Inter-study variability in population pharmacokinetic meta-analysis: when and how to estimate it?

Penetration of Antibiotics into Subcutaneous and Intramuscular Interstitial Fluid: A Meta-Analysis of Microdialysis Studies in Adults

BACKGROUND AND OBJECTIVE

The interstitial fluid of tissues is the effect site for antibiotics targeting extracellular pathogens. Microdialysis studies investigating these concentrations in muscle and subcutaneous tissue have reported notable variability in tissue penetration. This study aimed to comprehensively summarise the existing data on interstitial fluid penetration in these tissues and to identify potential factors influencing antibiotic distribution.

METHODS

A literature review was conducted, focusing on subcutaneous and intramuscular microdialysis studies of antibiotics in both adult healthy volunteers and patients. Random-effect meta-analyses were used to aggregate effect size estimates of tissue penetration. The primary parameter of interest was the unbound penetration ratio, which represents the ratio of the area under the concentration-time curve in interstitial fluid relative to the area under the concentration-time curve in plasma, using unbound concentrations.

RESULTS

In total, 52 reports were incorporated into this analysis. The unbound antibiotic exposure in the interstitial fluid of healthy volunteers was, on average, 22% lower than in plasma. The unbound penetration ratio values were higher after multiple dosing but did not significantly differ between muscle and subcutaneous tissue. Unbound penetration ratio values were lower for acids and bases compared with neutral antibiotics. Neither the molecular weight nor the logP of the antibiotics accounted for the variations in the unbound penetration ratio. Obesity was associated with lower interstitial fluid penetration. Conditions such as sepsis, tissue inflammation and tissue ischaemia were not significantly associated with altered interstitial fluid penetration.

CONCLUSIONS

This study highlights the variability and generally lower exposure of unbound antibiotics in the subcutaneous and intramuscular interstitial fluid compared with exposure in plasma. Future research should focus on understanding the therapeutic relevance of these differences and identify key covariates that may influence them.

Pooled analysis of three pharmacokinetic studies comparing biosimilar MB02 and reference bevacizumab

AIM

The aim of this study was to add robustness and provide further evidence on the bioequivalence, safety and immunogenicity between MB02 and reference bevacizumab. No similar study has been performed before with a biosimilar monoclonal antibody.

METHODS

Population analysis by pooling data from three independent pharmacokinetic (PK) studies was performed. The studies had a single-dose, double-blind, three-arm, parallel-group design and two studies, MB02-A-02-17 and MB02-A-05-18, compared MB02 to EU- and US-bevacizumab in Caucasian subjects, while study MB02-A-04-18 compared MB02 and EU-bevacizumab in Japanese participants. Primary endpoints included maximum observed serum concentration (Cmax ), area under the serum concentration-time curve (AUC) from time zero and extrapolated to infinity (AUC0-∞ ) and AUC from time zero to the time of last quantifiable concentration (AUC0-t ). Secondary endpoints included other PK parameters, safety and immunogenicity. A sensitivity analysis using actual protein concentration as a correction factor was applied to primary PK parameters.

RESULTS

Point estimates and 90% confidence intervals for the geometric mean ratios of primary PK parameters for MB02, EU- and US-bevacizumab were all contained within the predefined bioequivalence margins (80%-125%) for all pairwise comparisons. The same results for all pairwise comparisons were observed when protein-corrected primary PK parameters were analyzed. Safety and immunogenicity were similar between MB02 and the EU- and US-reference bevacizumab in healthy subjects.

CONCLUSIONS

This pooled analysis of three comparable PK studies further supports the bioequivalence of biosimilar MB02 to EU- and US-reference bevacizumab. No clinically meaningful differences in safety or immunogenicity were observed.

Model-based assessment of combination therapies - ranking of radiosensitizing agents in oncology

BACKGROUND

To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data.

METHODS

We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves.

RESULTS

The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 [Formula: see text] of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication.

CONCLUSIONS

A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

Investigation of simplified physiologically-based pharmacokinetic models in rat and human

Whole-body physiologically-based pharmacokinetic (PBPK) models have many applications in drug research and development. It is often necessary to inform these models with animal or clinical data, updating model parameters, and making the model more predictive for future applications. This provides an opportunity and a challenge given the large number of parameters of such models. The aim of this work was to propose new mechanistic model structures with reduced complexity allowing for parameter optimization. These models were evaluated for their ability to estimate realistic values for unbound tissue to plasma partition coefficients (Kpu) and simulate observed pharmacokinetic (PK) data. Two approaches are presented: using either established kinetic lumping methods based on tissue time constants (drug-dependent) or a novel clustering analysis to identify tissues sharing common Kpu values or Kpu scalars based on similarities of tissue composition (drug-independent). PBPK models derived from these approaches were assessed using PK data of diazepam in rats and humans. Although the clustering analysis produced minor differences in tissue grouping depending on the method used, two larger groups of tissues emerged. One including the kidneys, liver, spleen, gut, heart, and lungs, and another including bone, brain, muscle, and pancreas whereas adipose and skin were generally considered distinct. Overall, a subdivision into four tissue groups appeared most physiologically relevant in terms of tissue composition. Several models were found to have similar abilities to describe diazepam i.v. data as empirical models. Comparability of estimated Kpus to experimental Kpu values for diazepam was one criterion for selecting the appropriate PK model structure.

Evaluation and Application of Population Pharmacokinetic Models for Identifying Delayed Methotrexate Elimination in Patients With Primary Central Nervous System Lymphoma

Objective: Several population pharmacokinetic (popPK) models have been developed to determine the sources of methotrexate (MTX) PK variability. It remains unknown if these published models are precise enough for use or if a new model needs to be built. The aims of this study were to 1) assess the predictability of published models and 2) analyze the potential risk factors for delayed MTX elimination.

Methods: A total of 1458 MTX plasma concentrations, including 377 courses (1–17 per patient), were collected from 77 patients who were receiving high-dose MTX for the treatment of primary central nervous system lymphoma in Huashan Hospital. PopPK analysis was performed using the NONMEM® software package. Previously published popPK models were selected and rebuilt. A new popPK model was then constructed to screen potential covariates using a stepwise approach. The covariates were included based on the combination of theoretical mechanisms and data properties. Goodness-of-fit plots, bootstrap, and prediction- and simulation-based diagnostics were used to determine the stability and predictive performance of both the published and newly built models. Monte Carlo simulations were conducted to qualify the influence of risk factors on the incidence of delayed elimination.

Results: Among the eight evaluated published models, none presented acceptable values of bias or inaccuracy. A two-compartment model was employed in the newly built model to describe the PK of MTX. The estimated mean clearance (CL/F) was 4.91 L h⁻¹ (relative standard error: 3.7%). Creatinine clearance, albumin, and age were identified as covariates of MTX CL/F. The median and median absolute prediction errors of the final model were -10.2 and 36.4%, respectively. Results of goodness-of-fit plots, bootstrap, and prediction-corrected visual predictive checks indicated the high predictability of the final model.

Conclusions: Current published models are not sufficiently reliable for cross-center use. The elderly patients and those with renal dysfunction, hypoalbuminemia are at higher risk of delayed elimination.

A physiologically based pharmacokinetic model of clopidogrel in populations of European and Japanese ancestry: An evaluation of CYP2C19 activity

Treatment response to clopidogrel is associated with CYP2C19 activity through the formation of the active H4 metabolite. The aims of this study were to develop a physiologically based pharmacokinetic (PBPK) model of clopidogrel and its metabolites for populations of European ancestry, to predict the pharmacokinetics in the Japanese population by CYP2C19 phenotype, and to investigate the effect of clinical and demographic factors. A PBPK model was developed and verified to describe the two metabolic pathways of clopidogrel (H4 metabolite, acyl glucuronide metabolite) for a population of European ancestry using plasma data from published studies. Subsequently, model predictions in the Japanese population were evaluated. The effects of CYP2C19 activity, fluvoxamine coadministration (CYP2C19 inhibitor), and population‐specific factors (age, sex, BMI, body weight, cancer, hepatic, and renal dysfunction) on the pharmacokinetics of clopidogrel and its metabolites were then characterized. The predicted/observed ratios for clopidogrel and metabolite exposure parameters were acceptable (twofold acceptance criteria). For all CYP2C19 phenotypes, steady‐state AUC_0‐τ of the H4 metabolite was lower for the Japanese (e.g., EM, 7.69 [6.26–9.45] ng·h/ml; geometric mean [95% CI]) than European (EM, 24.8 [20.4–30.1] ng·h/ml, p < .001) population. In addition to CYP2C19‐poor metabolizer phenotype, fluvoxamine coadministration, hepatic, and renal dysfunction were found to reduce H4 metabolite but not acyl glucuronide metabolite concentrations. This is the first PBPK model describing the two major metabolic pathways of clopidogrel, which can be applied to populations of European and Japanese ancestry by CYP2C19 phenotype. The differences between the two populations appear to be determined primarily by the effect of varying CYP2C19 liver activity.

Second-generation TNFα turnover model for improved analysis of test compound interventions in LPS challenge studies

This study presents a non-linear mixed effects model describing tumour necrosis factor alpha (TNFα) release after lipopolysaccharide (LPS) provocations in absence or presence of anti-inflammatory test compounds. Inter-occasion variability and the pharmacokinetics of two test compounds have been added to this second-generation model, and the goal is to produce a framework of how to model TNFα response in LPS challenge studies in vivo and demonstrate its general applicability regardless of occasion or type of test compound. Model improvements based on experimental data were successfully implemented and provided a robust model for TNFα response after LPS provocation, as well as reliable estimates of the median pharmacodynamic parameters. The two test compounds, Test Compound A and roflumilast, showed 81.1% and 74.9% partial reduction of TNFα response, respectively, and the potency of Test Compound A was estimated to 0.166 µmol/L. Comparing this study with previously published work reveals that our model leads to biologically reasonable output, handles complex data pooled from different studies, and highlights the importance of accurately distinguishing the stimulatory effect of LPS from the inhibitory effect of the test compound.

Population pharmacokinetic modeling of pyrotinib in patients with HER2-positive advanced or metastatic breast cancer

OBJECTIVE

Pyrotinib, a new oral irreversible pan-ErbB tyrosine kinase inhibitor (TKI), has been approved in China for the treatment of HER2-positive advanced or metastatic breast cancer. This study aimed to conduct a population pharmacokinetics (PK) analysis of pyrotinib and to evaluate the impact of patient characteristics on pyrotinib's PK.

METHOD

A total of 1152 samples, provided by 59 adult female patients from two phase I clinical trials, were analyzed by nonlinear mixed-effects modeling. Monte Carlo simulation was conducted to assess impact of covariates on the exposure to pyrotinib.

RESULTS

The PK of pyrotinib was adequately described by a one-compartment model with first-order absorption and elimination. Patient's age and total protein levels could affect pyrotinib's apparent volume of distribution, and concomitant use of montmorillonite could decrease the bioavailability of pyrotinib by 50.3%. No PK interactions were observed between capecitabine and pyrotinib.

CONCLUSION

In this study, a population PK model of pyrotinib was developed to determine the influence of patient characteristics on the PK of pyrotinib. While patient age and total protein levels can significantly affect the apparent distribution volume of pyrotinib, the magnitude of the impact was limited, thus no dosage adjustment was recommended. Furthermore, concomitant use of montmorillonite for diarrhea needs to be taken with precaution.

Integrated Data Analysis of Six Clinical Studies Points Toward Model-Informed Precision Dosing of Tamoxifen

Introduction

At tamoxifen standard dosing, ∼20% of breast cancer patients do not reach proposed target endoxifen concentrations >5.97 ng/mL. Thus, better understanding the large interindividual variability in tamoxifen pharmacokinetics (PK) is crucial. By applying non-linear mixed-effects (NLME) modeling to a pooled ‘real-world’ clinical PK database, we aimed to (i) dissect several levels of variability and identify factors predictive for endoxifen exposure and (ii) assess different tamoxifen dosing strategies for their potential to increase the number of patients reaching target endoxifen concentrations.

Methods

Tamoxifen and endoxifen concentrations with genetic and demographic data of 468 breast cancer patients from six reported studies were used to develop a NLME parent-metabolite PK model. Different levels of variability on model parameters or measurements were investigated and the impact of covariates thereupon explored. The model was subsequently applied in a simulation-based comparison of three dosing strategies with increasing degree of dose individualization for a large virtual breast cancer population. Interindividual variability of endoxifen concentrations and the fraction of patients at risk for not reaching target concentrations were assessed for each dosing strategy.

Results and Conclusions

The integrated NLME model enabled to differentiate and quantify four levels of variability (interstudy, interindividual, interoccasion, and intraindividual). Strong influential factors, i.e., CYP2D6 activity score, drug–drug interactions with CYP3A and CYP2D6 inducers/inhibitors and age, were reliably identified, reducing interoccasion variability to <20% CV. Yet, unexplained interindividual variability in endoxifen formation remained large (47.2% CV). Hence, therapeutic drug monitoring seems promising for achieving endoxifen target concentrations. Three tamoxifen dosing strategies [standard dosing (20 mg QD), CYP2D6-guided dosing (20, 40, and 60 mg QD) and individual model-informed precision dosing (MIPD)] using three therapeutic drug monitoring samples (5–120 mg QD) were compared, leveraging the model. The proportion of patients at risk for not reaching target concentrations was 22.2% in standard dosing, 16.0% in CYP2D6-guided dosing and 7.19% in MIPD. While in CYP2D6-guided- and standard dosing interindividual variability in endoxifen concentrations was high (64.0% CV and 68.1% CV, respectively), it was considerably reduced in MIPD (24.0% CV). Hence, MIPD demonstrated to be the most promising strategy for achieving target endoxifen concentrations.

The application of control charts in regulated bioanalysis for monitoring long-term reproducibility

In regulated bioanalysis, the acceptance of results is batch-wise. When during clinical development derived pharmacokinetic or pharmacodynamic results from different studies will be combined or compared, it is recommendable to monitor the long-term reproducibility of bioanalytical assays. Long-term reproducibility can be evaluated by control charts generated from control samples included in each batch. We present a methodology for the implementation, construction and evaluation of control charts next to the regular batch acceptance of bioanalytical results. Decision rules can be set up for a statistical evaluation of the results. Violation of a decision rule may lead to a root-cause investigation and corrective actions to improve assay robustness. Three examples of control charts, for pharmacokinetic and pharmacodynamic analytes are presented.

External evaluation of population pharmacokinetic models for ciclosporin in adult renal transplant recipients

AIMS

Several population pharmacokinetic (popPK) models for ciclosporin (CsA) in adult renal transplant recipients have been constructed to optimize the therapeutic regimen of CsA. However, little is known about their predictabilities when extrapolated to different clinical centres. Therefore, this study aimed to externally evaluate the predictive ability of CsA popPK models and determine the potential influencing factors.

METHODS

A literature search was conducted and the predictive performance was determined for each selected model using an independent data set of 62 patients (471 predose and 500 2-h postdose concentrations) from our hospital. Prediction-based diagnostics and simulation-based normalized prediction distribution error were used to evaluate model predictability. The influence of prior information was assessed using Bayesian forecasting. Additionally, potential factors influencing model predictability were investigated.

RESULTS

Seventeen models extracted from 17 published popPK studies were assessed. Prediction-based diagnostics showed that ethnicity potentially influenced model transferability. Simulation-based normalized prediction distribution error analyses indicated misspecification in most of the models, especially regarding variance. Bayesian forecasting demonstrated that the predictive performance of the models substantially improved with 2-3 prior observations. The predictability of nonlinear Michaelis-Menten models was superior to that of linear compartmental models when evaluating the impact of structural models, indicating the underlying nonlinear kinetics of CsA. Structural model, ethnicity, covariates and prior observations potentially affected model predictability.

CONCLUSIONS

Structural model is the predominant factor influencing model predictability. Incorporation of nonlinear kinetics in CsA popPK modelling should be considered. Moreover, Bayesian forecasting substantially improved model predictability.

Analyzing the Potential Root Causes of Variability of Pharmacokinetics in Preclinical Species

The purpose of this research was to assess variability in pharmacokinetic profiles (PK variability) in preclinical species and identify the risk factors associated with the properties of a drug molecule that contribute to the variability. Exposure data in mouse, rat, dog, and monkey for a total of 16,592 research compounds studied between 1999 and 2013 were included in the analysis. Both in vivo study parameters and in silico/experimental physicochemical properties of the molecules were analyzed. Areas under the plasma concentration vs time curves (AUC) were used to assess PK variability. PK variability was calculated as the ratio of the highest AUC within a defined set of AUC values (AUC_max) over the lowest AUC within that set (AUC_min). Both intra- and inter-animal variability were analyzed, with intra-animal exposures found to be more variable than inter-animal exposures. While several routes of administration were initially studied, the analysis was focused on the oral route, which corresponds to the large majority of data points and displays higher variability than the subcutaneous, intraperitoneal, or intravenous routes. The association between inter-animal PK variability and physical properties was studied, and low solubility, high administered dose, high preclinical dose number (PDo), and pH-dependent solubility were found to be associated with high variability in exposures. Permeability-as assessed by the measured permeability coefficient in the LLC-PK1 cell line-was also considered but appeared to only have a weak association with variability. Consistent with these findings, BCS class I and III compounds were found to be less prone to PK variability than BCS class II and IV compounds. A modest association of PK variability with clearance was observed while the association with bioavailability, a higher PK variability for compounds with lower bioavailability, appeared to be more pronounced. Finally, two case studies that highlight PK variability issues are described, and successful mitigation strategies are presented.

Population Pharmacokinetic Model of Doxycycline Plasma Concentrations Using Pooled Study Data

The literature presently lacks a population pharmacokinetic analysis of doxycycline. This study aimed to develop a population pharmacokinetic model of doxycycline plasma concentrations that could be used to assess the power of bioequivalence between Doryx delayed-release tablets and Doryx MPC. Doxycycline pharmacokinetic data were available from eight phase 1 clinical trials following single/multiple doses of conventional-release doxycycline capsules, Doryx delayed-release tablets, and Doryx MPC under fed and fasted conditions. A population pharmacokinetic model was developed in a stepwise manner using NONMEM, version 7.3. The final covariate model was developed according to a forward inclusion (P < 0.01) and then backward deletion (P < 0.001) procedure. The final model was a two-compartment model with two-transit absorption compartments. Structural covariates in the base model included formulation effects on relative bioavailability (F), absorption lag (ALAG), and the transit absorption rate (KTR) under the fed status. An absorption delay (lag) for the fed status (FTLAG2 = 0.203 h) was also included in the model as a structural covariate. The fed status was observed to decrease F by 10.5%, and the effect of female sex was a 14.4% increase in clearance. The manuscript presents the first population pharmacokinetic model of doxycycline plasma concentrations following oral doxycycline administration. The model was used to assess the power of bioequivalence between Doryx delayed-release tablets and Doryx MPC, and it could potentially be used to critically examine and optimize doxycycline dose regimens.

Modeling of free fatty acid dynamics: insulin and nicotinic acid resistance under acute and chronic treatments

Nicotinic acid (NiAc) is a potent inhibitor of adipose tissue lipolysis. Acute administration results in a rapid reduction of plasma free fatty acid (FFA) concentrations. Sustained NiAc exposure is associated with tolerance development (drug resistance) and complete adaptation (FFA returning to pretreatment levels). We conducted a meta-analysis on a rich pre-clinical data set of the NiAc–FFA interaction to establish the acute and chronic exposure-response relations from a macro perspective. The data were analyzed using a nonlinear mixed-effects framework. We also developed a new turnover model that describes the adaptation seen in plasma FFA concentrations in lean Sprague–Dawley and obese Zucker rats following acute and chronic NiAc exposure. The adaptive mechanisms within the system were described using integral control systems and dynamic efficacies in the traditional \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$I_{\text{max}}$$\end{document}Imax model. Insulin was incorporated in parallel with NiAc as the main endogenous co-variate of FFA dynamics. The model captured profound insulin resistance and complete drug resistance in obese rats. The efficacy of NiAc as an inhibitor of FFA release went from 1 to approximately 0 during sustained exposure in obese rats. The potency of NiAc as an inhibitor of insulin and of FFA release was estimated to be 0.338 and 0.436 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\upmu {\text{M}}}$$\end{document}μM, respectively, in obese rats. A range of dosing regimens was analyzed and predictions made for optimizing NiAc delivery to minimize FFA exposure. Given the exposure levels of the experiments, the importance of washout periods in-between NiAc infusions was illustrated. The washout periods should be \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sim$$\end{document}∼2 h longer than the infusions in order to optimize 24 h lowering of FFA in rats. However, the predicted concentration-response relationships suggests that higher AUC reductions might be attained at lower NiAc exposures.

Population pharmacokinetic drug-drug interaction pooled analysis of existing data for rifabutin and HIV PIs

OBJECTIVES

Extensive but fragmented data from existing studies were used to describe the drug-drug interaction between rifabutin and HIV PIs and predict doses achieving recommended therapeutic exposure for rifabutin in patients with HIV-associated TB, with concurrently administered PIs.

METHODS

Individual-level data from 13 published studies were pooled and a population analysis approach was used to develop a pharmacokinetic model for rifabutin, its main active metabolite 25-O-desacetyl rifabutin (des-rifabutin) and drug-drug interaction with PIs in healthy volunteers and patients who had HIV and TB (TB/HIV).

RESULTS

Key parameters of rifabutin affected by drug-drug interaction in TB/HIV were clearance to routes other than des-rifabutin (reduced by 76%-100%), formation of the metabolite (increased by 224% in patients), volume of distribution (increased by 606%) and distribution to the peripheral compartment (reduced by 47%). For des-rifabutin, clearance was reduced by 35%-76% and volume of distribution increased by 67%-240% in TB/HIV. These changes resulted in overall increased exposure to rifabutin in TB/HIV patients by 210% because of the effects of PIs and 280% with ritonavir-boosted PIs.

CONCLUSIONS

Given together with non-boosted or ritonavir-boosted PIs, rifabutin at 150 mg once daily results in similar or higher exposure compared with rifabutin at 300 mg once daily without concomitant PIs and may achieve peak concentrations within an acceptable therapeutic range. Although 300 mg of rifabutin every 3 days with boosted PI achieves an average equivalent exposure, intermittent doses of rifamycins are not supported by current guidelines.

Designing More Efficient Preclinical Experiments: A Simulation Study in Chemotherapy-Induced Myelosupression

A new more efficient preclinical study design (referred to as a compact design) is proposed that removes the need for satellite animals for the collection of toxicokinetic (TK) data by sampling from the main study animals, taking no more than one sample in 24 h to build up a full profile over the course of the study. The compact design’s performance was tested with a simulation study, using an example of chemotherapy-induced myelosupression in rats. Data sets were simulated from a model based on available data, following both the compact design and a traditional design using satellite animals, with 100 studies being simulated for each. The effect of the compact design on parameter and variance estimates for the TK and neutrophil models were investigated, as well as the potential effect of interoccasion variability (IOV). The compact design performed equally as well as the traditional design, and had little impact on parameter or variation estimates, indicating that it would be a suitable alternative to traditional satellite designs while reducing the number of animals required. When IOV was present but not accounted for during the TK analysis some parameter estimates were biased and interindividual variation and residual errors inflated; this was reduced by allowing for IOV in the analysis. Using the compact design removes the need for a satellite group, reducing the number of animals required, without affecting the ability to model the data. If large IOV is suspected, caution should be exercised to avoid parameter estimation bias, and inflation of variability and residual error.

Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationships--a PKPD meta-analysis

BACKGROUND AND PURPOSE

Preclinical cardiovascular safety studies (CVS) have been compared between facilities with respect to their sensitivity to detect drug-induced QTc prolongation (ΔQTc). Little is known about the consistency of quantitative ΔQTc predictions that are relevant for translation to humans.

EXPERIMENTAL APPROACH

We derived typical ΔQTc predictions at therapeutic exposure (ΔQTcTHER ) with 95% confidence intervals (95%CI) for 3 Kv 11.1 (hERG) channel blockers (moxifloxacin, dofetilide and sotalol) from a total of 14 CVS with variable designs in the conscious dog. Population pharmacokinetic-pharmacodynamic (PKPD) analysis of each study was followed by a meta-analysis (pooling 2-6 studies including 10-32 dogs per compound) to derive meta-predictions of typical ΔQTcTHER . Meta-predictions were used as a reference to evaluate the consistency of study predictions and to relate results to those found in the clinical literature.

KEY RESULTS

The 95%CIs of study-predicted ΔQTcTHER comprised in 13 out of 14 cases the meta-prediction. Overall inter-study variability (mean deviation from meta-prediction at upper level of therapeutic exposure) was 30% (range: 1-69%). Meta-ΔQTcTHER predictions for moxifloxacin, dofetilide and sotalol overlapped with reported clinical QTc prolongation when expressed as %-prolongation from baseline.

CONCLUSIONS AND IMPLICATIONS

Consistent exposure-ΔQTc predictions were obtained from single preclinical dog studies of highly variable designs by systematic PKPD analysis, which is suitable for translational purposes. The good preclinical-clinical pharmacodynamic correlations obtained suggest that such an analysis should be more routinely applied to increase the informative and predictive value of results obtained from animal experiments.

Population pharmacokinetics of mycophenolic acid and its main glucuronide metabolite: a comparison between healthy Chinese and Caucasian subjects receiving mycophenolate mofetil

PURPOSE

Mycophenolate mofetil (MMF), a prodrug of the immunosuppressive agent mycophenolic acid (MPA), is widely used for prophylaxis of solid organ transplant rejection. MPA is primarily metabolized to 7-O-mycophenolic acid glucuronide (MPAG), an inactive metabolite that undergoes enterohepatic recirculation (EHC). This study assessed ethnic differences in the pharmacokinetics (PK) of MPA and MPAG between healthy Chinese and Caucasian subjects using population PK analysis.

METHODS

Data were pooled from 132 healthy subjects (80 Chinese, 52 Caucasians) in eight clinical studies in which MMF was administered in a single oral dose. Population PK analysis was performed using NONMEM®.

RESULTS

The PK of MPA and MPAG were best described by a five-chain compartment model, including a gallbladder compartment for EHC and a transit absorption model. Ethnicity was significantly correlated with the apparent clearance (CL/F) and volume of distribution (V/F) of MPAG but not those of MPA. Weight was identified as a covariate and was correlated with the PK of MPA and MPAG. MPA CL/F was 11.5 L/h for a 70-kg healthy subject, and the MPAG CL/F values were 1.36 and 1.90 L/h for 70-kg Chinese and Caucasian individuals, respectively. Internal and external evaluation indicated model validity.

CONCLUSIONS

This is the first population PK analysis to evaluate ethnic differences in the PK of MPA and MPAG in healthy Chinese and Caucasian subjects. No differences were observed in the PK of MPA between healthy Chinese and Caucasian subjects. Although, the MPAG CL/F was approximately 40 % higher in Caucasians, this finding may not be clinically relevant.

Integration of data from multiple sources for simultaneous modelling analysis: experience from nevirapine population pharmacokinetics

AIMS

To propose a modelling strategy to efficiently integrate data from different sources in one simultaneous analysis, using nevirapine population pharmacokinetic data as an example.

METHODS

Data from three studies including 115 human immunodeficiency virus-infected South African adults were used. Patients were on antiretroviral therapy regimens including 200 mg nevirapine twice daily and sampled at steady state. A development process was suggested, implemented in NONMEM7 and the final model evaluated with an external data set.

RESULTS

A stepwise approach proved efficient. Model development started with the intensively sampled data. Data were added sequentially, using visual predictive checks for inspecting their compatibility with the existing model. Covariate exploration was carried out, and auxiliary regression models were designed for imputation of missing covariates. Nevirapine pharmacokinetics was described by a one-compartment model with absorption through two transit compartments. Body size was accounted for using allometric scaling. The model included a mixture of two subpopulations with different typical values of clearance, namely fast (3.12 l h(-1)) and slow metabolizers (1.45 l h(-1)), with 17% probability of belonging to the latter. Absorption displayed large between-occasion variability, and food slowed the absorption mean transit time from 0.6 to 2.5 h. Concomitant antitubercular treatment including rifampicin typically decreased bioavailability by 39%, with significant between-subject variability. Visual predictive checks of external validation data indicated good predictive performance.

CONCLUSIONS

The development strategy succeeded in integrating data from different sources to produce a model with robust parameter estimates. This work paves the way for the creation of a nevirapine mega-model, including additional data from numerous diverse sources.

A model for glucose, insulin, and beta-cell dynamics in subjects with insulin resistance and patients with type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a progressive, metabolic disorder characterized by reduced insulin sensitivity and loss of beta-cell mass (BCM), resulting in hyperglycemia. Population pharmacokinetic-pharmacodynamic (PKPD) modeling is a valuable method to gain insight into disease and drug action. A semi-mechanistic PKPD model incorporating fasting plasma glucose (FPG), fasting insulin, insulin sensitivity, and BCM in patients at various disease stages was developed. Data from 3 clinical trials (phase II/III) with a peroxisome proliferator-activated receptor agonist, tesaglitazar, were used to develop the model. In this, a modeling framework proposed by Topp et al was expanded to incorporate the effects of treatment and impact of disease, as well as variability between subjects. The model accurately described FPG and fasting insulin data over time. The model included a strong relation between insulin clearance and insulin sensitivity, predicted 40% to 60% lower BCM in T2DM patients, and realistic improvements of BCM and insulin sensitivity with treatment. The treatment response on insulin sensitivity occurs within the first weeks, whereas the positive effects on BCM arise over several months. The semi-mechanistic PKPD model well described the heterogeneous populations, ranging from nondiabetic, insulin-resistant subjects to long-term treated T2DM patients. This model also allows incorporation of clinical-experimental studies and actual observations of BCM.

Investigation of PK-PD drug-drug interaction between acenocoumarol and amoxicillin plus clavulanic acid

A pharmacokinetic-pharmacodynamic (PK-PD) drug-drug interaction between acenocoumarol and amoxicillin + clavulanic acid antibiotic was assessed in eight healthy volunteers, using a population PK-PD) model. Each subject received at day 1 a single dose of 8 mg of acenocoumarol. Then 1 g of amoxicillin + 250 mg of clavulanic acid was given from days 3 to 9. On day 8, each subject received a single dose of 8 mg of acenocoumarol concomitantly with the antibiotic combination. Eleven blood samples were taken during 48 h following each acenocoumarol administration. Acenocoumarol plasma concentrations and prothrombin time were measured at each sampling time. We first identified the structural PK model by pooling data from this trial with individual data from other acenocoumarol PK trials. An indirect response model was used to fit PD data. Models were built using a non-linear mixed effect modelling approach with nonmem software. Covariates were tested on PK and PD parameters, including antibiotic treatment. Acenocoumarol PK data were fitted by a two-compartment, first-order input model with log normal inter-individual variability. Weight and antibiotic treatment were found to improve significantly the fit of PK data with a 15% decrease in acenocoumarol clearance with concomitant antibiotics (P < 0.05). An indirect response model was successfully applied to the PK-PD data of acenocoumarol. No covariate, including antibiotic treatment effect, significantly affected PT. Drug-drug interaction was demonstrated at the PK level, without any PD corollary.

Recommended reading in population pharmacokinetic pharmacodynamics

Developing the skills or expertise to create useful population pharmacokinetic-pharmacodynamic models can be a daunting task-the level of mathematical and statistical complexity is such that newcomers to the field are frequently overwhelmed. A good place to start in learning the field is to read articles in the literature. However, the number of articles dealing with population pharmacokinetic pharmacodynamics is exponentially increasing on a yearly basis, so choosing which articles to read can be difficult. The purpose of this review is to provide a recommended reading list for newcomers to the field. The list was chosen based on perceived impact of the article in the field, the quality of the article, or to highlight some important detail contained within the article. After reading the articles in the list, it is believed that the reader will have a broad overview of the field and have a sound foundation for more-detailed reading of the literature.

Clinical Trial Simulation: A Tool for Understanding Study Failures and Preventing Them

Execution models describe protocol deviations from a specified study design. When a clinical trial is planned, it is generally supposed that it will be executed according to a specific protocol that defines all aspects of the experimental design, from its beginning to its completion. Adherence to the protocol will allow estimation of the treatment outcome (safety and efficacy) with sufficient statistical power, or at least that is what is assumed. In reality, however, deviations from the protocol may lead to failure of the study to achieve its stated aims. In anticipation of protocol deviations that contribute to inflated residual variability and decreased study statistical power, trial designers tend to overpower studies in a rather arbitrary way. It is difficult to estimate quantitatively the consequences of one protocol deviation on statistical study power and, a fortiori, it is almost impossible to do it for a combination of protocol deviations. One way to study the consequences of model deviations is by using modelling and simulation techniques, and more specifically longitudinal stochastic models that can describe individual behaviours. Thus, execution models are powerful tools for identifying weaknesses or limitations in a proposed study design, which may be anticipated, avoided or resolved in order to increase robustness of the study design prior to implementation of the actual clinical study. As such, they are an integral component of clinical trial simulation and an essential tool in clinical trial design.

Meta-analysis of pharmacokinetic data of veterinary drugs using the Food Animal Residue Avoidance Databank: oxytetracycline and procaine penicillin G

Investigators frequently face the quandary of how to interpret the often times disparate pharmacokinetic parameter values reported in the literature. Combining of data from multiple studies (meta-analysis) is a useful tool in pharmacokinetics. Few studies have explored the use of meta-analysis for veterinary species. Even fewer studies have explored the potential strengths and weaknesses of the various methods of performing a meta-analysis. Therefore, in this study we performed a meta-analysis for oxytetracycline (OTC) and procaine penicillin G (PPG) given intramuscularly to cattle. The analysis included 28 individual data sets from 18 published papers for PPG (288 data points), and 41 individual data sets from 25 published papers for OTC (489 data points). Three methods were used to calculate the parameters. The first was a simple statistical analysis of the parameter values reported in each paper. The second method was a standard Two-Stage Method (TSM) using the mean concentration vs. time data extracted from each paper. The third method was the use of nonlinear mixed effect modeling (NMEM) of the concentration vs. time data reported in the various papers, treating the mean data as if each set came from an individual animal. The results of this evaluation indicate that all three methods generate comparable mean parameter estimates for OTC and PPG. The only significant difference noted was for OTC absorption half-lives taken from the published literature, a difference attributable to the use of an alternative method of parameter calculation. The NMEM procedure offers the possibility of including covariates such as dose, age, and weight. In this study the covariates did not influence the derived parameters. A combination approach to meta-analysis of published mean data is recommended, where the TSM is the first step, followed by the NMEM approach.

Population pharmacokinetic modelling of darifenacin and its hydroxylated metabolite using pooled data, incorporating saturable first-pass metabolism, CYP2D6 genotype and formulation-dependent bioavailability

AIMS

A model describing the population pharmacokinetics of darifenacin and its hydroxylated metabolite was developed from a combined analysis of 18 studies. The relationships between explanatory covariates and pharmacokinetic parameters were explored.

METHODS

Plasma concentration data from 337 individuals were pooled from 17 Phase 1 studies (median 28/33 darifenacin/metabolite observations per healthy subject), and one Phase 2 study (median 7/7 darifenacin/metabolite observations per subject) encompassing one intravenous and five different oral formulations (1-45 mg).

RESULTS

Non-linear Mixed Effects Models (NONMEM Version VI) described both the population pharmacokinetics of darifenacin and its hydroxylated metabolite with a two-compartment disposition model with first order absorption. The values (mean +/- standard error of the mean) for clearance (CL) and volume of distribution of the central compartment were 40.2 +/- 2.0 l h-1 and 34.7 +/- 4.6 l h-1, respectively, in a typical male CYP2D6 homozygote-extensive metabolizer (Hom-EM). The absolute bioavailability (F) of darifenacin in a Hom-EM after doses of 7.5, 15 or 30 mg extended release formulation (CR) was 15, 19 and 25%, respectively. Factors influencing F were formulation (70-110% higher for CR compared with immediate release following equivalent daily doses), CYP2D6 genotype [heterozygote-extensive metabolizers (Het-EM) and poor metabolizers (PM) experienced 40 and 90%, respectively, higher exposure than Hom-EM irrespective of dose administered] and saturable first-pass metabolism (dose nonlinearity 1.05-1.43-fold). Race affected F, which was 56% lower in Japanese males. The CYP3A4 inhibitors ketoconazole and erythromycin increased F to approximately 100% and ketoconazole decreased CL by 67.5%. CL was 31% lower in females and 10% lower at night. Formulation affected the metabolite absorption/formation rate. Ketoconazole and erythromycin administration resulted in a decrease of 61.2 and 28.8% in exposure to the metabolite, respectively. The covariates race, gender and circadian rhythm accounted for only approximately half of the variability in the estimated exposures to darifenacin.

CONCLUSIONS

The pooled analysis provided a descriptive integration of all characteristics and covariates of the pharmacokinetics of darifenacin and its metabolite, enabling interpolation and extrapolation of these key factors.

Pharmacology of cytotoxic agents: a helpful tool for building dose adjustment guidelines in the elderly

Aging is associated with multidimensional changes, including alterations in physiological functions, co-morbidities and poly-medications. These changes may lead to modifications in the absorption, distribution, metabolism and excretion of drugs. The lack of a scientific basis for optimal drug dosing in the elderly is a major problem. The development and validation of guidelines are therefore essential to improve treatment administration and monitoring in elderly patients. Even though it has been widely demonstrated that standard therapies used in adults may be of great benefit in the elderly, there may be a higher incidence of toxicity. This could be avoided by using dosage individualization based on a sound knowledge of the physiological factors implicated in the pharmacokinetic (PK) characteristics of the drugs administered and in their observed pharmacodynamic (PD) effects in each patient. The so-called "population modeling" approach renders such studies feasible by allowing the analysis of PK-PD relationships from sparse observational data.

Physiologically based modelling of inhibition of metabolism and assessment of the relative potency of drug and metabolite: dextromethorphan vs. dextrorphan using quinidine inhibition

AIMS

To define the relative antitussive effect of dextromethorphan (DEX) and its primary metabolite dextrorphan (DOR) after administration of DEX.

METHODS

Data were analysed from a double-blind, randomized cross-over study in which 22 subjects received the following oral treatments: (i) placebo; (ii) 30 mg DEX hydro-bromide; (iii) 60 mg DEX hydro-bromide; and (iv) 30 mg DEX hydro-bromide preceded at 1 h by quinidine HCl (50 mg). Cough was elicited using citric acid challenge. Pharmacokinetic data from all non-placebo arms of the study were fitted simultaneously. The parameters were then used as covariates in a link PK-PD model of cough suppression using data from all treatment arms.

RESULTS

The best-fit PK model assumed two- and one-compartment PK models for DEX and DOR, respectively, and competitive inhibition of DEX metabolism by quinidine. The intrinsic clearance of DEX estimated from the model ranged from 59 to 1536 l x h(-1), which overlapped with that extrapolated from in vitro data (12-261 l x h(-1)) and showed similar variation (26- vs. 21-fold, respectively). The inhibitory effect of quinidine ([I]/Ki) was 19 (95% confidence interval of mean: 18-20) with an estimated average Ki of 0.017 microM. Although DEX and DOR were both active, the potency of the antitussive effect of DOR was 38% that of DEX. A sustained antitussive effect was related to slow removal of DEX/DOR from the effect site (ke0 = 0.07 h(-1)).

CONCLUSIONS

Physiologically based PK modelling with perturbation of metabolism using an inhibitor allowed evaluation of the antitussive potency of DOR without the need for separate administration of DOR.