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Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations
The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572360/ https://www.ncbi.nlm.nih.gov/pubmed/28575552 http://dx.doi.org/10.1002/psp4.12208 |
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author | Hall, RG Pasipanodya, JG Swancutt, MA Meek, C Leff, R Gumbo, T |
author_facet | Hall, RG Pasipanodya, JG Swancutt, MA Meek, C Leff, R Gumbo, T |
author_sort | Hall, RG |
collection | PubMed |
description | The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44–150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum‐likelihood solution via the expectation‐maximization algorithm. Artificial intelligence‐based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration‐dependent, optimal dapsone doses need to be personalized for obese patients. |
format | Online Article Text |
id | pubmed-5572360 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-55723602017-08-30 Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations Hall, RG Pasipanodya, JG Swancutt, MA Meek, C Leff, R Gumbo, T CPT Pharmacometrics Syst Pharmacol Original Articles The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44–150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum‐likelihood solution via the expectation‐maximization algorithm. Artificial intelligence‐based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration‐dependent, optimal dapsone doses need to be personalized for obese patients. John Wiley and Sons Inc. 2017-07-13 2017-08 /pmc/articles/PMC5572360/ /pubmed/28575552 http://dx.doi.org/10.1002/psp4.12208 Text en © 2017 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs (http://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. |
spellingShingle | Original Articles Hall, RG Pasipanodya, JG Swancutt, MA Meek, C Leff, R Gumbo, T Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title | Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title_full | Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title_fullStr | Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title_full_unstemmed | Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title_short | Supervised Machine‐Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations |
title_sort | supervised machine‐learning reveals that old and obese people achieve low dapsone concentrations |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5572360/ https://www.ncbi.nlm.nih.gov/pubmed/28575552 http://dx.doi.org/10.1002/psp4.12208 |
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