Chemotherapy Drug Scheduling for the Induction Treatment of patients with Acute Myeloid Leukemia
E. Pefani, N. Panoskaltsis, A. Mantalaris, M.C. Georgiadis, E.N. Pistikopoulos, Imperial College London
Volume 61, Issue 7, Page:2049-2056
Treatment for Acute Myeloid Leukaemia (AML) with chemotherapy may result in acute and long-term life-threatening complications due to drug toxicity. Only relatively few patient-and leukemia-specific factors are taken into consideration in current protocols and choice of treatment often depends on the treating physician’s experience. With the advent of novel treatments and large amounts of patient- and leukemia-specific genomic data, there is a clear need for a systematic approach to the design and execution of chemotherapy regimens. We address these challenges in AML treatment by deriving a mathematical model that combines the leukemia-specific actions on the cell cycle (i.e. drug target) with patient-specific pharmacology of the drugs (pharmacokinetics).
The proposed model combines critical targets of drug actions on the cell cycle, together with pharmacokinetic (PK) and pharmacodynamic (PD) aspects providing a complete description of drug diffusion and action after administration. Tumour-specific characteristics, such as tumour burden and cell cycle times, as well as patient-specific characteristics, such as gender, age, weight and height, are incorporated into the model in order to gain insights into the personalised cell dynamics during treatment. Chemotherapy process is afterwards presented as an optimisation scheduling algorithm with aim to obtain the chemotherapeutic schedule which would maximise leukemic cell kill (therapeutic efficacy) whilst minimising death of the normal cell population, thereby reducing toxicities.
This work presents the potential for improved treatment design in AML therapy, dependent on disease and individual patient characteristics. This design would provide the opportunity to personalise treatment protocols for gold standard intensive and non-intensive therapies as well as for novel drugs through the use of model-based optimisation methods.
Keywords: Mathematical modelling, chemotherapy optimisation, cell cycle models, pharmacokinetics, pharmacodynamics