Dynamic population pharmacokinetic-pharmacodynamic modelling and simulation supports similar efficacy in glycosylated haemoglobin response with once or twice-daily dosing of canagliflozin.

AIM: Canagliflozin is an SGLT2 inhibitor approved for the treatment of type-2 diabetes. A dynamic population pharmacokinetic-pharmacodynamic (PK/PD) model relating 24-h canagliflozin exposure profiles to effects on glycosylated haemoglobin was developed to compare the efficacy of once-daily and twice-daily dosing. METHODS: Data from two clinical studies, one with once-daily, and the other with twice-daily dosing of canagliflozin as add-on to metformin were used (n = 1347). An established population PK model was used to predict full 24-h profiles from measured trough concentrations and/or baseline covariates. The dynamic PK/PD model incorporated an Emax relationship between 24-h canagliflozin exposure and HbA1c-lowering with baseline HbA1c affecting the efficacy. RESULTS: Internal and external model validation demonstrated that the model adequately predicted HbA1c-lowering for canagliflozin once-daily and twice-daily dosing regimens. The differences in HbA1c reduction between the twice-daily and daily mean profiles were minimal (at most 0.023% for 100 mg total daily dose [TDD] and 0.011% for 300 mg TDD, up to week 26, increasing with time and decreasing with TDD) and not considered clinically meaningful. CONCLUSIONS: Simulations using this model demonstrated the absence of clinically meaningful between-regimen differences in efficacy, supported the regulatory approval of a canagliflozin-metformin immediate release fixed-dose combination tablet and alleviated the need for an additional clinical study.

Impact of saturable distribution in compartmental PK models: dynamics and practical use.

We explore the impact of saturable distribution over the central and the peripheral compartment in pharmacokinetic models, whilst assuming that back flow into the central compartiment is linear. Using simulations and analytical methods we demonstrate characteristic tell-tale differences in plasma concentration profiles of saturable versus linear distribution models, which can serve as a guide to their practical applicability. For two extreme cases, relating to (i) the size of the peripheral compartment with respect to the central compartment and (ii) the magnitude of the back flow as related to direct elimination from the central compartment, we derive explicit approximations which make it possible to give quantitative estimates of parameters. In three appendices we give detailed explanations of how these estimates are derived. They demonstrate how singular perturbation methods can be successfully employed to gain insight in the dynamics of multi-compartment pharmacokinetic models. These appendices are also intended to serve as an introductory tutorial to these ideas.

The variability in beta-cell function in placebo-treated subjects with type 2 diabetes: application of the weight-HbA1c-insulin-glucose (WHIG) model.

AIM: The weight-glycosylated haemoglobin (HbA1C)-insulin-glucose (WHIG) model describes the effects of changes in weight on insulin sensitivity (IS) in newly diagnosed, obese subjects receiving placebo treatment. This model was applied to a wider population of placebo-treated subjects, to investigate factors influencing the variability in IS and ß-cell function. METHODS: The WHIG model was applied to the WHIG dataset (Study 1) and two other placebo datasets (Studies 2 and 3). Studies 2 and 3 consisted of nonobese subjects and subjects with advanced type 2 diabetes mellitus (T2DM). Body weight, fasting serum insulin (FSI), fasting plasma glucose (FPG) and HbA1c were used for nonlinear mixed-effects modelling (using NONMEM v7.2 software). Sources of interstudy variability (ISV) and potential covariates (age, gender, diabetes duration, ethnicity, compliance) were investigated. RESULTS: An ISV for baseline parameters (body weight and ß-cell function) was required. The baseline ß-cell function was significantly lower in subjects with advanced T2DM (median difference: Study 2: 15.6%, P < 0.001; Study 3: 22.7%, P < 0.001) than in subjects with newly diagnosed T2DM (Study 1). A reduction in the estimated insulin secretory response in subjects with advanced T2DM was observed but diabetes duration was not a significant covariate. CONCLUSION: The WHIG model can be used to describe the changes in weight, IS and ß-cell function in the diabetic population. IS remained relatively stable between subjects but a large ISV in ß-cell function was observed. There was a trend towards decreasing ß-cell responsiveness with diabetes duration, and further studies, incorporating subjects with a longer history of diabetes, are required.

Modeling the Disease Progression from Healthy to Overt Diabetes in ZDSD Rats.

Studying the critical transitional phase between healthy to overtly diabetic in type 2 diabetes mellitus (T2DM) is of interest, but acquiring such clinical data is impractical due to ethical concerns and would require a long study duration. A population model using Zucker diabetic Sprague-Dawley (ZDSD) rats was developed to describe this transition through altering insulin sensitivity (IS, %) as a result of accumulating excess body weight and ß-cell function (BCF, %) to affect glucose-insulin homeostasis. Body weight, fasting plasma glucose (FPG), and fasting serum insulin (FSI) were collected biweekly over 24 weeks from ZDSD rats (n = 23) starting at age 7 weeks. A semi-mechanistic model previously developed with clinical data was adapted to rat data with BCF and IS estimated relative to humans. Non-linear mixed-effect model estimation was performed using NONMEM. Baseline IS and BCF were 41% compared to healthy humans. BCF was described with a non-linear rise which peaked at 14 weeks before gradually declining to a negligible level. A component for excess growth reflecting obesity was used to affect IS, and a glucose-dependent renal effect exerted a two- to sixfold increase on the elimination of glucose. A glucose-dependent weight loss effect towards the end of experiment was implemented. A semi-mechanistic model to describe the dynamics of glucose and insulin was successfully developed for a rat population, transitioning from healthy to advanced diabetes. It is also shown that weight loss can be modeled to mimic the glucotoxicity phenomenon seen in advanced hyperglycemia.

Population Pharmacokinetic Modeling of Canagliflozin in Healthy Volunteers and Patients with Type 2 Diabetes Mellitus.

BACKGROUND AND OBJECTIVES: Canagliflozin is an orally active, reversible, selective sodium-glucose co-transporter-2 inhibitor. A population pharmacokinetic (popPK) model of canagliflozin, including relevant covariates as sources of inter-individual variability, was developed to describe phase I, II, and III data in healthy volunteers and in patients with type 2 diabetes mellitus (T2DM). METHODS: The final analysis included 9061 pharmacokinetic (PK) samples from 1616 volunteers enrolled in nine phase I, two phase II, and three phase III studies and was performed using NONMEM(®) 7.1. Inter-individual variability was evaluated using an exponential model and the residual error model was additive in the log domain. The first-order conditional estimation method with interaction was applied and the model was parameterized in terms of rate constants. Covariate effects were explored graphically on empirical Bayes estimates of PK parameters, as shrinkage was low. Clinical relevance of statistically significant covariates was evaluated. The predictive properties of the model were illustrated by prediction-corrected visual predictive checks. RESULTS: A two-compartment PK model with lag-time and sequential zero- and first-order absorption and first-order elimination best described the observed data. Sex, age, and weight on apparent volume of distribution of the central compartment, body mass index on first-order absorption rate constant, and body mass index and over-encapsulation on lag-time, and estimated glomerular filtration rate (eGFR, by MDRD equation), dose, and genetic polymorphism (carriers of UGT1A9*3 allele) on elimination rate constant were identified as statistically significant covariates. The prediction-corrected visual predictive checks revealed acceptable predictive performance of the model. CONCLUSION: The popPK model adequately described canagliflozin PK in healthy volunteers and in patients with T2DM. Because of the small magnitude of statistically significant covariates, they were not considered clinically relevant. However, dosage adjustments are recommended for T2DM patients with renal impairment (eGFR ≥60 mL/min/1.73 m(2): 100 or 300 mg/day; eGFR of 45 to <60 mL/min/1.73 m(2): 100 mg/day).

The method of averaging applied to pharmacokinetic/pharmacodynamic indirect response models.

The computational effort required to fit the pharmacodynamic (PD) part of a pharmacokinetic/pharmacodynamic (PK/PD) model can be considerable if the differential equations describing the model are solved numerically. This burden can be greatly reduced by applying the method of averaging (MAv) in the appropriate circumstances. The MAv gives an approximate solution, which is expected to be a good approximation when the PK profile is periodic (i.e. repeats its values in regular intervals) and the rate of change of the PD response is such that it is approximately constant over a single period of the PK profile. This paper explains the basis of the MAv by means of a simple mathematical derivation. The NONMEM® implementation of the MAv using the abbreviated FORTRAN function FUNCA is described and explained. The application of the MAv is illustrated by means of an example involving changes in glycated hemoglobin (HbA1c%) following administration of canagliflozin, a selective sodium glucose co-transporter 2 inhibitor. The PK/PD model applied to these data is fitted with NONMEM® using both the MAv and the standard method using a numerical differential equation solver (NDES). Both methods give virtually identical results but the NDES method takes almost 8 h to run both the estimation and covariance steps, whilst the MAv produces the same results in less than 30 s. An outline of the NONMEM® control stream and the FORTRAN code for the FUNCA function is provided in the appendices.

Characterization of the ZDSD Rat: A Translational Model for the Study of Metabolic Syndrome and Type 2 Diabetes.

Metabolic syndrome and T2D produce significant health and economic issues. Many available animal models have monogenic leptin pathway mutations that are absent in the human population. Development of the ZDSD rat model was undertaken to produce a model that expresses polygenic obesity and diabetes with an intact leptin pathway. A lean ZDF rat with the propensity for beta-cell failure was crossed with a polygenetically obese Crl:CD (SD) rat. Offspring were selectively inbred for obesity and diabetes for >30 generations. In the current study, ZDSD rats were followed for 6 months; routine clinical metabolic endpoints were included throughout the study. In the prediabetic metabolic syndrome phase, ZDSD rats exhibited obesity with increased body fat, hyperglycemia, insulin resistance, dyslipidemia, glucose intolerance, and elevated HbA1c. As disease progressed to overt diabetes, ZDSD rats demonstrated elevated glucose levels, abnormal oral glucose tolerance, increases in HbA1c levels, reductions in body weight, increased insulin resistance with decreasing insulin levels, and dyslipidemia. The ZDSD rat develops prediabetic metabolic syndrome and T2D in a manner that mirrors the development of metabolic syndrome and T2D in humans. ZDSD rats will provide a novel, translational animal model for the study of human metabolic diseases and for the development of new therapies.

Profiling the subjective effects of Δ9-tetrahydrocannabinol using visual analogue scales.

The subjective effects of cannabis and its main psychoactive component Δ(9) -tetrahydrocannabinol (THC) have played an important part in determining the therapeutic potential of cannabinoid agonists and antagonists. The effects mainly consist of feeling high, changes in perception, feelings of relaxation and occasionally dysphoric reactions. These effects are captured by two of the most frequently used visual analogue scales (VASs) in clinical (pharmacologic) research to measure subjective effects: VAS Bond and Lader (alertness, calmness and mood) and VAS Bowdle (psychedelic effects). In this analysis, the effects of THC on these VASs were compared within a total of 217 subjects who participated in 10 different studies. Not surprisingly, the item feeling high was found to be the best predictor for the effect of THC. Three separate clusters that describe the spectrum of subjective effects of THC were identified using different statistical methods, consisting of VAS "time", "thoughts" and "high" ("perception"), VAS "drowsy", "muzzy", "mentally slow" and "dreamy" ("relaxation") and VAS "voices", "meaning" and "suspicious" ("dysphoria"). These results provide experimental evidence that THC can evoke different classes of effects. These distinct subjective clusters could represent effects on various systems in the brain, which can be used to further differentiate the involvement of endocannabinoid systems in health and disease.

A mechanism-based disease progression model for comparison of long-term effects of pioglitazone, metformin and gliclazide on disease processes underlying Type 2 Diabetes Mellitus.

Effective long-term treatment of Type 2 Diabetes Mellitus (T2DM) implies modification of the disease processes that cause this progressive disorder. This paper proposes a mechanism-based approach to disease progression modeling of T2DM that aims to provide the ability to describe and quantify the effects of treatment on the time-course of the progressive loss of beta-cell function and insulin-sensitivity underlying T2DM. It develops a population pharmacodynamic model that incorporates mechanism-based representations of the homeostatic feedback relationships between fasting levels of plasma glucose (FPG) and fasting serum insulin (FSI), and the physiological feed-forward relationship between FPG and glycosylated hemoglobin A1c (HbA1c). This model was developed on data from two parallel one-year studies comparing the effects of pioglitazone relative to metformin or sulfonylurea treatment in 2,408 treatment-naïve T2DM patients. It was found that the model provided accurate descriptions of the time-courses of FPG and HbA1c for different treatment arms. It allowed the identification of the long-term effects of different treatments on loss of beta-cell function and insulin-sensitivity, independently from their immediate anti-hyperglycemic effects modeled at their specific sites of action. Hence it avoided the confounding of these effects that is inherent in point estimates of beta-cell function and insulin-sensitivity such as the widely used HOMA-%B and HOMA-%S. It was also found that metformin therapy did not result in a reduction in FSI levels in conjunction with reduced FPG levels, as expected for an insulin-sensitizer, whereas pioglitazone therapy did. It is concluded that, although its current implementation leaves room for further improvement, the mechanism-based approach presented here constitutes a promising conceptual advance in the study of T2DM disease progression and disease modification.