Integrated Exposure-Response of Dupilumab in Children, Adolescents, and Adults With Atopic Dermatitis Using Categorical and Continuous Efficacy Assessments: A Population Analysis.

BACKGROUND: While the majority of patients with atopic dermatitis (AD) achieve disease control with dupilumab treatment, there is variability in which patients achieve clear disease. The predictors of these responses are currently unclear. Integrated models were developed to evaluate the exposure-response (E-R) relationship of dupilumab in children, adolescents, and adults with AD. METHODS: Data from six Phase II and III clinical studies were pooled (2,366 adults [> 18 years], 243 adolescents [≥ 12 to < 18 years] and 359 children [≥ 6 to < 12 years]) for model development. Efficacy was assessed using the Eczema Area and Severity Index (EASI) and Investigator's Global Assessment (IGA). Indirect response models were applied to link measures of efficacy and functional serum dupilumab concentrations. The covariates on individual placebo-corrected response were assessed. Clinical trial scenarios were simulated to compare E-R relationships across age groups. Safety was not explored. RESULTS: After correcting for differences in placebo response and dupilumab exposure: 1) older age, higher body weight, lower baseline thymus and activation-regulated chemokine, and Asian race were associated with slightly lower EASI response, and no clear covariates were identified on IGA response; 2) clinical trial simulations generally showed slightly higher response at a given dupilumab concentration in children compared to adults and adolescents with severe and moderate AD. CONCLUSIONS: The collectively tested covariates explain some of the variability in dupilumab response in patients with AD. Patients in all age groups showed adequate response to dupilumab; however, children showed slightly higher drug effects compared to adults and adolescents at equivalent concentrations.

Pharmacokinetic/pharmacodynamic modeling of ketoprofen and flunixin at piglet castration and tail-docking.

This study performed population-pharmacokinetic/pharmacodynamic (pop-PK/PD) modeling of ketoprofen and flunixin in piglets undergoing routine castration and tail-docking, utilizing previously published data. Six-day-old male piglets (8/group) received either ketoprofen (3.0 mg/kg) or flunixin (2.2 mg/kg) intramuscularly. Two hours post-dose, piglets were castrated and tail docked. Inhibitory indirect response models were developed utilizing plasma cortisol or interstitial fluid prostaglandin E2 (PGE2) concentration data. Plasma IC50 for ketoprofen utilizing PGE2 as a biomarker was 1.2 µg/ml, and ED50 for was 5.83 mg/kg. The ED50 calculated using cortisol was 4.36 mg/kg; however, the IC50 was high, at 2.56 µg/ml. A large degree of inter-individual variability (124.08%) was also associated with the cortisol IC50 following ketoprofen administration. IC50 for flunixin utilizing cortisol as a biomarker was 0.06 µg/ml, and ED50 was 0.51 mg/kg. The results show that the currently marketed doses of ketoprofen (3.0 mg/kg) and flunixin (2.2 mg/kg) correspond to drug responses of 33.97% (ketoprofen-PGE2), 40.75% (ketoprofen-cortisol), and 81.05% (flunixin-cortisol) of the maximal possible responses. Given this information, flunixin may be the best NSAID to use in mitigating castration and tail-docking pain at the current label dose.

Dalbavancin Population Pharmacokinetic Modeling and Target Attainment Analysis.

Dalbavancin is indicated for the treatment of acute bacterial skin and skin structure infections caused by susceptible gram-positive microorganisms. This analysis represents the update of the population pharmacokinetics (popPK) modeling and target attainment simulations performed with data from the single-dose safety and efficacy study and an unrelated but substantial revision of the preclinical pharmacokinetic/pharmacodynamic target (fAUC/MIC, free area under concentration-time curve/minimum inhibitory concentration ratio). A 3-compartment distribution model with first-order elimination provided an appropriate fit, with typical dalbavancin clearance of 0.05 L/h and total volume of distribution of ∼15 L. Impact of intrinsic factors was modest, although statistically significant (P < .05) relationships with total clearance were found for the following covariates: creatinine clearance, weight, and albumin - dose adjustment was only indicated for severe renal impairment. Under the new nonclinical target, simulations of the popPK model projected that >99% of subjects would achieve the nonclinical target at MIC values up to and including 2 mg/L.

Integration of Food Animal Residue Avoidance Databank (FARAD) empirical methods for drug withdrawal interval determination with a mechanistic population-based interactive physiologically based pharmacokinetic (iPBPK) modeling platform: example for flunixin meglumine administration.

Violative chemical residues in animal-derived food products affect food safety globally and have impact on the trade of international agricultural products. The Food Animal Residue Avoidance Databank program has been developing scientific tools to provide appropriate withdrawal interval (WDI) estimations after extralabel drug use in food animals for the past three decades. One of the tools is physiologically based pharmacokinetic (PBPK) modeling, which is a mechanistic-based approach that can be used to predict tissue residues and WDIs. However, PBPK models are complicated and difficult to use by non-modelers. Therefore, a user-friendly PBPK modeling framework is needed to move this field forward. Flunixin was one of the top five violative drug residues identified in the United States from 2010 to 2016. The objective of this study was to establish a web-based user-friendly framework for the development of new PBPK models for drugs administered to food animals. Specifically, a new PBPK model for both cattle and swine after administration of flunixin meglumine was developed. Population analysis using Monte Carlo simulations was incorporated into the model to predict WDIs following extralabel administration of flunixin meglumine. The population PBPK model was converted to a web-based interactive PBPK (iPBPK) framework to facilitate its application. This iPBPK framework serves as a proof-of-concept for further improvements in the future and it can be applied to develop new models for other drugs in other food animal species, thereby facilitating the application of PBPK modeling in WDI estimation and food safety assessment.

Phxnlme: An R package that facilitates pharmacometric workflow of Phoenix NLME analyses.

BACKGROUND AND OBJECTIVE: Pharmacometric analyses are integral components of the drug development process, and Phoenix NLME is one of the popular software used to conduct such analyses. To address current limitations with model diagnostic graphics and efficiency of the workflow for this software, we developed an R package, Phxnlme, to facilitate its workflow and provide improved graphical diagnostics. METHODS: Phxnlme was designed to provide functionality for the major tasks that are usually performed in pharmacometric analyses (i.e. nonlinear mixed effects modeling, basic model diagnostics, visual predictive checks and bootstrap). Various estimation methods for modeling using the R package are made available through the Phoenix NLME engine. The Phxnlme R package utilizes other packages such as ggplot2 and lattice to produce the graphical output, and various features were included to allow customizability of the output. Interactive features for some plots were also added using the manipulate R package. RESULTS: Phxnlme provides enhanced capabilities for nonlinear mixed effects modeling that can be accessed using the phxnlme() command. Output from the model can be graphed to assess the adequacy of model fits and further explore relationships in the data using various functions included in this R package, such as phxplot() and phxvpc.plot(). Bootstraps, stratified up to three variables, can also be performed to obtain confidence intervals around the model estimates. With the use of an R interface, different R projects can be created to allow multi-tasking, which addresses the current limitation of the Phoenix NLME desktop software. In addition, there is a wide selection of diagnostic and exploratory plots in the Phxnlme package, with improvements in the customizability of plots, compared to Phoenix NLME. CONCLUSIONS: The Phxnlme package is a flexible tool that allows implementation of the analytical workflow of Phoenix NLME with R, with features for greater overall efficiency and improved customizable graphics. Phxnlme is freely available for download on the CRAN repository (https://cran.r-project.org/web/packages/Phxnlme/).

Application of an NLME-Stochastic Deconvolution Approach to Level A IVIVC Modeling.

Stochastic deconvolution is a parameter estimation method that calculates drug absorption using a nonlinear mixed-effects model in which the random effects associated with absorption represent a Wiener process. The present work compares (1) stochastic deconvolution and (2) numerical deconvolution, using clinical pharmacokinetic (PK) data generated for an in vitro-in vivo correlation (IVIVC) study of extended release (ER) formulations of a Biopharmaceutics Classification System class III drug substance. The preliminary analysis found that numerical and stochastic deconvolution yielded superimposable fraction absorbed (Fabs) versus time profiles when supplied with exactly the same externally determined unit impulse response parameters. In a separate analysis, a full population-PK/stochastic deconvolution was applied to the clinical PK data. Scenarios were considered in which immediate release (IR) data were either retained or excluded to inform parameter estimation. The resulting Fabs profiles were then used to model level A IVIVCs. All the considered stochastic deconvolution scenarios, and numerical deconvolution, yielded on average similar results with respect to the IVIVC validation. These results could be achieved with stochastic deconvolution without recourse to IR data. Unlike numerical deconvolution, this also implies that in crossover studies where certain individuals do not receive an IR treatment, their ER data alone can still be included as part of the IVIVC analysis.

Pharmacokinetics of 14 C-ortho-phenylphenol following intravenous administration in pigs.

Workers in the USA are exposed to industrial formulations, which may be toxic. These formulations often contain preservatives or biocides such as ortho-phenylphenol (OPP). There are limited data describing OPP following intravenous administration to assess truly the clearance of this chemical in humans and other species. In vivo experiments were conducted in pigs to determine related pharmacokinetic parameters. 14 C-OPP was administered as an intravenous bolus dose. Blood, feces, urine and tissue samples were collected for analysis by liquid scintillation. Data were analyzed using non-compartmental and compartmental pharmacokinetic model approaches. These data fitted a three-compartment model and showed that the half-life of 14 C-OPP following the intravenous bolus in pigs was 46.26 ± 10.01 h. The kidneys play a crucial role in clearance of 14 C-OPP with a large percentage of the dose being found in the urine (70.3 ± 6.9% dose). Comparisons with other species suggest that 14 C-OPP clearance in pigs (2.48 ml h-1 kg-1 ) is less than that in humans (18.87 ml h-1 kg-1 ) and rats (35.51 ml h-1 kg-1 ). Copyright © 2016 John Wiley & Sons, Ltd.

Assessment of penetrant and vehicle mixture properties on transdermal permeability using a mixed effect pharmacokinetic model of ex vivo porcine skin.

The accurate prediction of the rate and extent of transdermal absorption from topical exposure to chemical mixtures would be beneficial in risk assessment and drug delivery applications. The isolated perfused porcine skin flap (IPPSF) has been used as an ex vivo model for assessing transdermal absorption from topical exposures. A mixed effect, pharmacokinetic tissue model was used to model finite dose, transdermal, absorption data from IPPSF experiments for 12 penetrants dosed in up to 10 different vehicles. The model was able to identify permeability constant, while accounting for between and within unit variability, across the entire data set. This approach provides a platform for exploring the relationship between covariates (chemical descriptors and functions thereof) and the model parameters. Successive models were employed that reduced the overall variability in the parameter estimate by modeling the parameters as functions of the covariates. Log kp was initially modeled as a function of LogP and MW of the pure penetrant (adjusted r2  = 0.48). The addition of mixture factors to account for the different dosing vehicles further improved the relationship: to r2  = 0.56 with Connolly molecular area (CMA) and r2  = 0.78 with the further addition of total polar surface area difference (TPSAd). The pharmacokinetic model and quantitative structure property relationship (QSPR) developed for the IPPSF may be relevant to clinical transdermal formulation development. Copyright © 2016 John Wiley & Sons, Ltd.

Stochastic nonlinear mixed effects: a metformin case study.

In nonlinear mixed effect (NLME) modeling, the intra-individual variability is a collection of errors due to assay sensitivity, dosing, sampling, as well as model misspecification. Utilizing stochastic differential equations (SDE) within the NLME framework allows the decoupling of the measurement errors from the model misspecification. This leads the SDE approach to be a novel tool for model refinement. Using Metformin clinical pharmacokinetic (PK) data, the process of model development through the use of SDEs in population PK modeling was done to study the dynamics of absorption rate. A base model was constructed and then refined by using the system noise terms of the SDEs to track model parameters and model misspecification. This provides the unique advantage of making no underlying assumptions about the structural model for the absorption process while quantifying insufficiencies in the current model. This article focuses on implementing the extended Kalman filter and unscented Kalman filter in an NLME framework for parameter estimation and model development, comparing the methodologies, and illustrating their challenges and utility. The Kalman filter algorithms were successfully implemented in NLME models using MATLAB with run time differences between the ODE and SDE methods comparable to the differences found by Kakhi for their stochastic deconvolution.

Quantification of vehicle mixture effects on in vitro transdermal chemical flux using a random process diffusion model.

The effect of vehicle mixtures on transdermal permeation has been studied using transient flux profiles from porcine skin flow through diffusion cells. Such data characteristically exhibit a large amount of variability between treatments (vehicle and penetrant combinations) as well as noise within treatments. A novel mathematical model has been used that describes longitudinal variation as a time varying diffusivity. Between treatment variability was described by a mixed effects model. A quantitative structure property relationship (QSPR) was developed to describe the effects of the penetrant and vehicle mixture properties on the mean diffusivity and partition coefficient in the membrane. The relationship included terms for the logP and molecular weight of the penetrant and the refractive index of the vehicle mixture with R(2)>0.95 for K and >0.9 for partition coefficient (as K⋅D). This analysis improved on previous work, finding a more parsimonious model with higher predictability, while still identifying the mixture refractive index as a key descriptor in predicting vehicle effects. The concordance with established and expected relationships lends confidence to this new methodology for evaluating transient, finite dose, transdermal flux data collected using traditional experimental methods.

Onartuzumab with or without bevacizumab in combination with weekly paclitaxel does not prolong QTc or adversely affect other ECG parameters in patients with locally recurrent or metastatic triple-negative breast cancer.

PURPOSE: The potential effect of onartuzumab, when administered with or without bevacizumab in combination with weekly paclitaxel, on the corrected QT interval (QTc) and other electrocardiogram (ECG) parameters, was investigated in a randomized, phase 2 study OAM4861g of first- or second-line therapy in patients with locally recurrent or metastatic triple-negative breast cancer. METHODS: Triplicate 12-lead ECGs were recorded at screening, pre- and post-dose on day 1 of cycles 1, 2, and 4, and at the study drug discontinuation visit (SDDV). Onartuzumab serum samples were collected pre- and post-dose on day 1 of cycles 1-4 and at the SDDV. Fridericia's correction was applied to QT recordings (QTcF), and change from baseline (ΔQTcF) was calculated. Post-baseline measurements were reported as baseline-adjusted control arm (placebo plus bevacizumab plus paclitaxel)-corrected values (ΔΔQTcF). Categorical ECG findings were noted. Linear mixed effects modeling evaluated a potential concentration-ΔQTcF relationship. RESULTS: Out of 185 enrolled patients, 165 patients had ECG-evaluable data for analyses. Similar ΔQTcF and ΔΔQTcF values were observed across all treatment arms, with mean increase <10 and <7 ms, respectively, across all time points. Similar changes in heart rate, PR interval, and QRS duration were noted across all treatment arms. Incidences of abnormal ECG findings of clinical interest were comparable in the onartuzumab-containing arms and the control arm. No concentration-ΔQTcF relationship was evident at onartuzumab serum concentrations up to 1,200 µg/ml. CONCLUSIONS: These data suggest that onartuzumab, at the dose and exposures studied in this clinical trial, does not meaningfully affect the QTcF interval.

Development of a mixed-effect pharmacokinetic model for vehicle modulated in vitro transdermal flux of topically applied penetrants.

Transient flux profiles from in vitro flow-through cell experiments exhibit different characteristics depending upon the properties of the penetrants and vehicle mixtures applied. To enable discrimination of the chemical properties contributing to these differences, a consistent mathematical model should first be developed. A mixed effects modeling framework was used so that models can be estimated with as few parameters as possible, while also quantifying variability and accounting for correlation in the data. The models account for diffusion and binding within the membrane as well as dynamics on the diffusion coefficient. The models explain key features of the data, such as: lag time, sharp peaks in flux, two terminal phases, and low flux profiles. The models with dynamic diffusivity fit the data better than those without-particularly the sharp peaks. The significance of changing diffusivity over time suggests that vehicle effects are transient and are more accurately estimated when dynamics are modeled.

Modeling of pharmacokinetic systems using stochastic deconvolution.

In environments where complete mechanistic knowledge of the system dynamics is not available, a synergy of first-principle concepts, stochastic methods and statistical approaches can provide an efficient, accurate, and insightful strategy for model development. In this work, a system of ordinary differential equations describing system pharmacokinetics (PK) was coupled to a Wiener process for tracking the absorption rate coefficient, and was embedded in a nonlinear mixed effects population PK formalism. The procedure is referred to as "stochastic deconvolution" and it is proposed as a diagnostic tool to inform on a mapping function between the fraction of the drug absorbed and the fraction of the drug dissolved when applying one-stage methods to in vitro-in vivo correlation modeling. The goal of this work was to show that stochastic deconvolution can infer an a priori specified absorption profile given dense observational (simulated) data. The results demonstrate that the mathematical model is able to accurately reproduce the simulated data in scenarios where solution strategies for linear, time-invariant systems would assuredly fail. To this end, PK systems that are representative of Michaelis-Menten kinetics and enterohepatic circulation were investigated. Furthermore, the solution times are manageable using a modest computer hardware platform.

Analysis of level A in vitro-in vivo correlations for an extended-release formulation with limited bioavailability.

A two-stage, numerical deconvolution approach was employed to develop level A in vitro-in vivo correlations using data for three formulations of an extended-release oral dosage form. The in vitro dissolution data for all formulations exhibited near-complete dissolution within the time frame of the test. The pharmacokinetic concentration-time profiles for 16 subjects in a cross-over study demonstrated notably limited bioavailability for the slowest formulation. These data were used as the basis for the IVIVC model development. Two models were identified that satisfied the nominal requirements for a conclusive internal predictability of the IVIVC, provided that all three formulations were used as internal datasets. These were a simple linear model with absorption cut-off and a piecewise-linear variable absorption scale model. A subsequent cross-validation of the models' robustness indicated that neither model predicted satisfactorily the pharmacokinetic characteristics of all formulations in a conclusive manner. The piecewise-linear variable absorption scale model provided the most accurate results, particularly with respect to the prediction of the slowest formulation's pharmacokinetic metrics. But this latter model also involved additional free parameters compared with the simple linear model with absorption cut-off. It is argued that more complex IVIVC models with extra parameterization require comprehensive validation to ascertain the accuracy and robustness of the model. In order to achieve this, it is necessary to ensure a complete suite of supporting datasets for internal and external validation, irrespective of the mathematical approach used subsequently to develop the IVIVC.