Abstract
The quantitative description of individual observations in non-linear mixed effects models over time is complicated when the studied biomarker has a pulsatile release (e.g. insulin, growth hormone, luteinizing hormone). Unfortunately, standard non-linear mixed effects population pharmacodynamic models such as turnover and precursor response models (with or without a cosinor component) are unable to quantify these complex secretion profiles over time. In this study, the statistical power of standard statistical methodology such as 6 post-dose measurements or the area under the curve from 0 to 12 h post-dose on simulated dense concentration–time profiles of growth hormone was compared to a deconvolution-analysis-informed modelling approach in different simulated scenarios. The statistical power of the deconvolution-analysis-informed approach was determined with a Monte-Carlo Mapped Power analysis. Due to the high level of intra- and inter-individual variability in growth hormone concentrations over time, regardless of the simulated effect size, only the deconvolution-analysis informed approach reached a statistical power of more than 80% with a sample size of less than 200 subjects per cohort. Furthermore, the use of this deconvolution-analysis-informed modelling approach improved the description of the observations on an individual level and enabled the quantification of a drug effect to be used for subsequent clinical trial simulations.
Highlights
Drugs are administered with the aim to affect various physiological, biochemical and behavioral clinical markers to the benefit of patients
In this study we investigate the impact of several standard statistical methods and the potential advantage of applying a deconvolution-analysis-informed model on growth hormone data in a simulated clinical trial
The high variability in the simulated pulsatile profiles resulted in broad 95% confidence intervals when using the 6 postdose measurements or the AUC0-12 h outcomes in all scenarios, indicating that these analysis methods are sensitive to the highly variable nature of growth hormone secretion. These two statistical methods show a clear overlap, with low statistical powers in general, showing that, even though the AUC0-12 h was based on a dense growth hormone concentration–time profile, 200 subjects per group did not reach an 80% power in all tested effect sizes
Summary
Drugs are administered with the aim to affect various physiological, biochemical and behavioral clinical markers to the benefit of patients. Endogenous compounds that have a very short half-life and are released in pulses do not show such a nicely flowing curvature, as can be seen for endogenous growth hormone secretion presented, and are less easy to capture using cosine functions or pool models. This release pattern is commonly encountered in endocrine systems (e.g. growth hormone, glucose, luteinizing hormone, cortisol) which creates a challenge for the correct description of such a profile in a modelling approach
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