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首页> 外文期刊>Asian Journal of Pharmaceutical Sciences >A quality by design (QbD) case study on enteric-coated pellets: Screening of critical variables and establishment of design space at laboratory scale
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A quality by design (QbD) case study on enteric-coated pellets: Screening of critical variables and establishment of design space at laboratory scale

机译:肠溶微丸的设计质量(QbD)案例研究:筛选关键变量并在实验室规模建立设计空间

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Abstract The study aims to prepare naproxen enteric-coated pellets (NAP-ECPs) by fluid-bed coating using QbD principle. Risk assessment was firstly performed by using failure mode and effect analysis (FMEA) methodology. A Plackett–Burman design was then used for assessment of the most important variables affecting enteric-coated pellets characteristics. A Box–Behnken design was subsequently used for investigating the main, interactive, and quadratic effects of these variables on the response. By {FMEA} we discovered that eight factors should be considered to be high/important risk variables as compared with others. The responses of acid resistance and cumulative drug release were taken as critical quality attributes (CQAs). Pareto ranking analyses indicated that the coating weight gain (X7), triethyl citrate percentage (X1) and glycerol monostearate percentage (X2) were the most significant factors affecting the selected responses out of the eight high-risk variables. Optimization with response surface method (RSM) further fully clarified the relationship between X7, X1, {X2} and CQAs, and design space was established based on the constraints set on the responses. Due to the extreme coincidence of the predicted value generated by model with the observed value, the accuracy and robustness of the model were confirmed. It could be concluded that a promising NAP-ECPs was successfully designed using QbD approach in a laboratory scale.
机译:摘要研究旨在利用QbD原理通过流化床包衣制备萘普生肠溶丸(NAP-ECPs)。风险评估是首先使用故障模式和效果分析(FMEA)方法进行的。然后使用Plackett-Burman设计评估影响肠溶丸剂特性的最重要变量。 Box-Behnken设计随后用于调查这些变量对响应的主要,交互作用和二次影响。通过 {FMEA },我们发现与其他因素相比,八个因素应被视为高/重要风险变量。耐酸和累积药物释放的响应被视为关键质量属性(CQA)。 Pareto排名分析表明,涂层重量增加(X7),柠檬酸三乙酯百分比(X1)和单硬脂酸甘油酯百分比(X2)是影响八个高风险变量中所选响应的最重要因素。使用响应面方法(RSM)进行的优化进一步充分阐明了X7,X1, {X2 }与CQAs之间的关系,并根据响应设置的约束建立了设计空间。由于模型生成的预测值与观测值极为一致,因此可以确认模型的准确性和鲁棒性。可以得出结论,在实验室规模上使用QbD方法成功设计了有希望的NAP-ECP。

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