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Correlation of Drug Substance Particle Size Distribution with Other Bulk Properties to Predict Critical Quality Attributes

机译:药物粒度分布与其他体积特性的相关性,以预测关键的质量属性

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The purpose of this study was to correlate the particle size distribution (PSD) of a development molecule (1) with other bulk properties such as surface area and powder flow, to enable prediction of critical quality attributes (CQA) of the drug substance to support a roller compaction formulation process. Dry dispersion laser diffraction, wet dispersion laser diffraction, and wet dispersion image analysis PSD methods were developed for 1. Twenty eight (28) process batches were characterized by all three methods, and the resulting data were correlated to other bulk properties to determine if these correlations could be used to predict CQA values for future process batches. PSD metrics measured with wet dispersion laser diffraction and wet dispersion image analysis methods were found to have significantly better correlation to other bulk properties when compared to the metrics measured using the dry dispersion laser diffraction method. The correlations generated were found to be useful to predict resulting surface area and flow function values during process development. On the basis of these correlations, a target PSD range was identified for 1 that could support the roller compaction process. In order to further validate this methodology, various grades of lactose monohydrate were then used as a second model system to test correlation between PSD and other bulk properties.
机译:这项研究的目的是将显影分子(1)的粒径分布(PSD)与其他体积特性(例如表面积和粉末流动)相关联,从而能够预测支持的原料药的关键质量属性(CQA)辊压成型工艺。针对1种样品开发了干分散激光衍射,湿分散激光衍射和湿分散图像分析PSD方法。通过这三种方法对二十八(28)个工艺批次进行了表征,并将所得数据与其他本体性质相关联,以确定是否这些相关性可用于预测未来处理批次的CQA值。与使用干分散激光衍射方法测得的度量相比,使用湿分散激光衍射和湿分散图像分析方法测得的PSD度量与其他体积特性具有显着更好的相关性。发现所产生的相关性对于预测工艺开发过程中产生的表面积和流量函数值很有用。基于这些相关性,确定了可以支持压路机压实过程的目标PSD范围1。为了进一步验证该方法,然后将各种等级的乳糖一水合物用作第二个模型系统,以测试PSD与其他体积特性之间的相关性。

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