278255 Model-Based Individualized Treatment for Acute Lymphoblastic Leukemia

Wednesday, October 31, 2012
Hall B (Convention Center )
Jayachandran Devaraj1, Ann E. Rundell2, Robert E. Hannemann3 and Doraiswami Ramkrishna1, (1)School of Chemical Engineering, Purdue University, West Lafayette, IN, (2)Biomedical Engineering, Purdue University, West Lafayette, IN, (3)Chemical Engineering, Purdue University, West Lafayette, IN

Acute Lymphoblastic Leukemia (ALL) constitutes a major type of cancer among children and childhood ALL accounts for more than 60% of all ALL cases. Although 5–year survival rates are impressive in ALL, long-term childhood survivors endure several medical and socio-economic miseries related to their earlier treatment. It has been known for many decades that individuals differ genetically and phenotypically which significantly influence the way they respond to treatment. Thus, inter-patient variation in drug response presents a great challenge in determining the optimum dose for an individual patient.

In the present form of chemotherapy administration, patients are started with an average dose of drug and ‘titrated’ to a maximum tolerated dose through trial and error approach. During this process, patients undergo several dose modifications and encounter severe episodes of acute side-effects.  Many retrospective studies show that such endeavors are responsible for the long-term medical complication. A possible alternative would be a system theoretic framework adequately empowered by statistical and optimal control theory.  Such a quantitative approach, if implemented carefully, will greatly enhance the decision making capabilities of the treating physicians.

In this work, we propose a model-based approach for individualizing the treatment for ALL patients using a chemotherapeutic agent 6-Mercaptopurine (6-MP). Pharmacogenomic and other cell count measurements obtained from individual patients are used to individualize the model. Models account for the drug disposition in the body, metabolic conversions and its effect on various cell populations. A Bayesian approach is adopted to identify the parameters for individual patients. The models can be utilized to predict the expected treatment response and optimal treatment condition for a given patient with few measurements. This approach holds a great potential to tailor the treatment based on patients’ genetic and phenotypic make up as opposed to the current practice. Ultimately, this will help to reduce the therapy burden and improve the quality-of-life among ALL patients.

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