Comparing non-hierarchical models: application to non-linear mixed effects modeling

Drug-metabolism mechanism: Knowledge-based population pharmacokinetic approach for characterizing clobazam drug-drug interactions

A metabolic mechanism-based characterization of antiepileptic drug-drug interactions (DDIs) with clobazam in patients with Lennox-Gastaut syndrome (LGS) was performed using a population pharmacokinetic (PPK) approach. To characterize potential DDIs with clobazam, pharmacokinetic (PK) data from 153 patients with LGS in study OV-1012 (NCT00518713) and 18 healthy participants in bioavailability study OV-1017 were pooled. Antiepileptic drugs (AEDs) were grouped based on their effects on the cytochrome P450 (CYP) isozymes responsible for the metabolism of clobazam and its metabolite, N-desmethylclobazam (N-CLB): CYP3A inducers (phenobarbital, phenytoin, and carbamazepine), CYP2C19 inducers (valproic acid, phenobarbital, phenytoin, and carbamazepine), or CYP2C19 inhibitors (felbamate, oxcarbazepine). CYP3A4 inducers-which did not affect the oral clearance of clobazam-significantly increased the formation of N-CLB by 9.4%, while CYP2C19 inducers significantly increased the apparent elimination rate of N-CLB by 10.5%, resulting in a negligible net change in the PK of the active metabolite. CYP2C19 inhibitors did not affect N-CLB elimination. Because concomitant use of AEDs that are either CYP450 inhibitors or inducers with clobazam in the treatment of LGS patients had negligible to no effect on clobazam PK in this study, dosage adjustments may not be required for clobazam in the presence of the AEDs investigated here.

Preclinical examination of clofarabine in pediatric ependymoma: intratumoral concentrations insufficient to warrant further study

Clofarabine, a deoxyadenosine analog, was an active anticancer drug in our in vitro high-throughput screening against mouse ependymoma neurospheres. To characterize the clofarabine disposition in mice for further preclinical efficacy studies, we evaluated the plasma and central nervous system disposition in a mouse model of ependymoma. A plasma pharmacokinetic study of clofarabine (45 mg/kg, IP) was performed in CD1 nude mice bearing ependymoma to obtain initial plasma pharmacokinetic parameters. These estimates were used to derive D-optimal plasma sampling time points for cerebral microdialysis studies. A simulation of clofarabine pharmacokinetics in mice and pediatric patients suggested that a dosage of 30 mg/kg IP in mice would give exposures comparable to that in children at a dosage of 148 mg/m(2). Cerebral microdialysis was performed to study the tumor extracellular fluid (ECF) disposition of clofarabine (30 mg/kg, IP) in the ependymoma cortical allografts. Plasma and tumor ECF concentration-time data were analyzed using a nonlinear mixed effects modeling approach. The median unbound fraction of clofarabine in mouse plasma was 0.79. The unbound tumor to plasma partition coefficient (K pt,uu: ratio of tumor to plasma AUCu,0-inf) of clofarabine was 0.12 ± 0.05. The model-predicted mean tumor ECF clofarabine concentrations were below the in vitro 1-h IC50 (407 ng/mL) for ependymoma neurospheres. Thus, our results show the clofarabine exposure reached in the tumor ECF was below that associated with an antitumor effect in our in vitro washout study. Therefore, clofarabine was de-prioritized as an agent to treat ependymoma, and further preclinical studies were not pursued.

Population pharmacokinetics of clofarabine and its metabolite 6-ketoclofarabine in adult and pediatric patients with cancer

Clofarabine for injection is a second-generation nucleoside analog approved in the United States (Clolar(®)) and Europe (Evoltra(®)) for the treatment of pediatric relapsed or refractory acute lymphoblastic leukemia. This report describes the population pharmacokinetics of clofarabine and its metabolite 6-ketoclofarabine in adult and pediatric patients with hematologic malignancies or solid tumors. Clofarabine pharmacokinetics were best described by a 2-compartment model with linear elimination and first-order absorption after oral administration. Clofarabine was rapidly absorbed following oral administration with a mean absorption time of less than 2 h and bioavailability of 57.5%. The important covariates affecting clofarabine pharmacokinetics were age, weight, and estimated creatinine clearance (eCrCL). No difference in pharmacokinetics was observed between sexes, races, or disease type. The elimination half-life was dependent on all the covariates but was generally less than 7 h in all cases. A difference in clofarabine pharmacokinetics was observed between adults and children. For a pediatric patient 3 years old weighing 16 kg with an eCrCL of 138 mL/min/1.73 m(2), the population estimates for total systemic clearance and volume of distribution at steady-state were 18.3 L/h (1.14 L/h/kg) and 92.9 L (5.81 L/kg), respectively. α- and β-half-life were 0.9 and 4.4 h, respectively. For an elderly patient 82 years old weighing 96 kg with an eCrCL of 46 mL/min/1.73 m(2), the population estimates for CL and Vdss were 21.5 L/h (0.22 L/h/kg) and 257.4 L (268 L/kg), respectively. α- and β-half-life were 0.5 and 10.6 h, respectively. Because of the difference in pharmacokinetics, adults have higher exposure than children given a similar dose standardized to body surface area. The exact mechanism of this difference is not understood. As eCrCL decreased, exposure increased due to reduced total systemic clearance. In the case of moderate (eCrCL 30 to 60 mL/min/1.73 m(2)) and severe (eCrCL <30 mL/min/1.73 m(2)) renal impairment, dose reduction may be needed to maintain similar exposure in an equivalent patient of the same age, weight, and normal renal function after both oral and intravenous administration. 6-Ketoclofarabine was a minor metabolite with peak plasma concentrations occurring about 1 h after the start of the infusion and having a metabolite ratio averaging less than 5% and not more than 8% for any particular individual. 6-Ketoclofarabine was rapidly cleared from plasma with an average apparent half-life of 4.9 h (range 3.9 to 6.2 h). No accumulation of 6-ketoclofarabine was observed with predose samples all below the limit of quantification on Days 8 and 15. Further monitoring of 6-ketoclofarabine is not required in future studies.

Methodological comparison of in vitro binding parameter estimation: sequential vs. simultaneous non-linear regression

PURPOSE

Analysis of simulated data was compared using sequential (NLR) and simultaneous non-linear regression (SNLR) to evaluate precision and accuracy of ligand binding parameter estimation.

METHODS

Commonly encountered experimental error, specifically residual error of binding measurements (RE), experiment-to-experiment variability (BEV) and non-specific binding (B(NS)), were examined for impact of parameter estimation using both methods. Data from equilibrium, dissociation, association and non-specific binding experiments were fit simultaneously (SNLR) using NONMEM VI compared to the common practice of analyzing data from each experiment separately and assigning these as exact values (NLR) for estimation of the subsequent parameters.

RESULTS

The greatest contributing factor to bias and variability in parameter estimation was RE of the measured concentrations of ligand bound; however, SNLR provided more accurate and less bias estimates. Subtraction of B(NS) from total ligand binding data provided poor estimation of specific ligand binding parameters using both NLR and SNLR. Additional methods examined demonstrated that the use of SNLR provided better estimation of specific binding parameters, whereas there was considerable bias using NLR. NLR cannot account for BEV, whereas SNLR can provide approximate estimates of BEV.

CONCLUSION

SNLR provided superior resolution of parameter estimation in both precision and accuracy compared to NLR.

Evaluation of uncertainty parameters estimated by different population PK software and methods

The uncertainty associated with parameter estimations is essential for population model building, evaluation, and simulation. Summarized by the standard error (SE), its estimation is sometimes questionable. Herein, we evaluate SEs provided by different non linear mixed-effect estimation methods associated with their estimation performances. Methods based on maximum likelihood (FO and FOCE in NONMEM, nlme in Splus, and SAEM in MONOLIX) and Bayesian theory (WinBUGS) were evaluated on datasets obtained by simulations of a one-compartment PK model using 9 different designs. Bootstrap techniques were applied to FO, FOCE, and nlme. We compared SE estimations, parameter estimations, convergence, and computation time. Regarding SE estimations, methods provided concordant results for fixed effects. On random effects, SAEM and WinBUGS, tended respectively to under or over-estimate them. With sparse data, FO provided biased estimations of SE and discordant results between bootstrapped and original datasets. Regarding parameter estimations, FO showed a systematic bias on fixed and random effects. WinBUGS provided biased estimations, but only with sparse data. SAEM and WinBUGS converged systematically while FOCE failed in half of the cases. Applying bootstrap with FOCE yielded CPU times too large for routine application and bootstrap with nlme resulted in frequent crashes. In conclusion, FO provided bias on parameter estimations and on SE estimations of random effects. Methods like FOCE provided unbiased results but convergence was the biggest issue. Bootstrap did not improve SEs for FOCE methods, except when confidence interval of random effects is needed. WinBUGS gave consistent results but required long computation times. SAEM was in-between, showing few under-estimated SE but unbiased parameter estimations.

Population pharmacokinetics and bioavailability of motexafin gadolinium (Xcytrin®) in CD1 mice following intravenous and intraperitoneal injection

Motexafin gadolinium (Xcytrin) is an expanded porphyrin macrocyclic compound under development for the treatment of several types of cancer. Currently clinical trials and non-clinical pharmacology and toxicology studies are ongoing. The goals of this open label, four arm, non-crossover bioavailability study were to explore motexafin gadolinium pharmacokinetics, determine the i.p. bioavailability, and define a pharmacokinetic model suitable for descriptive and predictive use. Mice received one or seven daily i.v. or i.p. injections (40 mg/kg) then blood samples were collected and analyzed. Plasma concentration data were modelled using population pharmacokinetic methods and a two compartment model was the most appropriate model. The stability and predictive performance of the model were evaluated using bootstrap procedures. The accuracy of the predicted concentrations was 8.3%. Motexafin gadolinium was rapidly cleared from the plasma and although T(1/2beta) was 12.9 h there was no accumulation following seven doses. The i.p. bioavailability was 87.4% and higher plasma concentrations were sustainable for a longer period with i.p. dosing. V(c) was larger than the blood volume and the tissue compartment volume was 38% of V(c), suggesting motexafin gadolinium was not widely distributed into less well perfused tissues. The pharmacokinetic profile in this study was similar to that in oncology patients administered multiple doses of motexafin gadolinium. The unbiased model yields reliable parameter estimates and insight into the pharmacokinetics of motexafin gadolinium in mice, is suitable for both descriptive and predictive purposes, and is a valuable tool in the planning, analysis, and interpretation of pharmacology and toxicology studies in mice.

Covariate Detection in Population Pharmacokinetics Using Partially Linear Mixed Effects Models

PURPOSE

To introduce partially linear mixed effects models (PLMEMs), to illustrate their use, and to compare the power and Type I error rate in detecting a covariate effect with nonlinear mixed effects modeling using NONMEM.

METHODS

Sparse concentration-time data from males and females (1:1) were simulated under a 1-compartment oral model where clearance was sex-dependent. All possible combinations of number of subjects (50, 75, 100, 150, 250), samples per subject (2, 4, 6), and clearance multipliers (1 to 1.25) were generated. Data were analyzed with and without sex as a covariate using PLMEM (maximum likelihood estimation) and NONMEM (first-order conditional estimation). Four covariate screening methods were examined: NONMEM using the likelihood ratio test (LRT), PLMEM using the LRT, PLMEM using Wald's test, and analysis of variance (ANOVA) of the empirical Bayes estimates (EBEs) for CL treating sex as a categorical variable. The percent of simulations rejecting the null hypothesis of no covariate effect at the 0.05 level was determined. 300 simulations were done to calculate power curves and 1000 simulations were done (with no covariate effect) to calculate Type I error rate. Actual implementation of PLMEMs is illustrated using previously published teicoplanin data.

RESULTS

Type I error rates were similar between PLMEM and NONMEM using the LRT, but were inflated (as high as 36%) based on PLMEM using Wald's test. Type I error rate tended to increase as the number of observations per subject increased for the LRT methods. Power curves were similar between the PLMEM and NONMEM LRT methods and were slightly more than the power curve using ANOVA on the EBEs of CL. 80% power was achieved with 4 samples per subject and 50 subjects total when the effect size was approximately 1.07, 1.07, 1.08, and 1.05 for LRT using PLMEMs, LRT using NONMEM, ANOVA on the EBEs, and Wald's test using PLMEMs, respectively.

CONCLUSIONS

PLMEM and NONMEM covariate screening using the LRT had similar Type I error rates and power under the data generating model. PLMEMs offers a viable alternative to NONMEM-based covariate screening.

Population pharmacokinetics of clofarabine, a second-generation nucleoside analog, in pediatric patients with acute leukemia

The population pharmacokinetics of plasma clofarabine and intracellular clofarabine triphosphate were characterized in pediatric patients with acute leukemias. Traditional model-building techniques with NONMEM were used. Covariates were entered into the base model using a forward selection significance level of .05 and a backwards deletion criterion of .005. Model performance, stability, and influence analysis were assessed using the nonparametric bootstrap and n-1 jackknife. Simulations were used to understand the relationship between important covariates and exposure. A 2-compartment model with weight (scaled to a 40-kg reference patient) modeled as a power function on all pharmacokinetic parameters (0.75 on clearance-related terms and 1.0 on volume-related terms) was fit to plasma clofarabine concentrations (n = 32). White blood cell (WBC) count, modeled as a power function (scaled to a WBC count of 10 x 10(3)/microL), was a significant predictor of central volume with power term 0.128 +/- 0.0314. A reference patient had a systemic clearance of 32.8 L/h (27% between-subject variability [BSV]), a central volume of 115 L (56% BSV), an intercompartmental clearance of 20.5 L/h (27% BSV), and a peripheral volume of 94.5 L (39% BSV). Intracellular clofarabine triphosphate concentrations were modeled using a random intercept model without any covariates. The average predicted concentration was 11.6 +/- 2.62 microM (80% BSV), and although clofarabine triphosphate half-life could not be definitively estimated, its value was taken to be longer than 24 hours. The results confirm that clofarabine should continue being dosed on a per-squaremeter or per-body-weight basis.

Population pharmacokinetics III: design, analysis, and application of population pharmacokinetic Studies

OBJECTIVE

To present a framework within which population pharmacokinetic (PPK) studies should be designed and analyzed and discuss the application of developed PPK models.

METHODS

Information on PPK was retrieved from a MEDLINE search (1979-December 2003) of the literature and a bibliographic evaluation of review articles and books. This information is used in conjunction with experience to explain the design and analysis of PPK studies. Also, examples are included to demonstrate the usefulness of PPK.

SYNTHESIS

A great deal of thought must be given to the design and analysis of PPK studies (ie, development of PPK models). Models are of 2 primary types--descriptive and predictive--and the process applied to these models is necessarily different. An approach that ensures model applicability is presented.

CONCLUSIONS

PPK models have great utility, and the applications are many. They are very different from single-subject pharmacokinetic models and therefore require different approaches to model estimation.

Population pharmacokinetics and pharmacodynamics of ciclesonide

Ciclesonide is a novel glucocorticoid that is converted into ciclesonide--active principle (CIC-AP) in the lung. The study objectives were to identify a structural model for population pharmacokinetic (PK) analysis of CIC-AP using nonlinear mixed-effects modeling, assess the influence of select covariates on PK and/or pharmacodynamic (PD) parameters, and investigate the effects of CIC-AP on endogenous cortisol. Pooled concentration data from nine phase I studies (dose: 400-3600 micrograms) involving healthy and asthmatic patients were included in the PK analysis. There were 151 subjects (3300 observations) for the CIC-AP population PK analysis. Various models examined inter- and intrasubject variability for the PK parameters. Population estimates of the PK parameters of clearance and volume of distribution were 396 L/h (64.8% co-efficient of variation [CV]) and 1190 L (41.2% CV), respectively. Pharmacodynamic population estimates included maximum cortisol release rate, 3140 ng/h (5.4% CV). The EC50 of CIC-AP was 0.88 ng/mL. Ciclesonide is a safe corticosteroid that causes negligible cortisol suppression. The disposition and effect of CIC-AP can be described using mixed-effect modeling. The estimated EC50 is similar to mean Cmax from an 800-micrograms dose, further suggesting CIC-AP has little effect on cortisol suppression.

Estimating inestimable standard errors in population pharmacokinetic studies: the bootstrap with Winsorization

A simulation study was performed to determine how inestimable standard errors could be obtained when population pharmacokinetic analysis is performed with the NONMEM software on data from small sample size phase I studies. Plausible sets of concentration-time data for nineteen subjects were simulated using an incomplete longitudinal population pharmacokinetic study design, and parameters of a drug in development that exhibits two compartment linear pharmacokinetics with single dose first order input. They were analyzed with the NONMEM program. Standard errors for model parameters were computed from the simulated parameter values to serve as true standard errors of estimates. The nonparametric bootstrap approach was used to generate replicate data sets from the simulated data and analyzed with NONMEM. Because of the sensitivity of the bootstrap to extreme values, winsorization was applied to parameter estimates. Winsorized mean parameters and their standard errors were computed and compared with their true values as well as the non-winsorized estimates. Percent bias was used to judge the performance of the bootstrap approach (with or without winsorization) in estimating inestimable standard errors of population pharmacokinetic parameters. Winsorized standard error estimates were generally more accurate than non-winsorized estimates because the distribution of most parameter estimates were skewed, sometimes with heavy tails. Using the bootstrap approach combined with winsorization, inestimable robust standard errors can be obtained for NONMEM estimated population pharmacokinetic parameters with > or = 150 bootstrap replicates. This approach was also applied to a real data set and a similar outcome was obtained. This investigation provides a structural framework for estimating inestimable standard errors when NONMEM is used for population pharmacokinetic modeling involving small sample sizes.

Xpose--an S-PLUS based population pharmacokinetic/pharmacodynamic model building aid for NONMEM

The building of population pharmacokinetic/pharmacodynamic (PK/PD) models is a time consuming and complicated task. This is partly due the lack of specialized tools for the visualization and exploration requirements of this type of analysis. In this paper we present Xpose, a model building aid for population PK/PD analysis using NONMEM, which simplifies the task of producing documentation, data set checkout plots, goodness of fit plots and graphical model comparison. It also facilitates covariate model building by the use of stepwise generalized additive modeling (GAM), bootstrap of the GAM analyses and tree based modeling. The plots and analyses are presented in the form of a text based menu system and the only thing the user has to do is to make NONMEM produce one or more table files named in a specific way.