Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction
Physiologically‐based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug–drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin‐media...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-09-01
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| Series: | CPT: Pharmacometrics & Systems Pharmacology |
| Online Access: | https://doi.org/10.1002/psp4.12458 |
| _version_ | 1828853142133932032 |
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| author | Shinji Yamazaki Chester Costales Sarah Lazzaro Soraya Eatemadpour Emi Kimoto Manthena V. Varma |
| author_facet | Shinji Yamazaki Chester Costales Sarah Lazzaro Soraya Eatemadpour Emi Kimoto Manthena V. Varma |
| author_sort | Shinji Yamazaki |
| collection | DOAJ |
| description | Physiologically‐based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug–drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin‐mediated intestinal P‐glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis‐Menten constant (Km) to be intrinsic, we focused on the scaling factors for maximal efflux rate (Jmax) to adequately recover clinically observed results. Next, we predicted rifampin‐mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates. |
| first_indexed | 2024-12-13T00:07:38Z |
| format | Article |
| id | doaj.art-e2efb64d465b4efebe167697e4a2b216 |
| institution | Directory Open Access Journal |
| issn | 2163-8306 |
| language | English |
| last_indexed | 2024-12-13T00:07:38Z |
| publishDate | 2019-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | CPT: Pharmacometrics & Systems Pharmacology |
| spelling | doaj.art-e2efb64d465b4efebe167697e4a2b2162022-12-22T00:06:10ZengWileyCPT: Pharmacometrics & Systems Pharmacology2163-83062019-09-018963464210.1002/psp4.12458Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein InductionShinji Yamazaki0Chester Costales1Sarah Lazzaro2Soraya Eatemadpour3Emi Kimoto4Manthena V. Varma5Pharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development San Diego California USAPharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development Groton Connecticut USAPharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development Groton Connecticut USAPharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development Groton Connecticut USAPharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development Groton Connecticut USAPharmacokinetics, Dynamics and Metabolism Pfizer Worldwide Research & Development Groton Connecticut USAPhysiologically‐based pharmacokinetic (PBPK) modeling is a powerful tool to quantitatively describe drug disposition profiles in vivo, thereby providing an alternative to predict drug–drug interactions (DDIs) that have not been tested clinically. This study aimed to predict effects of rifampin‐mediated intestinal P‐glycoprotein (Pgp) induction on pharmacokinetics of Pgp substrates via PBPK modeling. First, we selected four Pgp substrates (digoxin, talinolol, quinidine, and dabigatran etexilate) to derive in vitro to in vivo scaling factors for intestinal Pgp kinetics. Assuming unbound Michaelis‐Menten constant (Km) to be intrinsic, we focused on the scaling factors for maximal efflux rate (Jmax) to adequately recover clinically observed results. Next, we predicted rifampin‐mediated fold increases in intestinal Pgp abundances to reasonably recover clinically observed DDI results. The modeling results suggested that threefold to fourfold increases in intestinal Pgp abundances could sufficiently reproduce the DDI results of these Pgp substrates with rifampin. Hence, the obtained fold increases can potentially be applicable to DDI prediction with other Pgp substrates.https://doi.org/10.1002/psp4.12458 |
| spellingShingle | Shinji Yamazaki Chester Costales Sarah Lazzaro Soraya Eatemadpour Emi Kimoto Manthena V. Varma Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction CPT: Pharmacometrics & Systems Pharmacology |
| title | Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction |
| title_full | Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction |
| title_fullStr | Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction |
| title_full_unstemmed | Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction |
| title_short | Physiologically‐Based Pharmacokinetic Modeling Approach to Predict Rifampin‐Mediated Intestinal P‐Glycoprotein Induction |
| title_sort | physiologically based pharmacokinetic modeling approach to predict rifampin mediated intestinal p glycoprotein induction |
| url | https://doi.org/10.1002/psp4.12458 |
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