Aims: This study aims to develop a quantitative semi-mechanistic model to describe the disease progression of leukemia during chemotherapy using circulating biomarkers in Korean adolescent population.
Methods: A routine clinical data set for 89 patients who were diagnosed as acute lymphoblastic leukemia (ALL) or acute myeloid leukemia (AML) for the first time between the age of 1 – 20 was collected from Severance hospital electric medical records (EMR) system. Lymphocyte (LYM) and platelet counts (PLT) were dependent variables, and age, WBC count, bone marrow transplantation and other laboratory results such as creatinine, ASL and ALT levels were covariates to be tested. A mechanistic model was used to describe LYM change with time during chemotherapy. A K-PD model was used to describe drug kinetics as blood concentration data were not available. LYM production was described by a single compartment representing proliferative cells, 3 transit compartments representing LYM maturation, and a single compartment representing blood LYM, where LYM production was assumed to be influenced by negative feedback from blood LYM and reduced by chemotherapy. Population modeling approach was performed using NONMEM 7.3.
Results: Due to data's hetrogeneity, patients treatment options were grouped into risk based protocol, standard risk (SR) and high risk (HR) based on the clinical practice. Drug effect was described by a linear model of effec-cite concenrtation obtianed from K-PD model, where drug doses were BSA standardized due to the use of multiple cytotoxic drugs during the treatment. Estimated parameters were 0.581, 0.458 and 1.19 for the slope of drug effect model for the subgroup of ALL_SR, ALL_HR and AML_HR, respectively, 0.425 day for MTT (mean transit time), and -0.00085 for gamma, which denotes the power of negative feedback component.
Conclusion: This work shows preliminary results for disease progression model of LYM during chemotherapy. Further analysis will include the analysis of PLT change, evaluation of potential covariates, and survival time analysis for these patients group.