Kayode Ogungbenro

Intravenous anakinra can achieve experimentally effective concentrations in the central nervous system within a therapeutic time window: results of a dose-ranging study

The naturally occurring antagonist of interleukin-1, IL-1RA, is highly neuroprotective experimentally, shows few adverse effects, and inhibits the systemic acute phase response to stroke. A single regime pilot study showed slow penetration into cerebrospinal fluid (CSF) at experimentally therapeutic concentrations. Twenty-five patients with subarachnoid hemorrhage (SAH) and external ventricular drains were sequentially allocated to five administration regimes, using intravenous bolus doses of 100 to 500 mg and 4 hours intravenous infusions of IL-1RA ranging from 1 to 10 mg per kg per hour. Choice of regimes and timing of plasma and CSF sampling was informed by pharmacometric analysis of pilot study data. Data were analyzed using nonlinear mixed effects modeling. Plasma and CSF concentrations of IL-1RA in all regimes were within the predicted intervals. A 500-mg bolus followed by an intravenous infusion of IL-1RA at 10 mg per kg per hour achieved experimentally therapeutic CSF concentrations of IL-1RA within 45 minutes. Experimentally, neuroprotective CSF concentrations in patients with SAH can be safely achieved within a therapeutic time window. Pharmacokinetic analysis suggests that IL-1RA transport across the blood–CSF barrier in SAH is passive. Identification of the practicality of this delivery regime allows further studies of efficacy of IL-1RA in acute cerebrovascular disease.

The population pharmacokinetics of R- and S-warfarin: effect of genetic and clinical factors

BACKGROUND Warfarin is a drug with a narrow therapeutic index and large interindividual variability in daily dosing requirements. Patients commencing warfarin treatment are at risk of bleeding due to excessive anticoagulation caused by overdosing. The interindividual variability in dose requirements is influenced by a number of factors, including polymorphisms in genes mediating warfarin pharmacology, co-medication, age, sex, body size and diet. AIMS To develop population pharmacokinetic models of both R- and S-warfarin using clinical and genetic factors and to identify the covariates which influence the interindividual variability in the pharmacokinetic parameters of clearance and volume of distribution in patients on long-term warfarin therapy. METHODS Patients commencing warfarin therapy were followed up for 26 weeks. Plasma warfarin enantiomer concentrations were determined in 306 patients for S-warfarin and in 309 patients for R-warfarin at 1, 8 and 26 weeks. Patients were also genotyped for CYP2C9 variants (CYP2C9*1,*2 and *3), two single-nucleotide polymorphisms (SNPs) in CYP1A2, one SNP in CYP3A4 and six SNPs in CYP2C19. A base pharmacokinetic model was developed using NONMEM software to determine the warfarin clearance and volume of distribution. The model was extended to include covariates that influenced the between-subject variability. RESULTS Bodyweight, age, sex and CYP2C9 genotype significantly influenced S-warfarin clearance. The S-warfarin clearance was estimated to be 0.144 l h⁻¹ (95% confidence interval 0.131, 0.157) in a 70 kg woman aged 69.8 years with the wild-type CYP2C9 genotype, and the volume of distribution was 16.6 l (95% confidence interval 13.5, 19.7). Bodyweight and age, along with the SNPs rs3814637 (in CYP2C19) and rs2242480 (in CYP3A4), significantly influenced R-warfarin clearance. The R-warfarin clearance was estimated to be 0.125 l h⁻¹ (95% confidence interval 0.115, 0.135) in a 70 kg individual aged 69.8 years with the wild-type CYP2C19 and CYP3A4 genotypes, and the volume of distribution was 10.9 l (95% confidence interval 8.63, 13.2). CONCLUSIONS Our analysis, based on exposure rather than dose, provides quantitative estimates of the clinical and genetic factors impacting on the clearance of both the S- and R-enantiomers of warfarin, which can be used in developing improved dosing algorithms.

Optimal Design for Multivariate Response Pharmacokinetic Models

We address the problem of designing pharmacokinetic experiments in multivariate response situations. Criteria, based on the Fisher information matrix, whose inverse according to the Rao–Cramer inequality is the lower bound of the variance–covariance matrix of any unbiased estimator of the parameters, have previously been developed for univariate response for an individual and a population. We extend these criteria to design individual and population studies where more than one response is measured, for example, when both parent drug and metabolites are measured in plasma, multi-compartment models, where measurements are taken at more than one site, or when drug concentration and pharmacodynamic data are collected simultaneously. We assume that measurements made at distinct times are independent, but measurements made of each concentration are correlated with a response variance–covariance matrix. We investigated a number of optimisation algorithms, namely simplex, exchange, adaptive random search, simulated annealing and a hybrid, to maximise the determinant of the Fisher information matrix as required by the D-optimality criterion. The multiresponse optimal design methodology developed was applied in two case studies, where the aim was to suggest optimal sampling times. The first was a restrospective iv infusion experiment aimed to characterise the disposition kinetics of tolcapone and its two metabolites in healthy volunteers. The second was a prospective iv bolus experiment designed to estimate the tissue disposition kinetics of eight beta-blockers in rat.

Methods and software tools for design evaluation in population pharmacokinetics–pharmacodynamics studies

Population pharmacokinetic (PK)-pharmacodynamic (PKPD) models are increasingly used in drug development and in academic research; hence, designing efficient studies is an important task. Following the first theoretical work on optimal design for nonlinear mixed-effects models, this research theme has grown rapidly. There are now several different software tools that implement an evaluation of the Fisher information matrix for population PKPD. We compared and evaluated the following five software tools: PFIM, PkStaMp, PopDes, PopED and POPT. The comparisons were performed using two models, a simple-one compartment warfarin PK model and a more complex PKPD model for pegylated interferon, with data on both concentration and response of viral load of hepatitis C virus. The results of the software were compared in terms of the standard error (SE) values of the parameters predicted from the software and the empirical SE values obtained via replicated clinical trial simulation and estimation. For the warfarin PK model and the pegylated interferon PKPD model, all software gave similar results. Interestingly, it was seen, for all software, that the simpler approximation to the Fisher information matrix, using the block diagonal matrix, provided predicted SE values that were closer to the empirical SE values than when the more complicated approximation was used (the full matrix). For most PKPD models, using any of the available software tools will provide meaningful results, avoiding cumbersome simulation and allowing design optimization.

Why has model-informed precision dosing not yet become common clinical reality?: Lessons from the past and a roadmap for the future

Patient groups prone to polypharmacy and special subpopulations are susceptible to suboptimal treatment. Refined dosing in special populations is imperative to improve therapeutic response and/or lowering the risk of toxicity. Model‐informed precision dosing (MIPD) may improve treatment outcomes by achieving the optimal dose for an individual patient. There is, however, relatively little published evidence of large‐scale utility and impact of MIPD, where it is often implemented as local collaborative efforts between academia and healthcare. This article highlights some successful applications of bringing MIPD to clinical care and proposes strategies for wider integration in healthcare. Considerations are brought up herein that will need addressing to see MIPD become “widespread clinical practice,” among those, wider interdisciplinary collaborations and the necessity for further evidence‐based efficacy and cost–benefit analysis of MIPD in healthcare. The implications of MIPD on regulatory policies and pharmaceutical development are also discussed as part of the roadmap.

The use of a modified Fedorov exchange algorithm to optimise sampling times for population pharmacokinetic experiments

We propose a new algorithm for optimising sampling times for population pharmacokinetic experiments using D-optimality. The algorithm was used in conjunction with the population Fisher information matrix as implemented in MATLAB (PFIM 1.1 and 1.2) to evaluate population pharmacokinetic designs. The new algorithm based on the classical Fedorov exchange algorithm optimises the determinant of the population Fisher information matrix. The performance of the new algorithm has been compared with other existing algorithms including simplex, simulated annealing and adaptive random search. The new algorithm performed better especially when dealing with complex designs at the expense of longer computing times.

Optimal Design of Pharmacokinetic Studies

Experimental design is fundamental to successful scientific investigation. Poorly designed experiments lead to the loss of information, which is costly and potentially unethical. Experiments can be designed in an optimal fashion to maximize the amount of information they provide. 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. In the case of population pharmacokinetic experiments, this involves the selection and a careful balance of a number of design factors, including the number and location of measurement times and the number of subjects to include in the study. 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 experiments and can also help to reduce both cost and time during drug development. This MiniReview provides an introduction to optimal design using examples taken from different pharmacokinetic experiments.

How many subjects are necessary for population pharmacokinetic experiments? Confidence interval approach

ObjectiveTo describe an approach using simulations for determining sample size for population pharmacokinetic experimentsMethodsWe address this problem by proposing method based on the estimation of the model parameters. The power to estimate the confidence interval of a parameter of choice to a particular precision is determined for different sample sizes by making stepwise increases in the sample size until the power is achieved. The method is based on simulation using previous information about the model and parameter estimates. Two examples are presented based on one-compartment and two-compartment first-order absorption models.ResultsSample size depends on the parameter of choice, the sampling designs and the method for the analysis of the collected data among other things. For the one-compartment first-order absorption model, assuming the parameter of choice is rate of absorption, the sample sizes required to estimate the 95% confidence interval within a 20% precision level with a power of 0.9 using the FO, FOCE and FOCE/INTERACTION methods in NONMEM and WinBUGS for a design that involved sampling at 0.01, 7.75 and 12 h (the population optimal design) are 20, 30, 30 and 30 respectively. For an extensive design (sampling at 0.5, 2, 4, 8, 12 and 24 h), the sample sizes are 20, 20, 20 and 30, respectively. For the two-compartment first-order absorption model, assuming the parameter of choice is initial volume of distribution, the sample sizes required to estimate the 95% confidence interval within a 50% precision level with a power of 0.8 for FO, FOCE/INTERACTION and WinBUGS were 50, 50 and 20, respectively.ConclusionThe determination of sample size using the confidence interval approach appears to be a pragmatic approach to determine the minimum number of subjects for a population pharmacokinetic experiment.

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.

Physiologically based pharmacokinetic modelling of methotrexate and 6-mercaptopurine in adults and children. Part 1: methotrexate

Methotrexate is an antimetabolite and antifolate drug that is widely used in the treatment of malignancies and auto-immune disorders. In childhood acute lymphoblastic leukaemia, methotrexate is often combined with 6-mercaptopurine and both of them have been shown to be very effective for maintenance of remission. Large variability in the pharmacokinetics of methotrexate has led to increasing use of therapeutic drug monitoring in its clinical use to identify patients with high risk of toxicity and optimise clinical outcome. A physiologically based pharmacokinetic model was developed for methotrexate for oral and intravenous dosing and adults and paediatric use. The model has compartments for stomach, gut lumen, enterocyte, gut tissue, spleen, liver vascular, liver tissue, gall bladder, systemic plasma, red blood cells, kidney vascular, kidney tissue, skin, bone marrow, thymus, muscle and rest of body. A mechanistic model was also developed for the kidney to account for renal clearance of methotrexate via filtration and secretion. Variability on system and drug specific parameters was incorporated in the model to reflect observed clinical data and assuming the same pathways in adults and children, age-dependent changes in body size, organ volumes and plasma flows, the model was scaled to children. The model was developed successfully for adults and parameters such as net secretion clearance, biliary transit time and red blood cell distribution and binding parameters were estimated from published adult profiles. A relationship between fraction absorbed and dose using reported mean bioavailability data in the literature was also established. The model also incorporates non-linear binding in some tissues that has been described in the literature. Predictions using this model provide adequate description of observed plasma concentration data in adults and children. The model can be used to predict plasma and tissue concentrations of methotrexate following intravenous and oral dosing in adults and children and therefore help to improve clinical outcome.