Patient specific multiscale modelling for plaque formation and progression

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

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 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.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-014-1490-4) contains supplementary material, which is available to authorized users.