Levels of circulating semaglutide determine reductions in HbA1c and body weight in people with type 2 diabetes.

Glucagon-like peptide-1 receptor agonists (GLP-1RA) are used for the treatment of type 2 diabetes. Whether clinically important responses and adverse events (AEs) are dependent on the route of administration has not been determined. We demonstrate that nearly identical exposure-response pharmacodynamic relationships are determined by plasma semaglutide levels achieved through oral versus injectable administration for changes in HbA1c, body weight, biomarkers of cardiovascular risk, and AEs such as nausea and vomiting. At typical exposure levels for oral semaglutide, the estimated response is 1.58% (oral) versus -1.62% (subcutaneous) for HbA1c and 3.77% (oral) versus 3.48% (subcutaneous) reduction in body weight relative to baseline after 6 months. Increased body weight is the most important variable associated with reduced semaglutide exposure for both formulations. Hence, interindividual variation in GLP-1R responsivity or route of administration are not major determinants of GLP-1RA effectiveness in the clinic.

Clinical Pharmacokinetics of Oral Semaglutide: Analyses of Data from Clinical Pharmacology Trials.

OBJECTIVE: The absorption, distribution and elimination of oral semaglutide, the first oral glucagon-like peptide-1 receptor agonist for treating type 2 diabetes, was investigated using a population pharmacokinetic model based on data from clinical pharmacology trials. METHODS: A previously developed, two-compartment pharmacokinetic model, based on subcutaneous and intravenous semaglutide, was extended to include data from six oral semaglutide trials conducted in either healthy volunteers or subjects with renal or hepatic impairment. Five trials employed multiple doses of oral semaglutide (5-10 mg) and one was a single-dose (10 mg) trial. In a separate analysis, the model was re-estimated using data from a trial in subjects with type 2 diabetes. RESULTS: The model accurately described concentration profiles across trials. Post-dose fasting time, co-ingestion of a large water volume, and body weight were the most important covariates affecting semaglutide exposure. Bioavailability was 0.8% when oral semaglutide was dosed using the recommended dosing conditions (30 min post-dose fasting time, administered with ≤ 120 mL of water), increasing with a longer post-dose fasting time and decreasing with higher water volume. Within-subject variability in bioavailability was 137%, which with once-daily dosing and a long half-life translates into 33% within-subject variability in steady-state exposure. There was no significant difference in oral bioavailability of semaglutide in healthy subjects and subjects with type 2 diabetes. CONCLUSIONS: The updated model provided a general characterisation of semaglutide pharmacokinetics following oral, subcutaneous and intravenous administration in healthy subjects and subjects with type 2 diabetes. Within-individual variation of oral bioavailability was relatively high, but reduced considerably at steady state. CLINICALTRIALS. GOV IDENTIFIERS: NCT01572753, NCT01619345, NCT02014259, NCT02016911, NCT02249871, NCT02172313, NCT02877355.

Population Pharmacokinetics of Semaglutide for Type 2 Diabetes.

INTRODUCTION: The aim of the present analysis was to characterise the absorption, distribution and elimination of semaglutide by means of population pharmacokinetic (PK) models using data from nine clinical pharmacology trials conducted in both healthy subjects and those with type 2 diabetes. METHODS: Data were obtained from trials with subcutaneous and intravenous administration of semaglutide that utilised frequent PK sampling and included a total of 353 subjects with 10,573 concentration values. RESULTS: Semaglutide PK properties across trials, drug product strengths and populations were well characterised by a two-compartment model with first-order absorption and elimination. For a typical subject with type 2 diabetes, clearance was estimated to be 0.0348 L/h [95% confidence interval (CI) 0.0327-0.0369 L/h], and the central and peripheral volumes were estimated to be 3.59 L (95% CI 3.28-3.90 L) and 4.10 L (95% CI 3.78-4.42 L), respectively (i.e. a total volume of distribution of 7.7 L). Interindividual variation was low (~ 15%) for both clearance and volumes of distribution, with low residual error (< 5%). Clearance and the total volume of distribution were approximately proportional to body weight. Minor differences were identified between healthy subjects and subjects with type 2 diabetes with respect to clearance and absorption rate, and between injection sites with respect to bioavailability. CONCLUSIONS: A novel two-compartment model was developed to provide the general characteristics of semaglutide absorption following subcutaneous administration, and of distribution and elimination across administration routes. Semaglutide PK was shown to be predictable across populations and administration routes and within subjects, and was primarily influenced by body weight. FUNDING: Novo Nordisk, Bagsværd, Denmark.

Impact on HbA1c and body weight of switching from other GLP-1 receptor agonists to semaglutide: A model-based approach.

AIMS: Semaglutide is a glucagon-like peptide-1 (GLP-1) analogue approved for the treatment of type 2 diabetes. The impact of switching treatment from another GLP-1 receptor agonist (GLP-1RA) to semaglutide was investigated by analyses of exposure-response models. METHODS: HbA1c and body weight time-course models were developed, using up to 30 weeks of observations from four trials in the semaglutide phase 3 programme. Given the recommended dosing for each GLP-1RA, pharmacokinetic profiles were simulated based on published population pharmacokinetic models and exposure was adjusted by the relative potencies to ensure that model predictions matched the effects observed in clinical trials. After 26 weeks of simulated treatment with liraglutide, dulaglutide or exenatide extended-release, simulated semaglutide treatment was initiated 1 day after the last once-daily dose of liraglutide and 1 week after the last once-weekly doses of dulaglutide or exenatide extended-release. RESULTS: The potency-adjusted total effective GLP-1RA concentration increased after switching from another GLP-1RA to semaglutide and was associated with reductions ranging from ~0.3% to ~0.8%-points for HbA1c and from ~2% to ~4% for body weight with semaglutide 1.0 mg. Temporary slight deteriorations in HbA1c were observed after switching to semaglutide 0.25 mg from liraglutide 1.2/1.8 mg or dulaglutide 1.5 mg. CONCLUSIONS: Exposure-response modelling suggests that switching to semaglutide from liraglutide, dulaglutide or exenatide extended-release results in further reductions in HbA1c and body weight. Initial slight deterioration in outcome values when switching to semaglutide 0.25 mg could be avoided by initiating semaglutide treatment at a higher dose.

Exposure-response analysis for evaluation of semaglutide dose levels in type 2 diabetes.

AIMS: To evaluate dose levels for semaglutide, a glucagon-like peptide-1 analogue approved for the treatment of type 2 diabetes, by examining the effects of demographic factors on efficacy and safety in an exposure-response analysis. METHODS: We analysed data from 1552 adults from four randomized phase III trials of 30 to 56 weeks' duration, investigating once-weekly semaglutide doses 0.5 and 1.0 mg. Exposure-response relationships were investigated using graphical and model-based techniques to assess the two dose levels and subgroups with the highest and lowest exposure and response. RESULTS: The population had the following demographic characteristics: baseline mean age between 53.2 and 58.4 years, glycated haemoglobin (HbA1c) between 64 and 67 mmol/mol (8.0% and 8.3%), body weight between 71.3 and 96.2 kg, and diabetes duration between 4.2 and 8.9 years. Exposure-response analysis showed a clear HbA1c and weight reduction across exposures after 30 weeks, irrespective of baseline values. The exposure-response for HbA1c was influenced by baseline HbA1c, and body weight exposure-response was influenced by sex, with limited impact of other factors. Analyses for relevant subgroups of baseline body weight, baseline HbA1c and sex indicated clinically relevant additional benefits with regard to HbA1c and weight with 1.0 vs 0.5 mg semaglutide. The proportion of participants reporting gastrointestinal (GI) side effects increased with increasing exposure, but was counteracted by tolerance development. CONCLUSIONS: The analysis showed that all subgroups obtained a clinically relevant benefit with semaglutide 0.5 mg and an additional benefit with semaglutide 1.0 mg. The increase in GI side effects with higher exposure was mitigated by gradually increasing the dose.

Liraglutide 3.0 mg for Weight Management: A Population Pharmacokinetic Analysis.

BACKGROUND AND OBJECTIVES: This analysis used a population pharmacokinetic approach to identify covariates that influence plasma exposure of liraglutide 3.0 mg, a glucagon-like peptide-1 (GLP-1) receptor agonist approved for weight management in overweight and obese individuals. METHODS: Samples for pharmacokinetic analysis were drawn at weeks 2, 12 and 28 of the phase IIIa SCALE Obesity and Prediabetes (N = 2339) and SCALE Diabetes (N = 584) trials. Dose proportionality of liraglutide in obese subjects was investigated using data from a phase II dose-finding study (N = 331). RESULTS: Dose-proportional exposure of liraglutide up to and including 3.0 mg was confirmed. Body weight and sex influenced exposure of liraglutide 3.0 mg, while age ≥70 years, race, ethnicity and baseline glycaemic status did not. Compared with a reference subject weighing 100 kg, exposure of liraglutide 3.0 mg was 44 % lower for a subject weighing 234 kg (90 % CI 41-47), 41 % higher for a subject weighing 60 kg (90 % CI 37-46), and 32 % higher (90 % CI 28-35) in females than males with the same body weight. Neither injection site nor renal function significantly influenced exposure of liraglutide 3.0 mg (post hoc analysis). CONCLUSION: Population pharmacokinetics of liraglutide up to and including 3.0 mg daily in overweight and obese adults demonstrated dose-proportional exposure, and limited effect of covariates other than sex and body weight. These findings were similar to those previously observed with liraglutide up to 1.8 mg in subjects with type 2 diabetes mellitus. Further analysis of exposure-response relationship and its effect on dose requirements is addressed in a separate publication.

Ethnic differences in insulin sensitivity, ß-cell function, and hepatic extraction between Japanese and Caucasians: a minimal model analysis.

CONTEXT: Ethnic differences have previously been reported for type 2 diabetes. OBJECTIVE: We aimed at assessing the potential differences between Caucasian and Japanese subjects ranging from normal glucose tolerance (NGT) to impaired glucose tolerance (IGT) and to type 2 diabetes. DESIGN: This was a cross-sectional study with oral glucose tolerance tests to assess ß-cell function, hepatic insulin extraction, and insulin sensitivity. PARTICIPANTS: PARTICIPANTS included 120 Japanese and 150 Caucasian subjects. MAIN OUTCOMES: Measures of ß-cell function, hepatic extraction, and insulin sensitivity were assessed using C-peptide, glucose, and insulin minimal models. RESULTS: Basal ß-cell function (Φ(b)) was lower in Japanese compared with Caucasians (P < .01). In subjects with IGT, estimates of the dynamic (Φ(d)) and static (Φ(s)) ß-cell responsiveness were significantly lower in the Japanese compared with Caucasians (P < .05). In contrast, values of insulin action showed higher sensitivity in the Japanese IGT subjects. Hepatic extraction was similar in NGT and IGT groups but higher in Japanese type 2 diabetic subjects (P < .01). Despite differences in insulin sensitivity, ß-cell function, and hepatic extraction, the disposition indices were similar between the 2 ethnic groups at all glucose tolerance states. Furthermore, the overall insulin sensitivity and ß-cell responsiveness for all glucose tolerance states were similar in Japanese and Caucasians after accounting for differences in body mass index. CONCLUSION: Our study provides evidence for a similar ability of Japanese and Caucasians to compensate for increased insulin resistance.

Body composition is the main determinant for the difference in type 2 diabetes pathophysiology between Japanese and Caucasians.

OBJECTIVE This cross-sectional clinical study compared the pathophysiology of type 2 diabetes in Japanese and Caucasians and investigated the role of demographic, genetic, and lifestyle-related risk factors for insulin resistance and ß-cell response. RESEARCH DESIGN AND METHODS A total of 120 Japanese and 150 Caucasians were enrolled to obtain comparable distributions of high/low BMI values across glucose tolerance states (normal glucose tolerance, impaired glucose tolerance, and type 2 diabetes), which were assessed by oral glucose tolerance tests. BMI in the two cohorts was distributed around the two regional cutoff values for obesity. RESULTS Insulin sensitivity was higher in Japanese compared with Caucasians, as indicated by the homeostatic model assessment of insulin resistance and Matsuda indices, whereas ß-cell response was higher in Caucasians, as measured by homeostatic model assessment of ß-cell function, the insulinogenic indices, and insulin secretion ratios. Disposition indices were similar for Japanese and Caucasians at all glucose tolerance states, indicating similar ß-cell response relative to the degree of insulin resistance. The main determinants for differences in metabolic indices were measures of body composition, such as BMI and distribution of adipose tissue. Differences in ß-cell response between Japanese and Caucasians were not statistically significant following adjustment by differences in BMI. CONCLUSIONS Our study showed similar disposition indices in Japanese and Caucasians and that the major part of the differences in insulin sensitivity and ß-cell response between Japanese and Caucasians can be explained by differences in body composition.

Mechanism-based population modelling for assessment of L-cell function based on total GLP-1 response following an oral glucose tolerance test.

GLP-1 is an insulinotropic hormone that synergistically with glucose gives rise to an increased insulin response. Its secretion is increased following a meal and it is thus of interest to describe the secretion of this hormone following an oral glucose tolerance test (OGTT). The aim of this study was to build a mechanism-based population model that describes the time course of total GLP-1 and provides indices for capability of secretion in each subject. The goal was thus to model the secretion of GLP-1, and not its effect on insulin production. Single 75 g doses of glucose were administered orally to a mixed group of subjects ranging from healthy volunteers to patients with type 2 diabetes (T2D). Glucose, insulin, and total GLP-1 concentrations were measured. Prior population data analysis on measurements of glucose and insulin were performed in order to estimate the glucose absorption rate. The individual estimates of absorption rate constants were used in the model for GLP-1 secretion. Estimation of parameters was performed using the FOCE method with interaction implemented in NONMEM VI. The final transit/indirect-response model obtained for GLP-1 production following an OGTT included two stimulation components (fast, slow) for the zero-order production rate. The fast stimulation was estimated to be faster than the glucose absorption rate, supporting the presence of a proximal-distal loop for fast secretion from L: -cells. The fast component (st3) = 8.64·10⁻5 [mg⁻¹]) was estimated to peak around 25 min after glucose ingestion, whereas the slower component (st4 = 26.2·10⁻5 [mg⁻¹]) was estimated to peak around 100 min. Elimination of total GLP-1 was characterised by a first-order loss. The individual values of the early phase GLP-1 secretion parameter (st3) were correlated (r = 0.52) with the AUC(0-60 min.) for GLP-1. A mechanistic population model was successfully developed to describe total GLP-1 concentrations over time observed after an OGTT. The model provides indices related to different mechanisms of subject abilities to secrete GLP-1. The model provides a good basis to study influence of different demographic factors on these components, presented mainly by indices of the fast- and slow phases of GLP-1 response.

Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.

Several articles have investigated stochastic differential equations (SDEs) in PK/PD models, but few have quantitatively investigated the benefits to predictive performance of models based on real data. Estimation of first phase insulin secretion which reflects beta-cell function using models of the OGTT is a difficult problem in need of further investigation. The present work aimed at investigating the power of SDEs to predict the first phase insulin secretion (AIR (0-8)) in the IVGTT based on parameters obtained from the minimal model of the OGTT, published by Breda et al. (Diabetes 50(1):150-158, 2001). In total 174 subjects underwent both an OGTT and a tolbutamide modified IVGTT. Estimation of parameters in the oral minimal model (OMM) was performed using the FOCE-method in NONMEM VI on insulin and C-peptide measurements. The suggested SDE models were based on a continuous AR(1) process, i.e. the Ornstein-Uhlenbeck process, and the extended Kalman filter was implemented in order to estimate the parameters of the models. Inclusion of the Ornstein-Uhlenbeck (OU) process caused improved description of the variation in the data as measured by the autocorrelation function (ACF) of one-step prediction errors. A main result was that application of SDE models improved the correlation between the individual first phase indexes obtained from OGTT and AIR (0-8) (r = 0.36 to r = 0.49 and r = 0.32 to r = 0.47 with C-peptide and insulin measurements, respectively). In addition to the increased correlation also the properties of the indexes obtained using the SDE models more correctly assessed the properties of the first phase indexes obtained from the IVGTT. In general it is concluded that the presented SDE approach not only caused autocorrelation of errors to decrease but also improved estimation of clinical measures obtained from the glucose tolerance tests. Since, the estimation time of extended models was not heavily increased compared to basic models, the applied method is concluded to have high relevance not only in theory but also in practice.

A matlab framework for estimation of NLME models using stochastic differential equations: applications for estimation of insulin secretion rates.

The non-linear mixed-effects model based on stochastic differential equations (SDEs) provides an attractive residual error model, that is able to handle serially correlated residuals typically arising from structural mis-specification of the true underlying model. The use of SDEs also opens up for new tools for model development and easily allows for tracking of unknown inputs and parameters over time. An algorithm for maximum likelihood estimation of the model has earlier been proposed, and the present paper presents the first general implementation of this algorithm. The implementation is done in Matlab and also demonstrates the use of parallel computing for improved estimation times. The use of the implementation is illustrated by two examples of application which focus on the ability of the model to estimate unknown inputs facilitated by the extension to SDEs. The first application is a deconvolution-type estimation of the insulin secretion rate based on a linear two-compartment model for C-peptide measurements. In the second application the model is extended to also give an estimate of the time varying liver extraction based on both C-peptide and insulin measurements.

Pharmacodynamic model of interleukin-21 effects on red blood cells in cynomolgus monkeys.

Interleukin-21 (IL-21) is a novel cytokine that is currently under clinical investigations as a potential anti-cancer agent. Like many other anti-cancer agents, including other interleukins, IL-21 is seen to produce a broad range of biological effects that may be related to both efficacy and safety of treatment. The present analysis investigates the observed pharmacodynamics effects on red blood cells following various treatment schedules of human IL-21 administrated to cynomolgus monkeys. These effects are described by a novel non-linear mixed-effects model that enabled separation of drug effects and sampling effects, the latter believed to be due partly to blood loss and partly to stress induced haemolysis in connection with blood sampling. Two different studies with a total of 9 different treatment groups of cynomolgus monkeys were used for model development. In conclusion, the model describes the IL-21 induced drop in red blood cells to be (1) caused by removal rather than suppression of production, consistent with increased reticulocyte concentration, and (2) considerably delayed compared to dosing, i.e. not related to the drop in red blood cells observed immediately post dose. It is believed that the structural model presented here can be used for other types of drug induced loss of red blood cells, whereas the mechanism for sampling related blood loss is relevant for investigations of anaemia in all pharmacological studies with smaller animals.

Mathematical beta cell model for insulin secretion following IVGTT and OGTT.

Evaluation of beta cell function is conducted by a variety of glucose tolerance tests and evaluated by a number of different models with less than perfect consistency among results obtained from different tests. We formulated a new approximation of the distributed threshold model for insulin secretion in order to approach a model for quantifying beta cell function, not only for one, but for several different experiments. Data was obtained from 40 subjects that had both an oral glucose tolerance test (OGTT) and an intravenous tolerance test (IVGTT) performed. Parameter estimates from the two experimental protocols demonstrate similarity, reproducibility, and indications of prognostic relevance. Useful first phase indexes comprise the steady state amount of ready releasable insulin A0 and the rate of redistribution krd, where both yield a considerable correlation (both r=0.67) between IVGTT and OGTT estimates. For the IVGTT, A0 correlates well (r=0.96) with the 10 min area under the curve of insulin above baseline, whereas krd represents a new and possibly more fundamental first phase index. For the useful second phase index gamma, a correlation of 0.75 was found between IVGTT and OGTT estimates.

Non-linear mixed-effects models with stochastic differential equations: implementation of an estimation algorithm.

Pharmacokinetic/pharmacodynamic modelling is most often performed using non-linear mixed-effects models based on ordinary differential equations with uncorrelated intra-individual residuals. More sophisticated residual error models as e.g. stochastic differential equations (SDEs) with measurement noise can in many cases provide a better description of the variations, which could be useful in various aspects of modelling. This general approach enables a decomposition of the intra-individual residual variation epsilon into system noise w and measurement noise e. The present work describes implementation of SDEs in a non-linear mixed-effects model, where parameter estimation was performed by a novel approximation of the likelihood function. This approximation is constructed by combining the First-Order Conditional Estimation (FOCE) method used in non-linear mixed-effects modelling with the Extended Kalman Filter used in models with SDEs. Fundamental issues concerning the proposed model and estimation algorithm are addressed by simulation studies, concluding that system noise can successfully be separated from measurement noise and inter-individual variability.

Stochastic differential equations in NONMEM: implementation, application, and comparison with ordinary differential equations.

PURPOSE: The objective of the present analysis was to explore the use of stochastic differential equations (SDEs) in population pharmacokinetic/pharmacodynamic (PK/PD) modeling. METHODS: The intra-individual variability in nonlinear mixed-effects models based on SDEs is decomposed into two types of noise: a measurement and a system noise term. The measurement noise represents uncorrelated error due to, for example, assay error while the system noise accounts for structural misspecifications, approximations of the dynamical model, and true random physiological fluctuations. Since the system noise accounts for model misspecifications, the SDEs provide a diagnostic tool for model appropriateness. The focus of the article is on the implementation of the Extended Kalman Filter (EKF) in NONMEM for parameter estimation in SDE models. RESULTS: Various applications of SDEs in population PK/PD modeling are illustrated through a systematic model development example using clinical PK data of the gonadotropin releasing hormone (GnRH) antagonist degarelix. The dynamic noise estimates were used to track variations in model parameters and systematically build an absorption model for subcutaneously administered degarelix. CONCLUSIONS: The EKF-based algorithm was successfully implemented in NONMEM for parameter estimation in population PK/PD models described by systems of SDEs. The example indicated that it was possible to pinpoint structural model deficiencies, and that valuable information may be obtained by tracking unexplained variations in parameters.