Virotherapy in Germany—Recent Activities in Virus Engineering, Preclinical Development, and Clinical Studies
Bibliographic record
Abstract
Virotherapy research involves the development, exploration, and application of oncolytic viruses that combine direct killing of cancer cells by viral infection, replication, and spread (oncolysis) with indirect killing by induction of anti-tumor immune responses. Oncolytic viruses can also be engineered to genetically deliver therapeutic proteins for direct or indirect cancer cell killing. In this review-as part of the special edition on "State-of-the-Art Viral Vector Gene Therapy in Germany"-the German community of virotherapists provides an overview of their recent research activities that cover endeavors from screening and engineering viruses as oncolytic cancer therapeutics to their clinical translation in investigator-initiated and sponsored multi-center trials. Preclinical research explores multiple viral platforms, including new isolates, serotypes, or fitness mutants, and pursues unique approaches to engineer them towards increased safety, shielded or targeted delivery, selective or enhanced replication, improved immune activation, delivery of therapeutic proteins or RNA, and redirecting antiviral immunity for cancer cell killing. Moreover, several oncolytic virus-based combination therapies are under investigation. Clinical trials in Germany explore the safety and potency of virotherapeutics based on parvo-, vaccinia, herpes, measles, reo-, adeno-, vesicular stomatitis, and coxsackie viruses, including viruses encoding therapeutic proteins or combinations with immune checkpoint inhibitors. These research advances represent exciting vantage points for future endeavors of the German virotherapy community collectively aimed at the implementation of effective virotherapeutics in clinical oncology.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".