Pharmacokinetic and pharmacodynamic properties of once-weekly insulin icodec in individuals with type 2 diabetes.

AIMS: To characterize the pharmacokinetic and pharmacodynamic properties of once-weekly insulin icodec in type 2 diabetes (T2D). MATERIALS AND METHODS: In an open-label trial, 46 individuals with T2D (18-75 years; body mass index 18.0-38.0 kg/m2 ; glycated haemoglobin ≤75 mmol/mol [≤9%]; basal insulin-treated) received subcutaneous once-weekly icodec for ≥8 weeks at individualized doses, aiming at a pre-breakfast plasma glucose concentration of 4.4 to 7.0 mmol/L (80-126 mg/dL) on the last three mornings of each weekly dosing interval. Frequent blood sampling to assess total serum icodec concentration (ie, albumin-bound and unbound) occurred from first icodec dose until 35 days after last dose. Icodec trough concentrations following initiation of once-weekly dosing were predicted by pharmacokinetic modelling. During the final 3 weeks of icodec treatment, while at steady state, the icodec glucose-lowering effect was assessed in three glucose clamps (target 7.5 mmol/L [135 mg/dL]): 0 to 36, 40 to 64 and 144 to 168 h post-dose, thus covering the initial, middle and last part of the 1-week dosing interval. Glucose-lowering effect during a complete dosing interval was predicted by pharmacokinetic-pharmacodynamic modelling. RESULTS: Model-predicted icodec steady state was attained after 3 to 4 weeks. At steady state, model-predicted daily proportions of glucose-lowering effect on days 1 to 7 of the 1-week dosing interval were 14.1%, 16.1%, 15.8%, 15.0%, 14.0%, 13.0% and 12.0%, respectively. Icodec duration of action was at least 1 week in all participants. Once-weekly icodec was overall safe and well tolerated in the current trial. CONCLUSIONS: The pharmacokinetic and pharmacodynamic characteristics of icodec in individuals with T2D support its potential as a once-weekly basal insulin.

Elucidating the Mechanism of Absorption of Fast-Acting Insulin Aspart: The Role of Niacinamide.

PURPOSE: Fast-acting insulin aspart (faster aspart) is a novel formulation of insulin aspart containing two additional excipients: niacinamide, to increase early absorption, and L-arginine, to optimize stability. The aim of this study was to evaluate the impact of niacinamide on insulin aspart absorption and to investigate the mechanism of action underlying the accelerated absorption. METHODS: The impact of niacinamide was assessed in pharmacokinetic analyses in pigs and humans, small angle X-ray scattering experiments, trans-endothelial transport assays, vascular tension measurements, and subcutaneous blood flow imaging. RESULTS: Niacinamide increased the rate of early insulin aspart absorption in pigs, and pharmacokinetic modelling revealed this effect to be most pronounced up to ~30-40 min after injection in humans. Niacinamide increased the relative monomer fraction of insulin aspart by ~35%, and the apparent permeability of insulin aspart across an endothelial cell barrier by ~27%. Niacinamide also induced a concentration-dependent vasorelaxation of porcine arteries, and increased skin perfusion in pigs. CONCLUSION: Niacinamide mediates the acceleration of initial insulin aspart absorption, and the mechanism of action appears to be multifaceted. Niacinamide increases the initial abundance of insulin aspart monomers and transport of insulin aspart after subcutaneous administration, and also mediates a transient, local vasodilatory effect.

The Effect of Various Degrees of Renal or Hepatic Impairment on the Pharmacokinetic Properties of Once-Weekly Insulin Icodec.

BACKGROUND AND OBJECTIVE: Icodec is a once-weekly insulin being developed to provide basal insulin coverage in diabetes mellitus. This study evaluated the effects of renal or hepatic impairment on icodec pharmacokinetics. METHODS: Two open-label, parallel-group, single-dose (1.5 U/kg subcutaneously) trials were conducted. In a renal impairment trial, 58 individuals were allocated to normal renal function (measured glomerular filtration rate ≥ 90 mL/min), mild (60 to < 90 mL/min), moderate (30 to < 60 mL/min) or severe (< 30 mL/min) renal impairment or end-stage renal disease. In a hepatic impairment trial, 25 individuals were allocated to normal hepatic function or mild (Child-Pugh Classification grade A), moderate (grade B) or severe (grade C) hepatic impairment. Blood was sampled frequently for a pharmacokinetic analysis until 35 days post-dose. RESULTS: The shape of the icodec pharmacokinetic profile was not affected by renal or hepatic impairment. Total icodec exposure was greater for mild (estimated ratio [95% confidence interval]: 1.12 [1.01; 1.24]), moderate (1.24 [1.12; 1.37]) and severe (1.28 [1.16; 1.42]) renal impairment, and for end-stage renal disease (1.14 [1.03; 1.28]), compared with normal renal function. It was also greater for mild (1.13 [1.00; 1.28]) and moderate (1.15 [1.02; 1.29]) hepatic impairment versus normal hepatic function. There was no statistically significant difference between severe hepatic impairment and normal hepatic function. Serum albumin levels (range 2.7-5.1 g/dL) did not statistically significantly influence icodec exposure. CONCLUSIONS: The clinical relevance of the slightly higher icodec exposure with renal or hepatic impairment is limited as icodec should be dosed according to individual need. No specific icodec dose adjustment is required in renal or hepatic impairment. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT03723785 and NCT04597697.

Predicting human half-life for insulin analogs: An inter-drug approach.

An inter-drug approach, applying pharmacokinetic information for insulin analogs in different animal species, rat, dog and pig, performed better compared to allometric scaling for human translation of intra-venous half-life and only required data from a single animal species for reliable predictions. Average fold error (AFE) between 1.2-1.7 were determined for all species and for multispecies allometric scaling AFE was 1.9. A slightly larger prediction error for human half-life was determined from in vitro human insulin receptor affinity data (AFE on 2.3-2.6). The requirements for the inter-drug approach were shown to be a span of at least 2 orders of magnitude in half-life for the included drugs and a shared clearance mechanism. The insulin analogs in this study were the five fatty acid protracted analogs: Insulin degludec, insulin icodec, insulin 320, insulin 338 and insulin 362, as well as the non-acylated analog insulin aspart.

Molecular Representations in Machine-Learning-Based Prediction of PK Parameters for Insulin Analogs.

Therapeutic peptides and proteins derived from either endogenous hormones, such as insulin, or de novo design via display technologies occupy a distinct pharmaceutical space in between small molecules and large proteins such as antibodies. Optimizing the pharmacokinetic (PK) profile of drug candidates is of high importance when it comes to prioritizing lead candidates, and machine-learning models can provide a relevant tool to accelerate the drug design process. Predicting PK parameters of proteins remains difficult due to the complex factors that influence PK properties; furthermore, the data sets are small compared to the variety of compounds in the protein space. This study describes a novel combination of molecular descriptors for proteins such as insulin analogs, where many contained chemical modifications, e.g., attached small molecules for protraction of the half-life. The underlying data set consisted of 640 structural diverse insulin analogs, of which around half had attached small molecules. Other analogs were conjugated to peptides, amino acid extensions, or fragment crystallizable regions. The PK parameters clearance (CL), half-life (T1/2), and mean residence time (MRT) could be predicted by using classical machine-learning models such as Random Forest (RF) and Artificial Neural Networks (ANN) with root-mean-square errors of CL of 0.60 and 0.68 (log units) and average fold errors of 2.5 and 2.9 for RF and ANN, respectively. Both random and temporal data splittings were employed to evaluate ideal and prospective model performance with the best models, regardless of data splitting, achieving a minimum of 70% of predictions within a twofold error. The tested molecular representations include (1) global physiochemical descriptors combined with descriptors encoding the amino acid composition of the insulin analogs, (2) physiochemical descriptors of the attached small molecule, (3) protein language model (evolutionary scale modeling) embedding of the amino acid sequence of the molecules, and (4) a natural language processing inspired embedding (mol2vec) of the attached small molecule. Encoding the attached small molecule via (2) or (4) significantly improved the predictions, while the benefit of using the protein language model-based encoding (3) depended on the used machine-learning model. The most important molecular descriptors were identified as descriptors related to the molecular size of both the protein and protraction part using Shapley additive explanations values. Overall, the results show that combining representations of proteins and small molecules was key for PK predictions of insulin analogs.

Impact of demographics and disease progression on the relationship between glucose and HbA1c.

CONTEXT: Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady-state (Herman & Cohen, 2012). OBJECTIVE: To assess the influence of demographic and disease progression-related covariates on the intercept of the estimated linear MPG-HbA1c relationship in a longitudinal model. DATA: Longitudinal patient-level data from 16 late-phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, BMI, gender, race, diabetes duration, and pre-trial treatment. Differences between trials were taken into account by estimating a trial-to-trial variability component. PARTICIPANTS: Participants included 47% females and 20% above 65years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races. ANALYSIS: Estimates of the change in the intercept of the MPG-HbA1c relationship due to the mentioned covariates were determined using a longitudinal model. RESULTS: The analysis showed that pre-trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG-HbA1c relationship. CONCLUSION: Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials.

Mathematical Modelling of Glucose-Dependent Insulinotropic Polypeptide and Glucagon-like Peptide-1 following Ingestion of Glucose.

The incretin hormones, glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1), play an important role in glucose homeostasis by potentiating glucose-induced insulin secretion. Furthermore, GLP-1 has been reported to play a role in glucose homeostasis by inhibiting glucagon secretion and delaying gastric emptying. As the insulinotropic effect of GLP-1 is preserved in patients with type 2 diabetes (T2D), therapies based on GLP-1 have been developed in recent years, and these have proven to be efficient in the treatment of T2D. The endogenous secretion of both GIP and GLP-1 is stimulated by glucose in the small intestine, and the release is dependent on the amount. In this work, we developed a semimechanistic model describing the release of GIP and GLP-1 after ingestion of various glucose doses in healthy volunteers and patients with T2D. In the model, the release of both hormones is stimulated by glucose in the proximal small intestine, and no differences in the secretion dynamics between healthy individuals and patients with T2D were identified after taking differences in glucose profiles into account.

Modeling of 24-hour glucose and insulin profiles in patients with type 2 diabetes mellitus treated with biphasic insulin aspart.

Insulin therapy for diabetes patients is designed to mimic the endogenous insulin response of healthy subjects and thereby generate normal blood glucose levels. In order to control the blood glucose in insulin-treated diabetes patients, it is important to be able to predict the effect of exogenous insulin on blood glucose. A pharmacokinetic/pharmacodynamic model for glucose homoeostasis describing the effect of exogenous insulin would facilitate such prediction. Thus the aim of this work was to extend the previously developed integrated glucose-insulin (IGI) model to predict 24-hour glucose profiles for patients with Type 2 diabetes following exogenous insulin administration. Clinical data from two trials were included in the analysis. In both trials, 24-hour meal tolerance tests were used as the experimental setup, where exogenous insulin (biphasic insulin aspart) was administered in relation to meals. The IGI model was successfully extended to include the effect of exogenous insulin. Circadian variations in glucose homeostasis were assessed on relevant parameters, and a significant improvement was achieved by including a circadian rhythm on the endogenous glucose production in the model. The extended model is a useful tool for clinical trial simulation and for elucidating the effect profile of new insulin products.

Glucose-lowering effect of insulin degludec is independent of subcutaneous injection region.

BACKGROUND AND OBJECTIVES: Patients with diabetes mellitus inject insulin in different regions of the body. This study investigated the pharmacokinetic and pharmacodynamic properties of insulin degludec (IDeg), a new-generation once-daily basal insulin with an ultra-long duration of action, after subcutaneous (SC) administration in different injection regions. METHODS: In this study, 20 healthy subjects received single SC doses of IDeg (0.4 U/kg; separated by 13-21 days) in the thigh, abdomen and deltoid in a randomised, open-label, single-centre, single-dose, complete crossover trial. Each dose was followed by a 24-h euglycaemic clamp and 120-h pharmacokinetic blood sampling. The obtained pharmacokinetic/pharmacodynamic profiles were extrapolated to steady state by simulation using a pharmacokinetic/pharmacodynamic model. RESULTS: Total IDeg exposure [area under the IDeg serum concentration-time curve 0-120 h after a single dose (AUCIDeg,0-120h,SD)] and maximum serum concentration [maximum IDeg serum concentration after a single dose (C max,IDeg,SD)] were higher (6-7 and 23-27 %, respectively) following a single SC dose in the deltoid or abdomen, compared with the thigh, as also observed with other insulin preparations. No statistical difference was observed in these measures between deltoid and abdominal administration. No pronounced differences were observed in the glucose-lowering effect of IDeg [area under the glucose infusion rate (GIR) curve 0-24 h after a single dose (AUCGIR,0-24h,SD) and maximum GIR after a single dose (GIRmax,SD)] when injected in the thigh, abdomen or deltoid (AUCGIR,0-24h,SD 2,572, 2,833 and 2,960 mg/kg, respectively). Simulated mean steady-state pharmacokinetic and pharmacodynamic profiles supported a flat and stable IDeg exposure and effect regardless of injection region, with comparable total glucose-lowering effects [area under the GIR curve at steady state (AUCGIR,τ,SS)] between the thigh, abdomen and deltoid. CONCLUSIONS: These findings support administering IDeg SC in the thigh, upper arm or abdominal wall without affecting IDeg absorption or effect at steady state.

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.