This study aimed to determine the stability (in terms of covariate selection) of a population pharmacokinetic model and evaluate its performance in the absence of a test data set. Data from 88 full-term infants, 11 of whom were human immunodeficiency virus (HIV)-seropositive, taking an antiinfective agent were analyzed using exploratory data analysis methods and the nonlinear mixed-effects modeling (NONMEM) program to obtain the final population pharmacokinetic model. The stability of the population pharmacokinetic model was tested using the nonparametric bootstrap approach in four steps: 1) with the base pharmacokinetic model, 100 bootstrap replicates of the original data were generated by sampling with replacement; 2) ascertainment that each bootstrap data replicate was described by the basic structural model using the NONMEM objective function; 3) generalized additive modeling (GAM) applied to empiric Bayesian estimates for covariate selection at alpha = 0.05 and a frequency (f) cutoff value of 0.50; and 4) NONMEM population model building using covariates selected in the third step with alpha = 0.005. Performance of the population pharmacokinetic model was evaluated using 200 additional bootstrap replicates of the data by fitting the model obtained in step 4 to them. Parameters obtained were compared with those obtained in the model stability step, and improved prediction error, a measure of predictive accuracy as an index of internal validation, was computed. The reciprocal of serum creatinine (RSC; f = 0.73) and HIV (f = 0.70) were selected by GAM as predictors of clearance (Cl). The population pharmacokinetic model obtained without the determination of model stability included RSC as a predictor of Cl, but the final model from the model stability step included both HIV and RSC as predictors of Cl. Final population pharmacokinetic parameters were obtained with this model fitted to the original data; however, the 95% confidence interval on the HIV status regression coefficient included zero, indicating no significance. The mean parameter estimates obtained with the additional 200 bootstrap replicates of data were within 15% of those obtained with the final model at the regression stability step. Bootstrap resampling procedure is useful for evaluating the stability and performance of a population model by repeatedly fitting it to the bootstrap samples when there is no test data set.

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 development.

In this study, rifampin (600 mg daily) was a more significant inducer of ethinyl estradiol and norethindrone clearance than rifabutin (300 mg daily), but neither agent reversed the suppression of ovulation caused by oral contraceptives. The carefully monitored oral contraceptive administration and the limited exposure to rifamycins may restrict the application of this study to clinical situations.

Declining pharmaceutical industry productivity is well recognized by drug developers, regulatory authorities and patient groups. A key part of the problem is that clinical studies are increasingly expensive, driven by the rising costs of conducting Phase II and III trials. It is therefore crucial to ensure that these phases of drug development are conducted more efficiently and cost-effectively, and that attrition rates are reduced. In this article, we argue that moving from the traditional clinical development approach based on sequential, distinct phases towards a more integrated view that uses adaptive design tools to increase flexibility and maximize the use of accumulated knowledge could have an important role in achieving these goals. Applications and examples of the use of these tools--such as Bayesian methodologies--in early- and late-stage drug development are discussed, as well as the advantages, challenges and barriers to their more widespread implementation.

PPK estimation methods that rely on the characterizing of mixed (fixed and random) effects are known to produce PPK parameter estimates that are less biased than those obtained using the naive and standard two-stage approaches. The NONMEM software is the most widely used software for the characterization of PPK.

The purpose of this study was to define model appropriateness, identifying the individual elements thereof, and to set out a framework within which model appropriateness could be determined for population pharmacokinetic (PPK) models. Model appropriateness was defined by stating the problem to be solved, with the intended use of the model being the pivotal event. The elements of model appropriateness were identified with the type of model (descriptive vs. predictive) determining which elements of model appropriateness need to be executed. An example is presented to show how model appropriateness is determined for the optimal application of PPK models. It was determined that PPK models are developed to solve problems. Model appropriateness depends on identifying the problem, as well as stating the intended use of the model, and requires evaluation of the model for goodness of fit, reliability, and stability if intended for descriptive purposes; for predictive models, validation would be an additional requirement. Descriptive models are used to explain variability in the pharmacokinetics (PK) of a drug, while predictive models are developed to extrapolate beyond the immediate study population. For those models used for predictive purposes, strong assumptions are made about the relationship to the underlying population from which the data were collected. As an example of determining model appropriateness, a PPK model for 5-fluorocytosine was developed, using NONMEM, version IV. The model was evaluated and validated by the process of percentile bootstrapping. From the PPK model, the range of expected serum concentrations based on two widely used dosing methods (Sanford and the University of California at San Diego [UCSD]) was simulated (Pharsight Trial Designer software). These results indicated that the UCSD method performed well and has the advantage of recommending convenient dosing intervals. In conclusion, considering and applying the principles of model appropriateness to PPK models will result in models that can be applied for their intended use with confidence. Model appropriateness was efficiently established and determined to address the problem of comparing competing dosing strategies.

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.

The purpose of this study was to define model appropriateness, identifying the individual elements thereof, and to set out a framework within which model appropriateness could be determined for population pharmacokinetic (PPK) models. Model appropriateness was defined by stating the problem to be solved, with the intended use of the model being the pivotal event. The elements of model appropriateness were identified with the type of model (descriptive vs. predictive) determining which elements of model appropriateness need to be executed. An example is presented to show how model appropriateness is determined for the optimal application of PPK models. It was determined that PPK models are developed to solve problems. Model appropriateness depends on identifying the problem, as well as stating the intended use of the model, and requires evaluation of the model for goodness of fit, reliability, and stability if intended for descriptive purposes; for predictive models, validation would be an additional requirement. Descriptive models are used to explain variability in the pharmacokinetics (PK) of a drug, while predictive models are developed to extrapolate beyond the immediate study population. For those models used for predictive purposes, strong assumptions are made about the relationship to the underlying population from which the data were collected. As an example of determining model appropriateness, a PPK model for 5-fluorocytosine was developed, using NONMEM, version IV. The model was evaluated and validated by the process of percentile bootstrapping. From the PPK model, the range of expected serum concentrations based on two widely used dosing methods (Sanford and the University of California at San Diego [UCSD]) was simulated (Pharsight Trial Designer software). These results indicated that the UCSD method performed well and has the advantage of recommending convenient dosing intervals. In conclusion, considering and applying the principles of model appropriateness to PPK models will result in models that can be applied for their intended use with confidence. Model appropriateness was efficiently established and determined to address the problem of comparing competing dosing strategies.