A Phase I Study of the Combination of Intravenous Reovirus Type 3 Dearing and Gemcitabine in Patients with Advanced Cancer
Bibliographic record
Abstract
PURPOSE: This study combined systemic administration of the oncolytic reovirus type 3 Dearing (reovirus) with chemotherapy in human subjects. We aimed to determine the safety and feasibility of combining reovirus administration with gemcitabine and to describe the effects of gemcitabine on the antireoviral immune response. EXPERIMENTAL DESIGN: Patients received reovirus in various doses, initially we dosed for five consecutive days but this was poorly tolerated. We amended the protocol to administer a single dose and administered up to 3 × 10(10) TCID(50). Toxicity was assessed by monitoring of clinical and laboratory measurements. We assessed antibody response by cytotoxicity neutralization assay. RESULTS: Sixteen patients received 47 cycles of reovirus. The two initial patients and one patient in the final cohort experienced dose limiting toxicity (DLT). The DLTs consisted of two asymptomatic grade 3 liver enzyme rises and one asymptomatic grade 3 troponin I rise. Common toxicities consisted of known reovirus and gemcitabine associated side effects. Further analysis showed a potential interaction between reovirus and gemcitabine in causing liver enzyme rises. Grade 3 rises in liver enzymes were associated with concomitant aminocetophen use. Importantly, the duration of the liver enzyme rise was short and reversible. Neutralizing antibody responses to reovirus were attenuated both in time-to-occurrence and peak height of the response. CONCLUSIONS: Reovirus at the dose of 1 × 10(10) TCID(50) can be safely combined with full dose gemcitabine. Combination of reovirus with gemcitabine affects the neutralizing antibody response and this could impact both safety and efficacy of this treatment schedule.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".