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Background <p>Counterfeit Botulinum Toxin A (BoNT-A) poses a growing global threat, particularly in aesthetic medicine where regulatory oversight is minimal and underreporting is widespread. Traditional pharmacovigilance systems such as FAERS and EudraVigilance fail to detect early counterfeit exposure due to reliance on delayed, structured reporting. As counterfeit incidents increase across multiple regions, a proactive, data-driven approach is urgently needed.</p> Objectives <p>This study aimed to develop and validate an AI-enabled surveillance system capable of detecting counterfeit BoNT-A exposures in real time, projecting regional risk through 2035, and reforming pharmacovigilance in deregulated aesthetic markets.</p> Methods <p>Over 2.5 million data points from 2015 to 2025 were analyzed, integrating adverse event databases, customs seizure records, patient forums, social media platforms, and global market data. Natural language processing models (BioBERT, RoBERTa, XLM-R) processed multilingual narratives. Counterfeit exposure probabilities were derived using probabilistic inference and anomaly detection. Forecasting models (ARIMA, Prophet, GNNs) projected long-term risk, and robustness was assessed through simulated crises.</p> Results <p>The AI system detected counterfeit exposure signals an average of 31 days before regulatory alerts, with over 86% spatial match accuracy. Platforms like RealSelf and Reddit showed &gt;91% concordance with known adverse event profiles. Forecasts project a global counterfeit exposure increase of 4.9% annually through 2035, with regional peaks in Turkey (risk score 0.76), Brazil, and India. South America is expected to exceed counterfeit-related AEs annually by 2035, a 70% rise. The model maintained &gt;87% accuracy in stress simulations and achieved a mean F1-score of 88.7% across six languages.</p> Conclusions <p>This study demonstrates the feasibility and urgency of AI-driven pharmacovigilance in aesthetic medicine. The BoNT-A Risk Burden Index offers a predictive, patient-centred tool to detect and mitigate counterfeit exposures before widespread harm occurs.</p> Level of Evidence IV <p>This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors <a href="http://www.springer.com/00266">www.springer.com/00266</a>.</p>

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AI-Enabled Surveillance and Modelling for Counterfeit Botulinum Toxin A: Risk Projection, Patient Safety, and Systemic Reform of Pharmacovigilance

  • 期刊论文

  • 发表日期：

    2026年2月11日

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  • Eqram Rahman,
  • Parinitha Rao,
  • Karim Sayed,
  • Alain Michon,
  • Nanze Yu,
  • Sotirios Ioannidis,
  • Patricia E. Garcia,
  • Woffles T. L. Wu,
  • Jean D. A. Carruthers,
  • William Richard Webb

摘要

Background

Counterfeit Botulinum Toxin A (BoNT-A) poses a growing global threat, particularly in aesthetic medicine where regulatory oversight is minimal and underreporting is widespread. Traditional pharmacovigilance systems such as FAERS and EudraVigilance fail to detect early counterfeit exposure due to reliance on delayed, structured reporting. As counterfeit incidents increase across multiple regions, a proactive, data-driven approach is urgently needed.

Objectives

This study aimed to develop and validate an AI-enabled surveillance system capable of detecting counterfeit BoNT-A exposures in real time, projecting regional risk through 2035, and reforming pharmacovigilance in deregulated aesthetic markets.

Methods

Over 2.5 million data points from 2015 to 2025 were analyzed, integrating adverse event databases, customs seizure records, patient forums, social media platforms, and global market data. Natural language processing models (BioBERT, RoBERTa, XLM-R) processed multilingual narratives. Counterfeit exposure probabilities were derived using probabilistic inference and anomaly detection. Forecasting models (ARIMA, Prophet, GNNs) projected long-term risk, and robustness was assessed through simulated crises.

Results

The AI system detected counterfeit exposure signals an average of 31 days before regulatory alerts, with over 86% spatial match accuracy. Platforms like RealSelf and Reddit showed >91% concordance with known adverse event profiles. Forecasts project a global counterfeit exposure increase of 4.9% annually through 2035, with regional peaks in Turkey (risk score 0.76), Brazil, and India. South America is expected to exceed counterfeit-related AEs annually by 2035, a 70% rise. The model maintained >87% accuracy in stress simulations and achieved a mean F1-score of 88.7% across six languages.

Conclusions

This study demonstrates the feasibility and urgency of AI-driven pharmacovigilance in aesthetic medicine. The BoNT-A Risk Burden Index offers a predictive, patient-centred tool to detect and mitigate counterfeit exposures before widespread harm occurs.

Level of Evidence IV

This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266.