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Background and ObjectiveInsulin degludec is a basal insulin with a slow and distinct absorption mechanism resulting in an ultra-long, flat, and stable pharmacokinetic profile in patients with diabetes mellitus. The aim of this study was to examine the effect of hepatic impairment on the single-dose pharmacokinetics of insulin degludec.MethodsTwenty-four subjects, allocated to one of four groups (n = 6 per group) based on level of hepatic impairment (normal hepatic function, Child–Pugh grade A, B, or C), were administered a single subcutaneous dose of 0.4 U/kg insulin degludec. Blood samples up to 120 h post-dose and fractionated urine samples were collected to measure pharmacokinetic parameters.ResultsNo difference was observed in pharmacokinetic parameters [area under the 120-h serum insulin degludec concentration–time curve (AUC120 h), maximum insulin degludec concentration (Cmax), and apparent clearance (CL/F)] for subjects with impaired versus normal hepatic function after a single dose of insulin degludec. The geometric mean [coefficient of variation (CV) %] AUC120 h values were 89,092 (16), 83,327 (15), 88,944 (23), and 79,846 (19) pmol·h/L for normal hepatic function and mild, moderate, and severe hepatic impairment, respectively. Simulated steady-state insulin degludec pharmacokinetic profiles showed an even distribution of exposure across a 24-h dosing interval regardless of hepatic function status.ConclusionsThe ultra-long pharmacokinetic properties of insulin degludec were preserved in subjects with hepatic impairment and there were no statistically significant differences in absorption or clearance compared with subjects with normal hepatic function.

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The minimal model was proposed in the late 1970s by Bergman et al. (Am. J. Physiol. 1979; 236(6):E667) as a powerful model consisting of three differential equations describing the glucose and insulin kinetics of a single individual. Considering the glucose and insulin simultaneously, the minimal model is a highly ill-posed estimation problem, where the reconstruction most often has been done by non-linear least squares techniques separately for each entity. The minimal model was originally specified for a single individual and does not combine several individuals with the advantage of estimating the metabolic portrait for a whole population. Traditionally it has been analysed in a deterministic set-up with only error terms on the measurements. In this work we adopt a Bayesian graphical model to describe the coupled minimal model that accounts for both measurement and process variability, and the model is extended to a population-based model. The estimation of the parameters are efficiently implemented in a Bayesian approach where posterior inference is made through the use of Markov chain Monte Carlo techniques. Hereby we obtain a powerful and flexible modelling framework for regularizing the ill-posed estimation problem often inherited in coupled stochastic differential equations. We demonstrate the method on experimental data from intravenous glucose tolerance tests performed on 19 normal glucose-tolerant subjects.