Literature DB >> 19626001

A pharmacodynamic Markov mixed-effects model for determining the effect of exposure to certolizumab pegol on the ACR20 score in patients with rheumatoid arthritis.

B D Lacroix1, M R Lovern, A Stockis, M L Sargentini-Maier, M O Karlsson, L E Friberg.   

Abstract

The American College of Rheumatology (ACR) 20% preliminary definition of improvement in rheumatoid arthritis (RA) (ACR20) is widely used in clinical trials to assess response to treatment. The objectives of this analysis were to develop an exposure-response model of ACR20 in subjects receiving treatment with certolizumab pegol and to predict clinical outcomes following various treatment schedules. At each visit, subjects were classified as being ACR20 responders or ACR20 nonresponders or as having dropped out. A Markov mixed-effects model was developed to investigate the effects of the drug on the transitions between the three defined states. Increasing certolizumab pegol exposure predicted an increasing probability of becoming a responder and remaining a responder, as well as a reduced probability of dropping out of treatment. Data from simulations of the ACR20 response rate support the use of dosing regimens of 400 mg at weeks 0, 2, and 4 followed by 200 mg every 2 weeks, or an alternative maintenance regimen of 400 mg every 4 weeks.

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Year:  2009        PMID: 19626001     DOI: 10.1038/clpt.2009.136

Source DB:  PubMed          Journal:  Clin Pharmacol Ther        ISSN: 0009-9236            Impact factor:   6.875


  25 in total

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2.  Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Authors:  Camille Vong; Martin Bergstrand; Joakim Nyberg; Mats O Karlsson
Journal:  AAPS J       Date:  2012-02-17       Impact factor: 4.009

3.  A Minimal Continuous-Time Markov Pharmacometric Model.

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Journal:  AAPS J       Date:  2017-06-20       Impact factor: 4.009

4.  A Markov chain model to evaluate the effect of CYP3A5 and ABCB1 polymorphisms on adverse events associated with tacrolimus in pediatric renal transplantation.

Authors:  Sherwin K B Sy; Jules Heuberger; Sireen Shilbayeh; Daniela J Conrado; Hartmut Derendorf
Journal:  AAPS J       Date:  2013-08-30       Impact factor: 4.009

5.  Approaches for modeling within subject variability in pharmacometric count data analysis: dynamic inter-occasion variability and stochastic differential equations.

Authors:  Chenhui Deng; Elodie L Plan; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2016-05-10       Impact factor: 2.745

Review 6.  Is there potential for therapeutic drug monitoring of biologic agents in rheumatoid arthritis?

Authors:  Carla Bastida; Virginia Ruíz; Mariona Pascal; Jordi Yagüe; Raimon Sanmartí; Dolors Soy
Journal:  Br J Clin Pharmacol       Date:  2017-01-18       Impact factor: 4.335

Review 7.  Pharmacokinetic/pharmacodynamic modeling in inflammation.

Authors:  Hoi-Kei Lon; Dongyang Liu; William J Jusko
Journal:  Crit Rev Biomed Eng       Date:  2012

Review 8.  Pharmacodynamic models for discrete data.

Authors:  Ines Paule; Pascal Girard; Gilles Freyer; Michel Tod
Journal:  Clin Pharmacokinet       Date:  2012-12       Impact factor: 6.447

9.  Latent variable indirect response modeling of categorical endpoints representing change from baseline.

Authors:  Chuanpu Hu; Zhenhua Xu; Alan M Mendelsohn; Honghui Zhou
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-12-30       Impact factor: 2.745

10.  A comprehensive molecular interaction map for rheumatoid arthritis.

Authors:  Gang Wu; Lisha Zhu; Jennifer E Dent; Christine Nardini
Journal:  PLoS One       Date:  2010-04-16       Impact factor: 3.240

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