An evaluation of population pharmacokinetics in therapeutic trials. Part II. Detection of a drug-drug interaction

Methods and strategies for assessing uncontrolled drug-drug interactions in population pharmacokinetic analyses: results from the International Society of Pharmacometrics (ISOP) Working Group

The purpose of this work was to present a consolidated set of guidelines for the analysis of uncontrolled concomitant medications (ConMed) as a covariate and potential perpetrator in population pharmacokinetic (PopPK) analyses. This white paper is the result of an industry-academia-regulatory collaboration. It is the recommendation of the working group that greater focus be given to the analysis of uncontrolled ConMeds as part of a PopPK analysis of Phase 2/3 data to ensure that the resulting outcome in the PopPK analysis can be viewed as reliable. Other recommendations include: (1) collection of start and stop date and clock time, as well as dose and frequency, in Case Report Forms regarding ConMed administration schedule; (2) prespecification of goals and the methods of analysis, (3) consideration of alternate models, other than the binary covariate model, that might more fully characterize the interaction between perpetrator and victim drug, (4) analysts should consider whether the sample size, not the percent of subjects taking a ConMed, is sufficient to detect a ConMed effect if one is present and to consider the correlation with other covariates when the analysis is conducted, (5) grouping of ConMeds should be based on mechanism (e.g., PGP-inhibitor) and not drug class (e.g., beta-blocker), and (6) when reporting the results in a publication, all details related to the ConMed analysis should be presented allowing the reader to understand the methods and be able to appropriately interpret the results.

Development of a pharmacokinetic interaction model for co-administration of simvastatin and amlodipine

A model for drug interaction between amlodipine and simvastatin was developed using concentration data obtained from a multiple-dose study consisting of single- and co-administration of amlodipine and simvastatin conducted in healthy Koreans. Amlodipine concentrations were assumed to influence the clearance of simvastatin and simvastatin acid, which as well as the oral bioavailability was allowed to vary depending on genetic polymorphisms of metabolic enzymes. Covariate effects on drug concentrations were also considered. The developed model yielded a 46% increase in simvastatin bioavailability and a 13% decrease in simvastatin clearance when amlodipine 10 mg was co-administered. When CYP3A4/5 polymorphisms were assessed by a mixture model, extensive metabolizers yielded a decrease in simvastatin bioavailability of 81% and a decrease in simvastatin clearance by 4.6 times as compared to poor metabolizers. Sixty percent of the usual dose was the optimal simvastatin dose that can minimize the interaction with amlodipine 10 mg. Age and weight had significant effects on amlodipine concentrations. In conclusion, this study has quantitatively described the pharmacokinetic interaction between simvastatin and amlodipine using a modeling approach. Given that the two drugs are often prescribed together, the developed model is expected to contribute to more efficient and safer drug treatment when they are co-administered.

Population-Based Assessments of Clinical Drug-Drug Interactions: Qualitative Indices or Quantitative Measures?

Population-based assessments of drug-drug interactions have become more common since the introduction and acceptance of the population pharmacokinetic approach. Unlike traditional methods, population-based studies provide clinically relevant results that can be applied directly to a target patient population. Furthermore, population-based studies do not demand the traditional requirements of intensive pharmacokinetic sampling, rigorous inpatient stays, or stringent assessment schedules. As such, the population-based approach can effectively be used to confirm known drug-drug interactions and further characterize anticipated interactions. A prospectively designed analysis can also reveal drug-drug interactions that might otherwise have gone undetected with traditional methods. Ultimately, these results could help to alleviate clinicians' concerns about using widely marketed drugs in combination therapies and also reduce patients' risk of experiencing unacceptable side effects. This article intends to provide a balanced overview of the population-based approach and its merits, drawbacks, and potential utility in the assessment of drug-drug interactions during clinical drug development.

Unaltered Etanercept Pharmacokinetics With Concurrent Methotrexate in Patients With Rheumatoid Arthritis

The purpose of this study was to evaluate the potential impact of concurrent weekly oral methotrexate administration on the pharmacokinetics of etanercept in patients with rheumatoid arthritis (RA) in a phase 3B trial. As part of a double-blind randomized trial of 682 patients with rheumatoid arthritis who received etanercept (25 mg subcutaneously twice weekly), methotrexate (weekly oral dose, median weekly dose: 20 mg), or etanercept (25 mg subcutaneously twice weekly) plus methotrexate (weekly oral dose, median weekly dose: 20 mg), serum etanercept concentrations were measured in a subset of patients. Serum samples for 98 randomly selected patients (48 receiving etanercept-alone treatment, 50 receiving etanercept plus methotrexate combination treatment) were analyzed to assess the pharmacokinetics of etanercept. A single blood sample was drawn from each patient at baseline and at the week 24 visit. Given the variable sampling time for patients in both groups, a population pharmacokinetic analysis using NONMEM was conducted for etanercept. A final covariate population pharmacokinetic model was constructed based on previously obtained etanercept data from both healthy subjects (n = 53) and patients with RA (n = 212) in 10 prior clinical trials. The predictive performance of the final model was assessed by both bootstrap and data-splitting validation approaches. The final model was then used to estimate Bayesian pharmacokinetic parameters for the patients in both treatments in the current trial. The potential effect of the concurrent administration of methotrexate on the pharmacokinetics of etanercept was examined by comparing the clearance values between 2 treatments using statistical criteria. A population 2-compartment model with first-order elimination from the central compartment and with either zero-order (intravenous administration) or first-order (subcutaneous administration) input was selected based on the data from the prior 10 etanercept clinical studies. The following pharmacokinetic parameters (typical value +/- standard error) were estimated: clearance (CL: 0.072 +/- 0.005 L/h), volume of distribution in the central compartment (V(c): 5.97 +/- 0.45 L), volume of distribution in the peripheral compartment (V(p): 2.05 +/- 0.32 L), intercompartment clearance (Q: 0.0645 +/- 0.0093 L/h), first-order absorption rate constant (k(a): 0.0282 +/- 0.0039 1/h), and absolute bioavailability for subcutaneous administration (F: 0.626 +/- 0.056). Interindividual variability of the pharmacokinetic parameters was quantified for CL (25.1%), V(c) (41.7%), k(a) (53.1%), and F (24.2%). Residual variability consisted of combined additive (11.4 ng/mL) and proportional error (49.9%). Both age (< 17 years) and body weight (< 60 kg) were found to be important covariates on CL. The results of both validation tests indicated the adequate predictive performance of the population model. Based on the bioequivalence criteria, the Bayesian-estimated clearance for patients receiving etanercept alone (mean: 0.070 L/h) was comparable to that for patients receiving a combination of etanercept and methotrexate (mean = 0.066 L/h). The pharmacokinetics of etanercept were not altered by the concurrent administration of methotrexate in patients with rheumatoid arthritis. Thus, no etanercept dose adjustment is needed for patients taking concurrent methotrexate.

Integrated pharmacokinetics and pharmacodynamics in drug development

Drug development is a complex, lengthy and expensive process. Pharmaceutical companies and regulatory authorities have recognised that the drug development process needs optimisation for efficiency in view of the return on investments. Pharmacokinetics and pharmacodynamics are the two main principles determining the relationship between dose and response. This article provides an update on integrated approaches towards drug development by linking pharmacokinetics, pharmacodynamics and disease aspects into mathematical models. Gradually, a transition is taking place from a rather empirical approach towards a modelling- and simulation-based approach to drug development. The main learning phases should be phases 0, I and II, whereas phase III studies should merely have a confirmatory purpose. In model-based drug development, mechanism-based mathematical models, which are iteratively refined along the path of development, incorporate the accumulating knowledge of the investigational drug, the disease and their mutual interference in different subsets of the target population. These models facilitate the design of the next study and improve the probability of achieving the projected efficacy and safety endpoints. In this article, several theoretical and practical aspects of an integrated approach towards drug development are discussed, together with some case studies from different therapeutic areas illustrating the application of pharmacokinetic/pharmacodynamic disease models at different stages of drug development.

Applications of population pharmacokinetics in current drug labelling

BACKGROUND AND OBJECTIVE

The application of population pharmacokinetics (PopPK) appears increasingly in drug labelling. The current study was to examine the use of PopPK in dose recommendation in drug-product labels.

METHOD

PopPK information was identified in the data sheets included in the physician desk reference (PDR). Electronic key word searches were conducted in the electronic library of PDR. The use of PopPK in the prescribing information, including the determination of dosing regimen, dosing in special populations and dose-adjustments was summarized and evaluated. The reliability and criteria for integrating the information derived from PopPK studies into the product labelling were discussed.

RESULTS AND DISCUSSION

Among more than 2500 items listed in the PDR, 88 listings were found to have PopPK information in the labelling. The information included general data (Gen) on pharmacokinetics (PK) and the effects of gender (sex), age, race, drug-drug interactions (DDI), smoking (Smk), alcohol consumption (Alc), disease state (Dis), renal impairment (Ren) and metabolic status (Met) on the PK parameters (Table). Whether there was an effect (+) or not (-) is also shown. Appendix 1 lists the products included in each category. Searches conducted at different times suggest an increase in both quantity and quality of PopPK data in drug development. PopPK is widely used in paediatric studies and the sample sizes in these studies are sometimes too small. The application of PopPK to protein drugs is increasing rapidly (Appendix 2). Several precautions should be exercised when PopPK is applied to protein drugs. When considering gender effects, different normalization methods for body weight have been used. The number of subjects included in the PopPK analysis should be given and the influence of the imbalance in any covariate should be investigated. PopPK-DDI results are particularly difficult to evaluate unless details about potentially influential factors such as dosing and sampling information for both drug and interacting drugs are given.

CONCLUSIONS

The use of PopPK to aid optimal dosing is increasing. Several noticeable problems raised usually avoid the acceptability of PopPK studies. More investigations are needed to inform the development of consensuses on these issues. There is an accelerating shift from PopPK to PopPK/PD. The limitations of such modelling should be recognized.

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.

Recommended reading in population pharmacokinetic pharmacodynamics

Developing the skills or expertise to create useful population pharmacokinetic-pharmacodynamic models can be a daunting task-the level of mathematical and statistical complexity is such that newcomers to the field are frequently overwhelmed. A good place to start in learning the field is to read articles in the literature. However, the number of articles dealing with population pharmacokinetic pharmacodynamics is exponentially increasing on a yearly basis, so choosing which articles to read can be difficult. The purpose of this review is to provide a recommended reading list for newcomers to the field. The list was chosen based on perceived impact of the article in the field, the quality of the article, or to highlight some important detail contained within the article. After reading the articles in the list, it is believed that the reader will have a broad overview of the field and have a sound foundation for more-detailed reading of the literature.

Clinical pharmacokinetics of drugs used to treat urge incontinence

Urge incontinence (also known as overactive bladder) is a common form of urinary incontinence, occurring alone or as a component of mixed urinary incontinence, frequently together with stress incontinence. Because of the pathophysiology of urge incontinence, anticholinergic/antispasmodic agents form the cornerstone of therapy. Unfortunately, the pharmacological activity of these agents is not limited to the urinary tract, leading to systemic adverse effects that often promote nonadherence. Although the pharmacokinetics of flavoxate, propantheline, scopolamine, imipramine/desipramine, trospium chloride and propiverine are also reviewed here, only for oxybutynin and tolterodine are there adequate efficacy/tolerability data to support their use in urge incontinence. Oxybutynin is poorly absorbed orally (2-11% for the immediate-release tablet formulation). Controlled-release oral formulations significantly prolong the time to peak plasma concentration and reduce the degree of fluctuation around the average concentration. Significant absorption occurs after intravesical (bladder) and transdermal administration, although concentrations of the active N-desethyl metabolite are lower after transdermal compared with oral administration, possibly improving tolerability. Food has been found to significantly affect the absorption of one of the controlled-release formulations of oxybutynin, enhancing the rate of drug release. Oxybutynin is extensively metabolised, principally via N-demethylation mediated by the cytochrome P450 (CYP) 3A isozyme. The pharmacokinetics of tolterodine are dependent in large part on the pharmacogenomics of the CYP2D6 and 3A4 isozymes. In an unselected population, oral bioavailability of tolterodine ranges from 10% to 74% (mean 33%) whereas in CYP2D6 extensive metabolisers and poor metabolisers mean bioavailabilities are 26% and 91%, respectively. Tolterodine is metabolised via CYP2D6 to the active metabolite 5-hydroxymethyl-tolterodine and via CYP3A to N-dealkylated metabolites. Urinary excretion of parent compound plays a minor role in drug disposition. Drug effect is based upon the unbound concentration of the so-called 'active moiety' (sum of tolterodine + 5-hydroxymethyl-tolterodine). Terminal disposition half-lives of tolterodine and 5-hydroxymethyl-tolterodine (in CYP2D6 extensive metabolisers) are 2-3 and 3-4 hours, respectively. Coadministration of antacid essentially converts the extended-release formulation into an immediate-release formulation. Knowledge of the pharmacokinetics of these agents may improve the treatment of urge incontinence by allowing the identification of individuals at high risk for toxicity with 'usual' dosages. In addition, the use of alternative formulations (controlled-release oral, transdermal) may also facilitate adherence, not only by reducing the frequency of drug administration but also by enhancing tolerability by altering the proportions of parent compound and active metabolite in the blood.

The role of population pharmacokinetics in drug development in light of the Food and Drug Administration's 'Guidance for Industry: population pharmacokinetics'

Population pharmacokinetics (PPK) has evolved from a discipline primarily applied to therapeutic drug monitoring to one that plays a significant role in clinical pharmacology in general and drug development in particular. In February 1999 the US Food and Drug Administration issued a 'Guidance for Industry: Population Pharmacokinetics' that sets out the mechanisms and philosophy of PPK and outlines its role in drug development. The application of PPK to the drug development process plays an important role in the efficient development of safe and effective drugs. PPK knowledge is essential for mapping the response surface, explaining subgroup differences, developing and evaluating competing dose administration strategies, and as an aid in designing future studies. The mapping of the response surface is done to maximise the benefit-risk ratio, so that the impact of the input profile and dose magnitude on beneficial and harmful pharmacological effects can be understood and applied to individual patients. PPK combined with simulation methods provides a tool for estimating the expected range of concentrations from competing dose administration strategies. Once extracted, this knowledge can be applied to labelling or used to assess various future study designs. PPK should be implemented across all phases of drug development. For preclinical studies, PPK can be applied to allometric scaling and toxicokinetic analyses, and is useful for determining 'first time in man' doses and explaining toxicological results. Phase I studies provide initial understanding of the structural model and the effect of possible covariates, and may later be used to evaluate PPK differences between patients and healthy individuals. Phase II studies provide the greatest opportunity to map the response surface. With these PPK models it is possible to gain an improved understanding of the role of the dose on the response surface and of the range of expected responses. In phase III and IV studies, PPK is implemented to further refine the PPK model and to explain unexpected responses. Planning for the implementation of PPK across all phases of drug development is necessary, as well as planning for individual PPK studies. Planning should include: defining important questions, identifying covariates and drug-drug interactions that need to be investigated, and identifying the applications and intended use of the model(s). The plan for each project must have a strategy for data management, data collection, data quality assurance, staff training for data collection, data analysis and model validation.

Drug-drug pharmacodynamic interaction detection by a nonparametric population approach. Influence of design and of interindividual variability

Population approaches are appealing methods for detecting then assessing drug-drug interactions mainly because they can cope with sparse data and quantify the interindividual pharmacokinetic (PK) and pharmacodynamic (PD) variability. Unfortunately these methods sometime fail to detect interactions expected on biochemical and/or pharmacological basis and the reasons of these false negatives are somewhat unclear. The aim of this paper is firstly to propose a strategy to detect and assess PD drug-drug interactions when performing the analysis with a nonparametric population approach, then to evaluate the influence of some design variates (i.e., number of subjects, individual measurements) and of the PD interindividual variability level on the performances of the suggested strategy. Two interacting drugs A and B are considered, the drug B being supposed to exhibit by itself a pharmacological action of no interest in this work but increasing the A effect. Concentrations of A and B after concomitant administration are simulated as well as the effect under various combinations of design variates and PD variability levels in the context of a controlled trial. Replications of simulated data are then analyzed by the NPML method, the concentration of the drug B being included as a covariate. In a first step, no model relating the latter to each PD parameter is specified and the NPML results are then proceeded graphically, and also by examining the expected reductions of variance and entropy of the estimated PD parameter distribution provided by the covariate. In a further step, a simple second stage model suggested by the graphic approach is introduced, the fixed effect and its associated variance are estimated and a statistical test is then performed to compare this fixed effect to a given value. The performances of our strategy are also compared to those of a non-population-based approach method commonly used for detecting interactions. Our results illustrate the relevance of our strategy in a case where the concentration of one of the two drugs can be included as a covariate and show that an existing interaction can be detected more often than with a usual approach. The prominent role of the interindividual PD variability level and of the two controlled factors is also shown.

The application of population pharmacokinetics to the drug development process

Population pharmacokinetics is playing an increasing role in clinical drug development. An overview of the population approach, including software and the advantages and limitations of the approach compared to the traditional approach to pharmacokinetic studies, is given. This paper also documents how the area has evolved over the past 15 years and addresses some of the issues that have arisen over the design and conduct of population studies. Finally, some alternative applications of the population approach are given for areas other than clinical drug development.

Expanding clinical applications of population pharmacodynamic modelling

Population pharmacokinetics or pharmacodynamics is the study of the variability in drug concentration or pharmacological effect between individuals when standard dosage regimens are administered. We provide an overview of pharmacokinetic models, pharmacodynamic models, population models and residual error models. We outline how population modelling approaches seek to explain interpatient variability with covariate analysis, and, in some approaches, to characterize the unexplained interindividual variability. The interpretation of the results of population modelling approaches is facilitated by shifting the emphasis from the perspective of the modeller to the perspective of the clinician. Both the explained and unexplained interpatient variability should be presented in terms of their impact on the dose-response relationship. Clinically relevant questions relating to the explained and unexplained variability in the population can be posed to the model, and confidence intervals can be obtained for the fraction of the population that is estimated to fall within a specific therapeutic range given a certain dosing regimen. Such forecasting can be used to develop optimal initial dosing guidelines. The development of population models (with random effects) permits the application of Bayes's formula to obtain improved estimates of an individual's pharmacokinetic and pharmacodynamic parameters in the light of observed responses. An important challenge to clinical pharmacology is to identify the drugs that might benefit from such adaptive-control-with-feedback dosing strategies. Drugs used for life threatening diseases with a proven pharmacokinetic-pharmacodynamic relationship, a small therapeutic range, large interindividual variability, small interoccasion variability and severe adverse effects are likely to be good candidates. Rapidly evolving changes in health care economics and consumer expectations make it unlikely that traditional drug development approaches will succeed in the future. A shift away from the narrow focus on rejecting the null hypothesis towards a broader focus on seeking to understand the factors that influence the dose-response relationship--together with the development of the next generation of software based on population models--should permit a more efficient and rational drug development programme.

Sparse drug concentration data analysis using a population approach: a valuable tool in clinical pharmacology

1. Drug concentration or pharmacological effect data collected from patients during therapy or as part of a Phase III or post-marketing study are generally sparse (i.e. one or a few observations per patient) in nature. 2. The population approach to analysing sparse drug concentration data provides a valuable tool for obtaining information about the pharmacokinetics of drugs in special patient groups (neonates, aged or critically ill), the importance of drug interactions in the clinic (using routinely collected blood concentration data) and for conducting a 'pharmacokinetic screen' in patients during early phase efficacy trials. 3. Using a population approach to analyse drug concentration-time data collected from patients during therapy or during an early phase investigation can complement information obtained from traditional pharmacokinetic/dynamic investigations to help gain a further insight into the factors that influence dosing guidelines.

Mixed-effect modeling for detection and evaluation of drug interactions: digoxin-quinidine and digoxin-verapamil combinations

Mixed-effect modeling has been suggested as a possible tool to detect and describe drug interactions in patient populations receiving drug combinations for the treatment of disease states. The mixed-effect modeling program, NONMEM, was used to measure the effects of the well-known digoxin-quinidine and digoxin-verapamil drug interactions in 294 patients receiving oral digoxin as hospital inpatients. Fourteen percent of the population took either quinidine or verapamil concurrently with digoxin (mean quinidine dose = 857 +/- 397 mg/day, verapamil = 261 +/- 110 mg/day). Two regression models for digoxin oral clearance were used. Model 1 used the knowledge that digoxin is eliminated by both renal and nonrenal routes (TVCL = ClNR+m.CrCl, where TVCL is the population digoxin oral clearance, ClNR is the nonrenal clearance, and m is the slope of the line that relates creatinine clearance (CrCl) to digoxin clearance); model 2 used a more conventional regression approach with a simple series of multipliers. For both models, quinidine administration decreased population digoxin oral clearance by approximately 45% and verapamil therapy decreased population digoxin oral clearance by approximately 30%. These values are similar to those found by traditional drug interaction studies conducted in small patient or normal subject populations. Mixed-effect modeling can detect clinically relevant drug interactions and produce information similar to that found in traditional pharmacokinetic crossover study designs.

Interaction between structural, statistical, and covariate models in population pharmacokinetic analysis

The influence of the choice of pharmacokinetic model on subsequent determination of covariate relationships in population pharmacokinetic analysis was studied using both simulated and real data sets. Simulations and data analysis were both performed with the program NONMEM. Data were simulated using a two-compartment model, but at late sample times, so that preferential selection of the two-compartment model should have been impossible. A simple categorical covariate acting on clearance was included. Initially, on the basis of a difference in the objective function values, the two-compartment model was selected over the one-compartment model. Only when the complexity of the one-compartment model was increased in terms of the covariate and statistical models was the difference in objective function values of the two structural models negligible. For two real data sets, with which the two-compartment model was not selected preferentially, more complex covariate relationships were supported with the one-compartment model than with the two-compartment model. Thus, the choice of structural model can be affected as much by the covariate model as can the choice of covariate model be affected by the structural model; the two choices are interestingly intertwined. A suggestion on how to proceed when building population pharmacokinetic models is given.

A microcomputer program to determine the precision of elimination rate constant and half-life estimates when sampling time is short

First-order elimination of drugs is often assumed in pharmacokinetics and elimination rate constant is then frequently determined by log-linear regression analysis from plasma concentration measurements. When the time which elapses between the first and the last plasma sample is short compared to the decay half-life, the elimination rate constant may not be determined with satisfactory precision, in particular because of analytical error. Application of basic principles of linear regression analysis allowed us to quantify the theoretical effect of analytical error on the determination of the drug elimination rate constant in that situation. It was highlighted that the precision of that determination could be efficiently improved by measuring samples in replicate, which should be recommended in practice. A user-friendly program was developed which can be used prospectively to optimize sampling strategy, and retrospectively to estimate the precision of parameter estimates. The program works on IBM PC and compatible microcomputers and is available on request.

Evaluation of hypothesis testing for comparing two populations using NONMEM analysis

In a simulation study of inference on population pharmacokinetic parameters, two methods of performing tests of hypotheses comparing two populations using NONMEM were evaluated. These two methods are the test based upon 95% confidence intervals and the likelihood ratio test. Data were simulated according to a monoexponential model and, in that context, power curves for each test were generated for (i) the ratio of mean clearance and (ii) the ratio of the population standard deviations of clearance. To generate the power curves, a range of these parameters was employed; other pharmacokinetic parameters were selected to reflect the variability typically present in a Phase II clinical trial. For tests comparing the means, the confidence interval tests had approximately the same power as the likelihood ratio tests and were consistently more faithful to the nominal level of significance. For comparison of the standard deviations, and when the volume of information available was relatively small, however, the likelihood ratio test was more able to detect differences between the two groups. These results were then compared to results on parameter estimation in order to gain insight into the question of power. As an example, the nonnormality of estimates of the ratio of standard deviations plays an important role in explaining the low power for the confidence interval tests. We conclude that, except for the situation of modeling standard deviations with only sparse information, NONMEM produces tests of significance that are effective at detecting clinically significant differences between two populations.

An evaluation of point and interval estimates in population pharmacokinetics using NONMEM analysis

In a simulation study of the estimation of population pharmacokinetic parameters, including fixed and random effects, the estimates and confidence intervals produced by NONMEM were evaluated. Data were simulated according to a monoexponential model with a wide range of design and statistical parameters, under both steady state (SS) and non-SS conditions. Within the range of values for population parameters commonly encountered in research and clinical settings, NONMEM produced parameter estimates for CL, V, sigma CL, and sigma epsilon which exhibit relatively small biases. As the range of variability increases, these biases became larger and more variable. An important exception was bias in the estimate for sigma V which was large even when the underlying variability was small. NONMEM standard error estimates are appropriate as estimates of standard deviation when the underlying variability is small. Except in the case of CL, standard error estimates tend to deteriorate as underlying variability increases. An examination of confidence interval coverage indicates that caution should be exercised when the usual 95% confidence intervals are used for hypothesis testing. Finally, simulation-based corrections of point and interval estimates are possible but corrections must be performed on a case-by-case basis.

Evaluation of population (NONMEM) pharmacokinetic parameter estimates

The application of population pharmacokinetic analysis has received increasing attention in the last few years. The main goal of this report is to make investigators aware of the necessity of independent evaluation of the results obtained from a population analysis based on observational studies. We also describe with the help of a specific example (a new synthetic opiate Alfentanil) how such evaluation can be performed for parameter estimates obtained with the software system NONMEM. The method differs depending on the type of serum concentration data that are used for the evaluation. A general method is described, based on the regression model used in NONMEM, that can test for bias in the estimates of fixed and random effects independent of the number of observations per patient and dosing. Since the procedure for testing for statistically significant bias in the prediction of the average concentration and its variability can be relatively complex, we propose that generally available program packages performing estimation of the pharmacokinetic parameters from observational data should contain the necessary software to evaluate the reliability of the parameter estimates on a second data set.

Pharmacokinetics of felbamate, a novel antiepileptic drug: application of mixed-effect modeling to clinical trials

Felbamate is a novel antiepileptic drug currently undergoing clinical trials in the United States. Serum felbamate concentration data from a phase II safety and efficacy trial were analyzed using NONMEM. A one-compartment, open model with first-order absorption and elimination was used. Body weight, sex, concurrent folic acid therapy, and phenytoin dose and dose:concentration ratio did not affect the estimates for felbamate clearance (Cl). Carbamazepine dose and dose:concentration ratio (CDCR) led to significant improvements in the objective function. The final models for felbamate clearance and volume of distribution (Vd) were as follows: Cl(L/hr) = 2.43 + 0.429*CDCR/240, Vd (L) = 51. The coefficient of variation of clearance was only about 12%, which may be indicative of the highly selective patient population. The clearance estimates are similar to those obtained in healthy volunteers and in patients receiving lower dosages of felbamate. The volume of distribution estimate, however, is slightly smaller than that reported previously. Valuable pharmacokinetic information can be obtained from the routine monitoring of serum concentrations during safety and efficacy trials.

The influence of assay variability on pharmacokinetic parameter estimation

The impact of assay variability on pharmacokinetic modeling was investigated. Simulated replications (150) of three "individuals" resulted in 450 data sets. A one-compartment model with first-order absorption was simulated. Random assay errors of 10, 20, or 30% were introduced and the ratio of absorption rate (Ka) to elimination rate (Ke) constants was 2, 10, or 20. The analyst was blinded as to the rate constants chosen for the simulations. Parameter estimates from the sequential method (Ke estimated with log-linear regression followed by estimation of Ka) and nonlinear regression with various weighting schemes were compared. NONMEM was run on the 9 data sets as well. Assay error caused a sizable number of curves to have apparent multicompartmental distribution or complex absorption kinetic characteristics. Routinely tabulated parameters (maximum concentration, area under the curve, and, to a lesser extent, mean residence time) were consistently overestimated as assay error increased. When Ka/Ke = 2, all methods except NONMEM underestimated Ke, overestimated Ka, and overestimated apparent volume of distribution. These significant biases increased with the magnitude of assay error. With improper weighting, nonlinear regression significantly overestimated Ke when Ka/Ke = 20. In general, however, the sequential approach was most biased and least precise. Although no interindividual variability was included in the simulations, estimation error caused large standard deviations to be associated with derived parameters, which would be interpreted as interindividual error in a nonsimulation environment. NONMEM, however, acceptably estimated all parameters and variabilities. Routinely applied pharmacokinetic estimation methods do not consistently provide unbiased answers. In the specific case of extended-release drug formulations, there is clearly a possibility that certain estimation methods yield Ka and relative bioavailability estimates that would be imprecise and biased.