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.

Bayesian approach to investigate a two-state mixed model of COPD exacerbations.

Chronic obstructive pulmonary disease (COPD) is a chronic obstructive disease of the airways. An exacerbation of COPD is defined as shortness of breath, cough, and sputum production. New therapies for COPD exacerbations are examined in clinical trials frequently based on the number of exacerbations that implies long-term study due to the high variability in occurrence and duration of the events. In this work, we expanded the two-state model developed by Cook et al. where the patient transits from an asymptomatic (state 1) to a symptomatic state (state 2) and vice versa, through investigating different semi-Markov models in a Bayesian context using data from actual clinical trials. Of the four models tested, the log-logistic model was shown to adequately characterize the duration and number of COPD exacerbations. The patient disease stage was found a significant covariate with an effect of accelerating the transition from asymptomatic to symptomatic state. In addition, the best dropout model (log-logistic) was incorporated in the final two-state model to describe the dropout mechanism. Simulation based diagnostics such as posterior predictive check (PPC) and visual predictive check (VPC) were used to assess the behaviour of the model. The final model was applied in three clinical trial data to investigate its ability to detect the drug effect: the drug effect was captured in all three datasets and in both directions (from state 1 to state 2 and vice versa). A practical design investigation was also carried out and showed the limits of reducing the number of subjects and study length on the drug effect identification. Finally, clinical trial simulation confirmed that the model can potentially be used to predict medium term (6-12 months) clinical trial outcome using the first 3 months data, but at the expense of showing a non-significant drug effect.

Correction to: Bayesian approach to investigate a two-state mixed model of COPD exacerbations.

The article [Bayesian approach to investigate a two-state mixed model of COPD exacerbations], written by [Anna Largajolli, Misba Beerahee, Shuying Yang], was originally published electronically on the publisher's internet portal (currently SpringerLink) on [13 June 2019] without open access. With the author(s)' decision to opt for Open Choice the copyright of the article changed on [November 2019] to © The Author(s) [2019] and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Operating characteristics of stepwise covariate selection in pharmacometric modeling.

Stepwise covariate modeling (SCM) is a widely used tool in pharmacometric analyses to identify covariates that explain between-subject variability (BSV) in exposure and exposure-response relationships. However, this approach has several potential weaknesses, including over-estimated covariate effect and incorrect selection of covariates due to collinearity. In this work, we investigated the operating characteristics (i.e., accuracy, precision, and power) of SCM in a controlled setting by simulating sixteen scenarios with up to four covariate relationships. The SCM analysis showed a decrease in the power to detect the true covariates as model complexity increased. Furthermore, false highly correlated covariates were frequently selected in place of or in addition to the true covariates. Relative root mean square errors (RMRSE) ranged from 1 to 51% for the fixed effects parameters, increased with the number of covariates included in the model, and were slightly higher than the RMRSE obtained with a simple re-estimation exercise with the true model (i.e., stochastic simulation and estimation). RMRSE for BSV increased with the number of covariates included in the model, with a covariance parameter RMRSE of almost 135% in the most complex scenario. Loose boundary conditions on the continuous covariate power relation appeared to have an impact on the covariate model selection in SCM. A stricter boundary condition helped achieve high power (> 90%), even in the most complex scenario. Finally, reducing the sample size in terms of number of subjects or number of samples proved to have an impact on the power to detect the correct model.

Response-Based Dosing for Ponatinib: Model-Based Analyses of the Dose-Ranging OPTIC Study.

Optimizing Ponatinib Treatment in CP-CML (OPTIC) was a randomized, phase II dose-optimization trial of ponatinib in chronic phase-chronic myeloid leukemia (CP-CML) resistant to ≥ 2 tyrosine kinase inhibitors or with T315I mutation. Patients were randomized to starting doses of 45-, 30-, or 15-mg ponatinib once daily. Patients receiving 45- or 30-mg reduced to 15-mg upon achievement of ≤ 1% BCR::ABL1IS (≥ molecular response with 2-log reduction (MR2)). The exposure-molecular response relationship was described using a four-state, discrete-time Markov model. Time-to-event models were used to characterize the relationship between exposure and arterial occlusive events (AOEs), grade ≥ 3 neutropenia, and thrombocytopenia. Increasing systemic exposures were associated with increasing probability of transitioning from no response to ≥ MR1, and from MR1 to ≥ MR1, with odds ratios of 1.63 (95% confidence interval (CI), 1.06-2.73) and 2.05 (95% CI, 1.53-2.89) for a 15-mg dose increase, respectively. Ponatinib exposure was a significant predictor of AOEs (hazard ratio (HR) 2.05, 95% CI, 1.43-2.93, for a 15-mg dose increase). In the exposure-safety models for neutropenia and thrombocytopenia, exposure was a significant predictor of grade ≥ 3 thrombocytopenia (HR 1.31, 95% CI, 1.05-1.64, for a 15-mg dose increase). Model-based simulations predicted a clinically meaningful higher rate of ≥ MR2 response at 12 months for the 45-mg starting dose (40.4%) vs. 30-mg (34%) and 15-mg (25.2%). The exposure-response analyses supported a ponatinib starting dose of 45 mg with reduction to 15 mg at response for patients with CP-CML.

Population pharmacokinetic analysis of the P2X3-receptor antagonist gefapixant.

Gefapixant, a P2X3-receptor antagonist, demonstrated objective and subjective efficacy in individuals with refractory or unexplained chronic cough. We report a population pharmacokinetic (PopPK) analysis that characterizes gefapixant pharmacokinetics (PKs), quantifies between- and within-participant variability, and evaluates the impact of intrinsic and extrinsic factors on gefapixant exposure. The PopPK model was initially developed using PK data from six phase I studies. Stepwise covariate method was utilized to identify covariates impacting PK parameters; the model was re-estimated and covariate effects were re-assessed after integrating PK data from three phase II and III studies. Simulations were conducted to evaluate the magnitude of covariate effects on gefapixant exposure. Of 1677 participants included in this data set, 1618 had evaluable PK records. Age, body weight, and sex had statistically significant, but not clinically relevant, effects on exposure. Degree of renal impairment (RI) had statistically significant and clinically relevant effects on exposure; exposure was 17% to 89% higher in those with versus without RI. Simulation results indicated that gefapixant 45 mg administered once daily to patients with severe RI has similar exposure to gefapixant 45 mg administered twice daily to patients with normal renal function. There were no significant effects of proton pump inhibitors or food. Of evaluated intrinsic and extrinsic factors, only RI had a clinically relevant effect on gefapixant exposure. Patients with mild or moderate RI do not require dosage adjustments; however, for patients with severe RI who are not on dialysis, gefapixant 45 mg once daily is recommended.

Mobocertinib Dose Rationale in Patients with Metastatic NSCLC with EGFR Exon 20 Insertions: Exposure-Response Analyses of a Pivotal Phase I/II Study.

Mobocertinib is an oral tyrosine kinase inhibitor approved for treatment of patients with locally advanced or metastatic non-small cell lung cancer (mNSCLC) with epidermal growth factor receptor gene (EGFR) exon 20 insertion (ex20ins) mutations previously treated with platinum-based chemotherapy. These exposure-response analyses assessed potential relationships between exposure and efficacy or safety outcomes in platinum-pretreated patients with EGFRex20ins-positive mNSCLC who received mobocertinib 160 mg once daily (q.d.) in a pivotal phase I/II study. A statistically significant relationship between the independent review committee-assessed objective response rate and molar sum exposure to mobocertinib and its active metabolites (AP32960 and AP32914) was not discernable using a longitudinal model of clinical response driven by normalized dynamic molar sum exposure or a static model of best clinical response based on time-averaged molar sum exposure. However, the longitudinal model suggested a trend for decreased probability of response with the change in mobocertinib molar sum exposure between the 160- and 120-mg doses (odds ratio: 0.78; 95% confidence interval: 0.55-1.10; P = 0.156). Time-averaged molar sum exposure was a significant predictor of the rate of grade ≥ 3 treatment-emergent adverse events (AEs). Taken together, these exposure-efficacy and exposure-safety results support a favorable benefit-risk profile for the approved mobocertinib 160-mg q.d. dose and dose modification guidelines for patients experiencing AEs.

Accelerating model-informed decisions for COVID-19 vaccine candidates using a model-based meta-analysis approach.

BACKGROUND: The COVID-19 pandemic has increased the need for innovative quantitative decision tools to support rapid development of safe and efficacious vaccines against SARS-CoV-2. To meet that need, we developed and applied a model-based meta-analysis (MBMA) approach integrating non-clinical and clinical immunogenicity and protection data. METHODS: A systematic literature review identified studies of vaccines against SARS-CoV-2 in rhesus macaques (RM) and humans. Summary-level data of 13 RM and 8 clinical trials were used in the analysis. A RM MBMA model was developed to quantify the relationship between serum neutralizing (SN) titres after vaccination and peak viral load (VL) post-challenge in RM. The translation of the RM MBMA model to a clinical protection model was then carried out to predict clinical efficacies based on RM data alone. Subsequently, clinical SN and efficacy data were integrated to develop three predictive models of efficacy - a calibrated RM MBMA, a joint (RM-Clinical) MBMA, and the clinical MBMA model. The three models were leveraged to predict efficacies of vaccine candidates not included in the model and efficacies against newer strains of SARS-CoV-2. FINDINGS: Clinical efficacies predicted based on RM data alone were in reasonable agreement with the reported data. The SN titre predicted to provide 50% efficacy was estimated to be about 21% of the mean human convalescent titre level, and that value was consistent across the three models. Clinical efficacies predicted from the MBMA models agreed with reported efficacies for two vaccine candidates (BBV152 and CoronaVac) not included in the modelling and for efficacies against delta variant. INTERPRETATION: The three MBMA models are predictive of protection against SARS-CoV-2 and provide a translational framework to enable early Go/No-Go and study design decisions using non-clinical and/or limited clinical immunogenicity data in the development of novel SARS-CoV-2 vaccines. FUNDING: This study was funded by Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA.

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.

Visual Predictive Check in Models with Time-Varying Input Function.

The nonlinear mixed effects models are commonly used modeling techniques in the pharmaceutical research as they enable the characterization of the individual profiles together with the population to which the individuals belong. To ensure a correct use of them is fundamental to provide powerful diagnostic tools that are able to evaluate the predictive performance of the models. The visual predictive check (VPC) is a commonly used tool that helps the user to check by visual inspection if the model is able to reproduce the variability and the main trend of the observed data. However, the simulation from the model is not always trivial, for example, when using models with time-varying input function (IF). In this class of models, there is a potential mismatch between each set of simulated parameters and the associated individual IF which can cause an incorrect profile simulation. We introduce a refinement of the VPC by taking in consideration a correlation term (the Mahalanobis or normalized Euclidean distance) that helps the association of the correct IF with the individual set of simulated parameters. We investigate and compare its performance with the standard VPC in models of the glucose and insulin system applied on real and simulated data and in a simulated pharmacokinetic/pharmacodynamic (PK/PD) example. The newly proposed VPC performance appears to be better with respect to the standard VPC especially for the models with big variability in the IF where the probability of simulating incorrect profiles is higher.

Serum ferritin is associated with non-alcoholic fatty liver disease and decreased Β-cell function in non-diabetic men and women.

AIMS: We sought to determine whether NAFLD is associated with poorer ß-cell function and if any ß-cell dysfunction is associated with abnormal markers of iron or inflammation. METHODS: This was a cross-sectional study of 15 non-diabetic adults with NAFLD and 15 non-diabetic age and BMI-matched controls. Insulin sensitivity was measured by isotope-labeled hyperinsulinemic-euglycemic clamps and ß-cell function by both oral (OGTT) and intravenous glucose tolerance tests. Liver and abdominal fat composition was evaluated by CT scan. Fasting serum levels of ferritin, transferrin-iron saturation, IL-6, TNFα and hsCRP were measured. RESULTS: Compared to controls, subjects with NAFLD had lower hepatic and systemic insulin sensitivity and ß-cell function was decreased as measured by the oral disposition index. Fasting serum ferritin and transferrin-iron saturation were higher in NAFLD and were positively associated with liver fat. Serum ferritin was negatively associated with ß-cell function measured by both oral and intravenous tests, but was not associated with insulin sensitivity. IL-6, TNFα and hsCRP did not differ between groups and did not correlate with serum ferritin, liver fat or measures of ß-cell function. CONCLUSIONS: These findings support a potential pathophysiological link between iron metabolism, liver fat and diabetes risk.

Identification of the glucose minimal model by stochastic nonlinear-mixed effects methods.

The nonlinear mixed effects models (NLMEM) are widespread modeling techniques in PKPD analysis and epidemiological studies because they can produce a description of not only the individual but also of the population features. Moreover, they are able to deal with individual data sparseness by borrowing the lack of information from the entire population. In this way, the NLMEM do not fail where instead other techniques, such as the traditional individual weighted least squares (WLS), sometimes do. The NLME approach relies on the maximization of a likelihood function that due to model parametric nonlinearity not always has an explicit solution. Various techniques have been proposed to solve this problem including the first order (FO) and the first order conditional (FOCE) estimation methods that approximate the likelihood function through a linearization; the expectation maximization algorithm (EM) that maximize the exact likelihood; the Bayesian estimation method where a third stage of variability, the distribution of the population parameters, is taken into account [1]. Recently, new estimation methods that rely on the EM algorithm have been implemented in the last release of the population software NONMEM [2]. These methods are: the iterative two stage (ITS), Monte Carlo importance sampling EM (IMP), Monte Carlo importance sampling EM assisted by Mode a Posteriori estimation (IMPMAP) and the Stochastic Approximation EM (SAEM). Moreover, another new method is available, the Markov Chain Monte Carlo Bayesian Analysis (BAYES), next to the more known FO and FOCE. With this article we want to complete the Denti et al [3] simulation study by evaluating the newest population methods applied on the IVGTT glucose minimal model.