Experience with Nonmem: Analysis of Routine Phenytoin Clinical Pharmacokinetic Data

Population pharmacokinetics of colistin and the relation to survival in critically ill patients infected with colistin susceptible and carbapenem-resistant bacteria

OBJECTIVES

The aim was to analyse the population pharmacokinetics of colistin and to explore the relationship between colistin exposure and time to death.

METHODS

Patients included in the AIDA randomized controlled trial were treated with colistin for severe infections caused by carbapenem-resistant Gram-negative bacteria. All subjects received a 9 million units (MU) loading dose, followed by a 4.5 MU twice daily maintenance dose, with dose reduction if creatinine clearance (CrCL) < 50 mL/min. Individual colistin exposures were estimated from the developed population pharmacokinetic model and an optimized two-sample per patient sampling design. Time to death was evaluated in a parametric survival analysis.

RESULTS

Out of 406 randomized patients, 349 contributed pharmacokinetic data. The median (90% range) colistin plasma concentration was 0.44 (0.14-1.59) mg/L at 15 minutes after the end of first infusion. In samples drawn 10 hr after a maintenance dose, concentrations were >2 mg/L in 94% (195/208) and 44% (38/87) of patients with CrCL ≤120 mL/min, and >120 mL/min, respectively. Colistin methanesulfonate sodium (CMS) and colistin clearances were strongly dependent on CrCL. High colistin exposure to MIC ratio was associated with increased hazard of death in the multivariate analysis (adjusted hazard ratio (95% CI): 1.07 (1.03-1.12)). Other significant predictors included SOFA score at baseline (HR 1.24 (1.19-1.30) per score increase), age and Acinetobacter or Pseudomonas as index pathogen.

DISCUSSION

The population pharmacokinetic model predicted that >90% of the patients had colistin concentrations >2 mg/L at steady state, but only 66% at 4 hr after start of treatment. High colistin exposure was associated with poor kidney function, and was not related to a prolonged survival.

Mathematical Models in the Description of Pregnane X Receptor (PXR)-Regulated Cytochrome P450 Enzyme Induction

The pregnane X receptor (PXR) is a drug/xenobiotic-activated transcription factor of crucial importance for major cytochrome P450 xenobiotic-metabolizing enzymes (CYP) expression and regulation in the liver and the intestine. One of the major target genes regulated by PXR is the cytochrome P450 enzyme (CYP3A4), which is the most important human drug-metabolizing enzyme. In addition, PXR is supposed to be involved both in basal and/or inducible expression of many other CYPs, such as CYP2B6, CYP2C8, 2C9 and 2C19, CYP3A5, CYP3A7, and CYP2A6. Interestingly, the dynamics of PXR-mediated target genes regulation has not been systematically studied and we have only a few mechanistic mathematical and biologically based models describing gene expression dynamics after PXR activation in cellular models. Furthermore, few indirect mathematical PKPD models for prediction of CYP3A metabolic activity in vivo have been built based on compartmental models with respect to drug–drug interactions or hormonal crosstalk. Importantly, several negative feedback loops have been described in PXR regulation. Although current mathematical models propose these adaptive mechanisms, a comprehensive mathematical model based on sufficient experimental data is still missing. In the current review, we summarize and compare these models and address some issues that should be considered for the improvement of PXR-mediated gene regulation modelling as well as for our better understanding of the quantitative and spatial dynamics of CYPs expression.

A Phase II pilot trial to evaluate safety and efficacy of ferroquine against early Plasmodium falciparum in an induced blood-stage malaria infection study

Background

Ferroquine (SSR97193) is a candidate anti-malarial currently undergoing clinical trials for malaria. To better understand its pharmacokinetic (PK) and pharmacodynamic (PD) parameters the compound was tested in the experimentally induced blood stage malaria infection model in volunteers.

Methods

Male and non-pregnant female aged 18–50 years were screened for this phase II, controlled, single-centre clinical trial. Subjects were inoculated with ~1800 viable Plasmodium falciparum 3D7A-infected human erythrocytes, and treated with a single-dose of 800 mg ferroquine. Blood samples were taken at defined time-points to measure PK and PD parameters. The blood concentration of ferroquine and its active metabolite, SSR97213, were measured on dry blood spot samples by ultra-performance liquid chromatography with tandem mass spectrometry (LC-MS/MS). Parasitaemia and emergence of gametocytes were monitored by quantitative PCR. Safety was determined by recording adverse events and monitoring clinical laboratory assessments during the course of the study.

Results

Eight subjects were enrolled into the study, inoculated with infected erythrocytes and treated with 800 mg ferroquine. Ferroquine was rapidly absorbed with maximal exposure after 4–8 and 4–12 h exposure for SSR97213. Non-compartmental PK analysis resulted in estimates for half-lives of 10.9 and 23.8 days for ferroquine and SSR97213, respectively. Parasite clearance as reported by parasite reduction ratio was 162.9 (95 % CI 141–188) corresponding to a parasite clearance half-life of 6.5 h (95 % CI: 6.4–6.7 h). PK/PD modelling resulted in a predicted minimal parasiticidal concentration of 20 ng/mL, and the single dosing tested in this study was predicted to maintain an exposure above this threshold for 454 h (37.8 days). Although ferroquine was overall well tolerated, transient elevated transaminase levels were observed in three subjects. Paracetamol was the only concomitant treatment among the two out of these three subjects that may have played a role in the elevated transaminases levels. No clinically significant ECG abnormalities were observed.

Conclusions

The parameters and PK/PD model derived from this study pave the way to the further rational development of ferroquine as an anti-malarial partner drug. The safety of ferroquine has to be further explored in controlled human trials.

Trial registration anzctr.org.au (registration number: ACTRN12613001040752), registered 18/09/2013

Electronic supplementary material

The online version of this article (doi:10.1186/s12936-016-1511-3) contains supplementary material, which is available to authorized users.

Modeling of rifampicin-induced CYP3A4 activation dynamics for the prediction of clinical drug-drug interactions from in vitro data

Induction of cytochrome P450 3A4 (CYP3A4) expression is often implicated in clinically relevant drug-drug interactions (DDI), as metabolism catalyzed by this enzyme is the dominant route of elimination for many drugs. Although several DDI models have been proposed, none have comprehensively considered the effects of enzyme transcription/translation dynamics on induction-based DDI. Rifampicin is a well-known CYP3A4 inducer, and is commonly used as a positive control for evaluating the CYP3A4 induction potential of test compounds. Herein, we report the compilation of in vitro induction data for CYP3A4 by rifampicin in human hepatocytes, and the transcription/translation model developed for this enzyme using an extended least squares method that can account for inherent inter-individual variability. We also developed physiologically based pharmacokinetic (PBPK) models for the CYP3A4 inducer and CYP3A4 substrates. Finally, we demonstrated that rifampicin-induced DDI can be predicted with reasonable accuracy, and that a static model can be used to simulate DDI once the blood concentration of the inducer reaches a steady state following repeated dosing. This dynamic PBPK-based DDI model was implemented on a new multi-hierarchical physiology simulation platform named PhysioDesigner.

Population pharmacokinetics in geriatric psychiatry

Although geriatric patients are the major recipients of drugs, most research during drug development is conducted in healthy younger adults. Safe and effective drug therapy in the elderly requires an understanding of both drug disposition and response in older individuals. One of the major issues in studying the elderly relates to the ability to study a large number of people in a minimally invasive way. Population pharmacokinetics can be used to model drug concentrations from a large population of sparsely sampled individuals. Population pharmacokinetics characterizes both the interindividual (between-subject) and intraindividual (within-subject) variability, and can identify factors that contribute to pharmacokinetic and pharmacodynamic variability. Population pharmacokinetics can be used to aid in designing large clinical trials by simulating virtual data based on the study design. It can also be used to assess consistency of drug exposure and evaluate its effect on clinical outcome. This article reviews the methods used in pharmacokinetic modeling, as well as providing examples of population pharmacokinetic modeling, highlighting its application to geriatric psychiatry.

[What's new in pediatric pharmacology?]

Only a minority of the drugs administered to children and infants have a pediatric labeling and have been sufficiently tested for efficacy, safety and correct pediatric dosing, which cannot necessarily be extrapolated from adult data. This situation is scientifically and ethically unacceptable. To address this problem, the suggestion is being made in several countries that more formal legal requirements should be introduced. In the United States, in 1997, a new legislation encouraged pharmaceutical companies to study medicines in children (for example, by offering the financial incentive of a six-month extension to patent exclusivity). However, there are undeniable difficulties in pediatric and neonatal studies. To minimize the risks of clinical investigation in children, appropriate methodologies should be used. New in vitro and in vivo methods are now available, taking into account pediatric characteristics.

Simulation of clinical trials

Computer simulation of clinical trials has evolved over the past two decades from a simple instructive game to "full" simulation models yielding pharmacologically sound, realistic trial outcomes. The need to make drug development more efficient and informative and the awareness that many industries make extensive use of simulation in product development have advanced considerably the use of simulation of clinical trials in pharmaceutical product development over the past decade. The structural and stochastic components of trial simulation models are explained as a prelude to a listing of representative simulation projects, reflecting investigative applications of statistical methods, trial design comparisons, and full simulation of new drugs being developed. Lessons learned from these projects are reviewed in the context of their current impact and potential for influencing the future of drug development.

Co-variables influencing 5-fluorouracil clearance during continuous venous infusion. A NONMEM analysis

The objective of this study was to attempt to identify patient co-variables which could influence interpatient variability in 5-fluorouracil (5-FU) clearance during a 5-day continuous venous infusion (CVI, cisplatin 100 mg/m2 day 1 then 5-FU 1 g/m2/day days 2-6). The analysis was performed using a NONMEM program according to a linear one-compartment model. A total of 186 cycles (2 samples per day, 8 a.m. and 5 p.m.) were analysed from 104 patients with various cancers (the majority of head and neck and oesophagus). The study co-variables were age, sex, body surface area, hepatic metastases, peripheral mononuclear cell dihydropyrimidine dehydrogenase activity (PMNC-DPD), liver enzymes, clock-time (8 a.m. versus 5 p.m.), elapsed time during CVI. The data showed that 5-FU clearance was significantly reduced by increased age, low PMNC-DPD, high serum alkaline phosphatase and elapsed time during infusion. These data may be useful for improving knowledge of predictive factors which can influence 5-FU exposure and thus predispose to toxic manifestations.

Nonparametric expectation maximization population modeling of ganciclovir

The use of the nonparametric expectation maximization (NPEM2) program to estimate pharmacokinetic parameters of ganciclovir in a group of patients with human immunodeficiency virus (HIV) and cytomegalovirus (CMV) infection was evaluated. A 10-point data set per patient obtained over 8 hours was analyzed. Mean pharmacokinetic parameters obtained included rate constant from the central to the peripheral compartment (KCP,3.1 hr-1), rate constant from the peripheral to the central compartment (KPC, 0.824 hr-1), slope of the volume of distribution to body weight (VS, 0.246 L/kg), and slope of clearance to creatinine clearance (Cl(cr)) and body weight (CLS,0.222L/hr/kg/100 mL/ min Cl(cr). Use of NPEM2 led to identification of a subset of patients with CMV retinitis who had a more rapid clearance of ganciclovir of 0.51 to 0.54 L/hr/kg/100 mL/min Cl(cr). Use of smaller, optimally timed samples of five, four, and three data points per patient produced mean pharmacokinetic parameter results consistent with the full ten-point data set. When Bayesian-derived parameter estimates using a five-point data set were compared with a traditional, nonlinear, least-square analysis of the entire ten-point data set, estimates of clearance were determined to be relatively unbiased and precise. The ability of NPEM2 to estimate pharmacokinetic parameters and to determine the population distribution of the parameters was demonstrated. By using points in the analysis chosen by D-optimal design theory, NPEM2 was able to give consistent parameter estimates with as few as three data points. Determination of the distribution appeared to have been dependent on the time points used, however. The approach of MAP-Bayesian analysis to derive patient-specific estimates using optimal samples and prior estimates from a previous population pharmacokinetic analysis for inclusion in subsequent pharmacodynamic analyses of drug exposure (area under the concentration-time curve) may enable development of exposure-response and exposure-toxicity relationships.

A method for prediction of phenytoin levels in the acute clinical setting

Phenytoin (PHT) administration is complicated by saturation kinetics within the therapeutic range, causing marked changes in drug concentration with small changes in dose. The "half-life" increases with concentration, varying from 8-24 hr up to weeks, making it difficult to obtain the steady state levels needed by most prediction algorithms and nomograms. A Bayesian prediction program (Epidose) is presented which explicitly models PHT absorption and elimination kinetics in the non-steady state. The algorithm accounts for the interdependency of closely spaced sequential samples. Estimates of future PHT concentration were made on 20 hospital inpatients, most of whom were acutely ill and received other medications. Future (mean = 4 day) PHT concentrations were predicted over a range from 4 to 22 micrograms/ml (mean 13.9 micrograms/ml) with a median absolute error of 1.0 microgram/ml. These data demonstrate that the program can be used for accurate PHT concentration predictions in sick patients.

A simplex procedure for fitting nonlinear pharmacokinetic models

A convenient and readily modifiable nonlinear regression procedure, based on two Pascal programs, is described. This procedure, suitable for running on both microcomputers and mainframes, is presented as utilized in ongoing clinical pharmacokinetic work. One program uses the simplex algorithm to fit data conforming to any of six models of drug disposition. In addition to the regression terms, it generates logarithmic plots of the function and calculates derived kinetic variables of drug distribution, elimination and clearance. This program is driven by another which batches together required data and parameter information to build a control file for solving multiple curve-fitting problems.

Population pharmacokinetic data and parameter estimation based on their first two statistical moments

A statistical model is set forth that can be taken as a very general description of most data sets that arise from population pharmacokinetic studies. A (nontraditional) method for estimating the parameters of the model--called the NONMEM method in previously published papers--is described. This method involves a linearization of the model. An investigation of the effect of this linearization is reported. This investigation is an empirical one, based on the simulation of data from special cases of the model and the application of a few different estimation methods to these data. From the limited evidence in this investigation it appears that the linearization per se does not significantly adversely affect the estimates.