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

GLP-1 Receptor Agonists Models for Type 1 Diabetes: A Narrative Review

BACKGROUND

Glucagon-like peptide 1 (GLP-1) is a hormone that promotes insulin secretion, delays gastric emptying, and inhibits glucagon secretion. The GLP-1 receptor agonists have been developed as adjunctive therapies for type 2 diabetes to improve glucose control. Recently, there has been an interest in introducing GLP-1 receptor agonists as adjunctive therapies in type 1 diabetes alongside automatic insulin delivery systems. The preclinical validation of these systems often relies on mathematical simulators that replicate the glucose dynamics of a person with diabetes. This review aims to explore mathematical models available in the literature to describe GLP-1 effects to be used in a type 1 diabetes simulator.

METHODS

Three databases were examined in the search for GLP-1 mathematical models. More than 1500 works were found after searching for specific keywords that were narrowed down to 39 works for full-text assessment.

RESULTS

A total of 23 works were selected describing GLP-1 pharmacokinetics and pharmacodynamics. However, none of the found models was designed for type 1 diabetes. An analysis is included of the available models' features that could be translated into a GLP-1 receptor agonist model for type 1 diabetes.

CONCLUSION

There is a gap in research in GLP-1 receptor agonists mathematical models for type 1 diabetes, which could be incorporated into type 1 diabetes simulators, providing a safe and inexpensive tool to carry out preclinical validations using these therapies.

A physiologically-based quantitative systems pharmacology model for mechanistic understanding of the response to alogliptin and its application in patients with renal impairment

Alogliptin is a highly selective inhibitor of dipeptidyl peptidase-4 and primarily excreted as unchanged drug in the urine, and differences in clinical outcomes in renal impairment patients increase the risk of serious adverse reactions. In this study, we developed a comprehensive physiologically-based quantitative systematic pharmacology model of the alogliptin-glucose control system to predict plasma exposure and use glucose as a clinical endpoint to prospectively understand its therapeutic outcomes with varying renal function. Our model incorporates a PBPK model for alogliptin, DPP-4 activity described by receptor occupancy theory, and the crosstalk and feedback loops for GLP-1-GIP-glucagon, insulin, and glucose. Based on the optimization of renal function-dependent parameters, the model was extrapolated to different stages renal impairment patients. Ultimately our model adequately describes the pharmacokinetics of alogliptin, the progression of DPP-4 inhibition over time and the dynamics of the glucose control system components. The extrapolation results endorse the dose adjustment regimen of 12.5 mg once daily for moderate patients and 6.25 mg once daily for severe and ESRD patients, while providing additional reflections and insights. In clinical practice, our model could provide additional information on the in vivo fate of DPP4 inhibitors and key regulators of the glucose control system.

Pharmacokinetics and Food Effect Between a 100-mg Sustained-Release Tablet and a 50-mg Immediate-Release Tablet of Vildagliptin in Healthy Subjects

Vildagliptin is one of the dipeptidyl peptidase-4 inhibitors. This study aimed to compare vildagliptin exposure between 50-mg immediate-release (IR) and 100-mg new sustained-release (SR) tablets, and evaluate the food effect on the pharmacokinetics (PKs) of vildagliptin. A randomized, open-label, 3-period, 3-treatment, 6-sequence crossover study was conducted on healthy subjects. During each period, subjects received the SR tablet either in the fasted (T1) or high-fat fed (T2) state once a day, or IR tablets administered twice a day in the fasted state (R). Blood samples for PK analysis were obtained serially up to 24 hours after dosing. Thirty-four subjects completed the study. The geometric mean ratios for the Cmax and AUC0-24h of T1 to R were 1.15 and 0.89, respectively. The corresponding values of T2 to T1 were 0.94 and 1.07, respectively. Vildagliptin exposure over 24 hours was similar between the SR and IR tablets. In addition, the PK profiles of the SR tablets were not altered by food. The SR tablets can be administered without a food effect and be an alternative option to IR tablets.

A Quantitative Systems Pharmacology Model of the Incretin Hormones GIP and GLP1, Glucagon, Glucose, Insulin, and the Small Molecule DPP-4 Inhibitor, Linagliptin

In the current study, we established a comprehensive quantitative systems pharmacology (QSP) model using linagliptin as the model drug, where drug disposition, drug intervention on dipeptidyl peptidase-4 (DPP-4), glucose-dependent insulinotropic peptide (GIP), Glucagon-like peptide-1 (GLP-1), glucagon, glucose, and insulin are integrated together with the cross talk and feedback loops incorporated among the whole glycemic control system. In the final linagliptin QSP model, the complicated disposition of linagliptin was characterized by a 2-compartment pharmacokinetic (PK) model with an enterohepatic cycling (EHC) component as well as target-mediated drug disposition (TMDD) processes occurring in both tissues and plasma, and the inhibitory effect of linagliptin on DPP-4 was determined by the linagliptin-DPP-4 complex in the central compartment based on target occupancy principle. The integrated GIP-GLP1-glucagon-glucose-insulin system contains five indirect response models as the "skeleton" structure with 12 feedback loops incorporated within the glucose control system. Our model adequately characterized the substantial nonlinear PK of linagliptin, time course of DPP-4 inhibition, as well as the kinetics of GIP, GLP-1, glucagon, and glucose simultaneously in humans. Our model provided valuable insights on linagliptin pharmacokinetics/pharmacodynamics and complicated glucose homeostasis. Since the glucose regulation modeling framework within the QSP model is "drug-independent", our model can be easily adopted by others to evaluate the effect of other DPP-4 inhibitors on the glucose control system. In addition, our QSP model, which contains more components than other reported glucose regulation models, can potentially be used to evaluate the effect of combination antidiabetic therapy targeting different components of glucose control system.

Population pharmacodynamic modeling of intramuscular and oral dexamethasone and betamethasone effects on six biomarkers with circadian complexities in Indian women

Population pharmacokinetic/pharmacodynamic (PK/PD) analysis was performed for extensive data for differing dosage forms and routes for dexamethasone (DEX) and betamethasone (BET) in 48 healthy nonpregnant Indian women in a partial and complex cross-over design. Single doses of 6 mg dexamethasone phosphate (DEX-P), betamethasone phosphate (BET-P), or 1:1 mixture of betamethasone phosphate and acetate (BET-PA) were administered orally (PO) or intramuscularly (IM) where each woman enrolled in a two-period cross-over study. Plasma concentrations collected over 96 h were described with a two-compartment model with differing PO and IM first-order absorption inputs. Overall, BET exhibited slower clearance, similar volume of distribution, faster absorption, and longer persistence than DEX with BET acetate producing extremely slow absorption but full bioavailability of BET. Six biomarkers were assessed over a 24-h baseline period with four showing circadian rhythms with complex baselines. These baselines and the strong responses seen after drug dosing were fitted with various indirect response models using the Laplace estimation methods in NONMEM 7.4. Both the PK and six biomarker responses were well-described with modest variability likely due to the homogeneous ages, weights, and ethnicities of the women. The drugs either inhibited or stimulated the influx processes with some models requiring joint inclusion of drug effects on circadian cortisol suppression. The biomarkers and order of sensitivity (lowest IC₅₀/SC₅₀ to highest) were: cortisol, T-helper cells, basophils, glucose, neutrophils, and T-cytotoxic cells. DEX sensitivities were generally greater than BET with corresponding mean ratios for these biomarkers of 2.86, 1.27, 1.72, 1.27, 2.69, and 1.06. Overall, the longer PK (e.g. half-life) of BET, but lesser PD activity (e.g. higher IC₅₀), produces single-dose response profiles that appear quite similar, except for the extended effects from BET-PA. This comprehensive population modeling effort provides the first detailed comparison of the PK profiles and six biomarker responses of five commonly used dosage forms of DEX and BET in healthy women.

Single-dose escalation study of yogliptin in healthy Chinese volunteers

BACKGROUND

Yogliptin is a novel xanthine dipeptidyl peptidase-4 (DPP-4) inhibitor targeting type 2 diabetes. After promising preclinical pharmacological studies, the first human trial of yogliptin was designed.

METHODS

A randomized, double-blind, parallel, placebo-controlled phase I single-dose escalation study was designed to evaluate the pharmacokinetics, pharmacodynamics, and tolerability after single oral doses of yogliptin in healthy Chinese subjects. Healthy subjects were assigned to nine cohorts, which received a single dose of yogliptin at 2.5, 5, 10, 25, 50, 100, 200, 400, or 600 mg. Two subjects in each cohort received placebo. Blood samples were collected before dosing and up to 192 h afterwards. Urine samples were collected until 120 h after dosing. Plasma and urine drug concentrations were determined using liquid chromatography coupled with tandem mass spectrometry, and DPP-4 activity was measured using a semi-quantitative, fluorescence-based kinetic assay.

RESULTS

A total of 104 subjects were enrolled, 103 of whom completed the study (mean age, 25.3 years; mean weight, 58.8 kg; mean BMI, 21.8 kg/m²). A total of 27 adverse events (AEs) occurred in 25 of 86 yogliptin subjects (29.1%), and 3 AEs occurred in 3 of 18 placebo subjects (16.7%). Yogliptin was absorbed with a median time of maximum observed concentration (T_max) of 3.0 h and was eliminated slowly with a t_1/2 of 25.45-43.84 h. The maximum observed concentration (C_max) and area under the curve (AUC) varied slightly more than dose-proportionally over the dose range from 2.5 to 400 mg. The fraction of drug excreted in urine ranged from 8.39% to 24.77%. Mean DPP-4 inhibition at 24 h after dosing ranged from 97.7% to 99.5%, and DPP-4 inhibition was >80% for 72 h at doses from 25 to 400 mg. DPP-4 inhibition was >80% for 1 week in the group receiving 400 mg.

CONCLUSION

Yogliptin was well tolerated in healthy subjects, with no dose-limiting toxicity observed in the range from 2.5 to 600 mg. Yogliptin inhibited plasma DPP-4 activity for 72 h at single doses of 25-200 mg and for 1 week at 400 mg, suggesting that once-weekly dosing of yogliptin is possible in type 2 diabetes patients.

TRIAL REGISTRATION

ChiCTR-IIR-17010311 (Chictr.org).

Evolving data analysis of an Oral Lipid Tolerance Test toward the standard for the Oral Glucose Tolerance Test: Cross species modeling effects of AZD7687 on plasma triacylglycerol

We have developed a novel mechanistic pharmacokinetic-pharmacodynamic (PK/PD) model to describe the time course of plasma triglyceride (TAG) after Oral Lipid Tolerance Test (OLTT) and the effects of AZD7687, an inhibitor of diacylglycerol acyltransferase 1 (DGAT1), in humans, rats, and mice. Pharmacokinetic and plasma TAG data were obtained both in animals and in two phase I OLTT studies. In the PK/PD model, the introduction of exogenous TAG is represented by a first order process. The endogenous production and removal of TAG from plasma are described with a turnover model. AZD7687 inhibits the contribution of exogenous TAG into circulation. One or two compartment models with first order absorption was used to describe the PK of AZD7687 for the different species. Nonlinear mixed effect modeling was used to fit the model to the data. The effects of AZD7687 on the plasma TAG time course during an OLTT as well as interindividual variability were well described by the model in all three species. Meal fat content or data from single vs repeated dosing did not affect model parameter estimates. Body mass index was found to be a significant covariate on the plasma TAG baseline. The system parameters of the model will facilitate analysis for other compounds and provide tools to bring the standard of OLTT data analysis closer to the analyses of Oral Glucose Tolerance Test data maximizing knowledge gain.

In vitro and in silico analysis of the effects of D2 receptor antagonist target binding kinetics on the cellular response to fluctuating dopamine concentrations

Background and Purpose

Target binding kinetics influence the time course of the drug effect (pharmacodynamics) both (i) directly, by affecting the time course of target occupancy, driven by the pharmacokinetics of the drug, competition with endogenous ligands and target turnover, and (ii) indirectly, by affecting signal transduction and homeostatic feedback. For dopamine D₂ receptor antagonists, it has been hypothesized that fast receptor binding kinetics cause fewer side effects, because part of the dynamics of the dopaminergic system is preserved by displacement of these antagonists.

Experimental Approach

Target binding kinetics of D₂ receptor antagonists and signal transduction after dopamine and D₂ receptor antagonist exposure were measured in vitro. These data were integrated by mechanistic modelling, taking into account competitive binding of endogenous dopamine and the antagonist, the turnover of the second messenger cAMP and negative feedback by PDE turnover.

Key Results

The proposed signal transduction model successfully described the cellular cAMP response for 17 D₂ receptor antagonists with widely different binding kinetics. Simulation of the response to fluctuating dopamine concentrations revealed that a significant effect of the target binding kinetics on the dynamics of the signalling only occurs at endogenous dopamine concentration fluctuations with frequencies below 1 min⁻¹.

Conclusions and Implications

Signal transduction and feedback are important determinants of the time course of drug effects. The effect of the D₂ receptor antagonist dissociation rate constant (k_off) is limited to the maximal rate of fluctuations in dopamine signalling as determined by the dopamine k_off and the cAMP turnover.

Utilization of data below the analytical limit of quantitation in pharmacokinetic analysis and modeling: promoting interdisciplinary debate

Traditionally, bioanalytical laboratories do not report actual concentrations for samples with results below the LOQ (BLQ) in pharmacokinetic studies. BLQ values are outside the method calibration range established during validation and no data are available to support the reliability of these values. However, ignoring BLQ data can contribute to bias and imprecision in model-based pharmacokinetic analyses. From this perspective, routine use of BLQ data would be advantageous. We would like to initiate an interdisciplinary debate on this important topic by summarizing the current concepts and use of BLQ data by regulators, pharmacometricians and bioanalysts. Through introducing the limit of detection and evaluating its variability, BLQ data could be released and utilized appropriately for pharmacokinetic research.

Anagliptin increases insulin-induced skeletal muscle glucose uptake via an NO-dependent mechanism in mice

AIMS/HYPOTHESIS

Recently, incretin-related agents have been reported to attenuate insulin resistance in animal models, although the underlying mechanisms remain unclear. In this study, we investigated whether anagliptin, the dipeptidyl peptidase 4 (DPP-4) inhibitor, attenuates skeletal muscle insulin resistance through endothelial nitric oxide synthase (eNOS) activation in the endothelial cells. We used endothelium-specific Irs2-knockout (ETIrs2KO) mice, which show skeletal muscle insulin resistance resulting from a reduction of insulin-induced skeletal muscle capillary recruitment as a consequence of impaired eNOS activation.

METHODS

In vivo, 8-week-old male ETIrs2KO mice were fed regular chow with or without 0.3% (wt/wt) DPP-4 inhibitor for 8 weeks to assess capillary recruitment and glucose uptake by the skeletal muscle. In vitro, human coronary arterial endothelial cells (HCAECs) were used to explore the effect of glucagon-like peptide 1 (GLP-1) on eNOS activity.

RESULTS

Treatment with anagliptin ameliorated the impaired insulin-induced increase in capillary blood volume, interstitial insulin concentration and skeletal muscle glucose uptake in ETIrs2KO mice. This improvement in insulin-induced glucose uptake was almost completely abrogated by the GLP-1 receptor (GLP-1R) antagonist exendin-(9-39). Moreover, the increase in capillary blood volume with anagliptin treatment was also completely inhibited by the NOS inhibitor. GLP-1 augmented eNOS phosphorylation in HCAECs, with the effect completely disappearing after exposure to the protein kinase A (PKA) inhibitor H89. These data suggest that anagliptin treatment enhances insulin-induced capillary recruitment and interstitial insulin concentrations, resulting in improved skeletal muscle glucose uptake by directly acting on the endothelial cells via NO- and GLP-1-dependent mechanisms in vivo.

CONCLUSIONS/INTERPRETATION

Anagliptin may be a promising agent to ameliorate skeletal muscle insulin resistance in obese patients with type 2 diabetes.

The Effects of a GLP-1 Analog on Glucose Homeostasis in Type 2 Diabetes Mellitus Quantified by an Integrated Glucose Insulin Model

In recent years, several glucagon-like peptide-1 (GLP-1)-based therapies for the treatment of type 2 diabetes mellitus (T2DM) have been developed. The aim of this work was to extend the semimechanistic integrated glucose-insulin model to include the effects of a GLP-1 analog on glucose homeostasis in T2DM patients. Data from two trials comparing the effect of steady-state liraglutide vs. placebo on the responses of postprandial glucose and insulin in T2DM patients were used for model development. The effect of liraglutide was incorporated in the model by including a stimulatory effect on insulin secretion. Furthermore, for one of the trials an inhibitory effect on glucose absorption was included to account for a delay in gastric emptying. As other GLP-1 receptor agonists have similar modes of action, it is believed that the model can also be used to describe the effect of other receptor agonists on glucose homeostasis.

Evaluating systems pharmacology models is different from evaluating standard pharmacokinetic-pharmacodynamic models

Based on the author's recent experience, there appears to be some confusion regarding the steps required to qualify a systems pharmacology model as adequate for the intended purpose. This manuscript outlines the model evaluation approach used in the author's recent publication¹on the systems pharmacology of a 5-lipoxygenase inhibitor and is an attempt to generate discussion on this topic within the pharmacometrics and systems pharmacology community.

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.

Mechanism-based model of parasite growth and dihydroartemisinin pharmacodynamics in murine malaria

Murine models are used to study erythrocytic stages of malaria infection, because parasite morphology and development are comparable to those in human malaria infections. Mechanism-based pharmacokinetic-pharmacodynamic (PK-PD) models for antimalarials are scarce, despite their potential to optimize antimalarial combination therapy. The aim of this study was to develop a mechanism-based growth model (MBGM) for Plasmodium berghei and then characterize the parasiticidal effect of dihydroartemisinin (DHA) in murine malaria (MBGM-PK-PD). Stage-specific (ring, early trophozoite, late trophozoite, and schizont) parasite density data from Swiss mice inoculated with Plasmodium berghei were used for model development in S-ADAPT. A single dose of intraperitoneal DHA (10 to 100 mg/kg) or vehicle was administered 56 h postinoculation. The MBGM explicitly reflected all four erythrocytic stages of the 24-hour P. berghei life cycle. Merozoite invasion of erythrocytes was described by a first-order process that declined with increasing parasitemia. An efflux pathway with subsequent return was additionally required to describe the schizont data, thus representing parasite sequestration or trapping in the microvasculature, with a return to circulation. A 1-compartment model with zero-order absorption described the PK of DHA, with an estimated clearance and distribution volume of 1.95 liters h(-1) and 0.851 liter, respectively. Parasite killing was described by a turnover model, with DHA inhibiting the production of physiological intermediates (IC(50), 1.46 ng/ml). Overall, the MBGM-PK-PD described the rise in parasitemia, the nadir following DHA dosing, and subsequent parasite resurgence. This novel model is a promising tool for studying malaria infections, identifying the stage specificity of antimalarials, and providing insight into antimalarial treatment strategies.

Study reanalysis using a mechanism-based pharmacokinetic/pharmacodynamic model of pramlintide in subjects with type 1 diabetes

This report describes a pharmacokinetic/pharmacodynamic model for pramlintide, an amylinomimetic, in type 1 diabetes mellitus (T1DM). Plasma glucose and drug concentrations were obtained following bolus and 2-h intravenous infusions of pramlintide at three dose levels or placebo in 25 T1DM subjects during the postprandial period in a crossover study. The original clinical data were reanalyzed by mechanism-based population modeling. Pramlintide pharmacokinetics followed a two-compartment model with zero-order infusion and first-order elimination. Pramlintide lowered overall postprandial plasma glucose AUC (AUC(net)) and delayed the time to peak plasma glucose after a meal (T (max)). The delay in glucose T (max) and reduction of AUC(net) indicate that overall plasma glucose concentrations might be affected by differing mechanisms of action of pramlintide. The observed increase in glucose T (max) following pramlintide treatment was independent of dose within the studied dose range and was adequately described by a dose-independent, maximum pramlintide effect on gastric emptying of glucose in the model. The inhibition of endogenous glucose production by pramlintide was described using a sigmoidal function with capacity and sensitivity parameter estimates of 0.995 for I (max) and 23.8 pmol/L for IC(50). The parameter estimates are in good agreement with literature values and the IC(50) is well within the range of postprandial plasma amylin concentrations in healthy humans, indicating physiological relevance of the pramlintide effect on glucagon secretion in the postprandial state. This model may prove to be useful in future clinical studies of other amylinomimetics or antidiabetic drugs with similar mechanisms of action.

Mechanism-based population pharmacokinetic modelling in diabetes: vildagliptin as a tight binding inhibitor and substrate of dipeptidyl peptidase IV

AIMS

To assess the pharmacokinetics of vildagliptin at different doses and build a mechanism-based population model that simultaneously describes vildagliptin pharmacokinetics and its effects on DPP-4 activity based on underlying physiology and biology.

METHODS

Vildagliptin concentrations and DPP-4 activity vs. time from 13 type 2 diabetic patients after oral vildagliptin 10, 25 or 100 mg and placebo twice daily for 28 days were co-modelled. NONMEM VI and S-ADAPT were utilized for population modelling.

RESULTS

A target-mediated drug disposition (TMDD) model accounting for capacity-limited high affinity binding of vildagliptin to DPP-4 in plasma and tissues had good predictive performance. Modelling the full time course of the vildagliptin-DPP-4 interaction suggested parallel vildagliptin dissociation from DPP-4 by a slow first-order process and hydrolysis by DPP-4 to an inactive metabolite as a disposition mechanism. Due to limited amounts of DPP-4, vildagliptin concentrations increased slightly more than dose proportionally. This newly proposed model and the parameter estimates are supported by published in vitro studies. Mean parameter estimates (inter-individual coefficient of variation) were: non-saturable clearance 36 l h−1 (25%), central volume of distribution 22 l (37%), half-life of dissociation from DPP-4 1.1 h (94%) and half-life of hydrolysis 6.3 h (81%).

CONCLUSIONS

Vildagliptin is both an inhibitor and substrate for DPP-4. By utilizing the TMDD approach, slow dissociation of vildagliptin from DPP-4 was found in patients and the half-life of hydrolysis by DPP-4 estimated. This model can be used to predict DPP-4 inhibition effects of other dosage regimens and be modified for other DPP-4 inhibitors to differentiate their properties.