Severe neutropenia is the major dose-liming toxicity of irinotecan-based chemotherapy. The objective was to assess to what extent a population pharmacokinetic/pharmacodynamic model including patient-specific demographic/clinical characteristics, individual pharmacokinetics, and absolute neutrophil counts (ANCs) can predict irinotecan-induced grade 4 neutropenia. A semimechanistic population pharmacokinetic/pharmacodynamic model was developed to describe neutrophil response over time in 197 patients with cancer receiving irinotecan. For covariate analysis, sex, race, age, pretreatment total bilirubin, and body surface area were evaluated to identify significant covariates on system-related parameters (mean transit time (MTT) and ɣ) and sensitivity to neutropenia effects of irinotecan and SN-38 (SLOPE). The model-based simulation was performed to assess the contribution of the identified covariates, individual pharmacokinetics, and baseline ANC alone or with incremental addition of weekly ANC up to 3 weeks on predicting irinotecan-induced grade 4 neutropenia. The time course of neutrophil response was described using the model assuming that irinotecan and SN-38 have toxic effects on bone marrow proliferating cells. Sex and pretreatment total bilirubin explained 10.5% of interindividual variability in MTT. No covariates were identified for SLOPE and γ. Incorporating sex and pretreatment total bilirubin (area under the receiver operating characteristic curve (AUC-ROC): 50%, 95% CI 50–50%) or with the addition of individual pharmacokinetics (AUC-ROC: 62%, 95% CI 53–71%) in the model did not result in accurate prediction of grade 4 neutropenia. However, incorporating ANC only at baseline and week 1 in the model achieved a good prediction (AUC-ROC: 78%, 95% CI 69–88%). These results demonstrate the potential applicability of a model-based approach to predict irinotecan-induced neutropenia, which ultimately allows for personalized intervention to maximize treatment outcomes.
Bibliographical noteFunding Information:
S.K. was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award Number T32GM086330. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The authors wish to express their sincere appreciation to Alan Forrest, who was instrumental in pharmacometric analysis of this project and a great mentor and friend to us all. He unfortunately passed away before this paper was submitted, and we all miss him greatly. We greatly appreciate Dr. Mark Ratain at the University of Chicago and Dr. Manish Sharma at START Midwest for providing primary data and their expertise throughout all aspects of our study.
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