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Journal of Clinical Oncology, Vol 26, No 12 (April 20), 2008: pp. 1917-1918 © 2008 American Society of Clinical Oncology. DOI: 10.1200/JCO.2007.15.4591
All Dex’ed Out With Nowhere to Go?Division of Clinical Pharmacology and Therapeutics, the Children's Hospital of Philadelphia, Philadelphia, PA 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.1 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.2 In this issue of the Journal of Clinical Oncology, Yang et al3 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 drug-drug interactions. The efficacy of corticosteroids in the treatment of lymphoblastic leukemia has been known for more than 50 years,4 and the optimal dose and choice of corticosteroid has been studied for almost as many decades.5,6 Yet, there is evidence that the dexamethasone dose, and presumably exposure, can impact antileukemia efficacy.5 Thus, the observation that dexamethasone exposure following administration of a fixed dose of 2.67 mg/m2 varies across an approximate 10-fold range may indeed have clinical consequences. Importantly, Yang et al3 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 Hoener7 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 ago8; in fact, the vincristine product label (Gensia Sicor Pharmaceuticals Inc, Irvine, CA)8a 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.9 As liver disease can modulate cytochrome P450 metabolism,10 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 al3 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.11,12 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.13 The two-stage approach undertaken by the authors, as discussed in the US Food and Drug Administration guidance on population pharmacokinetics,14 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: "Where do we go from here? " Perhaps reassuringly, all newly diagnosed patients with acute lymphocytic leukemia are exposed concomitantly to asparaginase and corticosteroid during induction chemotherapy, which results in a tendency toward greater dexamethasone exposures. Although we do not know the optimal dexamethasone exposure, steps that would minimize interpatient variation in drug exposure should be explored. The true value of this data set will only be realized if it can be translated successfully into the development of a dosing guidance that extends beyond body-surface area or body weight and that begins to incorporate covariates, such as serum albumin and age. Modeling and simulation techniques are available that would allow one to study proposed dosing guidance in silico before testing in the clinic.15 The application of advances in biomarker development and in modeling and simulation methodologies during clinical pharmacologic studies can move us closer to realizing a central tenet of personalized medicine: delivery of the right drug to the right patient at the right dose.16 AUTHORS’ DISCLOSURES OF POTENTIAL CONFLICTS OF INTEREST The author(s) indicated no potential conflicts of interest. AUTHOR CONTRIBUTIONS Conception and design: Peter C. Adamson, Jeffrey S. Barrett Manuscript writing: Peter C. Adamson, Jeffrey S. Barrett Final approval of manuscript: Peter C. Adamson, Jeffrey S. Barrett REFERENCES 1. Litos IK, Emmanouilidou E, Glynou KM, et al: Rapid genotyping of CYP2D6, CYP2C19, and TPMT polymorphisms by primer extension reaction in a dipstick format. Anal Bioanal Chem 389:1849-1857, 2007[CrossRef][Medline] 2. Lesko LJ: Personalized medicine: Elusive dream or imminent reality? Clin Pharmacol Ther 81:807-816, 2007[CrossRef][Medline] 3. Yang L, Panetta JC, Cai X, et al: Asparaginase may influence dexamethasone pharmacokinetics in acute lymphoblastic leukemia. J Clin Oncol 26:1932-1939, 2008 4. Farber S, Pinkel D, Sears EM, et al: Advances in chemotherapy of cancer in man. Adv Cancer Res 4:1-71, 1956[Medline] 5. Leikin SL, Brubaker C, Hartmann JR, et al: Varying prednisone dosage in remission induction of previously untreated childhood leukemia. Cancer 21:346-351, 1968[CrossRef][Medline] 6. Schwartz CL, Thompson EB, Gelber RD, et al: Improved response with higher corticosteroid dose in children with acute lymphoblastic leukemia. J Clin Oncol 19:1040-1046, 2001 7. Benet LZ, Hoener BA: Changes in plasma protein binding have little clinical relevance. Clin Pharmacol Ther 71:115-121, 2002[CrossRef][Medline] 8. Weiss HD, Walker MD, Wiernik PH: Neurotoxicity of commonly used antineoplastic agents. N Engl J Med 291:127-133, 1974[Medline] 8. U.S. Food and Drug Administration: Center for Drug Evaluation and Research, FDA Oncology Tools: Product label details in conventional order for vincristine. http://www.accessdata.fda.gov/scripts/cder/onctools/labels.cfm?GN=vincristine 9. Gentile DM, Tomlinson ES, Maggs JL, et al: Dexamethasone metabolism by human liver in vitro. Metabolite identification and inhibition of 6-hydroxylation. J Pharmacol Exp Ther 277:105-112, 1996 10. Frye RF, Zgheib NK, Matzke GR, et al: Liver disease selectively modulates cytochrome P450–mediated metabolism. Clin Pharmacol Ther 80:235-245, 2006[CrossRef][Medline] 11. Ribbing J, Jonsson EN: Power, selection bias and predictive performance of the population pharmacokinetic covariate model. J Pharmacokinet Pharmacodyn 31:109-134, 2004[CrossRef][Medline] 12. Wahlby U, Jonsson EN, Karlsson MO: Assessment of actual significance levels for covariate effects in NONMEM. J Pharmacokinet Pharmacodyn 28:231-252, 2001[CrossRef][Medline] 13. Panhard X, Mentre F: Evaluation by simulation of tests based on non-linear mixed-effects models in pharmacokinetic interaction and bioequivalence cross-over trials. Stat Med 24:1509-1524, 2005[CrossRef][Medline] 14. Guidance for Industry: Population Pharmacokinetics. Rockville, MD, US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research, 1999 15. Meibohm B, Laer S, Panetta JC, et al: Population pharmacokinetic studies in pediatrics: Issues in design and analysis. AAPS J 7:E475-87, 2005[CrossRef][Medline] 16. Piquette-Miller M, Grant DM: The art and science of personalized medicine. Clin Pharmacol Ther 81:311-315, 2007[CrossRef][Medline] Related Article
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Copyright © 2008 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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