Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling

Extension and validation of a physiologically based toxicokinetic model for risk assessment of aluminium exposure in humans

The safety of aluminium (Al) exposure from sources such as food, parenteral nutrition or adjuvanted medicinal products is still a matter of uncertainty. Since toxicokinetic studies in humans are lacking, model predictions are warranted for risk assessment. Recently, we established a physiologically based toxicokinetic (PBTK) model for Al built on a comprehensive toxicokinetic26 Al database, which could describe Al biokinetics in rats and human adults after single oral and intravenous doses of soluble Al salts. Since then, we have substantially amended the model, rendering it applicable to accurately represent children and their dynamically changing physiology (including maturating renal function in neonates and increased bone turnover during puberty). Also, additional sources of exposure were implemented, including vaccinations, subcutaneous allergen immunotherapies, food, antacids and parenteral nutrition. The model predictions in plasma and tissues were then compared to own published data and literature Al measurements after exposure from food (human reference values), parenteral nutrition (toxic levels in children and adults), adjuvanted allergen products or vaccines in rats and humans, and whole-body retention data. Al levels were predicted remarkably well, in plasma and toxicologically important tissues like bone, liver and brain. To our knowledge, this is the first Al PBTK model in humans ready for use in regulatory risk assessment, allowing to simulate Al exposure in children and adults from various sources of Al exposure like food and drinking water, Al contaminations in parenteral nutrition solutions, or poorly soluble Al complexes in medicinal products including Al-adjuvanted immunotherapeutics and vaccines.

Evaluation of the need for dosing adaptations in obese patients for surgical antibiotic prophylaxis: a model-based analysis of cefazolin pharmacokinetics

BACKGROUND

Cefazolin is used as a prophylactic antibiotic to reduce surgical site infections (SSIs). Obesity has been identified as a risk factor for SSIs. Cefazolin dosing recommendations and guidelines are currently inconsistent for obese patients. As plasma and target-site exposure might differ, pharmacokinetic data from the sites of SSIs are essential to evaluate treatment efficacy: these data can be obtained via tissue microdialysis. This analysis was designed to evaluate the need for dosing adaptations in obese patients for surgical prophylaxis.

METHODS

Data from 15 obese (BMImedian = 52.6 kg m-2) and 15 age- and sex-matched nonobese patients (BMImedian = 26.0 kg m-2) who received 2 g cefazolin i.v. infusion for infection prophylaxis were included in the analysis. Pharmacokinetic data from plasma and interstitial space fluid (ISF) of adipose tissue were obtained and analysed simultaneously using nonlinear mixed-effects modelling. Dosing regimens were evaluated by calculating the probability of target attainment (PTA) and the cumulative fraction of response (CFR) for plasma and ISF using unbound cefazolin concentration above minimum inhibitory concentration 100% of the time as target (fT>MIC = 100%). Dosing regimens were considered adequate when PTA and CFR were ≥90%.

RESULTS

Evaluation of cefazolin doses of 1 and 2 g with redosing at either 3 or 4 h by PTA and CFR in plasma and ISF found 2 g cefazolin with redosing at 4 h to be the most suitable dosing regimen for both obese and nonobese patients (PTA >90% and CFR >90% for both).

CONCLUSIONS

This model-based analysis, using fT>MIC = 100% as a target, showed that cefazolin dosing adaptations are not required for surgical prophylaxis in obese patients.

Information-theoretic evaluation of covariate distributions models

Statistical modelling of covariate distributions allows to generate virtual populations or to impute missing values in a covariate dataset. Covariate distributions typically have non-Gaussian margins and show nonlinear correlation structures, which simple descriptions like multivariate Gaussian distributions fail to represent. Prominent non-Gaussian frameworks for covariate distribution modelling are copula-based models and models based on multiple imputation by chained equations (MICE). While both frameworks have already found applications in the life sciences, a systematic investigation of their goodness-of-fit to the theoretical underlying distribution, indicating strengths and weaknesses under different conditions, is still lacking. To bridge this gap, we thoroughly evaluated covariate distribution models in terms of Kullback-Leibler (KL) divergence, a scale-invariant information-theoretic goodness-of-fit criterion for distributions. Methodologically, we proposed a new approach to construct confidence intervals for KL divergence by combining nearest neighbour-based KL divergence estimators with subsampling-based uncertainty quantification. In relevant data sets of different sizes and dimensionalities with both continuous and discrete covariates, non-Gaussian models showed consistent improvements in KL divergence, compared to simpler Gaussian or scale transform approximations. KL divergence estimates were also robust to the inclusion of latent variables and large fractions of missing values. While good generalization behaviour to new data could be seen in copula-based models, MICE shows a trend for overfitting and its performance should always be evaluated on separate test data. Parametric copula models and MICE were found to scale much better with the dimension of the dataset than nonparametric copula models. These findings corroborate the potential of non-Gaussian models for modelling realistic life science covariate distributions.

All You Need to Know About Allometric Scaling: An Integrative Review on the Theoretical Basis, Empirical Evidence, and Application in Human Pharmacology

Scaling approaches are used to describe or predict clearance for paediatric or obese populations from normal-weight adult values. Theoretical allometry assumes the existence of a universal bodyweight-based scaling relationship. Although theoretical allometry is highly disputed, it is commonly applied in pharmacological data analyses and clinical practice. The aim of the current review is to (1) increase pharmacologists' understanding of theoretical allometry to better understand the (implicit) assumptions and (dis)advantages and (2) highlight important methodological considerations with the application of this methodology. Theoretical allometry originated in an empirical, and later debated, observation by Kleiber of a scaling exponent of 0.75 between basal metabolic rate and body mass of mammals. The mathematical framework of West, Brown, and Enquist provides one possible explanation for this value. To date, multiple key assumptions of this framework have been disputed or disproven, and an increasing body of evidence is emerging against the existence of one universal allometric exponent. The promise of ease and universality of use that comes with theoretical approaches may be the reason they are so strongly sought after and defended. However, ecologists have suggested that the theory should move from a 'Newtonian approach', in which physical explanations are sought for a universal law and variability is of minor importance, to a 'Darwinian approach', in which variability is considered of primary importance for which evolutionary explanations can be found. No scientific support was found for the application of allometry for within-species scaling, so the application of basal metabolic rate-based scaling principles to clearance scaling remains unsubstantiated. Recent insights from physiologically based modelling approaches emphasise the interplay between drugs with different properties and physiological variables that underlie drug clearance, which drives the variability in the allometric scaling exponent in the field of pharmacology. To deal with this variability, drug-specific or patient-specific adaptations to theoretical allometric scaling are proposed, that introduce empiric elements and reduce the universality of the theory. The use of allometric scaling with an exponent of 0.75 may hold empirical merit for paediatric populations, except for the youngest individuals (aged ≤ 5 years). Nevertheless, biological interpretations and extrapolation potential attributed to models based on 0.75 allometric scaling are theoretically unfounded, and merits of the empirical application of this function should, as for all models, always be supported by appropriate model validation procedures. In this respect, it is not the value of the allometric exponent but the description and prediction of individual clearance values and drug concentrations that are of primary interest.

Comparison of the predictive accuracy of the physiologically based pharmacokinetic (PBPK) model and population pharmacokinetic (PPK) model of vancomycin in Japanese patients with MRSA infection

INTRODUCTION

The latest therapeutic drug monitoring guidelines for vancomycin (VCM) recommend that area under the concentration-time curve is estimated based on model-informed precision dosing and used to evaluate efficacy and safety. Therefore, we predicted VCM concentrations in individual methicillin-resistant Staphylococcus aureus-infected patients using existing a physiologically based pharmacokinetic (PBPK) model and 1- and 2-compartment population pharmacokinetic (PPK) models and confirmed and verified the accuracy of the PBPK model in estimating VCM concentrations with the PPK model.

METHODS

The subjects of the study are 20 patients, and the predicted concentrations were evaluated by comparing the observed and predicted trough and peak values of VCM concentrations for individual patients.

RESULTS

The results showed good correlation between the observed and predicted trough and peak concentrations of VCM was observed generally in the PBPK model, R2 values of 0.72, 0.62, and 0.40 with trough values of 0.49, 0.40, and 0.34 with peak values for PBPK model, 1-compartment, and 2-compartment model, respectively.

CONCLUSIONS

Although the performance of the PBPK model is not as predictive as the PPK model, generally similar predictive trends were obtained, suggesting that it may be a valuable tool for rapid and accurate prediction of AUC for VCM.

Development and Evaluation of a Physiologically Based Pharmacokinetic Model for Predicting Haloperidol Exposure in Healthy and Disease Populations

The physiologically based pharmacokinetic (PBPK) approach can be used to develop mathematical models for predicting the absorption, distribution, metabolism, and elimination (ADME) of administered drugs in virtual human populations. Haloperidol is a typical antipsychotic drug with a narrow therapeutic index and is commonly used in the management of several medical conditions, including psychotic disorders. Due to the large interindividual variability among patients taking haloperidol, it is very likely for them to experience either toxic or subtherapeutic effects. We intend to develop a haloperidol PBPK model for identifying the potential sources of pharmacokinetic (PK) variability after intravenous and oral administration by using the population-based simulator, PK-Sim. The model was initially developed and evaluated to predict the PK of haloperidol and its reduced metabolite in adult healthy population after intravenous and oral administration. After evaluating the developed PBPK model in healthy adults, it was used to predict haloperidol–rifampicin drug–drug interaction and was extended to tuberculosis patients. The model evaluation was performed using visual assessments, prediction error, and mean fold error of the ratio of the observed-to-predicted values of the PK parameters. The predicted PK values were in good agreement with the corresponding reported values. The effects of the pathophysiological changes and enzyme induction associated with tuberculosis and its treatment, respectively, on haloperidol PK, have been predicted precisely. For all clinical scenarios that were evaluated, the predicted values were within the acceptable two-fold error range.

High-Dosage Fosfomycin Results in Adequate Plasma and Target-Site Exposure in Morbidly Obese and Nonobese Nonhyperfiltration Patients

The objectives of this study were the identification in (morbidly) obese and nonobese patients of (i) the most appropriate body size descriptor for fosfomycin dose adjustments and (ii) adequacy of the currently employed dosing regimens. Plasma and target site (interstitial fluid of subcutaneous adipose tissue) concentrations after fosfomycin administration (8 g) to 30 surgery patients (15 obese/15 nonobese) were obtained from a prospective clinical trial. After characterization of plasma and microdialysis-derived target site pharmacokinetics via population analysis, short-term infusions of fosfomycin 3 to 4 times daily were simulated. The adequacy of therapy was assessed by probability of pharmacokinetic/pharmacodynamic target attainment (PTA) analysis based on the unbound drug-related targets of an %fT_>MIC (the fraction of time that unbound fosfomycin concentrations exceed the MIC during 24 h) of 70 and an fAUC_0-24h/MIC (the area under the concentration-time curve from 0 to 24 h for the unbound fraction of fosfomycin relative to the MIC) of 40.8 to 83.3. Lean body weight, fat mass, and creatinine clearance calculated via adjusted body weight (ABW) (CLCR_{CG_ABW}) of all patients (body mass index [BMI] = 20.1 to 52.0 kg/m²) explained a considerable proportion of between-patient pharmacokinetic variability (up to 31.0% relative reduction). The steady-state unbound target site/plasma concentration ratio was 26.3% lower in (morbidly) obese than nonobese patients. For infections with fosfomycin-susceptible pathogens (MIC ≤ 16 mg/L), intermittent "high-dosage" intravenous (i.v.) fosfomycin (8 g, three times daily) was sufficient to treat patients with a CLCR_{CG_ABW} of <130 mL/min, irrespective of the pharmacokinetic/pharmacodynamic indices considered. For infections by Pseudomonas aeruginosa with a MIC of 32 mg/L, when the index fAUC_0-24h/MIC is applied, fosfomycin might represent a promising treatment option in obese and nonobese patients, especially in combination therapy to complement β-lactams, in which carbapenem-resistant P. aeruginosa is critical. In conclusion, fosfomycin showed excellent target site penetration in obese and nonobese patients. Dosing should be guided by renal function rather than obesity status. (This study has been registered in the EU Clinical Trials Register under EudraCT no. 2012-004383-22.).

Physiology-based toxicokinetic modelling of aluminium in rat and man

A sufficient quantitative understanding of aluminium (Al) toxicokinetics (TK) in man is still lacking, although highly desirable for risk assessment of Al exposure. Baseline exposure and the risk of contamination severely limit the feasibility of TK studies administering the naturally occurring isotope 27Al, both in animals and man. These limitations are absent in studies with 26Al as a tracer, but tissue data are limited to animal studies. A TK model capable of inter-species translation to make valid predictions of Al levels in humans-especially in toxicological relevant tissues like bone and brain-is urgently needed. Here, we present: (i) a curated dataset which comprises all eligible studies with single doses of 26Al tracer administered as citrate or chloride salts orally and/or intravenously to rats and humans, including ultra-long-term kinetic profiles for plasma, blood, liver, spleen, muscle, bone, brain, kidney, and urine up to 150 weeks; and (ii) the development of a physiology-based (PB) model for Al TK after intravenous and oral administration of aqueous Al citrate and Al chloride solutions in rats and humans. Based on the comprehensive curated 26Al dataset, we estimated substance-dependent parameters within a non-linear mixed-effect modelling context. The model fitted the heterogeneous 26Al data very well and was successfully validated against datasets in rats and humans. The presented PBTK model for Al, based on the most extensive and diverse dataset of Al exposure to date, constitutes a major advancement in the field, thereby paving the way towards a more quantitative risk assessment in humans.

Similar Piperacillin/Tazobactam Target Attainment in Obese versus Nonobese Patients despite Differences in Interstitial Tissue Fluid Pharmacokinetics

Precision dosing of piperacillin/tazobactam in obese patients is compromised by sparse information on target-site exposure. We aimed to evaluate the appropriateness of current and alternative piperacillin/tazobactam dosages in obese and nonobese patients. Based on a prospective, controlled clinical trial in 30 surgery patients (15 obese/15 nonobese; 0.5-h infusion of 4 g/0.5 g piperacillin/tazobactam), piperacillin pharmacokinetics were characterized in plasma and at target-site (interstitial fluid of subcutaneous adipose tissue) via population analysis. Thereafter, multiple 3-4-times daily piperacillin/tazobactam short-term/prolonged (recommended by EUCAST) and continuous infusions were evaluated by simulation. Adequacy of therapy was assessed by probability of pharmacokinetic/pharmacodynamic target-attainment (PTA ≥ 90%) based on time unbound piperacillin concentrations exceed the minimum inhibitory concentration (MIC) during 24 h (%fT>MIC). Lower piperacillin target-site maximum concentrations in obese versus nonobese patients were explained by the impact of lean (approximately two thirds) and fat body mass (approximately one third) on volume of distribution. Simulated steady-state concentrations were 1.43-times, 95%CI = (1.27; 1.61), higher in plasma versus target-site, supporting targets of %fT>2×MIC instead of %fT>4×MIC during continuous infusion to avoid target-site concentrations constantly below MIC. In all obesity and renally impairment/hyperfiltration stages, at MIC = 16 mg/L, adequate PTA required prolonged (thrice-daily 4 g/0.5 g over 3.0 h at %fT>MIC = 50) or continuous infusions (24 g/3 g over 24 h following loading dose at %fT>MIC = 98) of piperacillin/tazobactam.

Spreadsheet-Based Minimal Physiological Models for the Prediction of Clearance of Therapeutic Proteins in Pediatric Patients

There is a growing interest in the use of physiologically based pharmacokinetic (PBPK) models as clinical pharmacology drug development tools. In PBPK modeling, not every organ or physiological parameter is required, leading to the development of a minimal PBPK (mPBPK) model, which is simple and efficient. The objective of this study was to streamline mPBPK modeling approaches and enable straightforward prediction of clearance of protein-based products in children. Four mPBPK models for scaling clearance from adult to children were developed and evaluated on Excel spreadsheets using (1) liver and kidneys; (2) liver, kidneys, and skin; (3) liver, kidneys, skin, and lymph; and (4) interstitial, lymph, and plasma volume. There were 35 therapeutic proteins with a total of 113 observations across different age groups (premature neonates to adolescents). For monoclonal and polyclonal antibodies, more than 90% of observations were within a 0.5- to 2-fold prediction error for all 4 methods. For nonantibodies, 79% to 100% of observations were within the 0.5- to 2-fold prediction error for the 4 different methods. Methods 1 and 4 provided the best results, >90% of the total observations were within the 0.5- to 2-fold prediction error for all 3 classes of protein-based products across a wide age range. The precision of clearance prediction was comparatively lower in children ≤2 years of age vs older children (>2 years of age) with methods 1 and 4 predicting 80% to 100% and 75% to 90% of observations within the 0.5- to 2-fold prediction error, respectively. The results of the study indicated that mPBPK models can be developed on spreadsheets, with acceptable performance for prediction of clearance.

A GFR-Based Method to Predict the Effect of Renal Impairment on the Exposure or Clearance of Renally Excreted Drugs: A Comparative Study Between a Simple GFR Method and a Physiologically Based Pharmacokinetic Model

Objective

The objective of this study was to compare the predictive performances of a glomerular filtration rate (GFR) model with a physiologically based pharmacokinetic (PBPK) model to predict total or renal clearance or area under the curve of renally excreted drugs in subjects with varying degrees of renal impairment.

Methods

From the literature, 11 studies were randomly selected in which total or renal clearance or area under the curve of drugs in subjects with different degrees of renal impairment were predicted by PBPK models. In these published studies, drugs were given to subjects intravenously or orally. The PBPK model was generally a whole-body model whereas the GFR model was as follows: Predicted total clearance (CL_T) = CL_T in healthy subjects × (GFR in RI/GFR in H), Predicted AUC = AUC in healthy subjects × (GFR in H/GFR in RI), where H is the healthy subjects and RI is renal impairment. The predicted clearance or area under the curve values using PBPK and GFR models were compared with the observed (experimental pharmacokinetic) values. The acceptable prediction error was within the 0.5- to 2-fold or 0.5- to 1.5-fold prediction error.

Results

There were 33 drugs with a total number of 101 observations (area under the curve, total and renal clearance in subjects with mild, moderate, and severe renal impairment). From PBPK and GFR models, out of 101 observations, 94 (93.1%) and 96 (95.0%) observations were within the 0.5- to 2-fold prediction error, respectively.

Conclusions

This study indicates that the predictive power of a simple GFR model is similar to a PBPK model for the prediction of clearance or area under the curve in subjects with renal impairment. The GFR method is simple, robust, and reliable and can replace complex empirical PBPK models.

Prediction of Clearance in Children from Adults Following Drug-Drug Interaction Studies: Application of Age-Dependent Exponent Model

Background and Objective

Pharmacokinetic drug–drug interaction (DDI) studies are conducted in adult subjects during drug development but there are limited studies that have characterized pharmacokinetic DDI studies in children. The objective of this study was to evaluate if the DDI clearance values from adults can be allometrically extrapolated from adults to children.

Methods

Fifteen drugs were included in this study and the age of the children ranged from premature neonates to adolescents (30 observations across the age groups). The age-dependent exponent (ADE) model was used to predict the clearance of drugs in children from adults following DDI studies.

Results

The prediction error of drug clearances following DDIs in children ranged from 4 to 67%. Of 30 observations, 17 (57%) and 27 (90%) observations had a prediction error ≤ 30% and ≤ 50%, respectively.

Conclusion

This study indicates that it is possible to predict the clearance of drugs with reasonable accuracy in children from adults following DDI studies using an ADE model. The method is simple, robust, and reliable and can replace other complex empirical models.

Clinical Determinants of Target Non-Attainment of Linezolid in Plasma and Interstitial Space Fluid: A Pooled Population Pharmacokinetic Analysis with Focus on Critically Ill Patients

OBJECTIVES

We aimed to assess linezolid pharmacokinetics in the plasma and interstitial space fluid (ISF) of patients with sepsis, diabetic foot infections or cystic fibrosis and healthy volunteers. The impacts of joint characteristics and disease on plasma and target-site exposure were to be identified together with the benefit of dose intensification in critically ill patients.

METHODS

Rich plasma (n = 1598) and ISF concentrations in subcutaneous adipose (n = 1430) and muscle tissue (n = 1089) measured by microdialysis were pooled from three clinical trials with 51 individuals receiving 600 mg of intravenous and oral linezolid. All data were analysed simultaneously by a population approach also considering methodological aspects of microdialysis. The impact of covariates on the attainment of the pharmacokinetic/pharmacodynamic targets, AUC/MIC = 100 (area under the concentration-time curve/minimum inhibitory concentration) and fT_>MIC = 99 % (time that unbound concentrations exceed the MIC), was assessed by deterministic and Monte Carlo simulations.

RESULTS

A two-compartment pharmacokinetic model with nonlinear elimination and tissue distribution factors accounting for differences between plasma and ISF concentrations adequately predicted all measurements. Clearance (CL) was highest in septic patients (11.2 L/h vs. CL_Healthy/CL_{Cystic fibrosis}/CL_Diabetic = 7.67/6.87/6.35 L/h). Penetration into subcutaneous adipose ISF was lowest in diabetic patients (-34.9 % compared with healthy volunteers). Creatinine clearance and total body weight further impacted linezolid exposure. To achieve timely efficacious therapy, front-loaded dosing and continuous infusion seemed beneficial in septic patients.

CONCLUSIONS

Our analysis suggests that after standard linezolid doses, particularly patients with sepsis and conserved renal function are at risk of not attaining pharmacokinetic/pharmacodynamic targets and would benefit from initial dose intensification.

Pharmacokinetic and pharmacodynamic modeling in BioGears

Pharmacokinetics/pharmacodynamics models were designed and integrated into the BioGears® physiology engine to address the need for real time drug effects for varying patients and injury profiles. Ten drugs were validated using experimental and subject matter expert data. The plasma concentration curves had a good fit with experimental data and 48 of 50 physiologic parameters displayed a less than 10% error compared to the validation data.

Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations

Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.

Drug disposition in obesity: toward evidence-based dosing

Obesity and morbid obesity are associated with many physiological changes affecting pharmacokinetics, such as increased blood volume, cardiac output, splanchnic blood flow, and hepatic blood flow. In obesity, drug absorption appears unaltered, although recent evidence suggests that this conclusion may be premature. Volume of distribution may vary largely, but the magnitude and direction of changes seem difficult to predict, with extrapolation on the basis of total body weight being the best approach to date. Changes in clearance may be smaller than in distribution, whereas there is growing evidence that the influence of obesity on clearance can be predicted on the basis of reported changes in the metabolic or elimination pathways involved. For obese children, we propose two methods to distinguish between developmental and obesity-related changes. Future research should focus on the characterization of physiological concepts to predict the optimal dose for each drug in the obese population.

Generating Virtual Patients by Multivariate and Discrete Re-Sampling Techniques

Purpose

Clinical Trial Simulations (CTS) are a valuable tool for decision-making during drug development. However, to obtain realistic simulation scenarios, the patients included in the CTS must be representative of the target population. This is particularly important when covariate effects exist that may affect the outcome of a trial. The objective of our investigation was to evaluate and compare CTS results using re-sampling from a population pool and multivariate distributions to simulate patient covariates.

Methods

COPD was selected as paradigm disease for the purposes of our analysis, FEV1 was used as response measure and the effects of a hypothetical intervention were evaluated in different populations in order to assess the predictive performance of the two methods.

Results

Our results show that the multivariate distribution method produces realistic covariate correlations, comparable to the real population. Moreover, it allows simulation of patient characteristics beyond the limits of inclusion and exclusion criteria in historical protocols.

Conclusion

Both methods, discrete resampling and multivariate distribution generate realistic pools of virtual patients. However the use of a multivariate distribution enable more flexible simulation scenarios since it is not necessarily bound to the existing covariate combinations in the available clinical data sets.

Electronic supplementary material

The online version of this article (doi:10.1007/s11095-015-1699-x) contains supplementary material, which is available to authorized users.

Monoclonal antibody disposition: a simplified PBPK model and its implications for the derivation and interpretation of classical compartment models

The structure, interpretation and parameterization of classical compartment models as well as physiologically-based pharmacokinetic (PBPK) models for monoclonal antibody (mAb) disposition are very diverse, with no apparent consensus. In addition, there is a remarkable discrepancy between the simplicity of experimental plasma and tissue profiles and the complexity of published PBPK models. We present a simplified PBPK model based on an extravasation rate-limited tissue model with elimination potentially occurring from various tissues and plasma. Based on model reduction (lumping), we derive several classical compartment model structures that are consistent with the simplified PBPK model and experimental data. We show that a common interpretation of classical two-compartment models for mAb disposition-identifying the central compartment with the total plasma volume and the peripheral compartment with the interstitial space (or part of it)-is not consistent with current knowledge. Results are illustrated for the monoclonal antibodies 7E3 and T84.66 in mice.

Pharmacokinetic-pharmacodynamic modeling of antibacterial drugs

Pharmacokinetic-pharmacodynamic (PKPD) modeling and simulation has evolved as an important tool for rational drug development and drug use, where developed models characterize both the typical trends in the data and quantify the variability in relationships between dose, concentration, and desired effects and side effects. In parallel, rapid emergence of antibiotic-resistant bacteria imposes new challenges on modern health care. Models that can characterize bacterial growth, bacterial killing by antibiotics and immune system, and selection of resistance can provide valuable information on the interactions between antibiotics, bacteria, and host. Simulations from developed models allow for outcome predictions of untested scenarios, improved study designs, and optimized dosing regimens. Today, much quantitative information on antibiotic PKPD is thrown away by summarizing data into variables with limited possibilities for extrapolation to different dosing regimens and study populations. In vitro studies allow for flexible study designs and valuable information on time courses of antibiotic drug action. Such experiments have formed the basis for development of a variety of PKPD models that primarily differ in how antibiotic drug exposure induces amplification of resistant bacteria. The models have shown promise for efficacy predictions in patients, but few PKPD models describe time courses of antibiotic drug effects in animals and patients. We promote more extensive use of modeling and simulation to speed up development of new antibiotics and promising antibiotic drug combinations. This review summarizes the value of PKPD modeling and provides an overview of the characteristics of available PKPD models of antibiotics based on in vitro, animal, and patient data.

Applications of minimal physiologically-based pharmacokinetic models

Conventional mammillary models are frequently used for pharmacokinetic (PK) analysis when only blood or plasma data are available. Such models depend on the quality of the drug disposition data and have vague biological features. An alternative minimal-physiologically-based PK (minimal-PBPK) modeling approach is proposed which inherits and lumps major physiologic attributes from whole-body PBPK models. The body and model are represented as actual blood and tissue (usually total body weight) volumes, fractions (f ( d )) of cardiac output with Fick's Law of Perfusion, tissue/blood partitioning (K ( p )), and systemic or intrinsic clearance. Analyzing only blood or plasma concentrations versus time, the minimal-PBPK models parsimoniously generate physiologically-relevant PK parameters which are more easily interpreted than those from mammillary models. The minimal-PBPK models were applied to four types of therapeutic agents and conditions. The models well captured the human PK profiles of 22 selected beta-lactam antibiotics allowing comparison of fitted and calculated K ( p ) values. Adding a classical hepatic compartment with hepatic blood flow allowed joint fitting of oral and intravenous (IV) data for four hepatic elimination drugs (dihydrocodeine, verapamil, repaglinide, midazolam) providing separate estimates of hepatic intrinsic clearance, non-hepatic clearance, and pre-hepatic bioavailability. The basic model was integrated with allometric scaling principles to simultaneously describe moxifloxacin PK in five species with common K ( p ) and f ( d ) values. A basic model assigning clearance to the tissue compartment well characterized plasma concentrations of six monoclonal antibodies in human subjects, providing good concordance of predictions with expected tissue kinetics. The proposed minimal-PBPK modeling approach offers an alternative and more rational basis for assessing PK than compartmental models.

CPT: Pharmacometrics and Systems Pharmacology

Welcome to the first issue of CPT: Pharmacometrics and Systems Pharmacology (CPT:PSP), a new journal from the American Society for Clinical Pharmacology and Therapeutics. CPT:PSP is a cross-disciplinary journal devoted to publishing advances in quantitative, model-based approaches as applied in pharmacology, (patho)physiology, and disease to aid the discovery, development, and utilization of human therapeutics. The emphasis of CPT:PSP will be on the application of modeling and simulation and the impact of Pharmacometrics and Systems Pharmacology on the discovery and development of innovative therapies.