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.

Quantitative model of the relationship between dipeptidyl peptidase-4 (DPP-4) inhibition and response: meta-analysis of alogliptin, saxagliptin, sitagliptin, and vildagliptin efficacy results

Dipeptidyl peptidase-4 (DPP-4) inhibition is a well- characterized treatment for type 2 diabetes mellitus (T2DM). The objective of this model-based meta-analysis was to describe the time course of HbA1c response after dosing with alogliptin (ALOG), saxagliptin (SAXA), sitagliptin (SITA), or vildagliptin (VILD). Publicly available data involving late-stage or marketed DPP-4 inhibitors were leveraged for the analysis. Nonlinear mixed-effects modeling was performed to describe the relationship between DPP-4 inhibition and mean response over time. Plots of the relationship between metrics of DPP-4 inhibition (ie, weighted average inhibition [WAI], time above 80% inhibition, and trough inhibition) and response after 12 weeks of daily dosing were evaluated. The WAI was most closely related to outcome, although other metrics performed well. A model was constructed that included fixed effects for placebo and drug and random effects for intertrial variability and residual error. The relationship between WAI and outcome was nonlinear, with an increasing response up to 98% WAI. Response to DPP-4 inhibitors could be described with a single drug effect. The WAI appears to be a useful index of DPP-4 inhibition related to HbA1c. Biomarker to response relationships informed by model-based meta-analysis can be leveraged to support study designs including optimization of dose, duration of therapy, and patient population.

Mechanistic modeling of the effects of glucocorticoids and circadian rhythms on adipokine expression

A mechanism-based model was developed to describe the effects of methylprednisolone (MPL), circadian rhythms, and the glucose/free fatty acid (FFA)/insulin system on leptin and adiponectin expression in white adipose tissue in rats. Fifty-four normal Wistar rats received 50 mg/kg MPL intramuscularly and were sacrificed at various times. An additional set of 54 normal Wistar rats were sacrificed at 18 time points across the 24-h light/dark cycle and served as controls. Measurements included plasma MPL, glucocorticoid receptor (GR) mRNA, leptin mRNA, adiponectin mRNA, plasma leptin, adiponectin, glucose, FFA, and insulin. MPL pharmacokinetics was described by a two-compartment model with two absorption components. All measured plasma markers and mRNA expression exhibited circadian patterns except for adiponectin and were described by Fourier harmonic functions. MPL caused significant down-regulation in GR mRNA with the nadir occurring at 5 h. MPL disrupted the circadian patterns in plasma glucose and FFA by stimulating their production. Plasma glucose and FFA subsequently caused an increase in plasma insulin. Furthermore, MPL disrupted the circadian patterns in leptin mRNA expression by stimulating its production. This rise was closely followed by an increase in plasma leptin. Both leptin mRNA and plasma leptin peaked at 12 h after MPL and eventually returned back to their circadian baselines. MPL and insulin had opposing effects on adiponectin mRNA expression and plasma adiponectin, which resulted in biphasic pharmacodynamic profiles. This small systems model quantitatively describes, integrates, and provides additional insights into various factors controlling adipokine gene expression.

Dynamic modeling of methylprednisolone effects on body weight and glucose regulation in rats

Influences of methylprednisolone (MPL) and food consumption on body weight (BW), and the effects of MPL on glycemic control including food consumption and the dynamic interactions among glucose, insulin, and free fatty acids (FFA) were evaluated in normal male Wistar rats. Six groups of animals received either saline or MPL via subcutaneous infusions at the rate of 0.03, 0.1, 0.2, 0.3 and 0.4 mg/kg/h for different treatment periods. BW and food consumption were measured twice a week. Plasma concentrations of MPL and corticosterone (CST) were determined at animal sacrifice. Plasma glucose, insulin, and FFA were measured at various times after infusion. Plasma MPL concentrations were simulated by a two-compartment model and used as the driving force in the pharmacodynamic (PD) analysis. All data were modeled using ADAPT 5. The MPL treatments caused reduction of food consumption and body weights in all dosing groups. The steroid also caused changes in plasma glucose, insulin, and FFA concentrations. Hyperinsulinemia was achieved rapidly at the first sampling time of 6 h; significant elevations of FFA were observed in all drug treatment groups; whereas only modest increases in plasma glucose were observed in the low dosing groups (0.03 and 0.1 mg/kg/h). Body weight changes were modeled by dual actions of MPL: inhibition of food consumption and stimulation of weight loss, with food consumption accounting for the input of energy for body weight. Dynamic models of glucose and insulin feedback interactions were extended to capture the major metabolic effects of FFA: stimulation of insulin secretion and inhibition of insulin-stimulated glucose utilization. These models of body weight and glucose regulation adequately captured the experimental data and reflect significant physiological interactions among glucose, insulin, and FFA. These mechanism-based PD models provide further insights into the multi-factor control of this essential metabolic system.

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.

Pharmacometrics 2020

This special issue of the Journal of Clinical Pharmacology is dedicated to pharmacometrics, covering topics related to methodological research, application to decisions, standardization, PhRMA survey, and growth strategy. Innovative methodological and technological advances in analyzing disease, drug, and trial data have equipped pharmacometricians with the know-how to influence high-level decisions, which in turn creates more pharmacometric opportunities. Pharmacometrics is revolutionizing drug development and regulatory decision making. To sustain the success and growth of this field, we need to up the ante. Strategic goals for pharmacometric groups in industry, regulatory agencies, and academia are proposed in this report. These goals should be of significance to all stakeholders who have a vested interest in drug development and therapeutics. The future of pharmacometrics depends on how well we all can deliver on the strategic goals.

Mechanism-based disease progression modeling of type 2 diabetes in Goto-Kakizaki rats

The dynamics of aging and type 2 diabetes (T2D) disease progression were investigated in normal [Wistar-Kyoto (WKY)] and diabetic [Goto-Kakizaki (GK)] rats and a mechanistic disease progression model was developed for glucose, insulin, and glycosylated hemoglobin (HbA1c) changes over time. The study included 30 WKY and 30 GK rats. Plasma glucose and insulin, blood glucose and HbA1c concentrations and hematological measurements were taken at ages 4, 8, 12, 16 and 20 weeks. A mathematical model described the development of insulin resistance (IR) and β-cell function with age/growth and diabetes progression. The model utilized transit compartments and an indirect response model to quantitate biomarker changes over time. Glucose, insulin and HbA1c concentrations in WKY rats increased to a steady-state at 8 weeks due to developmental changes. Glucose concentrations at 4 weeks in GK rats were almost twice those of controls, and increased to a steady-state after 8 weeks. Insulin concentrations at 4 weeks in GK rats were similar to controls, and then hyperinsulinemia occurred until 12-16 weeks of age indicating IR. Subsequently, insulin concentrations in GK rats declined to slightly below WKY controls due to β-cell failure. HbA1c showed a delayed increase relative to glucose. Modeling of HbA1c was complicated by age-related changes in hematology in rats. The diabetes model quantitatively described the glucose/insulin inter-regulation and HbA1c production and reflected the underlying pathogenic factors of T2D--IR and β-cell dysfunction. The model could be extended to incorporate other biomarkers and effects of various anti-diabetic drugs.

Optimal design of clinical tests for the identification of physiological models of type 1 diabetes in the presence of model mismatch

How to design a clinical test aimed at identifying in the safest, most precise and quickest way the subject-specific parameters of a detailed model of glucose homeostasis in type 1 diabetes is the topic of this article. Recently, standard techniques of model-based design of experiments (MBDoE) for parameter identification have been proposed to design clinical tests for the identification of the model parameters for a single type 1 diabetic individual. However, standard MBDoE is affected by some limitations. In particular, the existence of a structural mismatch between the responses of the subject and that of the model to be identified, together with initial uncertainty in the model parameters may lead to design clinical tests that are sub-optimal (scarcely informative) or even unsafe (the actual response of the subject might be hypoglycaemic or strongly hyperglycaemic). The integrated use of two advanced MBDoE techniques (online model-based redesign of experiments and backoff-based MBDoE) is proposed in this article as a way to effectively tackle the above issue. Online model-based experiment redesign is utilised to exploit the information embedded in the experimental data as soon as the data become available, and to adjust the clinical test accordingly whilst the test is running. Backoff-based MBDoE explicitly accounts for model parameter uncertainty, and allows one to plan a test that is both optimally informative and safe by design. The effectiveness and features of the proposed approach are assessed and critically discussed via a simulated case study based on state-of-the-art detailed models of glucose homeostasis. It is shown that the proposed approach based on advanced MBDoE techniques allows defining safe, informative and subject-tailored clinical tests for model identification, with limited experimental effort.

Pharmacokinetic/pharmacodynamic modeling of glucose clamp effects of inhaled and subcutaneous insulin in healthy volunteers and diabetic patients

The pharmacokinetics and pharmacodynamics (PK/PD) of inhaled insulin in humans have not been modeled previously. We rationalized a model for the effects of inhaled insulin on glucose infusion rate during a euglycemic clamp study based on the mechanism of insulin action and compared parameter estimates between subcutaneous and inhaled insulin in healthy and diabetic subjects. Published data from two studies in 11 healthy volunteers and 18 type 1 diabetes patients were digitized. The subjects received four different doses of inhaled insulin and one or three different doses subcutaneously at the start of a 10 h glucose clamp. All data were modeled simultaneously using NONMEM VI. Insulin pharmacokinetics were described by a one-compartment model with one (inhaled) or two (subcutaneous insulin) first-order absorption processes and first-order elimination. Insulin effects on glucose were described by an indirect response model. A biophase direct effect equation for the glucose infusion rate was implemented. Pharmacodynamic parameter estimates were 15.1 mg/min/kg for maximal glucose infusion rate (GIR(max)) and 88.0 mIU/L for SC(50) for diabetic patients and 62.9 mIU/L for healthy subjects. A PK/PD model based on fundamental principles of insulin action and glucose turnover suggests comparable potencies of inhaled and subcutaneous insulin.

Simple pharmacometric tools for oral anti-diabetic drug development: competitive landscape for oral non-insulin therapies in type 2 diabetes

The objectives were to develop a translational model that will help select doses for Phase-3 trials based on abbreviated Phase-2 trials and understand the competitive landscape for oral anti-diabetics based on efficacy, tolerability and ability to slow disease progression. Data for eight anti-diabetics with temporal profiles for fasting plasma glucose (FPG) and hemoglobin A1c (HbA1c) from 12 publications were digitized. The monotherapy data consisted of FPG and HbA1c profiles that were modeled using a transit compartment model. Evaluation of the competitive landscape utilized HbA1c literature data for 11 drugs. For the safety metric, tolerability issues with anti-diabetic drug classes were tabulated. For disease progression, the coefficient of failure method was used for assessing data from two long-term trials. The transit rate constants were remarkably consistent across 12 trials and represent system-specific/drug-independent parameters. The competitive landscape analysis showed that the primary efficacy metric fell into two groups of DeltaHbA1c: >0.8% or approximately 0.8%. On the safety endpoints, older agents showed some tolerability issues while the new agents were relatively safe. Among the different drug classes, only the thiazolidinediones appeared to slow disease progression but may also increase heart failure risk. In conclusion, the ability of system-specific parameters to predict HbA1c provides a tool to predict the expected efficacy profile from abbreviated dose-finding trials. To be commercially viable, new drugs should improve DeltaHbA1c by about 0.8% or more and possess safety profiles similar to newer anti-diabetic agents. Thus, this study proposes a suite of simple yet powerful tools to guide type-2-diabetes drug development.

A model for glucose, insulin, and beta-cell dynamics in subjects with insulin resistance and patients with type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity and loss of beta-cell mass (BCM), resulting in hyperglycemia. Population pharmacokinetic-pharmacodynamic (PKPD) modeling is a valuable method to gain insight into disease and drug action. A semi-mechanistic PKPD model incorporating fasting plasma glucose (FPG), fasting insulin, insulin sensitivity, and BCM in patients at various disease stages was developed. Data from 3 clinical trials (phase II/III) with a peroxisome proliferator-activated receptor agonist, tesaglitazar, were used to develop the model. In this, a modeling framework proposed by Topp et al was expanded to incorporate the effects of treatment and impact of disease, as well as variability between subjects. The model accurately described FPG and fasting insulin data over time. The model included a strong relation between insulin clearance and insulin sensitivity, predicted 40% to 60% lower BCM in T2DM patients, and realistic improvements of BCM and insulin sensitivity with treatment. The treatment response on insulin sensitivity occurs within the first weeks, whereas the positive effects on BCM arise over several months. The semi-mechanistic PKPD model well described the heterogeneous populations, ranging from nondiabetic, insulin-resistant subjects to long-term treated T2DM patients. This model also allows incorporation of clinical-experimental studies and actual observations of BCM.

An integrated model for the glucose-insulin system

The integrated glucose-insulin model was originally developed on a variety of intravenous glucose provocation experiments in healthy volunteers and type 2 diabetic patients. The model, which simultaneously describes time-courses of glucose and insulin based on mechanism-based components for production, elimination and homeostatic feedback, has been further extended to oral glucose provocations, meal tests and insulin administration. The model has been used to describe experiments ranging from 4 to 24 hr. Applications of the integrated glucose-insulin model include the clinical assessment of the mechanism of action of anti-diabetic drugs and the magnitude of their effects. Finally, the model was used for optimizing the design of provocation experiments.

An integrated glucose-insulin model to describe oral glucose tolerance test data in healthy volunteers

The extension of the previously developed integrated models for glucose and insulin (IGI) to include the oral glucose tolerance test (OGTT) in healthy volunteers could be valuable to better understand the differences between healthy individuals and those with type 2 diabetes mellitus (T2DM). Data from an OGTT in 23 healthy volunteers were used. Analysis was based on the previously developed intravenous model with extensions for glucose absorption and incretin effect on insulin secretion. The need for additional structural components was evaluated. The model was evaluated by simulation and a bootstrap. Multiple glucose and insulin concentration peaks were observed in most individuals as well as hypoglycemic episodes in the second half of the experiment. The OGTT data were successfully described by the extended basic model. An additional control mechanism of insulin on glucose production improved the description of the data. The model showed good predictive properties, and parameters were estimated with good precision. In conclusion, a previously presented integrated model has been extended to describe glucose and insulin concentrations in healthy volunteers following an OGTT. The characterization of the differences between the healthy and diabetic stages in the IGI model could potentially be used to extrapolate drug effect from healthy volunteers to T2DM.

Pharmacokinetic and pharmacodynamic modeling of a monoclonal antibody antagonist of glucagon receptor in male ob/ob mice

Elevated basal concentrations of glucagon and reduced postprandial glucagon suppression are partly responsible for the increased hepatic glucose production seen in type 2 diabetic patients. Recently, it was demonstrated that an antagonistic human monoclonal antibody (mAb) blocking glucagon receptor (GCGR) has profound glucose-lowering effects in various animal models. To further understand the effects on glucose homeostasis mediated by such an antibody, a pharmacokinetic-pharmacodynamic (PK-PD) study was conducted in a diabetic ob/ob mouse model. Four groups of ob/ob mice were randomized to receive single intraperitoneal administration of placebo, 0.6, 1, or 3 mg/kg of mAb GCGR, a fully human mAb against GCGR. The concentration-time data were used for noncompartmental and compartmental analysis. A semi-mechanistic PK-PD model incorporating the glucose-glucagon inter-regulation and the hypothesized inhibitory effect of mAb GCGR on GCGR signaling pathway via competitive inhibition was included to describe the disposition of glucose and glucagon over time. The pharmacokinetics of mAb GCGR was well characterized by a two-compartment model with parallel linear and nonlinear saturable eliminations. Single injection of mAb GCGR caused a rapid glucose-lowering effect with blood glucose concentrations returning to baseline by 4 to 18 days with increasing dose from 0.6 to 3 mg/kg. Elevation of glucagon concentrations was also observed in a dose-dependent manner. The results illustrated that the feedback relationship between glucose and glucagon in the presence of mAb GCGR could be quantitatively described by the developed model. The model may provide additional understanding in the underlying mechanism of GCGR antagonism by mAb.

Optimization of the intravenous glucose tolerance test in T2DM patients using optimal experimental design

Intravenous glucose tolerance test (IVGTT) provocations are informative, but complex and laborious, for studying the glucose-insulin system. The objective of this study was to evaluate, through optimal design methodology, the possibilities of more informative and/or less laborious study design of the insulin modified IVGTT in type 2 diabetic patients. A previously developed model for glucose and insulin regulation was implemented in the optimal design software PopED 2.0. The following aspects of the study design of the insulin modified IVGTT were evaluated; (1) glucose dose, (2) insulin infusion, (3) combination of (1) and (2), (4) sampling times, (5) exclusion of labeled glucose. Constraints were incorporated to avoid prolonged hyper- and/or hypoglycemia and a reduced design was used to decrease run times. Design efficiency was calculated as a measure of the improvement with an optimal design compared to the basic design. The results showed that the design of the insulin modified IVGTT could be substantially improved by the use of an optimized design compared to the standard design and that it was possible to use a reduced number of samples. Optimization of sample times gave the largest improvement followed by insulin dose. The results further showed that it was possible to reduce the total sample time with only a minor loss in efficiency. Simulations confirmed the predictions from PopED. The predicted uncertainty of parameter estimates (CV) was low in all tested cases, despite the reduction in the number of samples/subject. The best design had a predicted average CV of parameter estimates of 19.5%. We conclude that improvement can be made to the design of the insulin modified IVGTT and that the most important design factor was the placement of sample times followed by the use of an optimal insulin dose. This paper illustrates how complex provocation experiments can be improved by sequential modeling and optimal design.