An integrated model for the glucose-insulin system

Glucose Transporters Are Key Components of the Human Glucostat

Mouse models are extensively used in metabolic studies. However, inherent differences between the species, notably their blood glucose levels, hampered data translation into clinical settings. In this study, we confirmed GLUT1 to be the predominantly expressed glucose transporter in both adult and fetal human β-cells. In comparison, GLUT2 is detected in a small yet significant subpopulation of adult β-cells and is expressed to a greater extent in fetal β-cells. Notably, GLUT1/2 expression in INS+ cells from human stem cell-derived islet-like clusters (SC-islets) exhibited a closer resemblance to that observed in fetal islets. Transplantation of primary human islets or SC-islets, but not murine islets, lowered murine blood glucose to the human glycemic range, emphasizing the critical role of β-cells in establishing species-specific glycemia. We further demonstrate the functional requirements of GLUT1 and GLUT2 in glucose uptake and insulin secretion through chemically inhibiting GLUT1 in primary islets and SC-islets and genetically disrupting GLUT2 in SC-islets. Finally, we developed a mathematical model to predict changes in glucose uptake and insulin secretion as a function of GLUT1/2 expression. Collectively, our findings illustrate the crucial roles of GLUTs in human β-cells, and identify them as key components in establishing species-specific glycemic set points.

ARTICLE HIGHLIGHTS

Model-Informed Approaches to Support Drug Development for Patients With Obesity: A Regulatory Perspective

Obesity, which is defined as having a body mass index of 30 kg/m2 or greater, has been recognized as a serious health problem that increases the risk of many comorbidities (eg, heart disease, stroke, and diabetes) and mortality. The high prevalence of individuals who are classified as obese calls for additional considerations in clinical trial design. Nevertheless, gaining a comprehensive understanding of how obesity affects the pharmacokinetics (PK), pharmacodynamics (PD), and efficacy of drugs proves challenging, primarily as obese patients are seldom selected for enrollment at the early stages of drug development. Over the past decade, model-informed drug development (MIDD) approaches have been increasingly used in drug development programs for obesity and its related diseases as they use and integrate all available sources and knowledge to inform and facilitate clinical drug development. This review summarizes the impact of obesity on PK, PD, and the efficacy of drugs and, more importantly, provides an overview of the use of MIDD approaches in drug development and regulatory decision making for patients with obesity: estimating PK, PD, and efficacy in specific dosing scenarios, optimizing dose regimen, and providing evidence for seeking new indication(s). Recent review cases using MIDD approaches to support dose selection and provide confirmatory evidence for effectiveness for patients with obesity, including pediatric patients, are discussed. These examples demonstrate the promise of MIDD as a valuable tool in supporting clinical trial design during drug development and facilitating regulatory decision-making processes for the benefit of patients with obesity.

Altered glucose-dependent secretion of glucagon and ACTH is associated with insulin resistance, assessed by population analysis

This study aimed to characterize how the dysregulation of counter-regulatory hormones can contribute to insulin resistance and potentially to diabetes. Therefore, we investigated the association between insulin sensitivity and the glucose- and insulin-dependent secretion of glucagon, adrenocorticotropic hormone (ACTH), and cortisol in non-diabetic individuals using a population model analysis. Data, from hyperinsulinemic-hypoglycemic clamps, were pooled for analysis, including 52 individuals with a wide range of insulin resistance (reflected by glucose infusion rate 20-60 min; GIR20-60min). Glucagon secretion was suppressed by glucose and, to a lesser extent, insulin. The GIR20-60min and BMI were identified as predictors of the insulin effect on glucagon. At normoglycemia (5 mmol/L), a 90% suppression of glucagon was achieved at insulin concentrations of 16.3 and 43.4 µU/mL in individuals belonging to the highest and lowest quantiles of insulin sensitivity, respectively. Insulin resistance of glucagon secretion explained the elevated fasting glucagon for individuals with a low GIR20-60min. ACTH secretion was suppressed by glucose and not affected by insulin. The GIR20-60min was superior to other measures as a predictor of glucose-dependent ACTH secretion, with 90% suppression of ACTH secretion by glucose at 3.1 and 3.5 mmol/L for insulin-sensitive and insulin-resistant individuals, respectively. This difference may appear small but shifts the suppression range into normoglycemia for individuals with insulin resistance, thus, leading to earlier and greater ACTH/cortisol response when the glucose falls. Based on modeling of pooled glucose-clamp data, insulin resistance was associated with generally elevated glucagon and a potentiated cortisol-axis response to hypoglycemia, and over time both hormonal pathways may therefore contribute to dysglycemia and possibly type 2 diabetes.

Effect of Nigella sativa oil on pharmacokinetics and pharmacodynamics of gliclazide in rats

Nigella sativa oil (NSO) has been used widely for its putative anti-hyperglycemic activity. However, little is known about its potential effect on the pharmacokinetics and pharmacodynamics of antidiabetic drugs, including gliclazide. This study aimed to investigate herb-drug interactions between gliclazide and NSO in rats. Plasma concentrations of gliclazide (single oral and intravenous dose of 33 and 26.4 mg/kg, respectively) in the presence and absence of co-administration with NSO (52 mg/kg per oral) were quantified in healthy and insulin resistant rats (n = 5 for each group). Physiological and treatment-related factors were evaluated as potential influential covariates using a population pharmacokinetic modeling approach (NONMEM version 7.4). Clearance, volume of distribution and bioavailability of gliclazide were unaffected by disease state (healthy or insulin resistant). The concomitant administration of NSO resulted in higher systemic exposures of gliclazide by modulating bioavailability (29% increase) and clearance (20% decrease) of the drug. A model-independent analysis highlighted that pre-treatment with NSO in healthy rats was associated with a higher glucose lowering effect by up to 50% compared with that of gliclazide monotherapy, but not of insulin resistant rats. Although a similar trend in glucose reductions was not observed in insulin resistant rats, co-administration of NSO improved the sensitivity to insulin of this rat population. Natural product-drug interaction between gliclazide and NSO merits further evaluation of its clinical importance.

An Updated Organ-Based Multi-Level Model for Glucose Homeostasis: Organ Distributions, Timing, and Impact of Blood Flow

Glucose homeostasis is the tight control of glucose in the blood. This complex control is important, due to its malfunction in serious diseases like diabetes, and not yet sufficiently understood. Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. Over the last 10 years, this model has been used to insert new insights from the intra-cellular level into the larger whole-body perspective. However, the original cell-organ-body translation has during these years never been updated, despite several critical shortcomings, which also have not been resolved by other modeling efforts. For this reason, we here present an updated multi-level model. This model provides a more accurate sub-division of how much glucose is being taken up by the different organs. Unlike the original model, we now also account for the different dynamics seen in the different organs. The new model also incorporates the central impact of blood flow on insulin-stimulated glucose uptake. Each new improvement is clear upon visual inspection, and they are also supported by statistical tests. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. We hope that this model will serve as an improved basis for future data integration, useful for research and drug developments within diabetes.

A model-based approach to investigating the relationship between glucose-insulin dynamics and dapagliflozin treatment effect in patients with type 2 diabetes

AIMS

To develop a quantitative systems pharmacology model to describe the effect of dapagliflozin (a sodium-glucose co-transporter-2 [SGLT2] inhibitor) on glucose-insulin dynamics in type 2 diabetes mellitus (T2DM) patients, and to identify key determinants of treatment-mediated glycated haemoglobin (HbA1c) reduction.

MATERIALS AND METHODS

Glycaemic control during dapagliflozin treatment was mechanistically characterized by integrating components representing dapagliflozin pharmacokinetics (PK), glucose-insulin homeostasis, renal glucose reabsorption, and HbA1c formation. The model was developed using PK variables, glucose, plasma insulin, and urinary glucose excretion (UGE) from a phase IIa dapagliflozin trial in patients with T2DM (NCT00162305). The model was used to predict dapagliflozin-induced HbA1c reduction; model predictions were compared to actual data from phase III trials (NCT00528879, NCT00683878, NCT00680745 and NCT00673231).

RESULTS

The integrated glucose-insulin-dapagliflozin model successfully described plasma glucose and insulin levels, as well as UGE in response to oral glucose tolerance tests and meal intake. HbA1c reduction was also well predicted. The results show that dapagliflozin-mediated glycaemic control is anticorrelated to steady-state insulin concentration and insulin sensitivity.

CONCLUSIONS

The developed model framework is the first to integrate SGLT2 inhibitor mechanism of action with both short-term glucose-insulin dynamics and long-term glucose control (HbA1c). The results suggest that dapagliflozin treatment is beneficial in patients with inadequate glycaemic control from insulin alone and this benefit increases as insulin control diminishes.

Pharmacodynamic Drug-Drug Interactions

Pharmacodynamic drug-drug interactions (DDIs) occur when the pharmacological effect of one drug is altered by that of another drug in a combination regimen. DDIs often are classified as synergistic, additive, or antagonistic in nature, albeit these terms are frequently misused. Within a complex pathophysiological system, the mechanism of interaction may occur at the same target or through alternate pathways. Quantitative evaluation of pharmacodynamic DDIs by employing modeling and simulation approaches is needed to identify and optimize safe and effective combination therapy regimens. This review investigates the opportunities and challenges in pharmacodynamic DDI studies and highlights examples of quantitative methods for evaluating pharmacodynamic DDIs, with a particular emphasis on the use of mechanism-based modeling and simulation in DDI studies. Advancements in both experimental and computational techniques will enable the application of better, model-informed assessments of pharmacodynamic DDIs in drug discovery, development, and therapeutics.

Toward Better Understanding of Insulin Therapy by Translation of a PK-PD Model to Visualize Insulin and Glucose Action Profiles

Insulin replacement therapy is a fundamental treatment for glycemic control for managing diabetes. The engineering of insulin analogues has focused on providing formulations with action profiles that mimic as closely as possible the pattern of physiological insulin secretion that normally occurs in healthy individuals without diabetes. Hence, it may be helpful to practitioners to visualize insulin concentration profiles and associated glucose action profiles. Expanding on a previous analysis that established a pharmacokinetic (PK) model to describe typical profiles of insulin concentration over time following subcutaneous administration of various insulin formulations, the goal of the current analysis was to link the PK model to an integrated glucose‐insulin (IGI) systems pharmacology model. After the pharmacokinetic‐pharmacodynamic (PK‐PD) model was qualified by comparing model predictions with clinical observations, it was used to project insulin (PK) and glucose (PD) profiles of common insulin regimens and dosing scenarios. The application of the PK‐PD model to clinical scenarios was further explored by incorporating the impact of several hypothetical factors together, such as changing the timing or frequency of administration in a multiple‐dosing regimen over the course of a day, administration of more than 1 insulin formulation, or insulin dosing adjusted for carbohydrates in meals. Visualizations of insulin and glucose profiles for commonly prescribed regimens could be rapidly generated by implementing the linked subcutaneous insulin PK‐IGI model using the R statistical program (version 3.4.4) and a contemporary web‐based interface, which could enhance clinical education on glycemic control with insulin therapy.

The multistate tuberculosis pharmacometric model: a semi-mechanistic pharmacokinetic-pharmacodynamic model for studying drug effects in an acute tuberculosis mouse model

The Multistate Tuberculosis Pharmacometric (MTP) model, a pharmacokinetic-pharmacodynamic disease model, has been used to describe the effects of rifampicin on Mycobacterium tuberculosis (M. tuberculosis) in vitro. The aim of this work was to investigate if the MTP model could be used to describe the rifampicin treatment response in an acute tuberculosis mouse model. Sixty C57BL/6 mice were intratracheally infected with M. tuberculosis H37Rv strain on Day 0. Fifteen mice received no treatment and were sacrificed on Days 1, 9 and 18 (5 each day). Twenty-five mice received oral rifampicin (1, 3, 9, 26 or 98 mg·kg⁻¹·day⁻¹; Days 1–8; 5 each dose level) and were sacrificed on Day 9. Twenty mice received oral rifampicin (30 mg·kg⁻¹·day⁻¹; up to 8 days) and were sacrificed on Days 2, 3, 4 and 9 (5 each day). The MTP model was linked to a rifampicin population pharmacokinetic model to describe the change in colony forming units (CFU) in the lungs over time. The transfer rates between the different bacterial states were fixed to estimates from in vitro data. The MTP model described well the change in CFU over time after different exposure levels of rifampicin in an acute tuberculosis mouse model. Rifampicin significantly inhibited the growth of fast-multiplying bacteria and stimulated the death of fast- and slow-multiplying bacteria. The data did not support an effect of rifampicin on non-multiplying bacteria possibly due to the short duration of the study. The pharmacometric modelling framework using the MTP model can be used to perform investigations and predictions of the efficacy of anti-tubercular drugs against different bacterial states.

Dynamical modeling of liver Aquaporin-9 expression and glycerol permeability in hepatic glucose metabolism

Liver is crucial in the homeostasis of glycerol, an important metabolic intermediate. Plasma glycerol is imported by hepatocytes mainly through Aquaporin-9 (AQP9), an aquaglyceroporin channel negatively regulated by insulin in rodents. AQP9 is of critical importance in glycerol metabolism since hepatic glycerol utilization is rate-limited at the hepatocyte membrane permeation step. Glycerol kinase catalyzes the initial step for the conversion of the imported glycerol into glycerol-3-phosphate, a major substrate for de novo synthesis of glucose (gluconeogenesis) and/or triacyglycerols (lipogenesis). A model addressing the glucose-insulin system to describe the hepatic glycerol import and metabolism and the correlation with the glucose homeostasis is lacking so far. Here we consider a system of first-order ordinary differential equations delineating the relevance of hepatocyte AQP9 in liver glycerol permeability. Assuming the hepatic glycerol permeability as depending on the protein levels of AQP9, a mathematical function is designed describing the time course of the involvement of AQP9 in mouse hepatic glycerol metabolism in different nutritional states. The resulting theoretical relationship is derived fitting experimental data obtained with murine models at the fed, fasted or re-fed condition. While providing useful insights into the dynamics of liver AQP9 involvement in male rodent glycerol homeostasis our model may be adapted to the human liver serving as an important module of a whole body-model of the glucose metabolism both in health and metabolic diseases.

Characterizing the dynamic interaction among gastric emptying, glucose absorption, and glycemic control in nondiabetic obese adults

The effects of altered gastric emptying on glucose absorption and kinetics are not well understood in nondiabetic obese adults. The aim of this work was to develop a physiology-based model that can characterize and compare interactions among gastric emptying, glucose absorption, and glycemic control in nondiabetic obese and lean healthy adults. Dynamic glucose, insulin, and gastric emptying (measured with breath test) data from 12 nondiabetic obese and 12 lean healthy adults were available until 180 min after an oral glucose tolerance test (OGTT) with 10, 25, and 75 g of glucose. A physiology-based model was developed to characterize glucose kinetics applying nonlinear mixed-effects modeling with NONMEM7.3. Glucose kinetics after OGTT was described by a one-compartment model with an effect compartment to describe delayed insulin effects on glucose clearance. After the interactions between individual gastric emptying and glucose absorption profiles were accounted for, the glucose absorption rate was found to be similar in nondiabetic obese and lean controls. Baseline glucose concentration was estimated to be only marginally higher in nondiabetic obese subjects (4.9 vs. 5.2 mmol/l), whereas insulin-dependent glucose clearance in nondiabetic obese subjects was found to be cut in half compared with lean controls (0.052 vs. 0.029 l/min) and the insulin concentration associated with 50% of insulin-dependent glucose elimination rate was approximately twofold higher in nondiabetic obese subjects compared with lean controls (7.1 vs. 15.3 μU/ml). Physiology-based models can characterize and compare the dynamic interaction among gastric emptying, glucose absorption and glycemic control in populations of interest such as lean healthy and nondiabetic obese adults.

A Novel Insulin/Glucose Model after a Mixed-Meal Test in Patients with Type 1 Diabetes on Insulin Pump Therapy

Current closed-loop insulin delivery methods stem from sophisticated models of the glucose-insulin (G/I) system, mostly based on complex studies employing glucose tracer technology. We tested the performance of a new minimal model (GLUKINSLOOP 2.0) of the G/I system to characterize the glucose and insulin dynamics during multiple mixed meal tests (MMT) of different sizes in patients with type 1 diabetes (T1D) on insulin pump therapy (continuous subcutaneous insulin infusion, CSII). The GLUKINSLOOP 2.0 identified the G/I system, provided a close fit of the G/I time-courses and showed acceptable reproducibility of the G/I system parameters in repeated studies of identical and double-sized MMTs. This model can provide a fairly good and reproducible description of the G/I system in T1D patients on CSII, and it may be applied to create a bank of “virtual” patients. Our results might be relevant at improving the architecture of upcoming closed-loop CSII systems.

Requirements for multi-level systems pharmacology models to reach end-usage: the case of type 2 diabetes

We are currently in the middle of a major shift in biomedical research: unprecedented and rapidly growing amounts of data may be obtained today, from in vitro, in vivo and clinical studies, at molecular, physiological and clinical levels. To make use of these large-scale, multi-level datasets, corresponding multi-level mathematical models are needed, i.e. models that simultaneously capture multiple layers of the biological, physiological and disease-level organization (also referred to as quantitative systems pharmacology-QSP-models). However, today's multi-level models are not yet embedded in end-usage applications, neither in drug research and development nor in the clinic. Given the expectations and claims made historically, this seemingly slow adoption may seem surprising. Therefore, we herein consider a specific example-type 2 diabetes-and critically review the current status and identify key remaining steps for these models to become mainstream in the future. This overview reveals how, today, we may use models to ask scientific questions concerning, e.g., the cellular origin of insulin resistance, and how this translates to the whole-body level and short-term meal responses. However, before these multi-level models can become truly useful, they need to be linked with the capabilities of other important existing models, in order to make them 'personalized' (e.g. specific to certain patient phenotypes) and capable of describing long-term disease progression. To be useful in drug development, it is also critical that the developed models and their underlying data and assumptions are easily accessible. For clinical end-usage, in addition, model links to decision-support systems combined with the engagement of other disciplines are needed to create user-friendly and cost-efficient software packages.

Weight-HbA1c-insulin-glucose model for describing disease progression of type 2 diabetes

A previous semi‐mechanistic model described changes in fasting serum insulin (FSI), fasting plasma glucose (FPG), and glycated hemoglobin (HbA1c) in patients with type 2 diabetic mellitus (T2DM) by modeling insulin sensitivity and β‐cell function. It was later suggested that change in body weight could affect insulin sensitivity, which this study evaluated in a population model to describe the disease progression of T2DM. Nonlinear mixed effects modeling was performed on data from 181 obese patients with newly diagnosed T2DM managed with diet and exercise for 67 weeks. Baseline β‐cell function and insulin sensitivity were 61% and 25% of normal, respectively. Management with diet and exercise (mean change in body weight = −4.1 kg) was associated with an increase of insulin sensitivity (30.1%) at the end of the study. Changes in insulin sensitivity were associated with a decrease of FPG (range, 7.8–7.3 mmol/L) and HbA1c (6.7–6.4%). Weight change as an effector on insulin sensitivity was successfully evaluated in a semi‐mechanistic population model.

Prediction model for glucose metabolism based on lipid metabolism

OBJECTIVES

We developed a robust, long-term clinical prediction model to predict conditions leading to early diabetes using laboratory values other than blood glucose and insulin levels. Our model protects against missing data and noise that occur during long-term analysis.

METHODS

RESULTS of a 75-g oral glucose tolerance test (OGTT) were divided into three groups: diabetes, impaired glucose tolerance (IGT), and normal (n = 114, 235, and 325, respectively). For glucose metabolic and lipid metabolic parameters, near 30-day mean values and 10-year integrated values were compared. The relation between high-density lipoprotein cholesterol (HDL-C) and variations in HbA1c was analyzed in 158 patients. We also constructed a state space model consisting of an observation model (HDL-C and HbA1c) and an internal model (disorders of lipid metabolism and glucose metabolism) and applied this model to 116 cases.

RESULTS

The root mean square error between the observed HbA1c and predicted HbA1c was 0.25.

CONCLUSIONS

In the observation model, HDL-C levels were useful for prediction of increases in HbA1c. Even with numerous missing values over time, as occurs in clinical practice, clinically valid predictions can be made using this state space model.

Dose selection using a semi-mechanistic integrated glucose-insulin-glucagon model: designing phase 2 trials for a novel oral glucokinase activator

Selecting dosing regimens for phase 2 studies for a novel glucokinase activator LY2599506 is challenging due to the difficulty in modeling and assessing hypoglycemia risk. A semi-mechanistic integrated glucose-insulin-glucagon (GIG) model was developed in NONMEM based on pharmacokinetic, glucose, insulin, glucagon, and meal data obtained from a multiple ascending dose study in patients with Type 2 diabetes mellitus treated with LY2599506 for up to 26 days. The series of differential equations from the NONMEM model was translated into an R script to prospectively predict 24-h glucose profiles following LY2599506 treatment for 3 months for a variety of doses and dosing regimens. The reduction in hemoglobin A1c (HbA1c) at the end of the 3-month treatment was estimated using a transit compartment model based on the simulated fasting glucose values. Two randomized phase 2 studies, one with fixed dosing and the other employing conditional dose titration were conducted. The simulation suggested that (1) Comparable HbA1c lowering with lower hypoglycemia risk occurs with titration compared to fixed-dosing; and (2) A dose range of 50-400 mg BID provides either greater efficacy or lower hypoglycemia incidence or both than glyburide. The predictions were in reasonable agreement with the observed clinical data. The model predicted HbA1c reduction and hypoglycemia risk provided the basis for the decision to focus on the dose-titration trial and for the selection of doses for the demonstration of superiority of LY2599506 to glyburide. The integrated GIG model represented a valuable tool for the evaluation of hypoglycemia incidence.

Assessment of glycemic response to an oral glucokinase activator in a proof of concept study: application of a semi-mechanistic, integrated glucose-insulin-glucagon model

A proof of concept study was conducted to investigate the safety and tolerability of a novel oral glucokinase activator, LY2599506, during multiple dose administration to healthy volunteers and subjects with Type 2 diabetes mellitus (T2DM). To analyze the study data, a previously established semi-mechanistic integrated glucose-insulin model was extended to include characterization of glucagon dynamics. The model captured endogenous glucose and insulin dynamics, including the amplifying effects of glucose on insulin production and of insulin on glucose elimination, as well as the inhibitory influence of glucose and insulin on hepatic glucose production. The hepatic glucose production in the model was increased by glucagon and glucagon production was inhibited by elevated glucose concentrations. The contribution of exogenous factors to glycemic response, such as ingestion of carbohydrates in meals, was also included in the model. The effect of LY2599506 on glucose homeostasis in subjects with T2DM was investigated by linking a one-compartment, pharmacokinetic model to the semi-mechanistic, integrated glucose-insulin-glucagon system. Drug effects were included on pancreatic insulin secretion and hepatic glucose production. The relationships between LY2599506, glucose, insulin, and glucagon concentrations were described quantitatively and consequently, the improved understanding of the drug-response system could be used to support further clinical study planning during drug development, such as dose selection.

Quantifying the contribution of the liver to glucose homeostasis: a detailed kinetic model of human hepatic glucose metabolism

Despite the crucial role of the liver in glucose homeostasis, a detailed mathematical model of human hepatic glucose metabolism is lacking so far. Here we present a detailed kinetic model of glycolysis, gluconeogenesis and glycogen metabolism in human hepatocytes integrated with the hormonal control of these pathways by insulin, glucagon and epinephrine. Model simulations are in good agreement with experimental data on (i) the quantitative contributions of glycolysis, gluconeogenesis, and glycogen metabolism to hepatic glucose production and hepatic glucose utilization under varying physiological states. (ii) the time courses of postprandial glycogen storage as well as glycogen depletion in overnight fasting and short term fasting (iii) the switch from net hepatic glucose production under hypoglycemia to net hepatic glucose utilization under hyperglycemia essential for glucose homeostasis (iv) hormone perturbations of hepatic glucose metabolism. Response analysis reveals an extra high capacity of the liver to counteract changes of plasma glucose level below 5 mM (hypoglycemia) and above 7.5 mM (hyperglycemia). Our model may serve as an important module of a whole-body model of human glucose metabolism and as a valuable tool for understanding the role of the liver in glucose homeostasis under normal conditions and in diseases like diabetes or glycogen storage diseases.

Mechanism-based population modelling of the effects of vildagliptin on GLP-1, glucose and insulin in patients with type 2 diabetes

AIM

To build a mechanism-based population pharmacodynamic model to describe and predict the time course of active GLP-1, glucose and insulin in type 2 diabetic patients after treatment with various doses of vildagliptin.

METHODS

Vildagliptin concentrations, DPP-4 activity, active GLP-1, glucose and insulin concentrations from 13 type 2 diabetic patients after oral vildagliptin doses of 10, 25 or 100 mg and placebo twice daily for 28 days were co-modelled. The population PK/PD model was developed utilizing the MC-PEM algorithm in parallelized S-ADAPT version 1.56.

RESULTS

In the PD model, active GLP-1 production was stimulated by gastrointestinal intake of nutrients. Active GLP-1 was primarily metabolized by DPP-4 and an additional non-saturable pathway. Increased plasma glucose stimulated secretion of insulin which stimulated utilization of glucose. Active GLP-1 stimulated both glucose-dependent insulin secretion and insulin-dependent glucose utilization. Complete inhibition of DPP-4 resulted in an approximately 2.5-fold increase of active GLP-1 half-life.

CONCLUSIONS

The effects of vildagliptin in patients with type 2 diabetes on several PD endpoints were successfully described by the proposed model. The mechanisms of vildagliptin on glycaemic control could be evaluated from a variety of aspects such as effects of DPP-4 on GLP-1, effects of GLP-1 on insulin secretion and effects on hepatic and peripheral insulin sensitivity. The present model can be used to predict the effects of other dosage regimens of vildagliptin on DPP-4 inhibition, active GLP-1, glucose and insulin concentrations, or can be modified and applied to other incretin-related anti-diabetes therapies.

Pharmacokinetic and pharmacodynamic modeling of exendin-4 in type 2 diabetic Goto-Kakizaki rats

The pharmacokinetics (PK) and pharmacodynamics (PD) of exendin-4 were studied in type 2 diabetic Goto-Kakizaki rats after single doses at 0.5, 1, 5, or 10 μg/kg by intravenous administration and 5 μg/kg by subcutaneous administration. Plasma exendin-4, glucose, and insulin concentrations were determined. A target-mediated drug disposition model was used to characterize the PK of exendin-4. Glucose turnover was described by an indirect response model, with insulin stimulating glucose disposition. Insulin turnover was characterized by an indirect response model with a precursor compartment. After intravenous doses, exendin-4 rapidly disappeared from the circulation, whereas it exhibited rapid absorption (T(max) = 15-20 min) and incomplete bioavailability (F = 0.51) after the subcutaneous dose. Exendin-4 increased insulin release at 2 to 5 min with capacity S(max) = 6.91 and sensitivity SC₅₀ = 1.29 nM, followed by a rebound at 10 to 15 min and a slow return to the baseline. Glucose initially declined because of enhanced insulin secretion, and then gradually increased because of the activation of the neural system by exendin-4. The hyperglycemic action was modeled with increased hepatic glucose production with a linear factor S(RC) = 0.112 1/nM. The mechanistic PK/PD model satisfactorily described the disposition and effects of exendin-4 on glucose and insulin homeostasis in type 2 diabetic rats.