Semaglutide s.c. Once-Weekly in Type 2 Diabetes: A Population Pharmacokinetic Analysis.

INTRODUCTION: Semaglutide, a new treatment option approved for the treatment of patients with type 2 diabetes mellitus, is a glucagon-like peptide-1 receptor agonist to be injected subcutaneously once weekly. This analysis used a population pharmacokinetic model of semaglutide to identify clinically relevant covariates for exposure. METHODS: A total of 1612 patients with up to seven pharmacokinetic observations each were included in the analysis. All subjects had type 2 diabetes mellitus and were enrolled in one of five trials in the phase III development program for subcutaneous semaglutide once weekly (the SUSTAIN program). The treatment duration of the trials varied from 30 to 104 weeks. RESULTS: No clinically relevant effects on the exposure were seen for sex, age, race, ethnicity, renal function, or injection site used, and semaglutide exposure was stable over time. Of the covariates chosen, only body weight had a relevant effect on the exposure of semaglutide. Few subjects developed semaglutide antibodies, and the antibodies had no effect on exposure. Dose proportionality was shown for the 0.5 mg and 1.0 mg maintenance doses of semaglutide. CONCLUSION: The population pharmacokinetic study showed that semaglutide exposure is not affected by covariates other than body weight at either a maintenance dose of 0.5 or 1.0 mg semaglutide. Therefore, we conclude that no semaglutide dose adjustments are needed in different populations. This finding is to be further explored in an exposure-response analysis. TRIAL REGISTRATION: The trials were registered at ClinicalTrials.gov (identifiers: NCT02054897, NCT01930188, NCT01885208, NCT01720446 and NCT02207374). FUNDING: Novo Nordisk A/S, Bagsværd, Denmark.

Preserved pharmacokinetic exposure and distinct glycemic effects of insulin degludec and liraglutide in IDegLira, a fixed-ratio combination therapy.

Insulin degludec/liraglutide (IDegLira) is a novel fixed-ratio combination of the basal insulin insulin degludec (IDeg) and liraglutide, a glucagon-like peptide-1 analog. The pharmacokinetics (PK) and pharmacodynamics of IDegLira were assessed versus its components. A single-dose, randomized, 4-period crossover clinical pharmacology study in healthy subjects compared the bioavailability of IDegLira with its monocomponents. Dose proportionality, covariate effects on exposure, and exposure-response for change in glycated hemoglobin were analyzed based on data from a randomized treat-to-target phase 3 study in subjects with type 2 diabetes. Overall, the PK properties of IDeg and liraglutide were preserved for IDegLira. Liraglutide exposure was lower when dosed as IDegLira but met the criterion for equivalence. No relevant deviations from dose proportionality for the IDegLira components were observed. Covariate effects on exposure were consistent with previous results. Glycemic response to IDegLira was larger than with IDeg or liraglutide alone, reflecting their distinct glucose-lowering effects throughout the dose/exposure range.

Using stochastic differential equations for PK/PD model development.

A method for PK/PD model development based on stochastic differential equation models is proposed. The new method has a number of advantages compared to conventional methods. In particular, the new method avoids the exhaustive trial-and-error based search often conducted to determine the most appropriate model structure, because it allows information about the appropriate model structure to be extracted directly from data. This is accomplished through quantification of the uncertainty of the individual parts of an initial model, by means of which tools for performing model diagnostics can be constructed and guidelines for model improvement provided. Furthermore, the new method allows time-variations in key parameters to be tracked and visualized graphically, which allows important functional relationships to be revealed. Using simulated data, the performance of the new method is demonstrated by means of two examples. The first example shows how, starting from a simple assumption of linear PK, the method can be used to determine the correct nonlinear model for describing the PK of a drug following an oral dose. The second example shows how, starting from a simple assumption of no drug effect, the method can be used to determine the correct model for the nonlinear effect of a drug with known PK in an indirect response model.