Rahman Eqram, Carruthers Jean D A, Rao Parinitha, Yu Nanze, Philipp-Dormston Wolfgang G, Webb William Richard
From the Research and Innovation Hub, Innovation Aesthetics.
Department of Ophthalmology, University of British Columbia.
Plast Reconstr Surg. 2025 Apr 1;155(4):676e-688e. doi: 10.1097/PRS.0000000000011748. Epub 2024 Sep 16.
Botulinum toxin A (BoNT-A), derived from Clostridium botulinum , is widely used in medical and aesthetic treatments. Its clinical application extends from managing chronic conditions like cervical dystonia and migraine to reducing facial wrinkles. Despite its efficacy, a challenge associated with BoNT-A therapy is immunogenicity, where the immune system produces neutralizing antibodies (NAbs) against BoNT-A, reducing its effectiveness over time. This issue is important for patients requiring repeated treatments. The authors compared BoNT-A products, examining the factors influencing NAb development using advanced machine-learning techniques.
The authors analyzed data from randomized controlled trials involving 5 main BoNT-A products. Trials were selected on the basis of detailed reports of immunogenic responses to these treatments, particularly for glabellar lines. Machine-learning models, including logistic regression, random forest classifiers, and Bayesian logistic regression, were used to assess how treatment specifics and BoNT-A product types affect the development of NAbs.
Analysis of 14 studies with 8190 participants revealed that dosage and treatment frequency are key factors influencing the risk of NAb development. Among BoNT-A products, incobotulinumtoxinA shows the lowest, and abobotulinumtoxinA, the highest likelihood of inducing NAbs. The machine-learning and logistic regression findings indicated that treatment planning must consider these variables to minimize immunogenicity.
The study underscores the importance of understanding BoNT-A immunogenicity in clinical practice. By identifying the main predictors of NAb development and differentiating the immunogenic potential of BoNT-A products, the research provides insights for clinicians in optimizing treatment strategies. It highlights the need for careful treatment customization to reduce immunogenic risks, advocating for further research into the mechanisms of BoNT-A immunogenicity.
肉毒杆菌毒素A(BoNT-A)由肉毒梭菌产生,广泛应用于医学和美容治疗。其临床应用范围从治疗诸如颈部肌张力障碍和偏头痛等慢性病到减少面部皱纹。尽管其疗效显著,但与BoNT-A治疗相关的一个挑战是免疫原性,即免疫系统会产生针对BoNT-A的中和抗体(NAb),随着时间的推移降低其有效性。这个问题对于需要重复治疗的患者很重要。作者比较了BoNT-A产品,使用先进的机器学习技术研究影响NAb产生的因素。
作者分析了涉及5种主要BoNT-A产品的随机对照试验数据。试验是根据对这些治疗的免疫原性反应的详细报告挑选的,特别是针对眉间纹的报告。使用包括逻辑回归、随机森林分类器和贝叶斯逻辑回归在内的机器学习模型来评估治疗细节和BoNT-A产品类型如何影响NAb的产生。
对14项研究、8190名参与者的分析表明,剂量和治疗频率是影响NAb产生风险的关键因素。在BoNT-A产品中,incobotulinumtoxinA诱导产生NAb的可能性最低,而abobotulinumtoxinA最高。机器学习和逻辑回归结果表明,治疗计划必须考虑这些变量以将免疫原性降至最低。
该研究强调了在临床实践中了解BoNT-A免疫原性的重要性。通过确定NAb产生的主要预测因素并区分BoNT-A产品的免疫原性潜力,该研究为临床医生优化治疗策略提供了见解。它强调了需要仔细定制治疗以降低免疫原性风险,提倡对BoNT-A免疫原性机制进行进一步研究。