Literature DB >> 29687351

Feasibility of Exposure-Response Analyses for Clinical Dose-Ranging Studies of Drug Combinations.

Theodoros Papathanasiou1,2, Anders Strathe3, Andrew C Hooker4, Trine Meldgaard Lund5, Rune Viig Overgaard3.   

Abstract

The exposure-response relationship of combinatory drug effects can be quantitatively described using pharmacodynamic interaction models, which can be used for the selection of optimal dose combinations. The aim of this simulation study was to evaluate the reliability of parameter estimates and the probability for accurate dose identification for various underlying exposure-response profiles, under a number of different phase II designs. An efficacy variable driven by the combined exposure of two theoretical compounds was simulated and model parameters were estimated using two different models, one estimating all parameters and one assuming that adequate previous knowledge for one drug is readily available. Estimation of all pharmacodynamic parameters under a realistic, in terms of sample size and study design, phase II trial, proved to be challenging. Inaccurate estimates were found in all exposure-response scenarios, except for situations where no pharmacodynamic interaction was present, with the drug potency and interaction parameters being the hardest to estimate. When previous knowledge of the exposure-response relationship of one of the monocomponents is available, such information should be utilized, as it enabled relevant improvements in parameter estimation and in correct dose identification. No general trends for classification of the performance of the tested study designs across different scenarios could be identified. This study shows that pharmacodynamic interactions models can be used for the exposure-response analysis of clinical endpoints especially when accompanied by appropriate dose selection in regard to the expected drug potencies and appropriate trial size and if information regarding the exposure-response profile of one monocomponent is available.

Entities:  

Keywords:  drug-drug interactions; exposure-response; pharmacodynamics; phase II; response; surface

Mesh:

Substances:

Year:  2018        PMID: 29687351     DOI: 10.1208/s12248-018-0226-5

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


  26 in total

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Journal:  Anesthesiology       Date:  2000-06       Impact factor: 7.892

Review 2.  Pharmacodynamic parameter estimation: population size versus number of samples.

Authors:  Suzette Girgis; Sudhakar M Pai; Ihab G Girgis; Vijay K Batra
Journal:  AAPS J       Date:  2005-10-05       Impact factor: 4.009

Review 3.  Multi-target therapeutics: when the whole is greater than the sum of the parts.

Authors:  Grant R Zimmermann; Joseph Lehár; Curtis T Keith
Journal:  Drug Discov Today       Date:  2006-11-28       Impact factor: 7.851

Review 4.  Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies.

Authors:  Ting-Chao Chou
Journal:  Pharmacol Rev       Date:  2006-09       Impact factor: 25.468

Review 5.  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

6.  Population pharmacodynamic parameter estimation from sparse sampling: effect of sigmoidicity on parameter estimates.

Authors:  Sudhakar M Pai; Suzette Girgis; Vijay K Batra; Ihab G Girgis
Journal:  AAPS J       Date:  2009-07-24       Impact factor: 4.009

7.  Development of novel combination therapies.

Authors:  Janet Woodcock; Joseph P Griffin; Rachel E Behrman
Journal:  N Engl J Med       Date:  2011-02-16       Impact factor: 91.245

8.  Model selection and averaging of nonlinear mixed-effect models for robust phase III dose selection.

Authors:  Yasunori Aoki; Daniel Röshammar; Bengt Hamrén; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-11-04       Impact factor: 2.745

9.  BRAID: A Unifying Paradigm for the Analysis of Combined Drug Action.

Authors:  Nathaniel R Twarog; Elizabeth Stewart; Courtney Vowell Hammill; Anang A Shelat
Journal:  Sci Rep       Date:  2016-05-10       Impact factor: 4.379

10.  The effect of using a robust optimality criterion in model based adaptive optimization.

Authors:  Eric A Strömberg; Andrew C Hooker
Journal:  J Pharmacokinet Pharmacodyn       Date:  2017-04-06       Impact factor: 2.745

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  2 in total

1.  Optimizing Dose-Finding Studies for Drug Combinations Based on Exposure-Response Models.

Authors:  Theodoros Papathanasiou; Anders Strathe; Rune Viig Overgaard; Trine Meldgaard Lund; Andrew C Hooker
Journal:  AAPS J       Date:  2019-07-29       Impact factor: 4.009

2.  Demonstrating Contribution of Components of Fixed-Dose Drug Combinations Through Longitudinal Exposure-Response Analysis.

Authors:  Asbjørn Nøhr-Nielsen; Theis Lange; Julie Lyng Forman; Theodoros Papathanasiou; David J R Foster; Richard N Upton; Ole Jannik Bjerrum; Trine Meldgaard Lund
Journal:  AAPS J       Date:  2020-01-27       Impact factor: 4.009

  2 in total

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