Synergistic effects of herpes oncolytic virus and cyclophosphamide for recurrent malignant glioma: a narrative review
Bibliographic record
Abstract
Gliomas, comprising nearly 80% of brain malignancies, present a formidable challenge with glioblastomas being the most aggressive subtype. Despite multidisciplinary care, including surgery and chemoradiotherapy, the prognosis remains grim, emphasizing the need for innovative treatment strategies. The blood-brain barrier complicates drug access, and the diverse histopathology hinders targeted therapies. Oncolytic herpes viruses (oHSVs), particularly HSV1716, G207, and rQNestin34.5v, show promise in glioma treatment by selectively replicating in tumor cells. Preclinical and clinical studies demonstrate the safety and efficacy of oHSVs, with T-Vec being FDA-approved. However, challenges like viral delivery limitations and antiviral responses persist. The combination of oHSVs and combining cyclophosphamide (CPA) addresses these challenges, demonstrating increased transgene expression and viral activity. The immunosuppressive properties of CPA, particularly in metronomic schedules, enhance oHSV efficacy, supporting the development of this combination for recurrent malignant gliomas. CPA with oHSVs enhances viral oncolysis and extends survival. CPA's immunomodulatory effects, suppressing regulatory T cells, improve oHSV efficiency. While obstacles remain, this synergistic approach offers hope for improved outcomes, necessitating further research and clinical validation.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".