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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

Basic goodness-of-fit plots for the final model, including population and individual predicted vs. observed FEV1, conditional weighted residuals vs. time and individual predicted FEV1.
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Fig2: Basic goodness-of-fit plots for the final model, including population and individual predicted vs. observed FEV1, conditional weighted residuals vs. time and individual predicted FEV1.

Mentions: During covariate model building, disease severity at baseline, previous use of inhaled corticosteroids, gender and height were retained as significant covariates on Int_Dis whereas reversibility to salbutamol/salmeterol and disease severity at baseline were significant covariates on Emax for the active arm (salmeterol). All covariates were included in the final model in a as shown in Eqs. 14 and 15. A summary of the final parameter estimates including the aforementioned covariates is provided in Table II. All model parameters were well estimated, as shown by the relative low standard error of estimates (<50%) and by shrinkage levels <30% for all random effects parameter values. Basic goodness-of-fit plots also showed good model predictive performance (see Fig. 2).Fig. 2


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)

Basic goodness-of-fit plots for the final model, including population and individual predicted vs. observed FEV1, conditional weighted residuals vs. time and individual predicted FEV1.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig2: Basic goodness-of-fit plots for the final model, including population and individual predicted vs. observed FEV1, conditional weighted residuals vs. time and individual predicted FEV1.
Mentions: During covariate model building, disease severity at baseline, previous use of inhaled corticosteroids, gender and height were retained as significant covariates on Int_Dis whereas reversibility to salbutamol/salmeterol and disease severity at baseline were significant covariates on Emax for the active arm (salmeterol). All covariates were included in the final model in a as shown in Eqs. 14 and 15. A summary of the final parameter estimates including the aforementioned covariates is provided in Table II. All model parameters were well estimated, as shown by the relative low standard error of estimates (<50%) and by shrinkage levels <30% for all random effects parameter values. Basic goodness-of-fit plots also showed good model predictive performance (see Fig. 2).Fig. 2

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