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准确预测疫苗在实际储存条件下和温度骤变期间的稳定性。

Accurate prediction of vaccine stability under real storage conditions and during temperature excursions.

机构信息

Formulation & Stability Platform, Bioprocess R&D Department, Sanofi Pasteur, 1541 Avenue Marcel Mérieux, 69280 Marcy L'Étoile, France.

出版信息

Eur J Pharm Biopharm. 2018 Apr;125:76-84. doi: 10.1016/j.ejpb.2018.01.005. Epub 2018 Jan 16.

DOI:10.1016/j.ejpb.2018.01.005
PMID:29341899
Abstract

Due to their thermosensitivity, most vaccines must be kept refrigerated from production to use. To successfully carry out global immunization programs, ensuring the stability of vaccines is crucial. In this context, two important issues are critical, namely: (i) predicting vaccine stability and (ii) preventing product damage due to excessive temperature excursions outside of the recommended storage conditions (cold chain break). We applied a combination of advanced kinetics and statistical analyses on vaccine forced degradation data to accurately describe the loss of antigenicity for a multivalent freeze-dried inactivated virus vaccine containing three variants. The screening of large amounts of kinetic models combined with a statistical model selection approach resulted in the identification of two-step kinetic models. Predictions based on kinetic analysis and experimental stability data were in agreement, with approximately five percentage points difference from real values for long-term stability storage conditions, after excursions of temperature and during experimental shipments of freeze-dried products. Results showed that modeling a few months of forced degradation can be used to predict various time and temperature profiles endured by vaccines, i.e. long-term stability, short time excursions outside the labeled storage conditions or shipments at ambient temperature, with high accuracy. Pharmaceutical applications of the presented kinetics-based approach are discussed.

摘要

由于其对温度的敏感性,大多数疫苗从生产到使用都必须冷藏。为了成功实施全球免疫计划,确保疫苗的稳定性至关重要。在这种情况下,有两个重要问题至关重要,即:(i)预测疫苗稳定性和(ii)防止因超出推荐储存条件(冷链中断)的温度骤变而导致产品损坏。我们将先进的动力学和统计分析应用于疫苗强制降解数据,以准确描述包含三种变体的多价冷冻干燥灭活病毒疫苗的抗原性丧失。大量动力学模型的筛选结合统计模型选择方法,确定了两步动力学模型。基于动力学分析和实验稳定性数据的预测结果一致,在温度骤变和冷冻干燥产品的实验运输过程中,与真实值相比,长期稳定性储存条件下的预测值相差约五个百分点。结果表明,对几个月的强制降解进行建模可以用于预测疫苗所经历的各种时间和温度曲线,即长期稳定性、标签储存条件外的短时间温度骤变或在环境温度下运输,具有很高的准确性。本文还讨论了基于该动力学方法的药物应用。

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