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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

Schematic representation of the structural model. Bronchodilatory effect on FEV1 is described by an Emax function using a KPD model as input for drug exposure. The overall response to treatment accounts for the course of disease, i.e., the natural decrease in FEV1 over time, which has been parameterised in terms of an indirect response model. See text for further explanation of the parameters.
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Fig1: Schematic representation of the structural model. Bronchodilatory effect on FEV1 is described by an Emax function using a KPD model as input for drug exposure. The overall response to treatment accounts for the course of disease, i.e., the natural decrease in FEV1 over time, which has been parameterised in terms of an indirect response model. See text for further explanation of the parameters.

Mentions: Disease progression (trough FEV1 measurements) was modelled by a time–varying function. The treatment effect was described by an indirect response model using an Emax model in a multiplicative manner on Kin. In addition, a time-to-event model with constant hazard was used for dropout. A schematic representation of the structural model for disease progression and drug effects is shown in Fig. 1. The following disease progression and drug-related parameters were estimated: disease baseline intercept (Int_Dis), disease baseline slope (Slope_Dis), zero order input rate constant for the biological response (Kin), first order elimination rate constant (KDE), maximum drug effect (Emax) and the ratio between the apparent potency (EDK50) and Emax. Two parameters (β0 and β2) were necessary to model the dropout patterns. A constant hazard (β0) of 0.06 was estimated, which was increased in case of treatment failure (i.e., lower FEV1 values). The inclusion of β2 in the model led to a decrease of 15 points in the MOFV as compared to the model only including β0. The best fit was obtained with interindividual variability included on the disease slope, the disease baseline intercept, Emax, KDE, EDK50 and Kin. All covariance terms turned to be lower than 0.1 and their removal did not appear to decrease model performance or goodness of fit. Random effects were not imputed on dropout parameters. Residual errors were best described by an additive model. The parameter estimates for the base model are summarised in Table II.Fig. 1


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)

Schematic representation of the structural model. Bronchodilatory effect on FEV1 is described by an Emax function using a KPD model as input for drug exposure. The overall response to treatment accounts for the course of disease, i.e., the natural decrease in FEV1 over time, which has been parameterised in terms of an indirect response model. See text for further explanation of the parameters.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Schematic representation of the structural model. Bronchodilatory effect on FEV1 is described by an Emax function using a KPD model as input for drug exposure. The overall response to treatment accounts for the course of disease, i.e., the natural decrease in FEV1 over time, which has been parameterised in terms of an indirect response model. See text for further explanation of the parameters.
Mentions: Disease progression (trough FEV1 measurements) was modelled by a time–varying function. The treatment effect was described by an indirect response model using an Emax model in a multiplicative manner on Kin. In addition, a time-to-event model with constant hazard was used for dropout. A schematic representation of the structural model for disease progression and drug effects is shown in Fig. 1. The following disease progression and drug-related parameters were estimated: disease baseline intercept (Int_Dis), disease baseline slope (Slope_Dis), zero order input rate constant for the biological response (Kin), first order elimination rate constant (KDE), maximum drug effect (Emax) and the ratio between the apparent potency (EDK50) and Emax. Two parameters (β0 and β2) were necessary to model the dropout patterns. A constant hazard (β0) of 0.06 was estimated, which was increased in case of treatment failure (i.e., lower FEV1 values). The inclusion of β2 in the model led to a decrease of 15 points in the MOFV as compared to the model only including β0. The best fit was obtained with interindividual variability included on the disease slope, the disease baseline intercept, Emax, KDE, EDK50 and Kin. All covariance terms turned to be lower than 0.1 and their removal did not appear to decrease model performance or goodness of fit. Random effects were not imputed on dropout parameters. Residual errors were best described by an additive model. The parameter estimates for the base model are summarised in Table II.Fig. 1

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