blind deconvolution based data analysis software for transdermal alcohol biosensors |
| topic | deconvolution michaelis-menten boundary input and output. transdermal alcohol sensor distributed parameter system drinking diary pharmacokinetic modeling |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2016023 |
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