Prediction of Disease Progression, Treatment Response and Dropout in Chronic Obstructive Pulmonary Disease (COPD)

• Published in: Pharmaceutical research Vol. 32; no. 2; pp. 617 - 627
• Main Authors: Musuamba, F. T., Teutonico, D., Maas, H. J., Facius, A., Yang, S., Danhof, M., Della Pasqua, O.
• Format: Journal Article
• Language: English
• Published: Boston Springer US 01-02-2015; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Biochemistry; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Chronic obstructive pulmonary disease; Clinical trials; Disease Progression; Female; Forced Expiratory Volume - physiology; Humans; Male; Medical Law; Middle Aged; Patient Dropouts; Pharmaceutical sciences; Pharmacology/Toxicology; Pharmacy; Predictive Value of Tests; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - epidemiology; Pulmonary Disease, Chronic Obstructive - therapy; Research Paper; Treatment Outcome; KPD model; disease progression; disease modelling; dropout; chronic obstructive pulmonary disease
• ISSN: 0724-8741; 1573-904X
• DOI: 10.1007/s11095-014-1490-4

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 (FEV
1
), 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 FEV
1
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 E
max
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.