Prof Zvia Agur
Use of the Virtual Patient Technology to Improve Drug Safety: A Personalized Medicine Test Case
Neutropenia is the dose-limiting toxicity of the 3-weekly docetaxel (DOC) treatment schedule. A mathematical model for the pathological and physiological processes in a drug-treated patient, and for their interactions with the relevant pharmacological processes, was put forward (Virtual Patient technology) and used for predicting the effects of drug schedules on the efficacy and toxicity profiles in individual patients. In this work we validated the accuracy of the model in predicting individual neutrophil profiles of breast cancer patients subject to tri-weekly or once-weekly DOC schedules. Following validation, the model was used for suggesting an improved DOC schedule for a specific Mesenchymal Chondrosarcoma (MCS) patient.
The model was validated by correctly predicting the occurrence of grade 4 neutropenia in 81% of the patients (21 out of 26) recruited in two different sites. Model predictions of neutrophil count during and following treatment (up to 160 days) correlated well with the observed data (r = 0.63). This model supported further clinical results by suggesting that the once-weekly DOC regimen confers smaller toxicity than the tri-weekly regimen of the same total dose. Model predictions propose that schedules containing once-weekly Docetaxel in combination with intravenously applied Bevacizumab, will have a significantly higher efficacy/toxicity ratio in the MCS patient than the accepted standard of care. Weekly Docetaxel in the patient resulted in relief of pancytopenia and stable metastatic disease.
These results enforce the generality of the model in predicting the neutropenic effect of DOC in individual patients of various Caucasian populations, where the only input is the pretreatment neutrophil count and the planned therapy schedule. The model enables the clinician to tailor an individual DOC monotherapy or combination regimen for a patient, thus increasing the patient's safety and replacing trial-and error treatment planning by a more systematic methodology.