A Phase I Clinical Trial of Systemically Delivered NEMO Binding Domain Peptide in Dogs with Spontaneous Activated B-Cell like Diffuse Large B-Cell Lymphoma
Why this work is in the frame
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Bibliographic record
Abstract
Activated B-Cell (ABC) Diffuse Large B-Cell Lymphoma (DLBCL) is a common, aggressive and poorly chemoresponsive subtype of DLBCL, characterized by constitutive canonical NF-κB signaling. Inhibition of NF-κB signaling leads to apoptosis of ABC-DLBCL cell lines, suggesting targeted disruption of this pathway may have therapeutic relevance. The selective IKK inhibitor, NEMO Binding Domain (NBD) peptide effectively blocks constitutive NF-κB activity and induces apoptosis in ABC-DLBCL cells in vitro. Here we used a comparative approach to determine the safety and efficacy of systemic NBD peptide to inhibit constitutive NF-κB signaling in privately owned dogs with spontaneous newly diagnosed or relapsed ABC-like DLBCL. Malignant lymph nodes biopsies were taken before and twenty-four hours after peptide administration to determine biological effects. Intravenous administration of <2 mg/kg NBD peptide was safe and inhibited constitutive canonical NF-κB activity in 6/10 dogs. Reductions in mitotic index and Cyclin D expression also occurred in a subset of dogs 24 hours post peptide and in 3 dogs marked, therapeutically beneficial histopathological changes were identified. Mild, grade 1 toxicities were noted in 3 dogs at the time of peptide administration and one dog developed transient subclinical hepatopathy. Long term toxicities were not identified. Pharmacokinetic data suggested rapid uptake of peptide into tissues. No significant hematological or biochemical toxicities were identified. Overall the results from this phase I study indicate that systemic administration of NBD peptide is safe and effectively blocks constitutive NF-κB signaling and reduces malignant B cell proliferation in a subset of dogs with ABC-like DLBCL. These results have potential translational relevance for human ABC-DLBCL.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it