Virotherapy Research in Germany: From Engineering to Translation
Bibliographic record
Abstract
Virotherapy is a unique modality for the treatment of cancer with oncolytic viruses (OVs) that selectively infect and lyse tumor cells, spread within tumors, and activate anti-tumor immunity. Various viruses are being developed as OVs preclinically and clinically, several of them engineered to encode therapeutic proteins for tumor-targeted gene therapy. Scientists and clinicians in German academia have made significant contributions to OV research and development, which are highlighted in this review paper. Innovative strategies for "shielding," entry or postentry targeting, and "arming" of OVs have been established, focusing on adenovirus, measles virus, parvovirus, and vaccinia virus platforms. Thereby, new-generation virotherapeutics have been derived. Moreover, immunotherapeutic properties of OVs and combination therapies with pharmacotherapy, radiotherapy, and especially immunotherapy have been investigated and optimized. German investigators are increasingly assessing their OV innovations in investigator-initiated and sponsored clinical trials. As a prototype, parvovirus has been tested as an OV from preclinical proof-of-concept up to first-in-human clinical studies. The approval of the first OV in the Western world, T-VEC (Imlygic), has further spurred the involvement of investigators in Germany in international multicenter studies. With the encouraging developments in funding, commercialization, and regulatory procedures, more German engineering will be translated into OV clinical trials in the near future.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".