All dex'ed out with nowhere to go?

Since the publication of the human genome sequence more than 5 years ago, the pace of advances and the discussions surrounding the prospects of personalized medicine has increased rapidly. Technologic barriers are being overcome at an unimaginable pace, which results both in the discovery of genetic biomarkers and in the future of point-of-care testing that would allow physicians to check the status of critical genes related to pharmacotherapy. The field of clinical pharmacology lies at the crossroads of genotype and phenotype, as the dose exposure-response relationship is fundamental to the individualization of therapy based on pharmacogenetic data. For pharmacotherapy, population modeling is perhaps the most credible means to bridge the genotype-phenotype divide that allows for determination of which patient covariates matter and which do not. In this issue of the Journal of Clinical Oncology, Yang et al report the results of a population pharmacokinetic study of dexamethasone in 214 children with acute lymphoblastic leukemia and have found evidence to suggest that children who have recently been exposed to asparaginase have significantly greater dexamethasone exposures than other children. Older children and children with asparaginase allergies appear to have relatively higher apparent drug clearances, which could potentially result in a subtherapeutic exposure to corticosteroid. This study highlights the promise, as well as the potential pitfalls, of pharmacokinetic modeling approaches for understanding the basis of the wide interpatient variation in drug exposure that occurs with many anticancer drugs and the potential of modeling for assessing drugdrug interactions. The efficacy of corticosteroids in the treatment of lymphoblastic leukemia has been known for more than 50 years, and the optimal dose and choice of corticosteroid has been studied for almost as many decades. Yet, there is evidence that the dexamethasone dose, and presumably exposure, can impact antileukemia efficacy. Thus, the observation that dexamethasone exposure following administration of a fixed dose of 2.67 mg/m varies across an approximate 10-fold range may indeed have clinical consequences. Importantly, Yang et al found that serum albumin could serve as a potential biomarker of drug exposure: the lower the serum albumin, the greater the dexamethasone drug exposure. As the authors point out, the use of albumin as a biomarker in this case must be distinguished from albumin as a measure of potential protein binding. The albumin-dexamethasone relationship was inverse to what would be predicted for a decrease in protein binding; also, in most circumstances, changes in plasma protein binding are of only limited clinical significance. In an excellent analysis of this topic, Benet and Hoener distill fundamental clinical pharmacokinetic concepts into useful application and conclude that, for drugs administered orally and eliminated primarily by the liver, total exposure is essentially not affected by changes in protein binding. The likelihood of changes in albumin concentration directly impacting drug clearance of orally administered dexamethasone in a meaningful way is negligible; thus, low serum albumin—in this case—likely is serving as a biomarker of impaired hepatic function. The observation that asparaginase administration can impact the hepatic metabolism of anticancer drugs was suggested initially more than 30 years ago; in fact, the vincristine product label (Gensia Sicor Pharmaceuticals Inc, Irvine, CA) includes a recommendation that vincristine should be administered 12 to 24 hours before L-asparaginase administration because of the potential of a sequence-dependent drug interaction. To impact on dexamethasone disposition, impaired hepatic function necessarily must encompass the activity of CYP3A4, the primary enzyme responsible for dexamethasone metabolism. As liver disease can modulate cytochrome P450 metabolism, the proposed interaction appears likely between asparaginase-mediated effects on hepatic protein synthesis and CYP3A4 function. The study data collected for the analysis by Yang et al is rich, but it highlights the potential pitfalls of various modeling techniques. Of concern, the approach chosen by the authors does not permit the assessment of model performance to be evaluated, as is typically done with population studies. Alternative approaches, principally nonlinear mixed-effect modeling, generate diagnostics from which model appropriateness can be evaluated. In addition, analyses typically utilize simulation to examine the robustness and sensitivity of the model to parameter outcomes. The two-stage approach undertaken by the authors, as discussed in the US Food and Drug Administration guidance on population pharmacokinetics, is likely to overestimate interpatient variability. The approach is also reliant on the suitability of individual concentration-time data for individual pharmacokinetic characterization; that is not always the case with limited sampling approaches. The examination of model parameters for drug absorption highlights the concern that there may have been problems with the analytic approach taken. The authors found that the intrapatient variability of the absorption rate constant was 76.7%, whereas the interpatient variability was only 0.01%, which means that the model had far greater variation within a patient than between patients. This indicates that the absorption rate constant was unidentifiable. Despite these concerns, this unique data set and study are of significant potential value and lead to us to address the question: JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 26 NUMBER 12 APRIL 2