Literature DB >> 28912253

Physiologically Based Pharmacokinetic Model Predictions of Panobinostat (LBH589) as a Victim and Perpetrator of Drug-Drug Interactions.

Heidi J Einolf1, Wen Lin2, Christina S Won2, Lai Wang2, Helen Gu2, Dung Y Chun2, Handan He2, James B Mangold2.   

Abstract

Panobinostat (Farydak) is an orally active hydroxamic acid-derived histone deacetylase inhibitor used for the treatment of relapsed or refractory multiple myeloma. Based on recombinant cytochrome P450 (P450) kinetic analyses in vitro, panobinostat oxidative metabolism in human liver microsomes was mediated primarily by CYP3A4 with lower contributions by CYP2D6 and CYP2C19. Panobinostat was also an in vitro reversible and time-dependent inhibitor of CYP3A4/5 and a reversible inhibitor of CYP2D6 and CYP2C19. Based on a previous clinical drug-drug interaction study with ketoconazole (KTZ), the contribution of CYP3A4 in vivo was estimated to be ∼40%. Using clinical pharmacokinetic (PK) data from several trials, including the KTZ drug-drug interaction (DDI) study, a physiologically based pharmacokinetic (PBPK) model was built to predict panobinostat PK after single and multiple doses (within 2-fold of observed values for most trials) and the clinical DDI with KTZ (predicted and observed area under the curve ratios of 1.8). The model was then applied to predict the drug interaction with the strong CYP3A4 inducer rifampin (RIF) and the sensitive CYP3A4 substrate midazolam (MDZ) in lieu of clinical trials. Panobinostat exposure was predicted to decrease in the presence of RIF (65%) and inconsequentially increase MDZ exposure (4%). Additionally, PBPK modeling was used to examine the effects of stomach pH on the absorption of panobinostat in humans and determined that absorption of panobinostat is not expected to be affected by increases in stomach pH. The results from these studies were incorporated into the Food and Drug Administration-approved product label, providing guidance for panobinostat dosing recommendations when it is combined with other drugs.
Copyright © 2017 by The American Society for Pharmacology and Experimental Therapeutics.

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Year:  2017        PMID: 28912253     DOI: 10.1124/dmd.117.076851

Source DB:  PubMed          Journal:  Drug Metab Dispos        ISSN: 0090-9556            Impact factor:   3.922


  9 in total

1.  Predicting the Drug-Drug Interaction Mediated by CYP3A4 Inhibition: Method Development and Performance Evaluation.

Authors:  Hong-Can Ren; Yang Sai; Tao Chen; Chun Zhang; Lily Tang; Cheng-Guang Yang
Journal:  AAPS J       Date:  2021-12-10       Impact factor: 4.009

Review 2.  Applications, Challenges, and Outlook for PBPK Modeling and Simulation: A Regulatory, Industrial and Academic Perspective.

Authors:  Wen Lin; Yuan Chen; Jashvant D Unadkat; Xinyuan Zhang; Di Wu; Tycho Heimbach
Journal:  Pharm Res       Date:  2022-05-13       Impact factor: 4.580

3.  Dissolution and Translational Modeling Strategies Enabling Patient-Centric Drug Product Development: the M-CERSI Workshop Summary Report.

Authors:  Andreas Abend; Tycho Heimbach; Michael Cohen; Filippos Kesisoglou; Xavier Pepin; Sandra Suarez-Sharp
Journal:  AAPS J       Date:  2018-04-09       Impact factor: 4.009

Review 4.  Physiologically Based Pharmacokinetic Model Qualification and Reporting Procedures for Regulatory Submissions: A Consortium Perspective.

Authors:  Mohamad Shebley; Punam Sandhu; Arian Emami Riedmaier; Masoud Jamei; Rangaraj Narayanan; Aarti Patel; Sheila Annie Peters; Venkatesh Pilla Reddy; Ming Zheng; Loeckie de Zwart; Maud Beneton; Francois Bouzom; Jun Chen; Yuan Chen; Yumi Cleary; Christiane Collins; Gemma L Dickinson; Nassim Djebli; Heidi J Einolf; Iain Gardner; Felix Huth; Faraz Kazmi; Feras Khalil; Jing Lin; Aleksandrs Odinecs; Chirag Patel; Haojing Rong; Edgar Schuck; Pradeep Sharma; Shu-Pei Wu; Yang Xu; Shinji Yamazaki; Kenta Yoshida; Malcolm Rowland
Journal:  Clin Pharmacol Ther       Date:  2018-02-02       Impact factor: 6.875

5.  A Drug-Drug Interaction Study to Evaluate the Effect of TAS-303 on CYP3A Activity in the Small Intestine and Liver.

Authors:  Yuji Kumagai; Tomoe Fujita; Mika Maeda; Yoshinobu Sasaki; Makoto Nagaoka; Jinhong Huang; Toru Takenaka; Masaki Kawai
Journal:  J Clin Pharmacol       Date:  2020-02-05       Impact factor: 3.126

6.  Requirements to Establishing Confidence in Physiologically Based Pharmacokinetic (PBPK) Models and Overcoming Some of the Challenges to Meeting Them.

Authors:  Sheila Annie Peters; Hugues Dolgos
Journal:  Clin Pharmacokinet       Date:  2019-11       Impact factor: 6.447

7.  Application of Physiologically-Based Pharmacokinetic Modeling to Predict Gastric pH-Dependent Drug-Drug Interactions for Weak Base Drugs.

Authors:  Zhongqi Dong; Jia Li; Fang Wu; Ping Zhao; Sue-Chih Lee; Lillian Zhang; Paul Seo; Lei Zhang
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2020-07-31

8.  PDI inhibitor LTI6426 enhances panobinostat efficacy in preclinical models of multiple myeloma.

Authors:  Reeder M Robinson; Ashton P Basar; Leticia Reyes; Ravyn M Duncan; Hong Li; Nathan G Dolloff
Journal:  Cancer Chemother Pharmacol       Date:  2022-04-05       Impact factor: 3.288

9.  Comprehensive PBPK model to predict drug interaction potential of Zanubrutinib as a victim or perpetrator.

Authors:  Kun Wang; Xueting Yao; Miao Zhang; Dongyang Liu; Yuying Gao; Srikumar Sahasranaman; Ying C Ou
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2021-05-02
  9 in total

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