Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia

• Published in: Journal of theoretical biology Vol. 264; no. 3; pp. 990 - 1002
• Main Authors: Noble, Sarah L., Sherer, Eric, Hannemann, Robert E., Ramkrishna, Doraiswami, Vik, Terry, Rundell, Ann E.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 07-06-2010
• Subjects: Algorithms; Antimetabolites, Antineoplastic - therapeutic use; Biological systems; Child; Dose-Response Relationship, Drug; Drug scheduling; Erythrocyte Indices; Hematopoietic stem cell differentiation; Humans; Mercaptopurine - therapeutic use; Models, Biological; Nonlinear model predictive control; Personalized medicine; Precursor Cell Lymphoblastic Leukemia-Lymphoma - blood; Precursor Cell Lymphoblastic Leukemia-Lymphoma - diagnosis; Precursor Cell Lymphoblastic Leukemia-Lymphoma - drug therapy; Predictive Value of Tests; Sensitivity and Specificity; Treatment Outcome; Hematopoietic stem cell differentiation; Nonlinear model predictive control; Personalized medicine; Drug scheduling; Biological systems
• ISSN: 0022-5193; 1095-8541
• DOI: 10.1016/j.jtbi.2010.01.031

Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.