Nicolas Frey

A Comprehensive Hepatitis C Viral Kinetic Model Explaining Cure

We propose a model that characterizes and links the complexity and diversity of clinically observed hepatitis C viral kinetics to sustained virologic response (SVR)—the primary clinical end point of hepatitis C treatment, defined as an undetectable viral load at 24 weeks after completion of treatment)—in patients with chronic hepatitis C (CHC) who have received treatment with peginterferon α‐2a ± ribavirin. The new attributes of our hepatitis C viral kinetic model are (i) the implementation of a cure/viral eradication boundary, (ii) employment of all hepatitis C virus (HCV) RNA measurements, including those below the lower limit of quantification (LLOQ), and (iii) implementation of a population modeling approach. The model demonstrated excellent positive (99.3%) and negative (97.1%) predictive values for SVR as well as high sensitivity (96.6%) and specificity (99.4%). The proposed viral kinetic model provides a framework for mechanistic exploration of treatment outcome and permits evaluation of alternative CHC treatment options with the ultimate aim of developing and testing hypotheses for personalizing treatments in this disease.

An Integrated Model for Glucose and Insulin Regulation in Healthy Volunteers and Type 2 Diabetic Patients Following Intravenous Glucose Provocations

An integrated model for the regulation of glucose and insulin concentrations following intravenous glucose provocations in healthy volunteers and type 2 diabetic patients was developed. Data from 72 individuals were included. Total glucose, labeled glucose, and insulin concentrations were determined. Simultaneous analysis of all data by nonlinear mixed effect modeling was performed in NONMEM. Integrated models for glucose, labeled glucose, and insulin were developed. Control mechanisms for regulation of glucose production, insulin secretion, and glucose uptake were incorporated. Physiologically relevant differences between healthy volunteers and patients were identified in the regulation of glucose production, elimination rate of glucose, and secretion of insulin. The model was able to describe the insulin and glucose profiles well and also showed a good ability to simulate data. The features of the present model are likely to be of interest for analysis of data collected in antidiabetic drug development and for optimization of study design.

Population Pharmacokinetic Analysis of Tocilizumab in Patients With Rheumatoid Arthritis

Tocilizumab is a humanized anti‐interleukin‐6 (IL‐6) receptor monoclonal antibody that has demonstrated efficacy in the treatment of rheumatoid arthritis (RA). A population pharmacokinetic (PK) model was developed using nonlinear mixed effect modeling to describe the PK profile of tocilizumab and used to estimate interindividual variability and assess the influence of covariates on PK parameters. The model was constructed based on data collected from 1793 patients with moderate to severe RA who received tocilizumab (4 or 8 mg/kg), via intravenous infusion every 4 weeks, during 4 phase III clinical trials. Serum concentration‐time profiles of tocilizumab were adequately described by a 2‐compartment disposition model with parallel linear and nonlinear elimination kinetics. The 8‐mg/kg dose of tocilizumab, compared with the 4‐mg/kg dose, resulted in a more pronounced saturation of the nonlinear clearance pathway over the dosing interval, and this nonlinear clearance was representative of target‐mediated elimination due to tocilizumab binding to the IL‐6 receptor.

Semi‐mechanistic Population Pharmacokinetic Model of Multivalent Trastuzumab Emtansine in Patients with Metastatic Breast Cancer

Trastuzumab emtansine (T‐DM1) is an antibody–drug conjugate (ADC) composed of multiple molecules of the antimicrotubule agent DM1 linked to trastuzumab, a humanized anti–human epidermal growth factor receptor 2 (HER2) monoclonal antibody. Pharmacokinetics data from phase I (n = 52) and phase II (n = 111) studies in HER2‐positive metastatic breast cancer patients show a shorter terminal half‐life for T‐DM1 than for total trastuzumab (TTmAb). In this work, we translated prior preclinical modeling in monkeys to develop a semi‐mechanistic population pharmacokinetics model to characterize T‐DM1 and TTmAb concentration profiles. A series of transit compartments with the same disposition parameters was used to describe the deconjugation process from higher to lower drug‐to‐antibody ratios (DARs). The structure could explain the shorter terminal half‐life of T‐DM1 relative to TTmab. The final model integrates prior knowledge of T‐DM1 DARs from preclinical studies and could provide a platform for understanding and characterizing the pharmacokinetics of other ADC systems.

An integrated glucose-insulin model to describe oral glucose tolerance test data in healthy volunteers.

The extension of the previously developed integrated models for glucose and insulin (IGI) to include the oral glucose tolerance test (OGTT) in healthy volunteers could be valuable to better understand the differences between healthy individuals and those with type 2 diabetes mellitus (T2DM). Data from an OGTT in 23 healthy volunteers were used. Analysis was based on the previously developed intravenous model with extensions for glucose absorption and incretin effect on insulin secretion. The need for additional structural components was evaluated. The model was evaluated by simulation and a bootstrap. Multiple glucose and insulin concentration peaks were observed in most individuals as well as hypoglycemic episodes in the second half of the experiment. The OGTT data were successfully described by the extended basic model. An additional control mechanism of insulin on glucose production improved the description of the data. The model showed good predictive properties, and parameters were estimated with good precision. In conclusion, a previously presented integrated model has been extended to describe glucose and insulin concentrations in healthy volunteers following an OGTT. The characterization of the differences between the healthy and diabetic stages in the IGI model could potentially be used to extrapolate drug effect from healthy volunteers to T2DM.

Modeling of 24-Hour Glucose and Insulin Profiles of Patients With Type 2 Diabetes

A model able to simultaneously characterize and simulate 24‐hour glucose and insulin profiles following multiple meal tests was developed, extending an integrated glucose‐insulin model for oral glucose tolerance tests that was previously published. The analysis was based on glucose and insulin measurements from 59 placebo‐treated patients with type 2 diabetes. Circadian variations in glucose homeostasis were assessed on relevant parameters based on literature review. They were best described by a nighttime dip in insulin secretion between approximately 9 p.m. and 5 a.m. using a modulator function. The integrated glucose‐insulin model has thus been shown to be applicable to real‐life situations determined by multiple meals over the course of a day. This provides the basis for the analysis and simulation of long‐term glucose and insulin data. The model may also prove useful for understanding antidiabetic drug actions and requirements in the context of circadian changes in glucose‐insulin regulation.

Exposure-Exposure Relationship of Tocilizumab, an Anti–IL-6 Receptor Monoclonal Antibody, in a Large Population of Patients With Rheumatoid Arthritis

Relationships between tocilizumab exposure and response were evaluated using data from 4 phase III studies. Increased tocilizumab exposure was associated with improvements in Disease Activity Score using 28 joints (DAS28) and American College of Rheumatology (ACR) criteria and with a decrease in inflammation markers. A population pharmacokinetic/pharmacodynamic (PKPD) model was developed to describe data from 2 studies. An indirect‐response model with a sigmoid Emax (maximal drug effect) inhibitory drug effect on DAS28 “production” rate adequately described the relationship between tocilizumab concentration and DAS28. Mean minimum serum tocilizumab concentration at steady state was greater than the EC50 (concentration at which 50% of Emax on DAS28 is reached) with the 8‐mg/kg dose but not with the 4‐mg/kgdose. Simulations within a large rheumatoid arthritis (RA) population showed that DAS remission rates were 38% for 8 mg/kg and 24% for 4 mg/kg. Tocilizumab was more potent in RA patients with higher baseline interleukin‐6 levels, but this effect was not clinically significant. Other covariates (eg, presence of neutralizing antitocilizumab antibodies) did not demonstrate a clinically meaningful effect on tocilizumab DAS28 dose‐response relationships. These data support clinical observations that tocilizumab 8 mg/kg is more effective than 4 mg/kg in reducing disease activity.

An Integrated Model for the Glucose-Insulin System

The integrated glucose-insulin model was originally developed on a variety of intravenous glucose provocation experiments in healthy volunteers and type 2 diabetic patients. The model, which simultaneously describes time-courses of glucose and insulin based on mechanism-based components for production, elimination and homeostatic feedback, has been further extended to oral glucose provocations, meal tests and insulin administration. The model has been used to describe experiments ranging from 4 to 24 hr. Applications of the integrated glucose-insulin model include the clinical assessment of the mechanism of action of anti-diabetic drugs and the magnitude of their effects. Finally, the model was used for optimizing the design of provocation experiments.

Prediction of Shrinkage of Individual Parameters Using the Bayesian Information Matrix in Non-Linear Mixed Effect Models with Evaluation in Pharmacokinetics

ABSTRACTPurposeWhen information is sparse, individual parameters derived from a non-linear mixed effects model analysis can shrink to the mean. The objective of this work was to predict individual parameter shrinkage from the Bayesian information matrix (MBF). We 1) Propose and evaluate an approximation of MBF by First-Order linearization (FO), 2) Explore by simulations the relationship between shrinkage and precision of estimates and 3) Evaluate prediction of shrinkage and individual parameter precision.MethodsWe approximated MBF using FO. From the shrinkage formula in linear mixed effects models, we derived the predicted shrinkage from MBF. Shrinkage values were generated for parameters of two pharmacokinetic models by varying the structure and the magnitude of the random effect and residual error models as well as the design. We then evaluated the approximation of MBF FO and compared it to Monte-Carlo (MC) simulations. We finally compared expected and observed shrinkage as well as the predicted and estimated Standard Errors (SE) of individual parameters.ResultsMBF FO was similar to MBF MC. Predicted and observed shrinkages were close . Predicted and estimated SE were similar.ConclusionsMBF FO enables prediction of shrinkage and SE of individual parameters. It can be used for design optimization.

Identification of the Mechanism of Action of a Glucokinase Activator From Oral Glucose Tolerance Test Data in Type 2 Diabetic Patients Based on an Integrated Glucose‐Insulin Model

A mechanistic drug‐disease model was developed on the basis of a previously published integrated glucose‐insulin model by Jauslin et al. A glucokinase activator was used as a test compound to evaluate the models ability to identify a drugs mechanism of action and estimate its effects on glucose and insulin profiles following oral glucose tolerance tests. A kinetic‐pharmacodynamic approach was chosen to describe the drugs pharmacodynamic effects in a dose‐response‐time model. Four possible mechanisms of action of antidiabetic drugs were evaluated, and the corresponding affected model parameters were identified: insulin secretion, glucose production, insulin effect on glucose elimination, and insulin‐independent glucose elimination. Inclusion of drug effects in the model at these sites of action was first tested one‐by‐one and then in combination. The results demonstrate the ability of this model to identify the dual mechanism of action of a glucokinase activator and describe and predict its effects: Estimating a stimulating drug effect on insulin secretion and an inhibiting effect on glucose output resulted in a significantly better model fit than any other combination of effect sites. The model may be used for dose finding in early clinical drug development and for gaining more insight into a drug candidates mechanism of action.