A semi-mechanistic model of the relationship between average glucose and HbA1c in healthy and diabetic subjects

Safety, pharmacokinetics and pharmacodynamics of HRS-7535, a novel oral small molecule glucagon-like peptide-1 receptor agonist, in healthy participants: A phase 1, randomized, double-blind, placebo-controlled, single- and multiple-ascending dose, and food effect trial

AIM

To assess the safety, tolerability, pharmacokinetics (PKs) and pharmacodynamics of HRS-7535, a novel glucagon-like peptide-1 receptor agonist (GLP-1RA), in healthy participants.

MATERIALS AND METHODS

This phase 1 trial consisted of single-ascending dose (SAD), food effect (FE) and multiple-ascending dose (MAD) parts. In the SAD part, participants were randomized (6:2) to receive HRS-7535 (at doses of 15, 60 and 120 mg; administered orally once daily) or placebo. In the FE part, participants were randomized (8:2) to receive a single dose of 90-mg HRS-7535 or placebo, in both fed and fasted states. In the MAD part, participants were randomized (18:6) to receive daily HRS-7535 (120 mg [30/60/90/120-mg titration scheme]) or placebo for 28 days. The primary endpoints were safety and tolerability.

RESULTS

Nausea and vomiting were the most frequently reported AEs across all three parts. In the SAD part, the median Tmax was 5.98-5.99 hours and the geometric mean t1/2 was 5.28-9.08 hours across the HRS-7535 dosing range. In the MAD part, the median Tmax was 5.98-10.98 hours and the geometric mean t1/2 was 6.48-8.42 hours on day 28 in participants on HRS-7535. PKs were approximately dose-proportional. On day 29 in the MAD part, the mean (percentage) reduction in body weight from baseline was 4.38 kg (6.63%) for participants who received HRS-7535, compared with 0.8 kg (1.18%) for those participants who received a placebo.

CONCLUSIONS

HRS-7535 exhibited a safety and tolerability profile consistent with other GLP-1RAs and showed PKs suitable for once-daily dosing. These findings support further clinical development of HRS-7535 for type 2 diabetes.

A mechanistic modeling platform of SGLT2 inhibition: Implications for type 1 diabetes

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by abnormally high blood glucose concentrations due to dysfunction of the insulin-producing beta-cells in the pancreas. Dapagliflozin, an inhibitor of renal glucose reabsorption, has the potential to improve often suboptimal glycemic control in patients with T1DM through insulin-independent mechanisms and to partially mitigate the adverse effects associated with long-term insulin administration. In this work, we have adapted a systems pharmacology model of type 2 diabetes mellitus to describe the T1DM condition and characterize the effect of dapagliflozin on short- and long-term glycemic markers under various treatment scenarios. The developed platform serves as a quantitative tool for the in silico evaluation of the insulin-glucose-dapagliflozin crosstalk, optimization of the treatment regimens, and it can be further expanded to include additional therapies or other aspects of the disease.

Opportunities and Challenges of Disease Progression Modeling in Drug Development - An IQ Perspective

Disease progression modeling (DPM) represents an important model-informed drug development framework. The scientific communities support the use of DPM to accelerate and increase efficiency in drug development. This article summarizes International Consortium for Innovation & Quality (IQ) in Pharmaceutical Development mediated survey conducted across multiple biopharmaceutical companies on challenges and opportunities for DPM. Additionally, this summary highlights the viewpoints of IQ from the 2021 workshop hosted by the US Food and Drug Administration (FDA). Sixteen pharmaceutical companies participated in the IQ survey with 36 main questions. The types of questions included single/multiple choice, dichotomous, rank questions, and open-ended or free text. The key results show that DPM has different representation, it encompasses natural disease history, placebo response, standard of care as background therapy, and can even be interpreted as pharmacokinetic/pharmacodynamic modeling. The most common reasons for not implementing DPM as frequently seem to be difficulties in internal cross-functional alignment, lack of knowledge of disease/data, and time constraints. If successfully implemented, DPM can have an impact on dose selection, reduction of sample size, trial read-out support, patient selection/stratification, and supportive evidence for regulatory interactions. The key success factors and key challenges of disease progression models were highlighted in the survey and about 24 case studies across different therapeutic areas were submitted from various survey sponsors. Although DPM is still evolving, its current impact is limited but promising. The success of such models in the future will depend on collaboration, advanced analytics, availability of and access to relevant and adequate-quality data, collaborative regulatory guidance, and published examples of impact.

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.

Resistance to glycation in the zebra finch: Mass spectrometry-based analysis and its perspectives for evolutionary studies of aging

In humans, hyperglycemia is associated with protein glycation, which may contribute to aging. Strikingly, birds usually outlive mammals of the same body mass, while exhibiting high plasma glucose levels. However, how birds succeed in escaping pro-aging effects of glycation remains unknown. Using a specific mass spectrometry-based approach in captive zebra finches of known age, we recorded high glycaemia values but no glycated hemoglobin form was found. Still, we showed that zebra finch hemoglobin can be glycated in vitro, albeit only to a limited extent compared to its human homologue. This may be due to peculiar structural features, as supported by the unusual presence of three different tetramer populations with balanced proportions and a still bound cofactor that could be inositol pentaphosphate. High levels of the glycated forms of zebra finch plasma serotransferrin, carbonic anhydrase 2, and albumin were measured. Glucose, age or body mass correlations with either plasma glycated proteins or hemoglobin isoforms suggest that those variables may be future molecular tools of choice to monitor glycation and its link with individual fitness. Our molecular advance may help determine how evolution succeeded in associating flying ability, high blood glucose and long lifespan in birds.

Pharmacometrics Approaches and its Applications in Diabetes: An Overview

Type 2 diabetes mellitus is the most prevalent and progressive in nature. As the time progress, the multifaceted complications and comorbidities associated to diabetes worsen in the form of macrovascular or microvascular or both. Pharmacometrics modeling is a step forward in minimizing the risk or at least understanding the factors associated to its progression with the passage of time. These models investigate diabetes treatments effects and the progression factors with different viewpoints incorporating insulin-glucose dynamics, dose-response and pharmacokinetics, and pharmacodynamics relationships. Pharmacometrics modeling is an innovative approach in a sense that it is taking us away from the conventional analysis by providing all the opportunities in improving the decision-making in health sector. It has been suggested that we can achieve greater statistical power for determining drug effects through model-based evaluation than through traditional evaluations. The main aim of this review was to evaluate pharmacometrics approaches used in modeling diabetes progression through time and also the integrated models describing glucose-insulin dynamics.

Danuglipron (PF-06882961) in type 2 diabetes: a randomized, placebo-controlled, multiple ascending-dose phase 1 trial

Agonism of the glucagon-like peptide-1 receptor (GLP-1R) results in glycemic lowering and body weight loss and is a therapeutic strategy to treat type 2 diabetes (T2D) and obesity. We developed danuglipron (PF-06882961), an oral small-molecule GLP-1R agonist and found it had comparable efficacy to injectable peptidic GLP-1R agonists in a humanized mouse model. We then completed a placebo-controlled, randomized, double-blind, multiple ascending-dose phase 1 study ( NCT03538743 ), in which we enrolled 98 patients with T2D on background metformin and randomized them to receive multiple ascending doses of danuglipron or placebo for 28 d, across eight cohorts. The primary outcomes were assessment of adverse events (AEs), safety laboratory tests, vital signs and 12-lead electrocardiograms. Most AEs were mild, with nausea, dyspepsia and vomiting most commonly reported. There were no clinically meaningful AEs in laboratory values across groups. Heart rate generally increased with danuglipron treatment at day 28, but no heart-rate AEs were reported. Systolic blood pressure was slightly decreased and changes in diastolic blood pressure were similar with danuglipron treatment at day 28, compared with placebo. There were no clinically meaningful electrocardiogram findings. In this study in T2D, danuglipron was generally well tolerated, with a safety profile consistent with the mechanism of action of GLP-1R agonism.

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.

Improved Estimation of Glycated Hemoglobin from Continuous Glucose Monitoring and Past Glycated Hemoglobin Data

Background: Accurate estimation of glycated hemoglobin (HbA1c) from continuous glucose monitoring (CGM) remains challenging in clinic. We propose two statistical models and validate them in real-life conditions against the current standard, glucose management indicator (GMI). Materials and Methods: Modeling utilized routinely collected data from patients with type 1 diabetes from central Poland (eligibility criteria: age >1 year, diabetes duration >3 months, and CGM use between 01/01/2015 and 12/31/2019). CGM records were extracted from dedicated Medtronic/Abbott databases and cross-referenced with HbA1c values; 28-day periods preceding HbA1c measurement with >75% of the sensor-active time were analyzed. We developed a mixed linear regression, including glycemic variability indices and patient's ID (glucose variability-based patient specific model, GV-PS) intended for closed-group use and linear regression using patient-specific error of GMI (proportional error-based patient agnostic model, PE-PA) for general use. Models were validated with either new HbA1cs from closed-group patients or separate patient-HbA1c pool. External validation was performed with data from clinical trials. Performance metrics included bias, its 95% confidence interval (95% CI), coefficient of determination (R²), and root mean square error (RMSE). Results: We included 723 HbA1c-CGM pairs from 174 patients (mean age 9.9 ± 4.4 years and diabetes duration 3.7 ± 3.6 years). GMI yielded R² = 0.58, with different bias between Medtronic and Abbott devices [0.120% vs. -0.152%, P < 0.0001], and overall 95% CI = -0.9% to +1%, RMSE = 0.47%. GV-PS successfully captured patient-specific variance (closed-group validation: R² = 0.83, bias = 0.026%, 95% CI = -0.562% to 0.591%, RMSE = 0.31%). PE-PA performed similarly on new patients (R² = 0.76, bias = -0.069%, 95% CI = -0.790% to 0.653%, RMSE = 0.37%). In external validation GMI, GV-PS, and PE-PA produced 73.8%, 87.5%, and 91.0% predictions within 0.5% (5.5 mmol/mol) from the true value. Conclusion: Constructed models performed better than GMI. PE-PA provided an accurate estimate of HbA1c with fast and straightforward implementation.

Prior information for population pharmacokinetic and pharmacokinetic/pharmacodynamic analysis: overview and guidance with a focus on the NONMEM PRIOR subroutine

Abstract

Population pharmacokinetic analysis is used to estimate pharmacokinetic parameters and their variability from concentration data. Due to data sparseness issues, available datasets often do not allow the estimation of all parameters of the suitable model. The PRIOR subroutine in NONMEM supports the estimation of some or all parameters with values from previous models, as an alternative to fixing them or adding data to the dataset. From a literature review, the best practices were compiled to provide a practical guidance for the use of the PRIOR subroutine in NONMEM. Thirty-three articles reported the use of the PRIOR subroutine in NONMEM, mostly in special populations. This approach allowed fast, stable and satisfying modelling. The guidance provides general advice on how to select the most appropriate reference model when there are several previous models available, and to implement and weight the selected parameter values in the PRIOR function. On the model built with PRIOR, the similarity of estimates with the ones of the reference model and the sensitivity of the model to the PRIOR values should be checked. Covariates could be implemented a priori (from the reference model) or a posteriori, only on parameters estimated without prior (search for new covariates).

Graphic abstract

Electronic supplementary material

The online version of this article (10.1007/s10928-020-09695-z) contains supplementary material, which is available to authorized users.

Urinary glucose excretion after dapagliflozin treatment: An exposure-response modelling comparison between Japanese and non-Japanese patients diagnosed with type 1 diabetes mellitus

AIMS

To assess the dapagliflozin exposure-response relationship in Japanese and non-Japanese patients with type 1 diabetes mellitus (T1DM) and investigate if a dose adjustment is required in Japanese patients.

MATERIALS AND METHODS

Data from two clinical studies were used to develop a non-linear mixed effects model describing the relationship between dapagliflozin exposure (area under the concentration curve) and response (24-hour urinary glucose excretion [UGE]) in Japanese and non-Japanese patients with T1DM. The effects of patient-level characteristics (covariates; identified using a stepwise procedure) on response was also assessed. Simulations were performed using median-normalized covariate values.

RESULTS

Data from 84 patients were included. Average self-monitored blood glucose (SMBG) at day 7, change from baseline in total insulin dose at day 7, and baseline estimated glomerular filtration rate (eGFR) all had a significant effect on 24-hours UGE, with SMBG being the most influential. Dapagliflozin systemic exposure for matching doses and baseline eGFR was similar between Japanese and non-Japanese patients; however, higher SMBG and a greater reduction in total insulin dose was observed in the Japanese population. When the significant covariates were included, the model fit the data well for both populations, and accurately predicted exposure-response in the Japanese and non-Japanese populations, in agreement with the observed data.

CONCLUSIONS

There was no difference in dapagliflozin exposure-response in Japanese and non-Japanese patients with T1DM once differences in renal function, glycaemic control and insulin dose reductions between studies were considered. Therefore, no dose adjustment is recommended in Japanese patients with T1DM.

Efficacy of DPP-4 inhibitors, GLP-1 analogues, and SGLT2 inhibitors as add-ons to metformin monotherapy in T2DM patients: a model-based meta-analysis

AIMS

The aim of the present study was to quantitate the hypoglycaemic effects of dipeptidyl peptidase-4 inhibitors (DPP-4i), glucagon-like peptide-1 receptor agonists (GLP-1r) and sodium glucose cotransporter 2 inhibitors (SGLT2i) as add-on treatments to metformin monotherapy in patients with type 2 diabetes mellitus (T2DM) using a model-based meta-analysis (MBMA).

METHODS

A systematic literature search of public databases was conducted to develop models that describe the time courses of the fasting plasma glucose (FPG)- and haemoglobin A1c (HbA1c)-lowering effects of three antidiabetic classes using NONMEM 7.3.0.

RESULTS

Seventy-six publications were eligible for this study, and 873 FPG and 1086 HbA1c values were collected. We developed a physiological indirect response model that described the time courses of FPG and HbA1c and simulated reductions in these values 90 days after the initiation of add-on treatments. FPG and HbA1c reductions with once weekly exenatide, liraglutide and dulaglutide were greater than those with other drugs. Mean changes from baseline FPG and HbA1c with these drugs were as follows: exenatide (-22.5 and -16.6%), liraglutide (-22.1 and -16.3%), and dulaglutide (-19.3 and -14.3%). The hypoglycaemic effects of DPP-4i and SGLT2i were similar.

CONCLUSIONS

Once weekly exenatide, liraglutide and dulaglutide provided better hypoglycaemic effects among the antidiabetic drugs analysed. Long-acting GLP-1r appears to be more useful for T2DM patients inadequately controlled with metformin monotherapy.

Comparison of Power, Prognosis, and Extrapolation Properties of Four Population Pharmacodynamic Models of HbA1c for Type 2 Diabetes

Reusing published models saves time; time to be used for informing decisions in drug development. In antihyperglycemic drug development, several published HbA1c models are available but selecting the appropriate model for a particular purpose is challenging. This study aims at helping selection by investigating four HbA1c models, specifically the ability to identify drug effects (shape, site of action, and power) and simulation properties. All models could identify glucose effect nonlinearities, although for detecting the site of action, a mechanistic glucose model was needed. Power was highest for models using mean plasma glucose to drive HbA1c formation. Insulin contribution to power varied greatly depending on the drug target; it was beneficial only if the drug target was insulin secretion. All investigated models showed good simulation properties. However, extrapolation with the mechanistic model beyond 12 weeks resulted in drug effect overprediction. This investigation aids drug development in decisions regarding model choice if reusing published HbA1c models.

Steady-state relationship between average glucose, HbA1c and RBC lifespan

HbA1c is used to estimate average glucose. Previous studies showed linear relationship between average glucose and HbA1c. We made a new theoretical relationship using recently proposed Γ-like function model of erythrocyte lifespan. We showed the relationship between average glucose and HbA1c; we approximated it into a simple hyperbolic function: HbA1c=M_RBCk_gAG/(1+(2/3)M_RBCk_gAG), whose inverse function is easily obtained. Apparent linear relationship is an approximation of the curved relationship. Hyperbolic function would provide a more accurate approximation than a linear equation. Physicians should keep in mind the curved relationship and be aware that extremely high HbA1c indicates acceleratingly high glucose level.

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

CONTEXT

Several studies have shown that the relationship between mean plasma glucose (MPG) and glycated haemoglobin (HbA1c) may vary across populations. Especially race has previously been referred to shift the regression line that links MPG to HbA1c at steady-state (Herman & Cohen, 2012).

OBJECTIVE

To assess the influence of demographic and disease progression-related covariates on the intercept of the estimated linear MPG-HbA1c relationship in a longitudinal model.

DATA

Longitudinal patient-level data from 16 late-phase trials in type 2 diabetes with a total of 8927 subjects was used to study covariates for the relationship between MPG and HbA1c. The analysed covariates included age group, BMI, gender, race, diabetes duration, and pre-trial treatment. Differences between trials were taken into account by estimating a trial-to-trial variability component.

PARTICIPANTS

Participants included 47% females and 20% above 65years. 77% were Caucasian, 9% were Asian, 5% were Black and the remaining 9% were analysed together as other races.

ANALYSIS

Estimates of the change in the intercept of the MPG-HbA1c relationship due to the mentioned covariates were determined using a longitudinal model.

RESULTS

The analysis showed that pre-trial treatment with insulin had the most pronounced impact associated with a 0.34% higher HbA1c at a given MPG. However, race, diabetes duration and age group also had an impact on the MPG-HbA1c relationship.

CONCLUSION

Our analysis shows that the relationship between MPG and HbA1c is relatively insensitive to covariates, but shows small variations across populations, which may be relevant to take into account when predicting HbA1c response based on MPG measurements in clinical trials.

Glycated Hemoglobin, Plasma Glucose, and Erythrocyte Aging

BACKGROUND

The relationship between HbA1c and blood glucose averages has been characterized many times, yet, a unifying, mechanistic description is still lacking.

METHODS

We calculated the level of HbA1c from plasma glucose averages based solely on the in vivo rate of hemoglobin glycation, and the different turnover rates for erythrocytes of different ages. These calculations were then compared to the measured change of HbA1c due to changes in mean blood glucose (MBG), to complex models in the literature, and our own experiments.

RESULTS

Analysis of data on erythrocyte ageing patterns revealed that 2 separate RBC turnover mechanisms seem to be present. We calculated the mean red blood cell (RBC) life span within individuals to lie between 60 and 95 days. Comparison of expected HbA1c levels to data taken from continuous glucose monitors and finger-stick MBG yielded good agreement (r = .87, P < .0001). Experiments on the change with time of HbA1c induced by a change of MBG were in excellent agreement with our calculations (r = .98, P < .0001).

CONCLUSIONS

RBC turnover seems to be dominated by a constant rate of cell loss, and a mechanism that targets cells of a specific age. Average RBC life span is 80 ± 10.9 days. Of HbA1c change toward treatment goal value, 50% is reached in about 30 days. Many factors contribute to the ratio of glycated hemoglobin, yet we can make accurate estimations considering only the in vivo glycation constant, MBG, and the age distribution of erythrocytes.

Mechanistic modeling of hemoglobin glycation and red blood cell kinetics enables personalized diabetes monitoring

The amount of glycated hemoglobin (HbA1c) in diabetic patients' blood provides the best estimate of the average blood glucose concentration over the preceding 2 to 3 months. It is therefore essential for disease management and is the best predictor of disease complications. Nevertheless, substantial unexplained glucose-independent variation in HbA1c makes its reflection of average glucose inaccurate and limits the precision of medical care for diabetics. The true average glucose concentration of a nondiabetic and a poorly controlled diabetic may differ by less than 15 mg/dl, but patients with identical HbA1c values may have true average glucose concentrations that differ by more than 60 mg/dl. We combined a mechanistic mathematical model of hemoglobin glycation and red blood cell kinetics with large sets of within-patient glucose measurements to derive patient-specific estimates of nonglycemic determinants of HbA1c, including mean red blood cell age. We found that between-patient variation in derived mean red blood cell age explains all glucose-independent variation in HbA1c. We then used our model to personalize prospective estimates of average glucose and reduced errors by more than 50% in four independent groups of greater than 200 patients. The current standard of care provided average glucose estimates with errors >15 mg/dl for one in three patients. Our patient-specific method reduced this error rate to 1 in 10. Our personalized approach should improve medical care for diabetes using existing clinical measurements.

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.

Evaluation of the long-term durability and glycemic control of fasting plasma glucose and glycosylated hemoglobin for pioglitazone in Japanese patients with type 2 diabetes

BACKGROUND

This study applied a pharmacodynamic model-based approach to evaluate the long-term durability and glycemic control of pioglitazone in comparison with other oral glucose-lowering drugs in Japanese type 2 diabetes mellitus (T2DM) patients.

SUBJECTS AND METHODS

Japanese T2DM patients were enrolled in a prospective, randomized, open-label, blinded-end point study and received pioglitazone with or without other oral glucose-lowering drugs (excluding another thiazolidinedione [TZD]) (n=293) or oral glucose-lowering drugs excluding TZD (n=294). Treatment was adjusted to achieve glycosylated hemoglobin (HbA1c) <6.9%, and samples for fasting plasma glucose (FPG) and HbA1c were collected over 2.5-4 years. A simultaneous cascading indirect response model structure was applied to describe the time course of FPG and HbA1c. HbA1c levels were described using both an FPG-dependent and an FPG-independent function. To account for titration, drug effects for both treatment groups were implemented using a time-dependent Emax model.

RESULTS

Pioglitazone was superior in both time to maximum effect and the magnitude of reduction achieved in FPG and HbA1c. A greater reduction in median FPG (-21 mg/dL vs. -9 mg/dL) was observed with pioglitazone (P<0.05). Maximum drug effect for FPG was predicted to occur earlier (11 months) for pioglitazone than for the control group (14 months). The simulated additional reduction in FPG and HbA1c achieved with pioglitazone was predicted to be maintained beyond the currently observed study duration.

CONCLUSIONS

Pioglitazone was found to result in improved glycemic control and durability compared with control treatment. This model-based approach enabled the quantification of differences in FPG and HbA1c for both treatment groups and simulation to evaluate longer-term durability on FPG and HbA1c.

Validation of a hemoglobin A1c model in patients with type 1 and type 2 diabetes and its use to go beyond the averaged relationship of hemoglobin A1c and mean glucose level

Background

Glycated hemoglobin A_1c (HbA_1c) has been used as an index of glycemic control in the management, guidance, and clinical trials of diabetic patients for the past 35 years. The aim of this study was to validate the HbA_1c model in patients with type 1 and type 2 diabetes and to use it to support interpretation of HbA_1c in different clinical situations.

Methods

The HbA_1c model was identified in 30 patients (15 with type 1 diabetes and 15 with type 2 diabetes) by estimating the overall glycation rate constant (k), based on results of continuous glucose monitoring. The model was validated by assessing its ability to predict HbA_1c changes in cultures of erythrocytes in vitro and to reproduce results of the A1C-Derived Average Glucose (ADAG) study. The model was used to simulate the influence of different glucose profiles on HbA_1c.

Results

The mean k was equal to 1.296 ± 0.216 × 10⁻⁹ l mmol⁻¹ s⁻¹ with no difference between type 1 and type 2 diabetes. The mean coefficient of variation of k was equal to 16.7%. The model predicted HbA_1c levels in vitro with a mean absolute difference less than 0.3% (3.3 mmol/mol). It reproduced the linear relationship of HbA_1c and mean glucose levels established in the ADAG study. The simulation experiments demonstrated that during periods of unstable glycemic control, glycemic profiles with the same mean glucose might result in much different HbA_1c levels.

Conclusions

Patients with type 1 and type 2 diabetes are characterized by the same mean value of k, but there is considerable interindividual variation in the relationship of HbA_1c and mean glucose level. Results suggest that reciprocal changes in glycation rate and the life span of erythrocytes exist in a wide range of HbA_1c values. Thus, for an average patient with diabetes, no modifications of parameters of the glycation model are required to obtain meaningful HbA_1c predictions. Interpreting HbA_1c as a measure of the mean glucose is fully justified only in the case of stable glycemia. The model and more frequent tests of HbA_1c might be used to decrease ambiguity of interpreting HbA_1c in terms of glycemic control.

Methods for Predicting Diabetes Phase III Efficacy Outcome From Early Data: Superior Performance Obtained Using Longitudinal Approaches

The link between glucose and HbA1c at steady state has previously been described using steady-state or longitudinal relationships. We evaluated five published methods for prediction of HbA1c after 26/28 weeks using data from four clinical trials. Methods (1) and (2): steady-state regression of HbA1c on fasting plasma glucose and mean plasma glucose, respectively, (3) an indirect response model of fasting plasma glucose effects on HbA1c, (4) model of glycosylation of red blood cells, and (5) coupled indirect response model for mean plasma glucose and HbA1c. Absolute mean prediction errors were 0.61, 0.38, 0.55, 0.37, and 0.15% points, respectively, for Methods 1 through 5. This indicates that predictions improved by using mean plasma glucose instead of fasting plasma glucose, by inclusion of longitudinal glucose data and further by inclusion of longitudinal HbA1c data until 12 weeks. For prediction of trial outcome, the longitudinal models based on mean plasma glucose (Methods 4 and 5) had substantially better performance compared with the other methods.