Neutralizing Antibody Formation with OnabotulinumtoxinA (BOTOX®) Treatment from Global Registration Studies across Multiple Indications: A Meta-Analysis
Bibliographic record
Abstract
Though the formation of neutralizing antibodies (NAbs) during treatment with botulinum neurotoxin is rare, their presence may nonetheless affect the biological activity of botulinum toxin and negatively impact clinical response. The goal of this updated meta-analysis was to evaluate and characterize the rate of NAb formation using an expanded dataset composed of 33 prospective placebo-controlled and open-label clinical trials with nearly 30,000 longitudinal subject records prior to and following onabotulinumtoxinA treatment in 10 therapeutic and aesthetic indications. Total onabotulinumtoxinA doses per treatment ranged from 10 U to 600 U administered in ≤15 treatment cycles. The NAb formation at baseline and post-treatment was tested and examined for impact on clinical safety and efficacy. Overall, 27 of the 5876 evaluable subjects (0.5%) developed NAbs after onabotulinumtoxinA treatment. At study exit, 16 of the 5876 subjects (0.3%) remained NAb positive. Due to the low incidence of NAb formation, no clear relationship was discernable between positive NAb results and gender, indication, dose level, dosing interval, treatment cycles, or the site of injection. Only five subjects who developed NAbs post-treatment were considered secondary nonresponders. Subjects who developed NAbs revealed no other evidence of immunological reactions or clinical disorders. This comprehensive meta-analysis confirms the low NAb formation rate following onabotulinumtoxinA treatment across multiple indications, and its limited clinical impact on treatment safety and efficacy.
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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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.049 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".