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DEFINING DOSAGE REGIMENS OF ERLOTINIB AND GEFITINIB IN NON-SMALL CELL LUNG CANCER PATIENTS USING MODELLING AND SIMULATION (submitted in 2019)
European Statement
Clinical Pharmacy Services
Author(s)
SOFIA KONSTANTINIDOU, VANGELIS KARALIS
Why was it done?
Tyrosine kinase inhibitors (TKIs), like erlotinib and gefitinib, are widely used in anticancer therapy. However, after long term administration of TKIs, resistance is observed in the majority of patients. Thus, it is necessary to be able to define individualised dosage regimens for TKIs in cancer patients. Nowadays, modelling and simulation approaches represent the most powerful tool in the hands of clinical pharmacists towards precision medicine.
What was done?
Population pharmacokinetic (PK) – pharmacodynamic (PD) modelling was utilised to simulate erlotinib and gefitinib dosage regimens for non-small cell lung cancer. In silico clinical trials with virtual patients, of several resistance levels, were simulated in order to optimise pharmacotherapy and get better therapeutic outcomes.
How was it done?
The utilised PK/PD model and average parameter values were obtained from the study of Eigenmann and colleagues. This model was fully validated using statistical criteria and goodness of fit plots. In order to simulate many possible conditions that may occur in clinical practice, several different values of erlotinib and gefitinib clearance, absorption rate, pharmacodynamic characteristics (like tumor volume), and resistance were assessed. In addition, several dosage schemes were simulated. The entire modelling work was performed in Monolix® 2019R1.
What has been achieved?
Concentration vs. time and effect vs. time plots for the virtual patients were simulated for a variety of conditions and tumour resistance levels. For both TKIs, decrease of body clearance led to higher plasma concentrations, as well as more intense and longer duration of the effect (i.e. tumour volume shrinkage). Enhanced drug effect on resistant cells resulted in a decrease in tumour volume. In addition, a variety of concentration-time profiles were simulated, making it possible to choose the best regimen for each patient.
What next?
In this study, the use of modelling techniques led to the simulation of many conditions of patients and adjustment of dosage regimens according to their needs. Wider application of in silico methods using virtual patients will allow the design of the most appropriate individualised dosage schemes tailored to the patients’ requirements.