Literature DB >> 32291610

A Translational Physiologically Based Pharmacokinetics/Pharmacodynamics Framework of Target-Mediated Disposition, Target Inhibition and Drug-Drug Interactions of Bortezomib.

Shinji Iwasaki1, Andy Zhu2, Michael Hanley3, Karthik Venkatakrishnan3, Cindy Xia2.   

Abstract

Bortezomib is a potent 20S proteasome inhibitor approved for the treatment of multiple myeloma and mantle cell lymphoma. Despite the extensive clinical use of bortezomib, the mechanism of the complex time-dependent pharmacokinetics of bortezomib has not been fully investigated in context of its pharmacodynamics (PD) and drug-drug interaction (DDI) profiles. Here, we aimed to develop a mechanistic physiologically based (PB) PK/PD model to project PK, blood target inhibition and DDI of bortezomib in patients. A minimal PBPK/PD model consisting of six compartments was constructed using a bottom-up approach with pre-clinical data and human physiological parameters. Specifically, the target-mediated drug disposition (TMDD) of bortezomib in red blood cells (RBC), which determines target inhibition in blood, was characterized by incorporating the proteasome binding affinity of bortezomib and the proteasome concentration in RBC. The hepatic clearance and fraction metabolized by different CYP isoforms were estimated from in vitro metabolism and phenotyping experiments. The established model adequately characterized the multi-exponential and time-dependent plasma pharmacokinetics, target binding and blood proteasome inhibition of bortezomib. Further, the model was able to accurately predict the impact of a strong CYP3A inducer (rifampicin) and inhibitor (ketoconazole) on bortezomib exposure. In conclusion, the mechanistic PBPK/PD model successfully described the complex pharmacokinetics, target inhibition and DDIs of bortezomib in patients. This study illustrates the importance of incorporating target biology, drug-target interactions and in vitro clearance parameters into mechanistic PBPK/PD models and the utility of such models for pharmacokinetic, pharmacodynamic and DDI predictions.

Entities:  

Keywords:  bortezomib; drug–drug interaction; physiologically based pharmacokinetic/pharmacodynamic model; target-mediated drug disposition

Mesh:

Substances:

Year:  2020        PMID: 32291610     DOI: 10.1208/s12248-020-00448-x

Source DB:  PubMed          Journal:  AAPS J        ISSN: 1550-7416            Impact factor:   4.009


  31 in total

1.  Approval summary for bortezomib for injection in the treatment of multiple myeloma.

Authors:  Peter F Bross; Robert Kane; Ann T Farrell; Sophia Abraham; Kimberly Benson; Margaret E Brower; Sean Bradley; Jogarao V Gobburu; Anwar Goheer; Shwu-Luan Lee; John Leighton; Cheng Yi Liang; Richard T Lostritto; William D McGuinn; David E Morse; Atiqur Rahman; Lilliam A Rosario; S Leigh Verbois; Grant Williams; Yong-Cheng Wang; Richard Pazdur
Journal:  Clin Cancer Res       Date:  2004-06-15       Impact factor: 12.531

Review 2.  Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation Approaches: A Systematic Review of Published Models, Applications, and Model Verification.

Authors:  Jennifer E Sager; Jingjing Yu; Isabelle Ragueneau-Majlessi; Nina Isoherranen
Journal:  Drug Metab Dispos       Date:  2015-08-21       Impact factor: 3.922

3.  Prediction of human pharmacokinetics from preclinical information: comparative accuracy of quantitative prediction approaches.

Authors:  Natilie A Hosea; Wendy T Collard; Susan Cole; Tristan S Maurer; Rick X Fang; Hannah Jones; Shefali M Kakar; Yasuhiro Nakai; Bill J Smith; Rob Webster; Kevin Beaumont
Journal:  J Clin Pharmacol       Date:  2009-03-19       Impact factor: 3.126

4.  Population pharmacokinetics of the 11β-hydroxysteroid dehydrogenase type 1 inhibitor ABT-384 in healthy volunteers following single and multiple dose regimens.

Authors:  Guohua An; Wei Liu; David A Katz; Gerard J Marek; Walid Awni; Sandeep Dutta
Journal:  Biopharm Drug Dispos       Date:  2014-08-30       Impact factor: 1.627

Review 5.  Physiological parameters in laboratory animals and humans.

Authors:  B Davies; T Morris
Journal:  Pharm Res       Date:  1993-07       Impact factor: 4.200

6.  Pharmacokinetic and pharmacodynamic study of two doses of bortezomib in patients with relapsed multiple myeloma.

Authors:  Donna E Reece; Dan Sullivan; Sagar Lonial; Ann F Mohrbacher; Gurkamal Chatta; Chaim Shustik; Howard Burris; Karthik Venkatakrishnan; Rachel Neuwirth; William J Riordan; Michael Karol; Lisa L von Moltke; Milin Acharya; Peter Zannikos; A Keith Stewart
Journal:  Cancer Chemother Pharmacol       Date:  2010-03-20       Impact factor: 3.333

7.  Circulating proteasome levels are an independent prognostic factor for survival in multiple myeloma.

Authors:  Christian Jakob; Karl Egerer; Peter Liebisch; Seval Türkmen; Ivana Zavrski; Ulrike Kuckelkorn; Ulrike Heider; Martin Kaiser; Claudia Fleissner; Jan Sterz; Lorenz Kleeberg; Eugen Feist; Gerd-R Burmester; Peter-M Kloetzel; Orhan Sezer
Journal:  Blood       Date:  2006-11-09       Impact factor: 22.113

8.  Immunological methods to quantify and characterize proteasome complexes: development and application.

Authors:  Matthias Majetschak; Luis T Sorell
Journal:  J Immunol Methods       Date:  2008-03-03       Impact factor: 2.303

9.  Evaluation of the proteasome inhibitor MLN9708 in preclinical models of human cancer.

Authors:  Erik Kupperman; Edmund C Lee; Yueying Cao; Bret Bannerman; Michael Fitzgerald; Allison Berger; Jie Yu; Yu Yang; Paul Hales; Frank Bruzzese; Jane Liu; Jonathan Blank; Khristofer Garcia; Christopher Tsu; Larry Dick; Paul Fleming; Li Yu; Mark Manfredi; Mark Rolfe; Joe Bolen
Journal:  Cancer Res       Date:  2010-02-16       Impact factor: 12.701

10.  CYP2C19 pharmacogenetics in advanced cancer: compromised function independent of genotype.

Authors:  N A Helsby; W-Y Lo; K Sharples; G Riley; M Murray; K Spells; M Dzhelai; A Simpson; M Findlay
Journal:  Br J Cancer       Date:  2008-10-21       Impact factor: 7.640

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