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.

Pharmacometric modeling of drug adverse effects: an application of mixture models in schizophrenia spectrum disorder patients treated with clozapine

Clozapine has superior efficacy to other antipsychotics yet is underutilized due to its adverse effects, such as neutropenia, weight gain, and tachycardia. The current investigation aimed to introduce a pharmacometric approach to simultaneously model drug adverse effects, with examples from schizophrenia spectrum patients receiving clozapine. The adverse drug effects were represented as a function of time by incorporating a mixture model to describe individual susceptibility to the adverse effects. Applications of the proposed method were presented by analyzing retrospective data from patients' medical records in Psychiatric Clinic, Penang General Hospital. Tachycardia, weight gain, and absolute neutrophils count (ANC) decrease were best described by an offset, a piecewise linear, and a transient surge function, respectively. 42.9% of the patients had all the adverse effects, including weight gain (0.01 kg/m² increase every week over a baseline of 24.7 kg/m² until stabilizing at 279 weeks), ANC decrease (20% decrease from 4540 cells/µL week 12-20.8), and tachycardia (14% constant increase over a baseline of 87.9 bpm for a clozapine maintenance dose of 450 mg daily). 32.5% of the patients had only tachycardia, while the remaining 24.6% had none of the adverse effects. A new pharmacometric approach was proposed to describe adverse drug effects with examples of clozapine-induced weight gain, ANC drop, and tachycardia. The current approach described the longitudinal time changes of continuous data while assessing patient susceptibility. Furthermore, the model revealed the possible co-existence of ANC drop and weight gain; thus, neutrophil monitoring might predict future changes in body weight.

Optimization of trial duration to predict long-term HbA1c change with therapy: A pharmacometrics simulation-based evaluation

Glycated hemoglobin (HbA1c) is the main biomarker of diabetes drug development. However, because of its delayed turnover, trial duration is rarely shorter than 12 weeks, and being able to predict long-term HbA1c with precision using data from shorter studies would be beneficial. The feasibility of reducing study duration was therefore investigated in this study, assuming a model-based analysis. The aim was to investigate the predictive performance of 24- and 52-week extrapolations using data from up to 4, 6, 8 or 12 weeks, with six previously published pharmacometric models of HbA1c. Predictive performance was assessed through simulation-based dose-response predictions and model averaging (MA) with two hypothetical drugs. Results were consistent across the methods of assessment, with MA supporting the results derived from the model-based framework. The models using mean plasma glucose (MPG) or nonlinear fasting plasma glucose (FPG) effect, driving the HbA1c formation, showed good predictive performance despite a reduced study duration. The models, using the linear effect of FPG to drive the HbA1c formation, were sensitive to the limited amount of data in the shorter studies. The MA with bootstrap demonstrated strongly that a 4-week study duration is insufficient for precise predictions of all models. Our findings suggest that if data are analyzed with a pharmacometric model with MPG or FPG with a nonlinear effect to drive HbA1c formation, a study duration of 8 weeks is sufficient with maintained accuracy and precision of dose-response predictions.

Impact of Obesity on Postprandial Triglyceride Contribution to Glucose Homeostasis, Assessed with a Semimechanistic Model

The integrated glucose-insulin model is a semimechanistic model describing glucose and insulin after a glucose challenge. Similarly, a semiphysiologic model of the postprandial triglyceride (TG) response in chylomicrons and VLDL-V6 was recently published. We have developed the triglyceride-insulin-glucose-GLP-1 (TIGG) model by integrating these models and active GLP-1. The aim was to characterize, using the TIGG model, the postprandial response over 13 hours following a high-fat meal in 3 study populations based on body mass index categories: lean, obese, and very obese. Differential glucose and lipid regulation were observed between the lean population and obese or very obese populations. A population comparison revealed further that fasting glucose and insulin were elevated in obese and very obese when compared with lean; and euglycemia was achieved at different times postmeal between the obese and very obese populations. Postprandial insulin was incrementally elevated in the obese and very obese populations compared with lean. Postprandial chylomicrons TGs were similar across populations, whereas the postprandial TGs in VLDL-V6 were increased in the obese and very obese populations compared with lean. Postprandial active GLP-1 was diminished in the very obese population compared with lean or obese. The TIGG model described the response following a high-fat meal in individuals who are lean, obese, and very obese and provided insight into the possible regulation of glucose homeostasis in the extended period after the meal by utilizing lipids. The TIGG-model is the first model to integrate glucose and insulin regulation, incretin effect, and postprandial TGs response in chylomicrons and VLDL-V6.

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.

Mathematical Modeling for the Physiological and Clinical Investigation of Glucose Homeostasis and Diabetes

Mathematical modeling in the field of glucose metabolism has a longstanding tradition. The use of models is motivated by several reasons. Models have been used for calculating parameters of physiological interest from experimental data indirectly, to provide an unambiguous quantitative representation of pathophysiological mechanisms, to determine indices of clinical usefulness from simple experimental tests. With the growing societal impact of type 2 diabetes, which involves the disturbance of the glucose homeostasis system, development and use of models in this area have increased. Following the approaches of physiological and clinical investigation, the focus of the models has spanned from representations of whole body processes to those of cells, i.e., from in vivo to in vitro research. Model-based approaches for linking in vivo to in vitro research have been proposed, as well as multiscale models merging the two areas. The success and impact of models has been variable. Two kinds of models have received remarkable interest: those widely used in clinical applications, e.g., for the assessment of insulin sensitivity and β-cell function and some models representing specific aspects of the glucose homeostasis system, which have become iconic for their efficacy in describing clearly and compactly key physiological processes, such as insulin secretion from the pancreatic β cells. Models are inevitably simplified and approximate representations of a physiological system. Key to their success is an appropriate balance between adherence to reality, comprehensibility, interpretative value and practical usefulness. This has been achieved with a variety of approaches. Although many models concerning the glucose homeostasis system have been proposed, research in this area still needs to address numerous issues and tackle new opportunities. The mathematical representation of the glucose homeostasis processes is only partial, also because some mechanisms are still only partially understood. For in vitro research, mathematical models still need to develop their potential. This review illustrates the problems, approaches and contribution of mathematical modeling to the physiological and clinical investigation of glucose homeostasis and diabetes, focusing on the most relevant and stimulating models.

Modelling of fasting glucose-insulin dynamics from sparse data

With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.

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.

Study Design Selection in Early Clinical Anti-Hyperglycemic Drug Development: A Simulation Study of Glucose Tolerance Tests

In antidiabetic drug development, phase I studies usually involve short‐term glucose provocations. Multiple designs are available for these provocations (e.g., meal tolerance tests (MTTs) and graded glucose infusions (GGIs)). With a highly nonlinear, complex system as the glucose homeostasis, the various provocations will contribute with different information offering a rich choice. Here, we investigate the most appropriate study design in phase I for several hypothetical mechanisms of action of a study drug. Five drug effects in diabetes therapeutic areas were investigated using six study designs. Power to detect drug effect was assessed using the likelihood ratio test, whereas precision and accuracy of the quantification of drug effect was assessed using stochastic simulation and estimations. An overall summary was developed to aid designing the studies of antihyperglycemic drug development using model‐based analysis. This guidance is to be used when the integrated glucose insulin model is used, involving the investigated drug mechanisms of action.

Model-Based Evaluation of Proximal Sodium Reabsorption Through SGLT2 in Health and Diabetes and the Effect of Inhibition With Canagliflozin

Sodium-glucose cotransporter 2 inhibitors (SGLT2i) reduce glucose levels in diabetes by inhibiting renal glucose reabsorption in the proximal tubule (PT), resulting in urinary glucose excretion. A recent large cardiovascular outcomes trial suggested that the SGLT2i empagliflozin may also decrease risk of renal dysfunction. Because sodium (Na) and glucose reabsorption are coupled through SGLT2, it is hypothesized that the renal benefits may be derived from lowering Na reabsorption in the PT, which would lead to favorable renal hemodynamic changes. However, the quantitative contribution of SGLT2 to PT Na reabsorption, as well as the differences between healthy and diabetic subjects, and the impact of SGLT2i on PT Na reabsorption are unknown. In this study we extended an existing mathematical model of glucose dynamics to account for renal glucose filtration and excretion. We utilized this model to quantify glucose and Na reabsorption through SGLT2 in healthy, controlled, and uncontrolled diabetes and following treatment with canagliflozin. In healthy, controlled diabetic, and uncontrolled diabetic states, Na reabsorption through SGLT2 was found to be 5.7%, 11.5%, and 13.7% of total renal Na reabsorption, and 7.1% to 9.5%, 14.4% to 19.2%, and 17.1% to 22.8% of sodium reabsorption in the PT alone. The model predicted that treatment of controlled diabetes with canagliflozin returns PT Na reabsorption through SGLT2 to normal levels. The degree of increased PT Na reabsorption due to SGLT2 is likely sufficient to drive pathologic changes in renal hemodynamics, and restoration of normal Na reabsorption through SGLT2 may contribute to beneficial renal effects of SGLT2 inhibition.

Simulation-Based Evaluation of Dose-Titration Algorithms for Rapid-Acting Insulin in Subjects with Type 2 Diabetes Mellitus Inadequately Controlled on Basal Insulin and Oral Antihyperglycemic Medications

BACKGROUND

The purpose of this prospective, model-based simulation approach was to evaluate the impact of various rapid-acting mealtime insulin dose-titration algorithms on glycemic control (hemoglobin A1c [HbA1c]).

METHODS

Seven stepwise, glucose-driven insulin dose-titration algorithms were evaluated with a model-based simulation approach by using insulin lispro. Pre-meal blood glucose readings were used to adjust insulin lispro doses. Two control dosing algorithms were included for comparison: no insulin lispro (basal insulin+metformin only) or insulin lispro with fixed doses without titration.

RESULTS

Of the seven dosing algorithms assessed, daily adjustment of insulin lispro dose, when glucose targets were met at pre-breakfast, pre-lunch, and pre-dinner, sequentially, demonstrated greater HbA1c reduction at 24 weeks, compared with the other dosing algorithms. Hypoglycemic rates were comparable among the dosing algorithms except for higher rates with the insulin lispro fixed-dose scenario (no titration), as expected. The inferior HbA1c response for the "basal plus metformin only" arm supports the additional glycemic benefit with prandial insulin lispro.

CONCLUSIONS

Our model-based simulations support a simplified dosing algorithm that does not include carbohydrate counting, but that includes glucose targets for daily dose adjustment to maintain glycemic control with a low risk of hypoglycemia.

Influence of Meals and Glycemic Changes on QT Interval Dynamics

Thorough QT/QTc studies have become an integral part of early drug development programs, with major clinical and regulatory implications. This analysis expands on existing pharmacodynamic models of QT interval analysis by incorporating the influence of glycemic changes on the QT interval in a semimechanistic manner. A total of 21 healthy subjects enrolled in an open-label phase 1 pilot study and provided continuous electrocardiogram monitoring and plasma glucose and insulin concentrations associated with a 24-hour baseline assessment. The data revealed a transient decrease in QTc, with peak suppression occurring approximately 3 hours after the meal. A semimechanistic modeling approach was applied to evaluate temporal delays between meals and subsequent changes that might influence QT measurements. The food effect was incorporated into a model of heart rate dynamics, and additional delayed effects of the meal on QT were incorporated using a glucose-dependent hypothetical transit compartment. The final model helps to provide a foundation for the future design and analysis of QT studies that may be confounded by meals. This study has significant implications for QT study assessment following a meal or when a cohort is receiving a medication that influences postprandial glucose concentrations.

Application of the integrated glucose-insulin model for cross-study characterization of T2DM patients on metformin background treatment

AIM

The integrated glucose-insulin (IGI) model is a semi-mechanistic physiological model which can describe the glucose-insulin homeostasis system following various glucose challenge settings. The aim of the present work was to apply the model to a large and diverse population of metformin-only-treated type 2 diabetes mellitus (T2DM) patients and identify patient-specific covariates.

METHODS

Data from four clinical studies were pooled, including glucose and insulin concentration-time profiles from T2DM patients on stable treatment with metformin alone following mixed-meal tolerance tests. The data were collected from a wide range of patients with respect to the duration of diabetes and level of glycaemic control.

RESULTS

The IGI model was expanded by four patient-specific covariates. The level of glycaemic control, represented by baseline glycosylated haemoglobin was identified as a significant covariate for steady-state glucose, insulin-dependent glucose clearance and the magnitude of the incretin effect, while baseline body mass index was a significant covariate for steady-state insulin levels. In addition, glucose dose was found to have an impact on glucose absorption rate. The developed model was used to simulate glucose and insulin profiles in different groups of T2DM patients, across a range of glycaemic control, and it was found accurately to characterize their response to the standard oral glucose challenge.

CONCLUSIONS

The IGI model was successfully applied to characterize differences between T2DM patients across a wide range of glycaemic control. The addition of patient-specific covariates in the IGI model might be valuable for the future development of antidiabetic treatment and for the design and simulation of clinical studies.

Pharmacokinetic and Pharmacodynamic Drug Interaction Study of Piragliatin, a Glucokinase Activator, and Glyburide, a Sulfonylurea, in Type 2 Diabetic Patients

A glucokinase activator and a sulfonylurea might be coprescribed to synergize treatment success for type 2 diabetes (T2D). This clinical pharmacology study was designed to investigate the potential glucose-lowering effect or pharmacodynamic (PD), pharmacokinetic (PK), and safety/tolerability interactions between piragliatin and glyburide in T2D patients already taking glyburide but not adequately controlled. This was an open-label, multiple-dose, 3-period, single-sequence crossover design: on days -1, 6, and 12, PD and PK samples were drawn with glyburide alone (period 0), piragliatin + glyburide (period 1), and piragliatin alone (period 2) treatments. The glucose-lowering effect, including fasting plasma glucose (FPG), of piragliatin was more pronounced when it was administered concomitantly with glyburide as compared to piragliatin or glyburide administered alone. However, this enhancement cannot be explained by a potential PK interaction between piragliatin and glyburide. Other than hypoglycemia, there were no clinically relevant safety findings. Thus, the enhanced PD effect warrants further investigation to define the optimal dose combination between glucokinase activators and sulfonylureas with regard to efficacy, safety, and tolerability.

Effects of piragliatin, a glucokinase activator, on fasting and postprandial plasma glucose in patients with type 2 diabetes mellitus

To assess the safety, tolerability, pharmacokinetics (PK), and pharmacodynamics (PD) of piragliatin, a double-blind, randomized, placebo-controlled, multiple-ascending-doses study was conducted in patients with type 2 diabetes mellitus (T2D). Fifty-nine T2D patients were given piragliatin or placebo in a dose-escalation design as a single dose on day 1 followed by multiple doses on days 3 through 8 at doses of 10, 25, 50, 100, and 200 mg twice a day (BID) as well as 200 mg every day (QD). Blood and urine samples were collected for PK analysis. PD assessments included plasma glucose, insulin, C-peptide, glucagon, and GLP-1. Piragliatin exposure was dose proportional without appreciable accumulation or food effect. Piragliatin treatment at steady state yielded dose-dependent reductions up to 32.5% and 35.5% for the highest dose in fasting and postprandial plasma glucose. Piragliatin was well tolerated. Mild or moderate hypoglycemia with rapid recovery after sugar-containing drinks or scheduled meals was the only dose-limiting adverse event. It is concluded that multiple doses of piragliatin consistently showed rapid, dose-dependent glucose reduction of fasting and postprandial plasma glucose in T2D patients.

Exploratory effects of a strong CYP3A inhibitor (ketoconazole), a strong CYP3A inducer (rifampicin), and concomitant ethanol on piragliatin pharmacokinetics and pharmacodynamics in type 2 diabetic patients

Piragliatin is a CYP3A substrate; its inactive metabolite M4, formed through cytosolic reductase, is reversibly metabolized back to piragliatin through CYP3A. The impact of concomitant CYP3A modifiers thus cannot be predicted. Drinking alcohol under fasting conditions is associated with a recognized glucose-lowering effect, which might be synergistic with piragliatin's hypoglycemic effect. Two exploratory studies were conducted to examine these potential interactions in type 2 diabetes (T2D) patients: 16 completed an open-label, sequential 2-way crossover, 2-arm (randomized to ketoconazole and rifampicin) CYP3A study; another 18 participated in a double-blind, placebo-controlled, randomized 3-way crossover ethanol study. Administration of piragliatin (100-mg single dose) resulted in a 32% Cmax and 44% area under the curve (AUC∞ ) increase in piragliatin exposure without affecting glucose AUC0-6h following ketoconazole (400 mg QD × 5 days); 30% Cmax and 72% AUC∞ decrease in piragliatin exposure with a 13% increase in glucose AUC0-6h following rifampicin (600 mg QD × 5 days); and, unexpectedly, a 32% Cmax and 23% AUC0-6h decrease (no change in AUC∞ ) in piragliatin exposure with a 13% increase in glucose AUC0-6h following alcohol (40-g single dose). In conclusion, a strong CYP3A modifier or concomitant alcohol could lead to a change in exposure to piragliatin with a potential alteration in glucose-lowering effect.

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.

Pharmacokinetic-pharmacodynamic modelling of biomarker response to sitagliptin in healthy volunteers

Pharmacokinetic/pharmacodynamic (PK/PD) models can be useful tools in new drug development and also optimal drug therapy in patients. This study was designed to develop a PK/PD model of sitagliptin based on the physiology of incretin. The PK/PD data included information derived from two different studies. Study 1 was conducted as a one-sequence, three-period, repeated-dose, dose escalation (sitagliptin 25, 50 and 100 mg q.d.) design in twelve healthy volunteers. Study 2 was a first-in-man study for the newly developed dipeptidyl peptidase-4 (DPP-4) inhibitor in healthy volunteers. In study 1, blood samples were collected to measure sitagliptin concentrations, DPP-4 activity and active glucagon-like peptide-1 (GLP-1) concentrations. In study 2, only data from the 'placebo group' were used, and blood samples were collected to measure DPP-4 activity, active GLP-1 concentrations and glucose concentrations. A PK/PD analysis was conducted using a non-linear mixed effects modelling approach. Sitagliptin pharmacokinetics was modelled using a two-compartment model with first-order absorption. Changes in DPP-4 inhibition were linked to the PK model using a sigmoid Emax model, whereas the active GLP-1 changes were explained using an indirect response model; this model incorporated the glucose and DPP-4 inhibition models. The PK/PD model developed adequately described the changes in sitagliptin concentration, DPP-4 inhibition and active GLP-1 concentration in healthy volunteers.

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.