Literature DB >> 23315146

Modelling and simulation of placebo effect: application to drug development in schizophrenia.

Venkatesh Pilla Reddy1, Magdalena Kozielska, Rik de Greef, An Vermeulen, Johannes H Proost.   

Abstract

High and variable placebo effect (PE) within and among clinical trials can substantially affect conclusions about the efficacy of new drugs in the treatment of schizophrenia and other neuropsychiatric disorders. In recent years, it has become increasingly difficult to prove drug efficacy against placebo, and one of the reasons is that the placebo response has increased over recent years. The increased placebo response over the years is partly explained by unidentified parallel interventions, patient factors, issues with trial designs, and regional variability or demographic differences. In addition, a nocebo effect, which is undesirable effects a subject manifests after receiving placebo, e.g. extrapyramidal side effects, in placebo arms of antipsychotic trials could also influence the PE and clinical trial outcomes. Placebo effects (PEs) are a natural phenomenon and cannot be avoided completely in clinical trials. However, accounting for the PE via mixed effects modelling approaches could reduce bias in quantifying the overall effect size of the drug treatment. This review article focuses on the PE and its impact on schizophrenia clinical trial outcomes. The authors briefly describe the factors that lead to high and variable PE. Next, pharmacometric approaches to account for the PE and dropouts in schizophrenia clinical trials are described. Finally, some points are provided that could be considered while designing and optimizing antipsychotic trials via simulation approaches.

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Year:  2013        PMID: 23315146     DOI: 10.1007/s10928-012-9296-7

Source DB:  PubMed          Journal:  J Pharmacokinet Pharmacodyn        ISSN: 1567-567X            Impact factor:   2.745


  30 in total

Review 1.  Pharmacometrics at FDA: evolution and impact on decisions.

Authors:  J R Powell; J V S Gobburu
Journal:  Clin Pharmacol Ther       Date:  2007-05-30       Impact factor: 6.875

Review 2.  Methodological issues in current antipsychotic drug trials.

Authors:  Stefan Leucht; Stephan Heres; Johannes Hamann; John M Kane
Journal:  Schizophr Bull       Date:  2008-01-29       Impact factor: 9.306

3.  Trial watch: phase III and submission failures: 2007-2010.

Authors:  John Arrowsmith
Journal:  Nat Rev Drug Discov       Date:  2011-02       Impact factor: 84.694

4.  Modeling helps in understanding antidepressants.

Authors:  N Holford
Journal:  Clin Pharmacol Ther       Date:  2012-08       Impact factor: 6.875

5.  Recent developments in improving signal detection and reducing placebo response in psychiatric clinical trials.

Authors:  Craig H Mallinckrodt; Roy N Tamura; Yoko Tanaka
Journal:  J Psychiatr Res       Date:  2011-03-31       Impact factor: 4.791

Review 6.  Modelling placebo response in depression trials using a longitudinal model with informative dropout.

Authors:  Roberto Gomeni; Agnes Lavergne; Emilio Merlo-Pich
Journal:  Eur J Pharm Sci       Date:  2008-11-08       Impact factor: 4.384

7.  The association of dropout and outcome in trials of antipsychotic medication and its implications for dealing with missing data.

Authors:  Jonathan Rabinowitz; Ori Davidov
Journal:  Schizophr Bull       Date:  2008-01-22       Impact factor: 9.306

8.  Schizophrenia: do we really need placebo-controlled studies?

Authors:  J G Storosum; A J Elferink; B J van Zwieten
Journal:  Eur Neuropsychopharmacol       Date:  1998-12       Impact factor: 4.600

9.  Pharmacokinetic-pharmacodynamic modeling of severity levels of extrapyramidal side effects with markov elements.

Authors:  V Pilla Reddy; K J Petersson; A A Suleiman; A Vermeulen; J H Proost; L E Friberg
Journal:  CPT Pharmacometrics Syst Pharmacol       Date:  2012-09-26

10.  What is causing the reduced drug-placebo difference in recent schizophrenia clinical trials and what can be done about it?

Authors:  Aaron S Kemp; Nina R Schooler; Amir H Kalali; Larry Alphs; Ravi Anand; George Awad; Michael Davidson; Sanjay Dubé; Larry Ereshefsky; Georges Gharabawi; Andrew C Leon; Jean-Pierre Lepine; Steven G Potkin; An Vermeulen
Journal:  Schizophr Bull       Date:  2008-08-22       Impact factor: 9.306

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  1 in total

1.  Modeling near-continuous clinical endpoint as categorical: application to longitudinal exposure-response modeling of Mayo scores for golimumab in patients with ulcerative colitis.

Authors:  Chuanpu Hu; Omoniyi J Adedokun; Liping Zhang; Amarnath Sharma; Honghui Zhou
Journal:  J Pharmacokinet Pharmacodyn       Date:  2018-10-30       Impact factor: 2.745

  1 in total

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