A pragmatic approach to the design of population pharmacokinetic studies

Population pharmacokinetic analysis of enrofloxacin and its active metabolite ciprofloxacin after intravenous injection to cats with reduced kidney function

BACKGROUND

It is unknown if enrofloxacin accumulates in plasma of cats with reduced kidney function.

HYPOTHESIS

To determine if enrofloxacin and its active metabolite ciprofloxacin have reduced clearance in azotemic cats.

ANIMALS

Thirty-four cats hospitalized for clinical illness with variable degree of kidney function.

METHODS

Prospective study. After enrofloxacin (dose 5 mg/kg) administration to cats, sparse blood sampling was used to obtain 2 compartment population pharmacokinetic results using nonlinear mixed-effects modeling. Plasma enrofloxacin and ciprofloxacin concentrations were measured and summed to obtain the total fluoroquinolone concentration. A model of ciprofloxacin metabolism from enrofloxacin was created and evaluated for covariate effects on clearance, volume of distribution, and the metabolic rate of ciprofloxacin generation from enrofloxacin.

RESULTS

Body weight was the only covariate found to affect total fluoroquinolone volume of distribution (effect 1.63, SE 0.19, P < .01) and clearance (effect 1.63, SE 0.27, P < .01). Kidney function did not have a significant effect on total fluoroquinolone clearance (median 440.8 mL/kg/h (range 191.4-538.0 mL/kg/h) in cats with normal kidney function, 365.8 mL/kg/h (range 89.49-1092.0 mL/kg/h) in cats with moderate kidney dysfunction, and 308.5 mL/kg/h (range 140.20-480.0 mL/kg/h) in cats with severe kidney dysfunction (P = .64). Blood urea nitrogen concentration influenced the metabolic generation of ciprofloxacin from enrofloxacin (effect 0.51, SE 0.08, P < .01), but other markers of kidney function did not.

CONCLUSIONS AND CLINICAL IMPORTANCE

Adjustment of enrofloxacin dosage is not indicated for azotemic cats.

Current use and future potential of (physiologically based) pharmacokinetic modelling of radiopharmaceuticals: a review

Rationale: Physiologically based pharmacokinetic (PBPK) and population pharmacokinetic (PK) modelling approaches are widely accepted in non-radiopharmaceutical drug development and research, while there is no major role for these approaches in radiopharmaceutical development yet. In this review, a literature search was performed to specify different research purposes and questions that have previously been answered using both PBPK and population PK modelling for radiopharmaceuticals. Methods: The literature search was performed using the databases PubMed and Embase. Wide search terms included radiopharmaceutical, tracer, radioactivity, physiologically based pharmacokinetic model, PBPK, population pharmacokinetic model and nonlinear mixed-effects model. Results: Eight articles and twenty articles were included for this review based on this literature search for population PK modelling and PBPK modelling, respectively. Included population PK analyses showed to have an added value to develop predictive models for a population and to describe individual variability sources. Main purposes of PBPK models appeared related to optimizing treatment (planning), or more specifically: to find the optimal combination of peptide amount and radioactivity, to optimize treatment planning by reducing the number of measurements, to individualize treatment, to get insights in differences between pre-therapeutic and therapeutic scans or to understand inter-patient differences. Other main research subjects were regarding radiopharmaceutical comparisons, selecting ligands based on their peptide characteristics and gaining a better understanding of drug-drug interactions. Conclusions: The use of PK modelling approaches in radiopharmaceutical research remains scarce, but can be expanded to obtain a better understanding of PK and whole-body distribution of radiopharmaceuticals in general. PK modelling of radiopharmaceuticals has great potential for the nearby future and could contribute to the evolving research of radiopharmaceuticals.

The midpoint of cortical thinning between late childhood and early adulthood differs between individuals and brain regions: Evidence from longitudinal modelling in a 12-wave neuroimaging sample

Charting human brain maturation between childhood and adulthood is a fundamental prerequisite for understanding the rapid biological and psychological changes during human development. Two barriers have precluded the quantification of maturational trajectories: demands on data and demands on estimation. Using high-temporal resolution neuroimaging data of up to 12-waves in the HUBU cohort (N = 90, aged 7-21 years) we investigate changes in apparent cortical thickness across childhood and adolescence. Fitting a four-parameter logistic nonlinear random effects mixed model, we quantified the characteristic, s-shaped, trajectory of cortical thinning in adolescence. This approach yields biologically meaningful parameters, including the midpoint of cortical thinning (MCT), which corresponds to the age at which the cortex shows most rapid thinning - in our sample occurring, on average, at 14 years of age. These results show that, given suitable data and models, cortical maturation can be quantified with precision for each individual and brain region.

Random sparse sampling strategy using stochastic simulation and estimation for a population pharmacokinetic study

The purpose of this study was to use the stochastic simulation and estimation method to evaluate the effects of sample size and the number of samples per individual on the model development and evaluation. The pharmacokinetic parameters and inter- and intra-individual variation were obtained from a population pharmacokinetic model of clinical trials of amlodipine. Stochastic simulation and estimation were performed to evaluate the efficiencies of different sparse sampling scenarios to estimate the compartment model. Simulated data were generated a 1000 times and three candidate models were used to fit the 1000 data sets. Fifty-five kinds of sparse sampling scenarios were investigated and compared. The results showed that, 60 samples with three points and 20 samples with five points are recommended, and the quantitative methodology of stochastic simulation and estimation is valuable for efficiently estimating the compartment model and can be used for other similar model development and evaluation approaches.

Antibiotic dosing in children in Europe: can we grade the evidence from pharmacokinetic/pharmacodynamic studies - and when is enough data enough?

PURPOSE OF REVIEW

Antibiotics are prescribed more frequently to children than any other class of medication. Analysis of the evidence behind antimicrobial dosing regimes is imperative to improve clinical outcomes, minimize antimicrobial resistance development, and to identify priority research areas for the future. This review aims to promote debate amongst paediatricians, pharmacologists, and pharmacists about how to improve antimicrobial prescribing by considering methods to develop and disseminate optimal dosage information.

RECENT FINDINGS

There has been increasing use of population analyses to understand pharmacokinetic/pharmacodynamic (PK/PD) parameters in children. Nonlinear mixed effects modelling is widely accepted to be the method of choice for analyses of PK/PD data. However, communicating the quality of PK/PD studies is an equally important factor to allow clinicians to gauge the robustness of the evidence. The possibility of grading PK/PD studies is discussed, along with using systematic reviews and PK/PD meta-analysis for generating high-quality evidence.Many doses in existing formularies (including the British National Formulary for Children) are based on outdated evidence. The need to update formularies to account for new evidence, population changes (e.g. obesity), and changing patterns of resistance requires a more systematic evaluation of antimicrobial PK/PD relationships in children. The possibility of e-formularies with links directly to the evidence should be considered and regulators must also play a role in supporting the re-evaluation of off-patent dosing guidelines.

SUMMARY

Advancing our understanding of the evidence behind paediatric antimicrobial therapeutic regimens is essential to improve both clinical outcomes and patient safety. Using a combination of international collaboration, electronic communication, and PK/PD modelling techniques, we can now define the gaps in our knowledge base and develop the techniques to answer them.

Designing simple PK-PD studies in children

Conducting clinical pharmacology research studies in pediatric patients is challenging because of ethical and practical constraints but necessary to ensure that drugs are used safely and effectively in this population. Developments in laboratory analytical techniques, such as improved assay sensitivity and the use of alternative sample matrices, can reduce blood loss and offer less invasive blood sampling, causing less trauma to the patient and fewer ethical concerns. Recent advances in data analysis techniques, which aim to extract the maximum amount of useful information from small sample numbers, should be considered when planning a clinical trial and incorporated into the study design. Using 'population' methodology allows a more flexible sampling strategy that enables valuable data to be collected in the course of routine clinical practice, rather than in a rigid, and potentially artificial, setting. Integration of pharmacokinetics and pharmacodynamics and the application of physiological approaches and simulation techniques to the analysis and interpretation of drug concentration and effect data offer new opportunities that have particular relevance to pharmacological research in the field of pediatric anesthetics.

Drug Development for Pediatric Populations: Regulatory Aspects

Pediatric aspects are nowadays integrated early in the development process of a new drug. The stronger enforcement to obtain pediatric information by the regulatory agencies in recent years resulted in an increased number of trials in children. Specific guidelines and requirements from, in particular, the European Medicines Agency (EMA) and the Food and Drug Administration (FDA) form the regulatory framework. This review summarizes the regulatory requirements and strategies for pediatric drug development from an industry perspective. It covers pediatric study planning and conduct, considerations for first dose in children, appropriate sampling strategies, and different methods for data generation and analysis to generate knowledge about the pharmacokinetics (PK) and pharmacodynamics (PD) of a drug in children. The role of Modeling and Simulation (M&S) in pediatrics is highlighted-including the regulatory basis-and examples of the use of M&S are illustrated to support pediatric drug development.

Random serial sampling to evaluate efficacy of iron fortification: a randomized controlled trial of margarine fortification with ferric pyrophosphate or sodium iron edetate

BACKGROUND

Random serial sampling is widely used in population pharmacokinetic studies and may have advantages compared with conventional fixed time-point evaluation of iron fortification.

OBJECTIVE

Our objective was to validate random serial sampling to judge the efficacy of iron fortification of a low-fat margarine.

DESIGN

We conducted a 32-wk placebo-controlled, double-blind, iron-intervention trial in 18-40-y-old Swiss women (n = 142) with serum ferritin (SF) concentrations <25 μg/L. Women were randomly assigned to 3 groups to receive 20 g margarine, with 14 mg added iron as either micronized ground ferric pyrophosphate (MGFePP) or sodium iron edetate (NaFeEDTA), or placebo daily. We measured hemoglobin and iron status of subjects at 2 fixed time points (at baseline and the endpoint) plus 3 randomly assigned time points between 4 and 28 wk. With the use of bootstrapping, the number of observations per individual was reduced to 3 and then compared with the 5-time-point data. Mixed-effects models were used to estimate iron repletion over time for random sampling, and analysis of covariance was used for fixed time-point sampling.

RESULTS

Body iron stores increased in women who received MGFePP or NaFeEDTA compared with women who received placebo (P < 0.05). The increase in body iron stores with NaFeEDTA fortification was 2-3 times the increase with MGFePP fortification (P < 0.05); the difference was more marked in women with baseline SF concentrations <15 μg/L (P < 0.05). Random serial sampling reduced the required sample size per group to one-tenth of that for 2 fixed time points. Compared with the 5-time-point analysis, the 3-time-point sparse sampling generated comparable estimates of efficacy.

CONCLUSIONS

When used to evaluate the efficacy of iron fortificants, random serial sampling can reduce the sample size, invasiveness, and costs while increasing sensitivity. Random serial sampling more clearly describes the pattern of iron repletion and may prove useful in evaluating other micronutrient interventions.

Population pharmacokinetic model of the pregabalin-sildenafil interaction in rats: application of simulation to preclinical PK-PD study design

PURPOSE

Preliminary evidence has suggested a synergistic interaction between pregabalin and sildenafil for the treatment of neuropathic pain. The focus of this study was to determine the influence of sildenafil on the pharmacokinetics (PK) of pregabalin with the objective of informing the design of a quantitative pharmacodynamic (PD) study.

METHODS

The pharmacokinetics were determined in rats following 2-hr intravenous infusions of pregabalin at doses of 4 mg/kg/hr and 10 mg/kg/hr with and without a sildenafil bolus (2.2 mg) and steady state infusion (12 mg/kg/hr for 6 h). This PK model was utilized in a preclinical trial simulation with the aim of selecting the optimal sampling strategy to characterize the PK-PD profile in a future study. Eight logistically feasible PK sampling strategies were simulated in NONMEM and examined through trial simulation techniques.

RESULTS

A two-compartment population PK model best described pregabalin pharmacokinetics. Significant model covariates included either a binary effect of sildenafil administration (30.2% decrease in clearance) or a concentration-dependent effect due to sildenafil's active metabolite.

CONCLUSIONS

Analysis of simulations indicated that three post-PD samples had the best cost/benefit ratio by providing a significant increase in the precision (and minor improvement in bias) of both PK and PD parameters compared with no PK sampling.

Pediatric clinical pharmacology studies in Chagas disease: focus on Argentina

Chagas disease is a neglected parasitic disease endemic in the Americas. It mainly affects impoverished populations and the acute phase of the infection mostly affects children. Many cases have also been detected in nonendemic countries as a result of recent migratory trends. The chronic phase is relatively asymptomatic, but 30% of patients with chronic infection eventually develop cardiac and digestive complications that commonly lead to death or disability. Only two drugs are available for the treatment of Chagas disease, benznidazole and nifurtimox. These drugs have been shown to be effective in the treatment of both acute and early chronic phases in children, but the pharmacokinetics of these drugs have never been studied in this population. We have set out to conduct a pharmacokinetics study of benznidazole in a pediatric population with Chagas disease. The results of this study are expected to allow better estimation of the optimal doses and schedule of pharmacotherapy for Chagas disease in children.

Biosimulation of drug metabolism--a yeast based model

Computationally predicting the metabolic fates of drugs is a very complex task which is owed not only to the huge and diverse biochemical network in the living cell, but also to the majority of in vivo transformations that occur through the action of hepatocytes and gastro-intestinal micro-flora. Thus, xenobiotics are metabolised by more than a single cell type. However, the prediction of metabolic fates is definitely a problem worth solving since it would allow facilitate the development of drugs in a way less relying on animal testing. As a first step in this direction, PharmBiosim is being developed, a biosimulation tool which is based on substantial data reduction and on attributing metabolic fates of drug molecules to functional groups and substituents. This approach works with yeast as a model organism and is restricted to drugs that are mainly transformed by enzymes of the central metabolism, especially sugar metabolism. The reason for the latter is that the qualitative functioning of the involved biochemistry is very similar in diverse cell types involved in drug metabolism. Further it allows for using glycolytic oscillations as a tool to quantify interactions of a drug with this metabolic pathway.

Importance of characterizing determinants of variability in exposure: application to dasatinib in subjects with chronic myeloid leukemia

Characterizing the key determinants of variability in the exposure of orally administered drugs may be important in understanding the implications of exposure variability on clinical responses. In particular, partitioning overall variability into interoccasion variability (IOV) and interindividual variability (IIV) allows a better assessment of the clinical importance of exposure variability. The IOV characterizes the dose-to-dose variability in exposure within a subject and is likely to be less clinically relevant than IIV for chronically administered drugs as the effect of IOV averages out over repeated dosing. The main aims of this model-based analysis were (1) to characterize the IOV and IIV of dasatinib, a novel, orally administered, multitargeted kinase inhibitor of BCR-ABL and SRC family kinases that is indicated for the treatment of chronic myeloid leukemia and Philadelphia-positive acute lymphoblastic leukemia and (2) to demonstrate using simulated data that it is possible to estimate IIV and IOV in relative bioavailability (F(R)) of an orally administered drug, given an adequate sampling scheme. Variability in dasatinib exposure was estimated to be mainly due to IOV in F(R) (44% coefficient of variation [CV]) and, to a lesser extent, due to IIV in F(R) and IIV in clearance (32% and 25% CV, respectively). The IIV is expected to be more clinically relevant than IOV for chronically administered oral drugs such as dasatinib, as the overall variability in cumulative exposure will be mainly due to IIV. The analysis of simulated data demonstrated that models ignoring either IIV or IOV in F(R) resulted in upwardly biased estimates of interindividual or residual variability. Thus, it may be important to account for both IIV and IOV in F(R), particularly for orally administered agents that exhibit absorption-related variability in exposure.

Population pharmacokinetics of valproate in Chinese children with epilepsy

AIM

The aim of the present study is to establish a population pharmacokinetic (PPK) model of valproate (VPA) in Chinese epileptic children to promote the reasonable use of anti-epileptic drugs.

METHODS

Sparse data of VPA serum concentrations from 417 epileptic children were collected. These patients were divided into 2 groups: the PPK model group (n=317) and the PPK valid group (n=100). The PPK parameter values of VPA were calculated by NONMEM software using the data of the PPK model group. A basic model and a final model were set up. To validate the 2 models, the concentrations of PPK valid group were predicted by each model, respectively. The mean prediction error (MPE), mean squared prediction error (MSPE), root mean squared prediction error (RMSPE), weight residues (WRES), and the 95% confidence intervals (95% CI) were also calculated. Then, the values between the 2 models were compared.

RESULTS

The PPK of VPA was determined by a 1-compartment model with a first-order absorption process. The basic model was: Ka=3.09 (h(-1)), V/F=20.4 (L), CL/F=0.296 (L/h). The final model was: Ka=0.251+2.24 x (1-HS) (h(-1)), V/F=2.88+0.157 x WT (L), CL/F=0.106(0.98 x CO)+ 0.0157 x AGE (L/h). For the basic model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were -23.53 (-30.36, -16.70), 3728.96 (2872.72, 4585.20), 39.62 (34.34, 44.90), and -0.06 (-0.14, 0.02), respectively. For the final model, the MPE, MSPE, RMSPE, WRES, and the 95% CI were -1.16 (-4.85, 2.53), 1002.83 (1050.64, 1143.61), 23.04 (21.12, 24.96), and 0.08 (-0.04, 0.20), respectively. The final model was more optimal than the basic model.

CONCLUSION

The PPK model of VPA in Chinese epileptic children was successfully established. It will be valuable to facilitate individualized dosage regimens.

In silico prediction of clinical efficacy

Drug development is a high risk and costly process, and the ability to predict clinical efficacy in silico (in a computer) can save the pharmaceutical industry time and resources. Additionally, such an approach will result in more targeted, personalized therapies. To date, a number of in silico strategies have been developed to provide better information about the human response to novel therapies earlier in the drug development process. Some of the most prominent include physiological modeling of disease and disease processes, analytical tools for population pharmacodynamics, tools for the analysis of genomic expression data, Monte Carlo simulation technologies, and predictive biosimulation. These strategies are likely to contribute significantly to reducing the failure rate of drugs entering clinical trials.

Population pharmacokinetics/pharmacodynamics of anesthetics

In this article we review how population pharmacokinetic/pharmacodynamic (PD) modeling has evolved in the specialty of anesthesiology, how anesthesiology benefited from the mixed-effects approach, and which features of modeling need careful attention. Key articles from the anesthesiology literature are selected to discuss the modeling of typical anesthesiological PD end points, such as level of consciousness and analgesia, interactions between hypnotics and analgesics, estimation with poor and sometimes rich data sets from populations of various sizes, covariate detection, covariances between random effects, and Bayesian forecasting.