2003
Verona, Italy
Disease progression modelling; application of population analysis to distinguish between symptomatic and protective treatment effects.
Willem DeWinter(1), Joost DeJongh(1), Bart Ploeger(1), Richard Urquhart(2), Ian Moules(2), David Eckland(2), and Meindert Danhof(1)
(1)LAP&P Consultants, Leiden, The Netherlands. (2)Takeda Europe R&D, London, U.K.
Background: For certain diseases, drug treatment effects can only be evaluated relative to the disease progression over time. Model analysis of treatment effects requires that drug efficacy is expressed as an effect on the disease progression parameter(s). In earlier publications (1), the difference between symptomatic and protective effects of drug treatment has been discussed. For slowly progressing diseases, it is often difficult to discriminate between these types of effects, as the time-course of the clinical trial is limited relative to the duration of the disease. This problem becomes evident when the effect of a new drug is compared to that of existing treatments. In this case, not only the short-term efficacy is of interest. In particular, the difference in symptomatic and protective effects between the new drug and the existing reference therapies needs to be evaluated.
Methods: Results from two phase IV studies on treatment of type II (non-insulin dependent) diabetes melitus in over 2000 newly diagnosed patients were considered. Pioglitazone (Actos[R]), a new drug for treatment of type II diabetes, was compared to a reference treatment with either sulphonylurea or metformin for a treatment duration of up to one year. Disease progression was determined by taking blood samples for fasting plasma glucose (FPG) and glycated hemoglobin (HbA1c) over time. A population PD model was implemented in NONMEM in which the disease progression was modelled as a time dependent, saturable process that could be counter-acted by a protective or symptomatic treatment effect, or a mixture of both. FPG and HbA1c measurements were fitted to an indirect response model in which a cascade of events occurs in the following sequence: disease progression/treatment => FPG => HbA1c.
Results: The model could be fitted to the data for patients in each of the three treatment groups for both FPG and HbA1c. The model cascade for these two biomarkers adequately described that the initial onset of the drug effect on FPG is followed by HbA1c, which has a somewhat slower onset. It was observed that for Pioglitazone treated patients, most of the drug effect could be classified as being protective; FPG and HbA1c levels decreased to a level that remained relatively stable during the course of treatment. For the sulphonylurea reference treatment, a substantial part of the drug effect was classified as symptomatic; After an initial period of decrease following the start of treatment, FPG and HbA1c levels started to rise slowly during the course of treatment. The metformin reference treatment, had characteristics of both protective and symptomatic drug effects.
Discussion and conclusion: The present approach demonstrates that an appropriate disease progression model can be used to compare the effects on disease progression that are caused by different drugs. Besides the identification of quantitative differences between drugs, the model also offers the possibility to assess qualitative differences between drugs that become apparent during the course of treatment. The principle of the present analysis is not limited to diabetus melitus, but is applicable to a variety of chronic diseases with slow progression. In addition to retrospective analysis of trial results, this method has also been applied prospectively for the optimisation of trial designs in which identification of either protective or symptomatic drug effect is a primary objective.
References: [1] Modelling of Disease and Disease Progression, N. Holford; in ”Mechanism- Based PK/PD modelling as the basis for the development and validation of biomarkers”. COST B15 Expert Meeting; April 27-28, 2000, Leiden, The Netherlands.