Exploring Nonresponse to Botulinum Toxin in Aesthetics: Narrative Review of Key Trigger Factors and Effective Management Strategies
Bibliographic record
Abstract
Background: Nonresponse to botulinum toxin type A (BoNT-A) has been reported in both medical and aesthetic applications. Secondary nonresponse (SNR) occurs when BoNT-A is initially effective before failure commences at a later point. Most reported cases involve SNR in aesthetics. Several aspects of this complication remain elusive or controversial. Objective: We aimed to address unanswered questions regarding the prevalence and etiology of SNR. Additionally, we investigated the immunogenicity of BoNT-A formulations, mainly focusing on the development of neutralizing antibodies that hinder the toxin's pharmacologic effects. Furthermore, we sought to examine the management strategies for SNR. Methods: The PubMed and Google Scholar databases were searched from inception for articles on nonresponse to BoNT-A therapy. Articles were evaluated based on their contribution to the field. Expert opinions and panel recommendations regarding management and data gaps were also included in the review. Results: There are limited data on SNR prevalence in aesthetic applications compared to therapeutic uses. Trigger factors of SNR include improper handling of BoNT-A; incorrect injection practices; and impurities present in the formulation, such as clostridial complexing proteins that may increase immunogenicity. Other contributing factors include infection; patient characteristics; and treatment parameters that encompass an increased frequency of BoNT-A injections (ie, <3 months apart), higher cumulative dosages, elevated treatment dosages, and booster injections (retreatment within 3 weeks of the initial injection). Neutralizing antibodies developed with first-generation formulations, such as onabotulinumtoxinA and abobotulinumtoxinA that contain clostridial proteins, but not with second-generation BoNT-As, such as incobotulinumtoxinA and daxibotulinumtoxinA, which lack these proteins. Among patients who developed SNR after using first-generation BoNT-A for aesthetic purposes, switching to incobotulinumtoxinA therapy did not result in the development of immune responses. Switching to a protein-free BoNT-A formulation such as incobotulinumtoxinA upon development of SNR has been advocated. To effectively manage SNR, it is crucial to minimize the identified trigger factors. Conclusions: Nonresponse to BoNT-A is gaining importance in aesthetic treatments. Considering the potential for immunogenicity is essential when selecting a BoNT-A formulation. Preventing SNR is crucial, given the lack of solid data on effective treatments.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".