Moving oncolytic viruses into the clinic: clinical-grade production, purification, and characterization of diverse oncolytic viruses
Bibliographic record
Abstract
Oncolytic viruses (OVs) are unique anticancer agents based on their pleotropic modes of action, which include, besides viral tumor cell lysis, activation of antitumor immunity. A panel of diverse viruses, often genetically engineered, has advanced to clinical investigation, including phase 3 studies. This diversity of virotherapeutics not only offers interesting opportunities for the implementation of different therapeutic regimens but also poses challenges for clinical translation. Thus, manufacturing processes and regulatory approval paths need to be established for each OV individually. This review provides an overview of clinical-grade manufacturing procedures for OVs using six virus families as examples, and key challenges are discussed individually. For example, different virus features with respect to particle size, presence/absence of an envelope, and host species imply specific requirements for measures to ensure sterility, for handling, and for determination of appropriate animal models for toxicity testing, respectively. On the other hand, optimization of serum-free culture conditions, increasing virus yields, development of scalable purification strategies, and formulations guaranteeing long-term stability are challenges common to several if not all OVs. In light of the recent marketing approval of the first OV in the Western world, strategies for further upscaling OV manufacturing and optimizing product characterization will receive increasing attention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".