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Prediction of disease progression, treatment response and dropout in chronic obstructive pulmonary disease (COPD).

Musuamba FT, Teutonico D, Maas HJ, Facius A, Yang S, Danhof M, Della Pasqua O - Pharm. Res. (2014)

Bottom Line: Two parameters were necessary to model the dropout patterns, which was found to be partly linked to the treatment failure.Disease severity at baseline, previous use of corticosteroids, gender and height were significant covariates on disease baseline whereas disease severity and reversibility to salbutamol/salmeterol were significant covariates on Emax for salmeterol active arm.Incorporation of the various interacting factors into a single model will offer the basis for patient enrichment and improved dose rationale in COPD.

View Article: PubMed Central - PubMed

Affiliation: Gorlaeus Laboratories, Division of Pharmacology, Leiden Academic Centre for Drug Research, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.

ABSTRACT

Purpose: Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV1), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients.

Methods: Data from six phase II trials lasting up to 6 months were used. Disease progression (trough FEV1 measurements) was modelled by a time-varying function, whilst the treatment effect was described by an indirect response model. A time-to-event model was used for dropout

Results: All relevant parameters were characterised with acceptable precision. Two parameters were necessary to model the dropout patterns, which was found to be partly linked to the treatment failure. Disease severity at baseline, previous use of corticosteroids, gender and height were significant covariates on disease baseline whereas disease severity and reversibility to salbutamol/salmeterol were significant covariates on Emax for salmeterol active arm.

Conclusion: Incorporation of the various interacting factors into a single model will offer the basis for patient enrichment and improved dose rationale in COPD.

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Related in: MedlinePlus

Visual predictive check stratified by (a) treatment (left panel: placebo, right panel: 50 μg dose), (b) by previous use of inhaled corticosteroids [PISU] (left panel: no, right panel: yes), (c) by reversibility to salbutamol/salmeterol [REV] (left panel: non-reversible, right panel: reversible), and (d) by severity status [SEV] (left panel: non-severe, right panel severe). Grey dots: observed concentrations, black dotted lines: limits of the 95% prediction intervals for the observations, black dotted lines: limits of the 95% prediction intervals for the simulations, black continuous line: median line for the observations, red continuous line: median line for the simulations. See text for further details on the scenarios selected for the VPC.
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Fig3: Visual predictive check stratified by (a) treatment (left panel: placebo, right panel: 50 μg dose), (b) by previous use of inhaled corticosteroids [PISU] (left panel: no, right panel: yes), (c) by reversibility to salbutamol/salmeterol [REV] (left panel: non-reversible, right panel: reversible), and (d) by severity status [SEV] (left panel: non-severe, right panel severe). Grey dots: observed concentrations, black dotted lines: limits of the 95% prediction intervals for the observations, black dotted lines: limits of the 95% prediction intervals for the simulations, black continuous line: median line for the observations, red continuous line: median line for the simulations. See text for further details on the scenarios selected for the VPC.

Mentions: Validation of the final model by external validation procedures and bootstrapping yielded good predictive performance. All observed parameter values obtained by bootstrapping were found to be within the 90%-confidence interval (n = 100 bootstraps). In addition, as shown by the visual predictive check in Fig. 3, most of the observed data in the validation subset of data were distributed within the 5th and 95th percentiles of the prediction intervals. For the sake of clarity, similar predictive performance is observed when the response profiles were split by severity status (severity status being the covariate with the highest effect size) and by dose group. The overlap between predicted and original distributions shows that the model accurately captures drug, disease and dropout effects (Fig. 4).Fig. 3


Prediction of disease progression, treatment response and dropout in chronic obstructive pulmonary disease (COPD).

Musuamba FT, Teutonico D, Maas HJ, Facius A, Yang S, Danhof M, Della Pasqua O - Pharm. Res. (2014)

Visual predictive check stratified by (a) treatment (left panel: placebo, right panel: 50 μg dose), (b) by previous use of inhaled corticosteroids [PISU] (left panel: no, right panel: yes), (c) by reversibility to salbutamol/salmeterol [REV] (left panel: non-reversible, right panel: reversible), and (d) by severity status [SEV] (left panel: non-severe, right panel severe). Grey dots: observed concentrations, black dotted lines: limits of the 95% prediction intervals for the observations, black dotted lines: limits of the 95% prediction intervals for the simulations, black continuous line: median line for the observations, red continuous line: median line for the simulations. See text for further details on the scenarios selected for the VPC.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4300418&req=5

Fig3: Visual predictive check stratified by (a) treatment (left panel: placebo, right panel: 50 μg dose), (b) by previous use of inhaled corticosteroids [PISU] (left panel: no, right panel: yes), (c) by reversibility to salbutamol/salmeterol [REV] (left panel: non-reversible, right panel: reversible), and (d) by severity status [SEV] (left panel: non-severe, right panel severe). Grey dots: observed concentrations, black dotted lines: limits of the 95% prediction intervals for the observations, black dotted lines: limits of the 95% prediction intervals for the simulations, black continuous line: median line for the observations, red continuous line: median line for the simulations. See text for further details on the scenarios selected for the VPC.
Mentions: Validation of the final model by external validation procedures and bootstrapping yielded good predictive performance. All observed parameter values obtained by bootstrapping were found to be within the 90%-confidence interval (n = 100 bootstraps). In addition, as shown by the visual predictive check in Fig. 3, most of the observed data in the validation subset of data were distributed within the 5th and 95th percentiles of the prediction intervals. For the sake of clarity, similar predictive performance is observed when the response profiles were split by severity status (severity status being the covariate with the highest effect size) and by dose group. The overlap between predicted and original distributions shows that the model accurately captures drug, disease and dropout effects (Fig. 4).Fig. 3

Bottom Line: Two parameters were necessary to model the dropout patterns, which was found to be partly linked to the treatment failure.Disease severity at baseline, previous use of corticosteroids, gender and height were significant covariates on disease baseline whereas disease severity and reversibility to salbutamol/salmeterol were significant covariates on Emax for salmeterol active arm.Incorporation of the various interacting factors into a single model will offer the basis for patient enrichment and improved dose rationale in COPD.

View Article: PubMed Central - PubMed

Affiliation: Gorlaeus Laboratories, Division of Pharmacology, Leiden Academic Centre for Drug Research, P.O. Box 9502, 2300 RA, Leiden, The Netherlands.

ABSTRACT

Purpose: Drug development in chronic obstructive pulmonary disease (COPD) has been characterised by unacceptably high failure rates. In addition to the poor sensitivity in forced expiratory volume in one second (FEV1), numerous causes are known to contribute to this phenomenon, which can be clustered into drug-, disease- and design-related factors. Here we present a model-based approach to describe disease progression, treatment response and dropout in clinical trials with COPD patients.

Methods: Data from six phase II trials lasting up to 6 months were used. Disease progression (trough FEV1 measurements) was modelled by a time-varying function, whilst the treatment effect was described by an indirect response model. A time-to-event model was used for dropout

Results: All relevant parameters were characterised with acceptable precision. Two parameters were necessary to model the dropout patterns, which was found to be partly linked to the treatment failure. Disease severity at baseline, previous use of corticosteroids, gender and height were significant covariates on disease baseline whereas disease severity and reversibility to salbutamol/salmeterol were significant covariates on Emax for salmeterol active arm.

Conclusion: Incorporation of the various interacting factors into a single model will offer the basis for patient enrichment and improved dose rationale in COPD.

Show MeSH
Related in: MedlinePlus