Tepilamide Fumarate as a Novel Potentiator of Virus-Based Therapy
Bibliographic record
Abstract
Oncolytic virotherapy, using viruses such as vesicular stomatitis virus (VSVΔ51) and Herpes Simplex Virus-1 (HSV-1) to selectively attack cancer cells, faces challenges such as cellular resistance mediated by the interferon (IFN) response. Dimethyl fumarate (DMF) is used in the treatment of multiple sclerosis and psoriasis and is recognized for its anti-cancer properties and has been shown to enhance both VSVΔ51 and HSV-1 oncolytic activity. Tepilamide fumarate (TPF) is a DMF analog currently undergoing clinical trials for the treatment of moderate-to-severe plaque psoriasis. The aim of this study was to evaluate the potential of TPF in enhancing the effectiveness of oncolytic viruses. In vitro, TPF treatment rendered 786-0 carcinoma cells more susceptible to VSVΔ51 infection, leading to increased viral replication. It outperformed DMF in both increasing viral infection and increasing the killing of these resistant cancer cells and other cancer cell lines tested. Ex vivo studies demonstrated TPF's selective boosting of oncolytic virus infection in cancer cells without affecting healthy tissues. Effectiveness was notably high in pancreatic and ovarian tumor samples. Our study further indicates that TPF can downregulate the IFN pathway through a similar mechanism to DMF, making resistant cancer cells more vulnerable to viral infection. Furthermore, TPF's impact on gene therapy was assessed, revealing its ability to enhance the transduction efficiency of vectors such as lentivirus, adenovirus type 5, and adeno-associated virus type 2 across various cell lines. This data underscore TPF's potential role in not only oncolytic virotherapy but also in the broader application of gene therapy. Collectively, these findings position TPF as a promising agent in oncolytic virotherapy, warranting further exploration of its therapeutic potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".