Optimum blood sampling time windows for parameter estimation in population pharmacokinetic experiments

Guidelines for the experimental design of pharmacokinetic studies with nanomaterials in preclinical animal models

A shared feature in the value proposition of every nanomaterial-based drug delivery systems is the desirable improvement in the disposition (or ADME) and pharmacokinetic profiles of the encapsulated drug being delivered. Remarkable progress has been made towards understanding the complex and multifactorial relationships between pharmacokinetic profiles and nanomaterial physicochemical properties, biological interactions, species physiology, etc. These advances have fuelled the rational design of numerous nanomaterials with long-circulation times and improved tissue accumulation (e.g., in tumours). Unfortunately, a central weakness in many of these research efforts has been the inconsistent and insufficient characterisation of the pharmacokinetic profiles of nanomaterials in scientific reporting-a problem affecting the majoirty of of contemporary nanomaterials literature and innovative nanomaterials in early stages of preclinical development especially. Given the significant role of pharmacokinetic assessments to serve as guideposts for deciding whether to continue with the preclinical development and clinical translation of drug delivery systems, the prevalence of poor pharmacokinetic characterisations in nanomaterials research is particularly alarming. A conspicuous problem in many reports is the inappropriate selection of experimental designs and methodologies for studying nanomaterial pharmacokinetics, the consequences of which are increased uncertainty over the accurate interpretation of reported pharmacokinetic data and diminished experimental reproducibility throughout the field. Thus, there is renewed interest in the establishment of consistent and comprehensive strategies for designing preclinical experiments to assess the pharmacokinetics of nanomaterials with diverse physicochemical properties. Towards this end, herein are proposed simple guidelines for the experimental design of pharmacokinetic studies with nanomaterials drawn from the best research practices, principle strategies, and important considerations used in industry for collecting pharmacokinetic data in preclinical animal models. Specifically, key experimental design factors in these studies are identified and examined in the context of nanomaterials for optimality, including blood sampling strategy and technique, sample allocation and sampling time window, test species selection, experimental sources of pharmacokinetic variability, etc. Methods for noninvasive imaging-derived pharmacokinetic assessments of theranostic nanomaterials are also explored with particular focus on emission tomography imaging modalities. Taken together, this review will provide nanomaterial researchers with practical knowledge and pragmatic recommendations for selecting the best designs and methodologies for assessing the pharmacokinetic profiles of their nanomaterials, and hopefully maximise the chances of translational success of these innovative products into humans.

Mathematical modeling and simulation in animal health. Part III: Using nonlinear mixed-effects to characterize and quantify variability in drug pharmacokinetics

A common feature of human and veterinary pharmacokinetics is the importance of identifying and quantifying the key determinants of between-patient variability in drug disposition and effects. Some of these attributes are already well known to the field of human pharmacology such as bodyweight, age, or sex, while others are more specific to veterinary medicine, such as species, breed, and social behavior. Identification of these attributes has the potential to allow a better and more tailored use of therapeutic drugs both in companion and food-producing animals. Nonlinear mixed effects (NLME) have been purposely designed to characterize the sources of variability in drug disposition and response. The NLME approach can be used to explore the impact of population-associated variables on the relationship between drug administration, systemic exposure, and the levels of drug residues in tissues. The latter, while different from the method used by the US Food and Drug Administration for setting official withdrawal times (WT) can also be beneficial for estimating WT of approved animal drug products when used in an extralabel manner. Finally, NLME can also prove useful to optimize dosing schedules, or to analyze sparse data collected in situations where intensive blood collection is technically challenging, as in small animal species presenting limited blood volume such as poultry and fish.

Unrepaired Tetralogy of Fallot-related Pathophysiologic Changes Reduce Systemic Clearance of Etomidate in Children

BACKGROUND

Pathophysiologic changes in children with congenital heart disease may alter the effect of drugs by influencing the pharmacokinetics (PK). Considering the limited literature that describes the PK of etomidate in pediatric patients, especially in those with tetralogy of Fallot (TOF), our aim was to characterize the PK of etomidate and explore the effects of TOF.

METHODS

Twenty-nine pediatric patients (15 with TOF and 14 with normal cardiac anatomy) scheduled to undergo elective surgery under general anesthesia were recruited in the study. All children received etomidate 60 μg/kg/min intravenously until a bispectral index of ≤50 was reached for 5 seconds during anesthesia induction. Arterial blood samples were drawn and analyzed. Population analysis was performed by using NONMEM to define PK characteristics. The estimates were standardized to a 70-kg adult using a per-kilogram model.

RESULTS

Data consisting of 244 samples from 29 children with a mean age of 236 days (range, 86-360 days) were used, including a TOF group with a mean age of 250 days (range, 165-360 days) and a normal cardiac anatomy group with a mean age of 221 days (range, 86-360 days). A 3-compartment disposition model was best fitted to describe the PK of etomidate. The introduction of TOF as a covariate for systemic clearance (Cl1) improved the model and resulted in a significant reduction of objective function (Δobjective function = -7.33; P = .0068), which means that TOF was a significant covariate of Cl1, and the etomidate Cl1 in children with TOF (1.67 × (weight [WT]/70 kg) L/min) was lower than those with normal cardiac anatomy (2.28 × (WT/70 kg) L/min). Other PK parameter values were as follows: V1 = 8.05 × (WT/70 kg) L; V2 = 13.7 × (WT/70 kg) L; V3 = 41.3 × (WT/70 kg) L; Cl2 = 3.35 × (WT/70 kg) L/min; Cl3 = 0.563 × (WT/70 kg) L/min.

CONCLUSIONS

A decreased systemic clearance for etomidate in children with TOF resulted in a lower required infusion rate and variation with time to achieve the same plasma concentration and maintain an equivalent target concentration or have longer sedation and recovery times after bolus or continuous infusion than normal children.

Optimal sparse sampling for estimating ganciclovir/valganciclovir AUC in solid organ transplant patients using NONMEN

BACKGROUND

Ganciclovir and valganciclovir (GCV/VGCV) are used for the treatment and prophylaxis of cytomegalovirus in solid organ transplant (SOT) patients. An area under the time-concentration curve of 40-50 μg × h/mL is related to efficacy. Therapeutic drug monitoring could prevent suboptimal drug exposure and adverse events, but obtaining full concentration profiles is not feasible. Sampling optimization by developing a reliable and clinically applicable limited sampling strategy (LSS) may simplify dose adjustment.

METHODS

An LSS was developed using an original pharmacokinetic (PK) data set of 40 full profiles from 20 adult SOT patients. The LSS was developed based on population and Bayesian prediction approaches. Population PK parameters from a previous model were used for simulation or as priors (NONMEM version 7.2). Median percentage of prediction error and median of absolute percentage prediction error were calculated for plasma clearance (CL) and central compartment distribution volume (V(2)). Bias and precisions were compared using 1-way analysis of variance (SPSSv19.0).

RESULTS

Sampling windows were designed according to the PK profile previously observed with the entire set of data. The 4 windows selected were distributed from 0.5 to 1.5 hours, 2 to 3 hours, 4 to 5 hours, and 6 to 8 hours. Predose and concentrations beyond 8 hours were not considered in any case because simulated negative concentrations occurred in both cases. Predicted exposure using 3 sampling times (0.5-1.5, 4-5, and 6-8 hours) showed the best predictive performance, by either the population or Bayesian approaches. Bias and imprecision for CL and V(2) were 0 and 0.60%, and -0.78% and 0.78%, respectively.

CONCLUSIONS

GCV/VCG area under the time-concentration curve in SOT patients could be predicted with acceptable accuracy for clinical management and dose individualization using LSS. The estimator of GCV/VGC, using 3 concentrations measured at 0.5-1.5, 4-5, and 6-8 hours after drug intake, could be used for dose adjustment.

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models.

Optimal design of mixed-effects PK/PD models based on differential equations

There is a vast literature on the analysis of optimal design of nonlinear mixed-effects models (NLMMs) described by ordinary differential equations (ODEs) with analytic solution. However, much less has been published on the design of trials to fit such models with nonanalytic solution. In this article, we use the "direct" method to find parameter sensitivities, which are required during the optimization of models defined as ODEs, and apply them to find D-optimal designs for various specific situations relevant to population pharmacokinetic studies using a particular model with first-order absorption and elimination. In addition, we perform two simulation studies. The first one aims to show that the criterion computed from the development of the Fisher information matrix expression is a good measure to compare and optimize population designs, thus avoiding a large number of simulations; In the second one, a sensitivity analysis with respect to parameter misspecification allows us to compare the robustness of different population designs constructed in this article.

Application of optimal design methodologies in clinical pharmacology experiments

Pharmacokinetics and pharmacodynamics data are often analysed by mixed-effects modelling techniques (also known as population analysis), which has become a standard tool in the pharmaceutical industries for drug development. The last 10 years has witnessed considerable interest in the application of experimental design theories to population pharmacokinetic and pharmacodynamic experiments. Design of population pharmacokinetic experiments involves selection and a careful balance of a number of design factors. Optimal design theory uses prior information about the model and parameter estimates to optimize a function of the Fisher information matrix to obtain the best combination of the design factors. This paper provides a review of the different approaches that have been described in the literature for optimal design of population pharmacokinetic and pharmacodynamic experiments. It describes options that are available and highlights some of the issues that could be of concern as regards practical application. It also discusses areas of application of optimal design theories in clinical pharmacology experiments. It is expected that as the awareness about the benefits of this approach increases, more people will embrace it and ultimately will lead to more efficient population pharmacokinetic and pharmacodynamic experiments and can also help to reduce both cost and time during drug development.

Optimisation of sampling windows design for population pharmacokinetic experiments

This paper describes an approach for optimising sampling windows for population pharmacokinetic experiments. Sampling windows designs are more practical in late phase drug development where patients are enrolled in many centres and in out-patient clinic settings. Collection of samples under the uncontrolled environment at these centres at fixed times may be problematic and can result in uninformative data. Population pharmacokinetic sampling windows design provides an opportunity to control when samples are collected by allowing some flexibility and yet provide satisfactory parameter estimation. This approach uses information obtained from previous experiments about the model and parameter estimates to optimise sampling windows for population pharmacokinetic experiments within a space of admissible sampling windows sequences. The optimisation is based on a continuous design and in addition to sampling windows the structure of the population design in terms of the proportion of subjects in elementary designs, number of elementary designs in the population design and number of sampling windows per elementary design is also optimised. The results obtained showed that optimal sampling windows designs obtained using this approach are very efficient for estimating population PK parameters and provide greater flexibility in terms of when samples are collected. The results obtained also showed that the generalized equivalence theorem holds for this approach.

Optimal design of pulsed arterial spin labeling MRI experiments

Quantitative measurement of cerebral blood flow (CBF) using arterial spin labeling (ASL) MRI requires the acquisition of multiple inversion times (TIs) and the application of an appropriate kinetic model. The choice of these sampling times will have an impact on the precision of the estimated parameters. Here, optimal sampling schedule (OSS) design techniques, based on the Fisher Information approach, are applied in order to derive an optimal sampling scheme for pulsed arterial spin labeling (PASL) experiments. Such an approach should improve the precision of parameter estimation from experimental data, and provide a formal framework for optimally selecting a limited number of samples. In this study, we aimed to optimize the estimation precision of CBF and bolus arrival time from the PASL data. The performance of OSS was compared to a more standard evenly distributed sampling schedule (EDS) using both simulated and measured experimental data sets. It was found that OSS was able to significantly improve the precision of parameter estimation in PASL studies that sought to estimate either both CBF and bolus arrival time, or CBF alone.

A program for individual and population optimal design for univariate and multivariate response pharmacokinetic-pharmacodynamic models

The design of pharmacokinetic and pharmacodynamic experiments concerns a number of issues, among which are the number of observations and the times when they are taken. Often a model is used to describe these data and the pharmacokinetic-pharmacodynamic behavior of a drug. Knowledge of the data analysis model at the design stage is beneficial for collecting patient data for parameter estimation. A number of criteria for model-oriented experiments, which maximize the information content of the data, are available. In this paper we present a program, Popdes, to investigate the D-optimal design of individual and population multivariate response models, such as pharmacokinetic-pharmacodynamic, physiologically based pharmacokinetic, and parent drug and metabolites models. A pre-clinical and clinical pharmacokinetic-pharmacodynamic model describing the concentration-time profile and effect of an oncology compound in development is used for illustration.

Optimal design for multiresponse pharmacokinetic-pharmacodynamic models - dealing with unbalanced designs

This paper addresses the problem of determining D-optimal designs for multiresponse pharmacokinetic-pharmacodynamic (PKPD) experiments where data on each response variable can be collected at different times. Most previous multiresponse model optimal design applications have considered the case where all response variables are measured at the same time points. However in practice it may not be possible to have all responses measured at the same sampling times. We propose an optimal design method to take into account the unbalanced nature of the problem. The method developed was applied to a PKPD problem that involved describing the time course of drug plasma concentrations, heart rate and mean arterial blood pressure for both a fixed effects and mixed effects regression model. Additionally a simulation study was carried out in NONMEM for one such population optimal design problem.