Dynamics modeling and synergistic mechanisms of oncolytic virus-bortezomib combination therapy
Bibliographic record
Abstract
For cancer treatment, the combination therapy of oncolytic virus (OV) and bortezomib, a proteasome inhibitor, is a highly worthy research problem. We develop a nonlinear mathematical model that captures the dynamics of uninfected and infected tumor cells, free oncolytic virus particles, bortezomib and natural killer (NK) cells. We consider bortezomib administered periodically and integrate the bortezomib administration functions into the model. We explore the strategies for oncolytic virotherapy, an impulsive dose, where one viral dose is administered at several successive time points. We derive the OV infection threshold, [Formula: see text], which determines whether the viral infection will persist ([Formula: see text] or be eliminated ([Formula: see text]). We theoretically demonstrate that periodic injections of bortezomib and limited pulse injections of OV can eliminate tumor cells. Numerical simulations show the uninfected tumor cell population is significantly reduced when [Formula: see text], and the overall therapeutic efficacy of virotherapy shows a substantial improvement compared to cases where [Formula: see text]. Through global sensitivity analysis, we evaluate the impact of both OV- and bortezomib-related parameters on treatment outcomes. The results indicate that OV-related parameters substantially influence virotherapy efficacy, whereas bortezomib-related parameters have minimal impact on overall treatment success but do significantly affect the equilibrium level of uninfected tumor cells. Our results reveal how OV-Bortezomib combination therapy works synergistically, guiding better cancer treatment design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".