Physiologically-Based Mathematical Modelling of Neutrophil Dynamics during Concurrent Chemotherapy and Filgrastim Support
Bibliographic record
Abstract
Abstract A common and dose-limiting side effect of chemotherapy is the development of neutropenia (a reduction in neutrophil numbers). To avoid or mitigate drops in absolute neutrophil counts (ANCs), patients are typically given recombinant human granulocyte colony-stimulating factor (rhG-CSF/filgrastim) to minimise the myelosuppressive nature of anti-cancer treatments. Dosing recommendations for filgrastim after chemotherapy suggest treatment begin one day post-chemotherapy and continue for a given amount of time or until ANCs rise sufficiently. Indeed, filgrastim support in a given chemotherapy cycle can sometimes reach seven to ten days in a 14-day period. Due to the intricacy of neutrophil production from the hematopoeitic stem cells in addition to the complexity of the interactions of cytokines and their receptors, a complete understanding of the mechanisms underlying myelopoiesis remains elusive. Mathematical modelling of these processes is a method which provides a global view of the dynamics of blood cell production and helps to elucidate the implications of concurrent chemotherapy and rhG-CSF support upon the blood production system. Moreover, the mathematical treatment of myelopoiesis can suggest novel dosing regimens that may be more beneficial than current schedules by supporting currently-held hypotheses and/or revealing previously unstudied relationships and dynamics. In this study, we construct a physiologically-based model of myelopoiesis which incorporates an up-to-date understanding of the production of neutrophils with our group's previously published model of blood cell dynamics. This model is combined with pharmacokinetic and pharmcodynamic (PKPD) models of Zalypsis (PM00104), an anti-cancer drug currently in phase II clinical trials, and filgrastim, a myelostimulant. The physiological model of myelopoiesis directly relates observable delays in neutrophil production to temporal lags in the model through the use of delay differential equations. All parameters are comprehensively defined for an average patient by utilising previously published physiological and PKPD studies. The model is numerically implemented and simulated to compare its predictions to ANC time series of patients undergoing the CHOP14 protocol. Able to recreate previously published data, we then investigated the optimal timing of filgrastim administrations post-chemotherapy during 14-day periodic chemotherapy and examined the number of filgrastim administrations necessary to ward off neutropenia using this optimised timing. Our results indicate that delaying rhG-CSF administrations by six or seven days after the administration of chemotherapy lessens the myelosuppressive impact of anti-cancer treatment. In addition, we found that if filgrastim administration are started seven days post-chemotherapy, as few as three or four doses of rhG-CSF during a 14-day cycle would improve the ANC nadir experienced by an average patient during myelosuppressive chemotherapy. In all, our results suggest that it is possible to lessen the hematopoietic burden of chemotherapy on patients and that detailed physiological modelling of myelopoiesis is a useful tool to clinicians and researchers alike. Disclosures Off Label Use: We look at optimal dosing regimens of filgrastim during periodic chemotherapy in the context of physiological mathematical models. No clinical trials were undertaken and no patients underwent any regimen changes..
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".