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Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans

BACKGROUND: Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important. OBJECTIVE: To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethy...

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Autores principales: Min, Jee Sun, Kim, Doyun, Park, Jung Bae, Heo, Hyunjin, Bae, Soo Hyeon, Seo, Jae Hong, Oh, Euichaul, Bae, Soo Kyung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5028085/
https://www.ncbi.nlm.nih.gov/pubmed/27695293
http://dx.doi.org/10.2147/DDDT.S109141
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author Min, Jee Sun
Kim, Doyun
Park, Jung Bae
Heo, Hyunjin
Bae, Soo Hyeon
Seo, Jae Hong
Oh, Euichaul
Bae, Soo Kyung
author_facet Min, Jee Sun
Kim, Doyun
Park, Jung Bae
Heo, Hyunjin
Bae, Soo Hyeon
Seo, Jae Hong
Oh, Euichaul
Bae, Soo Kyung
author_sort Min, Jee Sun
collection PubMed
description BACKGROUND: Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important. OBJECTIVE: To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine. METHODS: The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol. RESULTS: The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition. CONCLUSION: This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor.
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spelling pubmed-50280852016-09-30 Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans Min, Jee Sun Kim, Doyun Park, Jung Bae Heo, Hyunjin Bae, Soo Hyeon Seo, Jae Hong Oh, Euichaul Bae, Soo Kyung Drug Des Devel Ther Original Research BACKGROUND: Evaluating the potential risk of metabolic drug–drug interactions (DDIs) is clinically important. OBJECTIVE: To develop a physiologically based pharmacokinetic (PBPK) model for sarpogrelate hydrochloride and its active metabolite, (R,S)-1-{2-[2-(3-methoxyphenyl)ethyl]-phenoxy}-3-(dimethylamino)-2-propanol (M-1), in order to predict DDIs between sarpogrelate and the clinically relevant cytochrome P450 (CYP) 2D6 substrates, metoprolol, desipramine, dextromethorphan, imipramine, and tolterodine. METHODS: The PBPK model was developed, incorporating the physicochemical and pharmacokinetic properties of sarpogrelate hydrochloride, and M-1 based on the findings from in vitro and in vivo studies. Subsequently, the model was verified by comparing the predicted concentration-time profiles and pharmacokinetic parameters of sarpogrelate and M-1 to the observed clinical data. Finally, the verified model was used to simulate clinical DDIs between sarpogrelate hydrochloride and sensitive CYP2D6 substrates. The predictive performance of the model was assessed by comparing predicted results to observed data after coadministering sarpogrelate hydrochloride and metoprolol. RESULTS: The developed PBPK model accurately predicted sarpogrelate and M-1 plasma concentration profiles after single or multiple doses of sarpogrelate hydrochloride. The simulated ratios of area under the curve and maximum plasma concentration of metoprolol in the presence of sarpogrelate hydrochloride to baseline were in good agreement with the observed ratios. The predicted fold-increases in the area under the curve ratios of metoprolol, desipramine, imipramine, dextromethorphan, and tolterodine following single and multiple sarpogrelate hydrochloride oral doses were within the range of ≥1.25, but <2-fold, indicating that sarpogrelate hydrochloride is a weak inhibitor of CYP2D6 in vivo. Collectively, the predicted low DDIs suggest that sarpogrelate hydrochloride has limited potential for causing significant DDIs associated with CYP2D6 inhibition. CONCLUSION: This study demonstrated the feasibility of applying the PBPK approach to predicting the DDI potential between sarpogrelate hydrochloride and drugs metabolized by CYP2D6. Therefore, it would be beneficial in designing and optimizing clinical DDI studies using sarpogrelate as an in vivo CYP2D6 inhibitor. Dove Medical Press 2016-09-14 /pmc/articles/PMC5028085/ /pubmed/27695293 http://dx.doi.org/10.2147/DDDT.S109141 Text en © 2016 Min et al. This work is published and licensed by Dove Medical Press Limited The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Min, Jee Sun
Kim, Doyun
Park, Jung Bae
Heo, Hyunjin
Bae, Soo Hyeon
Seo, Jae Hong
Oh, Euichaul
Bae, Soo Kyung
Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title_full Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title_fullStr Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title_full_unstemmed Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title_short Application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
title_sort application of physiologically based pharmacokinetic modeling in predicting drug–drug interactions for sarpogrelate hydrochloride in humans
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5028085/
https://www.ncbi.nlm.nih.gov/pubmed/27695293
http://dx.doi.org/10.2147/DDDT.S109141
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