In Vitro and In Vivo Characterization of a Fully Felinized Therapeutic Anti-Nerve Growth Factor Monoclonal Antibody for the Treatment of Pain in Cats
Bibliographic record
Abstract
BACKGROUND: Limited options are available for the treatment of pain in cats. Monoclonal antibodies (mAbs) that neutralize nerve growth factor (NGF) have demonstrated analgesic capacity in rodent models, people with osteoarthritis, and dogs with degenerative joint disease. HYPOTHESIS/OBJECTIVES: This study describes the design and characterization of a fully felinized anti-NGF monoclonal antibody. In vitro potency, pharmacokinetics, and the ability of the antibody to treat pain in a self-resolving, acute inflammation model were investigated in cats. ANIMALS: Thirty-eight cats at a research colony at Charles River Laboratories, Ireland. METHODS: Felinized anti-NGF mAb, NV-02, was produced using a complementary DNA (cDNA)-based method (PETization). Purified NV-02 was tested for affinity, potency, and immunoreactivity in vitro, then for safety and plasma pharmacokinetic distribution in vivo, and analgesic efficacy in a model of kaolin-induced inflammatory pain. RESULTS: Anti-NGF mAb, NV-02 neutralized NGF with high affinity and potency and did not bind complement. NV-02-administered SC had a plasma half-life of 7-15 days and was well tolerated at dosages up to 28 mg/kg. A dosage of 2 mg/kg NV-02 SC significantly decreased signs of lameness on day 2 (P = .0027), day 3 (P = .016), day 4, (P = .0063), day 5 (P = .0085), day 6 (P = .0014), and day 7 (P = .0034) after induction of inflammation. CONCLUSIONS AND CLINICAL IMPORTANCE: The high affinity, long plasma half-life, safety, and analgesic efficacy of felinized anti-NGF mAb (NV-02) support further investigation of the analgesic potential of this antibody in the cat.
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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 | 0.000 | 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) | 0.001 | 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".