Rong Deng1,2, Leonid Gibiansky3, Tong Lu4, Priya Agarwal4, Hao Ding4, Xiaobin Li4, Smita Kshirsagar4, Dan Lu4, Chunze Li4, Sandhya Girish4, Jue Wang4, Michelle Boyer5, Kathryn Humphrey5, Kevin J Freise6, Ahmed Hamed Salem6, John F Seymour7, Arnon P Kater8, Dale Miles4. 1. Genentech Inc., South San Francisco, CA, USA. deng.rong@gene.com. 2. Clinical Pharmacology, Genentech Research and Early Development, 1 DNA Way, MS463a, South San Francisco, CA, 94080, USA. deng.rong@gene.com. 3. QuantPharm, LLC, North Potomac, MD, USA. 4. Genentech Inc., South San Francisco, CA, USA. 5. Roche Products Limited, Welwyn Garden City, UK. 6. AbbVie, North Chicago, IL, USA. 7. Royal Melbourne Hospital, Peter MacCallum Cancer Centre, and University of Melbourne, Melbourne, VIC, Australia. 8. Department of Hematology, Amsterdam UMC, University of Amsterdam on behalf of HOVON CLL WG, Amsterdam, The Netherlands.
Abstract
BACKGROUND:Venetoclax is a selective B-cell lymphoma-2 (BCL-2) inhibitor approved for use as monotherapy or with rituximab in patients with chronic lymphocytic leukemia (CLL). The objectives of the current analysis of observed data from adult patients randomized to venetoclax-rituximab in the phase III MURANO study were to characterize venetoclax pharmacokinetics (PKs) using a Bayesian approach, evaluate whether a previously developed population PK model for venetoclax can describe the PKs of venetoclax when administered with rituximab, and to determine post hoc estimates of PK parameters for the exposure-response analysis. METHODS: Parameter estimates and uncertainty estimated by a population PK model were used as priors. Additional covariate effects (CLL risk status, geographic region, and 17p deletion [del(17p)] status) were added to the model. The updated model was used to describe venetoclax PKs after repeated dosing in combination with rituximab, and to determine post hoc estimates of PK parameters for exposure-response analysis. RESULTS: The PK analysis included 600 quantifiable venetoclax PK samples from 182 patients in the MURANO study. Model evaluation using standard diagnostic plots, visual predictive checks, and normalized prediction distribution error plots indicated no model deficiencies. There was no significant relationship between venetoclax apparent clearance (CL/F) and bodyweight, age, sex, mild and moderate hepatic and renal impairment, or coadministration of weak cytochrome P450 3A inhibitors. The chromosomal abnormality del(17p) and CLL risk status had no apparent effect on the PKs of venetoclax. A minimal increase in venetoclax CL/F (approximately 7%) was observed after coadministration with rituximab. CL/F was 30% lower in patients from Central and Eastern Europe (n = 60) or Asia (n = 4) compared with other regions (95% confidence interval [CI] 21-39%). Apparent central volume of distribution was 30% lower (95% CI 22-38%) in females (n = 56) compared with males (n = 126). No clinically significant impact of region or sex was observed on key safety and efficacy outcomes. CONCLUSIONS: The Bayesian model successfully characterized venetoclax PKs over time and confirmed key covariates affecting PKs in the MURANO study. The model was deemed appropriate for further use in simulations and for generating individual patient PK parameters for subsequent exposure-response evaluation.
RCT Entities:
BACKGROUND:Venetoclax is a selective B-cell lymphoma-2 (BCL-2) inhibitor approved for use as monotherapy or with rituximab in patients with chronic lymphocytic leukemia (CLL). The objectives of the current analysis of observed data from adult patients randomized to venetoclax-rituximab in the phase III MURANO study were to characterize venetoclax pharmacokinetics (PKs) using a Bayesian approach, evaluate whether a previously developed population PK model for venetoclax can describe the PKs of venetoclax when administered with rituximab, and to determine post hoc estimates of PK parameters for the exposure-response analysis. METHODS: Parameter estimates and uncertainty estimated by a population PK model were used as priors. Additional covariate effects (CLL risk status, geographic region, and 17p deletion [del(17p)] status) were added to the model. The updated model was used to describe venetoclax PKs after repeated dosing in combination with rituximab, and to determine post hoc estimates of PK parameters for exposure-response analysis. RESULTS: The PK analysis included 600 quantifiable venetoclax PK samples from 182 patients in the MURANO study. Model evaluation using standard diagnostic plots, visual predictive checks, and normalized prediction distribution error plots indicated no model deficiencies. There was no significant relationship between venetoclax apparent clearance (CL/F) and bodyweight, age, sex, mild and moderate hepatic and renal impairment, or coadministration of weak cytochrome P450 3A inhibitors. The chromosomal abnormality del(17p) and CLL risk status had no apparent effect on the PKs of venetoclax. A minimal increase in venetoclax CL/F (approximately 7%) was observed after coadministration with rituximab. CL/F was 30% lower in patients from Central and Eastern Europe (n = 60) or Asia (n = 4) compared with other regions (95% confidence interval [CI] 21-39%). Apparent central volume of distribution was 30% lower (95% CI 22-38%) in females (n = 56) compared with males (n = 126). No clinically significant impact of region or sex was observed on key safety and efficacy outcomes. CONCLUSIONS: The Bayesian model successfully characterized venetoclax PKs over time and confirmed key covariates affecting PKs in the MURANO study. The model was deemed appropriate for further use in simulations and for generating individual patient PK parameters for subsequent exposure-response evaluation.
Authors: Kevin J Freise; Aksana K Jones; Doerthe Eckert; Sven Mensing; Shekman L Wong; Rod A Humerickhouse; Walid M Awni; Ahmed Hamed Salem Journal: Clin Pharmacokinet Date: 2017-05 Impact factor: 6.447
Authors: Andrew J Souers; Joel D Leverson; Erwin R Boghaert; Scott L Ackler; Nathaniel D Catron; Jun Chen; Brian D Dayton; Hong Ding; Sari H Enschede; Wayne J Fairbrother; David C S Huang; Sarah G Hymowitz; Sha Jin; Seong Lin Khaw; Peter J Kovar; Lloyd T Lam; Jackie Lee; Heather L Maecker; Kennan C Marsh; Kylie D Mason; Michael J Mitten; Paul M Nimmer; Anatol Oleksijew; Chang H Park; Cheol-Min Park; Darren C Phillips; Andrew W Roberts; Deepak Sampath; John F Seymour; Morey L Smith; Gerard M Sullivan; Stephen K Tahir; Chris Tse; Michael D Wendt; Yu Xiao; John C Xue; Haichao Zhang; Rod A Humerickhouse; Saul H Rosenberg; Steven W Elmore Journal: Nat Med Date: 2013-01-06 Impact factor: 53.440
Authors: Aksana K Jones; Kevin J Freise; Suresh K Agarwal; Rod A Humerickhouse; Shekman L Wong; Ahmed Hamed Salem Journal: AAPS J Date: 2016-05-27 Impact factor: 4.009
Authors: John F Seymour; Thomas J Kipps; Barbara Eichhorst; Peter Hillmen; James D'Rozario; Sarit Assouline; Carolyn Owen; John Gerecitano; Tadeusz Robak; Javier De la Serna; Ulrich Jaeger; Guillaume Cartron; Marco Montillo; Rod Humerickhouse; Elizabeth A Punnoose; Yan Li; Michelle Boyer; Kathryn Humphrey; Mehrdad Mobasher; Arnon P Kater Journal: N Engl J Med Date: 2018-03-22 Impact factor: 91.245
Authors: Justin Guinney; Tao Wang; Teemu D Laajala; Kimberly Kanigel Winner; J Christopher Bare; Elias Chaibub Neto; Suleiman A Khan; Gopal Peddinti; Antti Airola; Tapio Pahikkala; Tuomas Mirtti; Thomas Yu; Brian M Bot; Liji Shen; Kald Abdallah; Thea Norman; Stephen Friend; Gustavo Stolovitzky; Howard Soule; Christopher J Sweeney; Charles J Ryan; Howard I Scher; Oliver Sartor; Yang Xie; Tero Aittokallio; Fang Liz Zhou; James C Costello Journal: Lancet Oncol Date: 2016-11-16 Impact factor: 41.316
Authors: Anna H-X P Chan Kwong; Elisa A M Calvier; David Fabre; Florence Gattacceca; Sonia Khier Journal: J Pharmacokinet Pharmacodyn Date: 2020-06-13 Impact factor: 2.745