Improving the use of G-CSF during chemotherapy using physiological mathematical modelling : a quantitative systems pharmacology approach

Dose-limitation or interruption of chemotherapeutic treatment is most often prompted by a decrease in circulating neutrophils, the most abundant white blood cell in the human body. Myelosuppression, or a reduction in absolute neutrophil counts (ANCs) by anti-cancer treatments, is precipitated by the nonspecific killing effect of chemotherapeutic drugs which have toxic effects on noncancerous cells. To mitigate this myelosuppressive effect, patients are frequently administered recombinant human granulocyte colony-stimulating factor (rhG-CSF), an exogenous form of the cytokine G-CSF, which stimulates neutrophil production and release into the blood stream. While the benefits of adjuvant treatment rhG-CSF during chemotherapy are well recognised, the protocols with which it is administered are not well defined and are frequently determined ad libitum by clinicians. To quantify and address the optimisation of the administration of rhG-CSF during chemotherapeutic treatment, we developed a physiological model of granulopoiesis which incorporates the contemporary understanding of  the production of neutrophils from the hematopoietic stem cells in the bone marrow. To this physiological model, we incorporated mechanistic pharmacokinetic/pharmacodynamic (PK/PD) models of two drugs, PM00104 (Zalypsis), a chemotherapeutic drug, and rhG-CSF (filgrastim). Through exhaustive parameter estimation using first principles and no data fitting, we successfully predicted clinical data from 172 patients for an average patient undergoing the CHOP14 protocol (6 cycles of 14-day periodic chemotherapy with rhG-CSF administered on days 4-13 post-chemotherapy). We then demonstrated that delaying the administration of rhG-CSF to 6 or 7 days post-chemotherapy allowed for a reduction in the number of filgrastim administrations from ten to four or even three while maintaining or improving the neutrophil nadir. We also investigated the effects of PK variability on the model's predictions by incorporating population PK (PopPK) models of both drugs. Using five different variability scenarios and cohorts of 500 in silico patients per scenario, we established that there are no statistically significant differences between a typical patient and the population in the model's predictions with respect to three crucial clinical endpoints, namely the time to ANC nadir, the ANC nadir, and the area under the concentration-effect curve. The model's robustness to PK variability allows for the scaling up from the individual to population level.  Motivated by the use of rhG-CSF in other disease-states, namely periodic pathologies like cyclical neutropenia, we next endeavoured to contextualise the model within dynamic diseases. By bringing to light that the cytokine paradigm is broken when exogenous cytokine mimetics are administered, we developed a novel physiological PK model for G-CSF incorporating both unbound and bound concentrations. The updated PK model prompted changes to the PD model since we could now track the concentrations of bound G-CSF. We showed that the mass-action equilibrium hypopthesis for bound and unbound drugs is not valid and led to overestimations of the renal clearance of G-CSF. We also successfully reproduced clinical data in a variety of settings (exogenous G-CSF alone, PM00104 alone, CHOP14 protocol) and clarified the mechanisms underlying the body's response to both drugs. Lastly, we discussed the potential of quantitative systems pharmacology in both drug development and translational medicine by using the physiological PK/PD model we developed.

Keyphrases: Quantitative systems pharmacology, Granulopoiesis, Mechanistic modelling, Physiologic pharmacokinetics/pharmacodynamics, Systems biology