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Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example
BACKGROUND AND OBJECTIVES: We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a ‘bottom-up’ approach) and population pharmacokinetic (popPK) confirmation (a ‘top-down’ approach), or in reverse order, depending on whe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916198/ https://www.ncbi.nlm.nih.gov/pubmed/26715215 http://dx.doi.org/10.1007/s40262-015-0356-1 |
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author | Feng, Sheng Shi, Jun Parrott, Neil Hu, Pei Weber, Cornelia Martin-Facklam, Meret Saito, Tomohisa Peck, Richard |
author_facet | Feng, Sheng Shi, Jun Parrott, Neil Hu, Pei Weber, Cornelia Martin-Facklam, Meret Saito, Tomohisa Peck, Richard |
author_sort | Feng, Sheng |
collection | PubMed |
description | BACKGROUND AND OBJECTIVES: We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a ‘bottom-up’ approach) and population pharmacokinetic (popPK) confirmation (a ‘top-down’ approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin. METHODS: A PBPK model was built using Simcyp(®) to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM(®) to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis. RESULTS: PBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results. CONCLUSION: As a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40262-015-0356-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4916198 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-49161982016-07-06 Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example Feng, Sheng Shi, Jun Parrott, Neil Hu, Pei Weber, Cornelia Martin-Facklam, Meret Saito, Tomohisa Peck, Richard Clin Pharmacokinet Original Research Article BACKGROUND AND OBJECTIVES: We propose a strategy for studying ethnopharmacology by conducting sequential physiologically based pharmacokinetic (PBPK) prediction (a ‘bottom-up’ approach) and population pharmacokinetic (popPK) confirmation (a ‘top-down’ approach), or in reverse order, depending on whether the purpose is ethnic effect assessment for a new molecular entity under development or a tool for ethnic sensitivity prediction for a given pathway. The strategy is exemplified with bitopertin. METHODS: A PBPK model was built using Simcyp(®) to simulate the pharmacokinetics of bitopertin and to predict the ethnic sensitivity in clearance, given pharmacokinetic data in just one ethnicity. Subsequently, a popPK model was built using NONMEM(®) to assess the effect of ethnicity on clearance, using human data from multiple ethnic groups. A comparison was made to confirm the PBPK-based ethnic sensitivity prediction, using the results of the popPK analysis. RESULTS: PBPK modelling predicted that the bitopertin geometric mean clearance values after 20 mg oral administration in Caucasians would be 1.32-fold and 1.27-fold higher than the values in Chinese and Japanese, respectively. The ratios of typical clearance in Caucasians to the values in Chinese and Japanese estimated by popPK analysis were 1.20 and 1.17, respectively. The popPK analysis results were similar to the PBPK modelling results. CONCLUSION: As a general framework, we propose that PBPK modelling should be considered to predict ethnic sensitivity of pharmacokinetics prior to any human data and/or with data in only one ethnicity. In some cases, this will be sufficient to guide initial dose selection in different ethnicities. After clinical trials in different ethnicities, popPK analysis can be used to confirm ethnic differences and to support dose justification and labelling. PBPK modelling prediction and popPK analysis confirmation can complement each other to assess ethnic differences in pharmacokinetics at different drug development stages. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s40262-015-0356-1) contains supplementary material, which is available to authorized users. Springer International Publishing 2015-12-29 2016 /pmc/articles/PMC4916198/ /pubmed/26715215 http://dx.doi.org/10.1007/s40262-015-0356-1 Text en © The Author(s) 2015 Open AccessThis article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Research Article Feng, Sheng Shi, Jun Parrott, Neil Hu, Pei Weber, Cornelia Martin-Facklam, Meret Saito, Tomohisa Peck, Richard Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title | Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title_full | Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title_fullStr | Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title_full_unstemmed | Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title_short | Combining ‘Bottom-Up’ and ‘Top-Down’ Methods to Assess Ethnic Difference in Clearance: Bitopertin as an Example |
title_sort | combining ‘bottom-up’ and ‘top-down’ methods to assess ethnic difference in clearance: bitopertin as an example |
topic | Original Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916198/ https://www.ncbi.nlm.nih.gov/pubmed/26715215 http://dx.doi.org/10.1007/s40262-015-0356-1 |
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