Pharmacokinetic modeling and simulation support for age- and weight-adjusted dosing of dabigatran etexilate in children with venous thromboembolism.

BACKGROUND: Dabigatran etexilate (DE), a direct oral thrombin inhibitor, has been evaluated in children with venous thromboembolism (VTE) using oral solution, pellets, or capsules. OBJECTIVES: This study evaluated DE pharmacokinetics (PK) in children with VTE and the appropriateness of a DE pediatric age- and weight-based dosing algorithm. PATIENTS/METHODS: A population PK model was fitted to data from four single-arm and one randomized, comparative pediatric VTE studies (358 children aged birth to <18 years; 2748 PK observations) and one healthy-adult study (32 males aged <40 years; 1523 PK observations) using nonlinear mixed-effects modeling. A stepwise, covariate, model-building procedure evaluated the influence of covariates (e.g., age, body weight, body surface area [BSA]-normalized renal function, and sex). The final model was used to evaluate the pediatric dosing algorithm, with simulations comparing pediatric trough exposure with reference exposure defined for the pediatric studies. RESULTS: The population PK of dabigatran was adequately described by a two-compartment model with first-order elimination and absorption. Age, weight, BSA-normalized renal function, and sex were statistically significant covariates (all P < .05). Apparent clearance increased with age (independently of body weight), diminished with decreasing BSA-normalized renal function, and was lower in females than males. All disposition parameters increased with body weight escalation (allometric scaling). Simulations confirmed that for all DE formulations, the final pediatric dosing algorithms achieved reference exposure without dose adjustment. CONCLUSIONS: Using a population PK model of DE for children with VTE, simulations showed that the final dosing algorithms were appropriate for all DE formulations; no dose titration was needed.

Optimal Designs for Model-Based Assessment of Insulin Sensitivity and Glucose Effectiveness.

The integrated minimal model allows assessment of clinical diagnosis indices, for example, insulin sensitivity (SI ) and glucose effectiveness (SG ), from data of the insulin-modified intravenous glucose tolerance test (IVGTT), which is laborious with an intense sampling schedule, up to 32 samples. The aim of this study was to propose a more informative, although less laborious, IVGTT design to be used for model-based assessment of SI and SG . The IVGTT design was optimized simultaneously for all design variables: glucose and insulin infusion doses, time of glucose dose and start of insulin infusion, insulin infusion duration, sampling times, and number of samples. Design efficiency was used to compare among different designs. The simultaneously optimized designs showed a profound higher efficiency than both standard rich (32 samples) and sparse (10 samples) designs. The optimized designs, after removing replicate sample times, were 1.9 and 7.1 times more efficient than the standard rich and sparse designs, respectively. After including practical aspects of the designs, for example, sufficient duration between samples and avoidance of prolonged hypoglycemia, we propose 2 practical designs with fewer sampling times and lower input of glucose and insulin than standard designs, constrained to prevent hypoglycemia. The optimized practical rich design is equally efficient in assessing SI and SG as the rich standard design, but with half the number of the samples, while the optimized practical sparse design has 1 less sample and requires 4.6 times fewer individuals for equal certainty when assessing SI and SG than the sparse standard design.

The integrated glucose insulin minimal model: An improved version.

This paper describes the improved integrated minimal model for healthy subjects and patients with type 2 diabetes and the work leading up to this model. The original integrated minimal model characterizes simultaneously glucose and insulin following intravenous glucose tolerance test (IVGTT) in healthy subjects and provides apart from estimates of indices for insulin sensitivity (Si) and glucose effectiveness (SG), also full simulation capabilities. However, this model was developed using IVGTT data of total glucose and consequently, the model cannot separate hepatic glucose production from glucose disposal. By fitting the original integrated minimal model to IVGTT data of labelled and total glucose, we show that all parameter estimates of the glucose sub-model were significantly different between the fits, in particular, SG, which was ~3 fold higher with total, compared to labelled glucose. In addition, the time profiles of hepatic glucose production, obtained from the model, were unphysiological in most subjects. To correct these flaws, we developed the improved integrated minimal model based on the non-integrated, two-compartment minimal model. The improved integrated minimal model showed physiologically plausible dynamic time profiles of hepatic glucose production and all parameter estimates were compatible with those reported in original publication of the non-integrated minimal model. The integrated minimal model offers the benefits of the original integrated minimal model with simulation capabilities, in presence of endogenous insulin, combined with the benefits of the non-integrated minimal model, which accurately estimates the clinical indices of insulin sensitivity and glucose effectiveness. In addition, the improved integrated minimal model describes, apart from healthy subjects, also patients with type 2 diabetes.

Translation Between Two Models; Application with Integrated Glucose Homeostasis Models.

PURPOSE: For some biological systems, there exist several models with somewhat different features and perspectives. We propose an evaluation method for NLME models by analyzing real and simulated data from the model of main interest using a structurally different, but similar, NLME model. We showcase this method using the Integrated Glucose Insulin (IGI) model and the Integrated Minimal Model (IMM). Additionally, we try to map parameters carrying similar information between the two models. METHODS: A bootstrap of real data and simulated datasets from both the IMM and IGI models were analyzed with the two models. Important parameters of the IMM were mapped to IGI parameters using a large IMM simulated dataset analyzed under the IGI model. RESULTS: Comparison of the parameters estimated from real data and data simulated with the IMM and analyzed with the IGI model demonstrated differences between real and IMM-simulated data. Comparison of the parameters estimated from real data and data simulated with the IGI model and analyzed with the IMM also demonstrated differences but to a lower extent. The strongest parameter correlations were found for: insulin-dependent glucose clearance (IGI) ~ insulin sensitivity (IMM); insulin-independent glucose clearance (IGI) ~ glucose effectiveness (IMM); and insulin effect parameter (IGI) ~ insulin action (IMM). CONCLUSIONS: We demonstrated a new approach to investigate models' ability to simulate real-life-like data, and the information captured in each model in comparison to real data, and the IMM clinically used parameters were successfully mapped to their corresponding IGI parameters.

Model-Based Conditional Weighted Residuals Analysis for Structural Model Assessment.

Nonlinear mixed effects models are widely used to describe longitudinal data to improve the efficiency of drug development process or increase the understanding of the studied disease. In such settings, the appropriateness of the modeling assumptions is critical in order to draw correct conclusions and must be carefully assessed for any substantial violations. Here, we propose a new method for structure model assessment, based on assessment of bias in conditional weighted residuals (CWRES). We illustrate this method by assessing prediction bias in two integrated models for glucose homeostasis, the integrated glucose-insulin (IGI) model, and the integrated minimal model (IMM). One dataset was simulated from each model then analyzed with the two models. CWRES outputted from each model fitting were modeled to capture systematic trends in CWRES as well as the magnitude of structural model misspecifications in terms of difference in objective function values (ΔOFVBias). The estimates of CWRES bias were used to calculate the corresponding bias in conditional predictions by the inversion of first-order conditional estimation method's covariance equation. Time, glucose, and insulin concentration predictions were the investigated independent variables. The new method identified correctly the bias in glucose sub-model of the integrated minimal model (IMM), when this bias occurred, and calculated the absolute and proportional magnitude of the resulting bias. CWRES bias versus the independent variables agreed well with the true trends of misspecification. This method is fast easily automated diagnostic tool for model development/evaluation process, and it is already implemented as part of the Perl-speaks-NONMEM software.

Variability Attribution for Automated Model Building.

We investigated the possible advantages of using linearization to evaluate models of residual unexplained variability (RUV) for automated model building in a similar fashion to the recently developed method "residual modeling." Residual modeling, although fast and easy to automate, cannot identify the impact of implementing the needed RUV model on the imprecision of the rest of model parameters. We used six RUV models to be tested with 12 real data examples. Each example was first linearized; then, we assessed the agreement in improvement of fit between the base model and its extended models for linearization and conventional analysis, in comparison to residual modeling performance. Afterward, we compared the estimates of parameters' variabilities and their uncertainties obtained by linearization to conventional analysis. Linearization accurately identified and quantified the nature and magnitude of RUV model misspecification similar to residual modeling. In addition, linearization identified the direction of change and quantified the magnitude of this change in variability parameters and their uncertainties. This method is implemented in the software package PsN for automated model building/evaluation with continuous data.

Model-Based Residual Post-Processing for Residual Model Identification.

The purpose of this study was to investigate if model-based post-processing of common diagnostics can be used as a diagnostic tool to quantitatively identify model misspecifications and rectifying actions. The main investigated diagnostic is conditional weighted residuals (CWRES). We have selected to showcase this principle with residual unexplained variability (RUV) models, where the new diagnostic tool is used to scan extended RUV models and assess in a fast and robust way whether, and what, extensions are expected to provide a superior description of data. The extended RUV models evaluated were autocorrelated errors, dynamic transform both sides, inter-individual variability on RUV, power error model, t-distributed errors, and time-varying error magnitude. The agreement in improvement in goodness-of-fit between implementing these extended RUV models on the original model and implementing these extended RUV models on CWRES was evaluated in real and simulated data examples. Real data exercise was applied to three other diagnostics: conditional weighted residuals with interaction (CWRESI), individual weighted residuals (IWRES), and normalized prediction distribution errors (NPDE). CWRES modeling typically predicted (i) the nature of model misspecifications, (ii) the magnitude of the expected improvement in fit in terms of difference in objective function value (ΔOFV), and (iii) the parameter estimates associated with the model extension. Alternative metrics (CWRESI, IWRES, and NPDE) also provided valuable information, but with a lower predictive performance of ΔOFV compared to CWRES. This method is a fast and easily automated diagnostic tool for RUV model development/evaluation process; it is already implemented in the software package PsN.

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 population PK-PD analysis of furosemide for BP lowering effect: A comparative study in primary and secondary hypertension.

Though numerous reports have demonstrated multiple mechanisms by which furosemide can exert its anti-hypertensive response. However, lack of studies describing PK-PD relationship for furosemide featuring its anti-hypertensive property has limited its usage as a blood pressure (BP) lowering agent. Serum concentrations and mean arterial BP were monitored following 40 and 80mgkg-1 multiple oral dose of furosemide in spontaneously hypertensive rats (SHR) and DOCA-salt induced hypertensive (DOCA-salt) rats. A simultaneous population PK-PD relationship using Emax model with effect compartment was developed to compare the anti-hypertensive efficacy of furosemide in these rat models. A two-compartment PK model with Weibull-type absorption and first-order elimination best described the serum concentration-time profile of furosemide. In the present study, post dose serum concentrations of furosemide were found to be lower than the EC50. The EC50 predicted in DOCA-salt rats was found to be lower (4.5-fold), whereas the tolerance development was higher than that in SHR model. The PK-PD parameter estimates, particularly lower values of EC50, Ke and Q in DOCA-salt rats as compared to SHR, pinpointed the higher BP lowering efficacy of furosemide in volume overload induced hypertensive conditions. Insignificantly altered serum creatinine and electrolyte levels indicated a favorable side effect profile of furosemide. In conclusion, the final PK-PD model described the data well and provides detailed insights into the use of furosemide as an anti-hypertensive agent.