Abstract: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. … Show more
“…In contrast, the latter non-model-based approaches conventionally only consider one single time point of the concentration-or effect-time profile (8). The utility of clinical trial simulations and model-based data analysis methods to inform the design of clinical studies has been demonstrated across various therapeutic fields (9)(10)(11). In pharmacogenomics, pharmacometric models have foremost been used to enhance the understanding of the impact of genetic variants on drug exposure and various outcomes rather than for clinical trial design.…”
Purpose
Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.
Methods
Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50–400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.
Results
The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar’s test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.
Conclusions
The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.
“…In contrast, the latter non-model-based approaches conventionally only consider one single time point of the concentration-or effect-time profile (8). The utility of clinical trial simulations and model-based data analysis methods to inform the design of clinical studies has been demonstrated across various therapeutic fields (9)(10)(11). In pharmacogenomics, pharmacometric models have foremost been used to enhance the understanding of the impact of genetic variants on drug exposure and various outcomes rather than for clinical trial design.…”
Purpose
Pharmacometric models provide useful tools to aid the rational design of clinical trials. This study evaluates study design-, drug-, and patient-related features as well as analysis methods for their influence on the power to demonstrate a benefit of pharmacogenomics (PGx)-based dosing regarding myelotoxicity.
Methods
Two pharmacokinetic and one myelosuppression model were assembled to predict concentrations of irinotecan and its metabolite SN-38 given different UGT1A1 genotypes (poor metabolizers: CLSN-38: -36%) and neutropenia following conventional versus PGx-based dosing (350 versus 245 mg/m2 (-30%)). Study power was assessed given diverse scenarios (n = 50–400 patients/arm, parallel/crossover, varying magnitude of CLSN-38, exposure-response relationship, inter-individual variability) and using model-based data analysis versus conventional statistical testing.
Results
The magnitude of CLSN-38 reduction in poor metabolizers and the myelosuppressive potency of SN-38 markedly influenced the power to show a difference in grade 4 neutropenia (<0.5·109 cells/L) after PGx-based versus standard dosing. To achieve >80% power with traditional statistical analysis (χ2/McNemar’s test, α = 0.05), 220/100 patients per treatment arm/sequence (parallel/crossover study) were required. The model-based analysis resulted in considerably smaller total sample sizes (n = 100/15 given parallel/crossover design) to obtain the same statistical power.
Conclusions
The presented findings may help to avoid unfeasible trials and to rationalize the design of pharmacogenetic studies.
“…Also, when simulating with the IMM and estimating with both models, ΔOFV Bias was lower for the IGI model estimations, as presented in Table I, concluding that the IGI model structural assumptions regarding glucose kinetics were less prone to significant misspecifications. Finally, the magnitude of the IMM glucose sub-model bias peaked to 20% of conditional prediction of glucose, which is a considerably high percentage for such integrated system, and in light of a previous study (14), the utilization of such model in drug development to explore drug effects enhancing glucose disappearance will result in the misleading conclusions of overestimating drug effects on insulin-independent glucose clearance and underestimating drug effects on insulin-dependent glucose clearance.…”
Section: Discussionmentioning
confidence: 82%
“…Upon glucose administration, insulin first-phase amount enters insulin first-phase compartment as a system response, then it is released into the insulin central compartment. The IGI model has been widely used in diabetes modeling with applications in exploring drug effects (12), disease progression (13), designing early clinical trials (14), and optimizing IVGTT design (15). Fig.…”
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 (ΔOFV
Bias
). 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.
Electronic supplementary material
The online version of this article (10.1208/s12248-019-0305-2) contains supplementary material, which is available to authorized users.
“…One of the appealing features of the IGI model is its ability to distinguish hepatic glucose production from glucose disappearance, facilitated by access to labelled glucose measurements during its development. The model had been successfully applied for exploring drug effects (17), disease progression (18), designing early clinical trials (19) and optimizing IVGTT design (20).…”
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-lifelike 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. KEY WORDS glucose effectiveness. insulin sensitivity. integrated glucose insulin model. integrated minimal model. NLMEPC
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