Combat Casualty Care Research Program, US Army Medical Research and Materiel Command, 504 Scott Street, Ft Detrick, MD 21702-5012, USA.
Expert Rev Neurother. 2010 Jan;10(1):93-103. doi: 10.1586/ern.09.134.
Botulinum neurotoxin is a pharmaceutical treatment used for an increasing number of neurological and non-neurological indications, symptoms and diseases. Despite the wealth of clinical reports that involve the timing of the therapeutic effects of this toxin, few studies have attempted to integrate these data into unified models. Secondary reactions have also been examined including the development of adverse events, resistance to repeated applications, and nerve terminal sprouting. Our primary intent for conducting this review was to gather relevant pharmacodynamic data from suitable biomedical literature regarding botulinum neurotoxins via the use of automated data-mining techniques. We envision that mathematical models will ultimately be of value to those who are healthcare decision makers and providers, as well as clinical and basic researchers. Furthermore, we hypothesize that the combination of this computer-intensive approach with mathematical modeling will predict the percentage of patients who will favorably or adversely respond to this treatment and thus will eventually assist in developing the increasingly important area of personalized medicine.
肉毒杆菌神经毒素是一种用于越来越多神经和非神经适应症、症状和疾病的药物治疗方法。尽管有大量涉及这种毒素治疗效果时间的临床报告,但很少有研究试图将这些数据整合到统一的模型中。还检查了继发性反应,包括不良反应的发展、对重复应用的耐药性和神经末梢发芽。我们进行这项综述的主要目的是通过使用自动化数据挖掘技术,从关于肉毒杆菌神经毒素的合适生物医学文献中收集相关药效学数据。我们设想,数学模型最终将对医疗保健决策者和提供者以及临床和基础研究人员有价值。此外,我们假设这种计算机密集型方法与数学建模的结合将预测对这种治疗有有利或不利反应的患者比例,从而最终有助于发展日益重要的个性化医学领域。