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首页> 外文期刊>Bulletin of the Korean Chemical Society >At‐Line Raman Spectroscopy Determination of Tablet Mass Gains during the Tablet Coating Process
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At‐Line Raman Spectroscopy Determination of Tablet Mass Gains during the Tablet Coating Process

机译:在线拉曼光谱测定片剂包衣过程中的片剂质量

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The coating process in the manufacturing of solid dosage forms is important for optimizing therapeutic efficacy because the coating is vital for controlling the drug release rate. The coating is most commonly applied by spraying a solution containing the coating material to form a film. In this study, core tablet samples were collected at regular intervals from four batches made using a scale‐up manufacturing coating process, and the Raman spectra were obtained to determine the mass increase of the coated tablets. To acquire the spectra, a wide area illumination scheme was used to sample a large area (28.3 mm2) with a 785‐nm laser. Additionally, we established a calibration model using the Raman spectra of three tablet batches and verified the accuracy of the model by predicting the tablet weights of a fourth batch. All spectra were preprocessed with baseline correction and normalization. The spectral range was 1700 to 300 cm?1, and partial least squares analysis was performed to establish a model with full cross‐validation. The accuracy was assessed by comparing the difference between the predicted weights using the model and that measured on an analytical balance, which was the reference test method. Standard error of cross‐validation (SECV) values and standard error of prediction (SEP) values for the fourth batch were 0.0023 and 0.0020 g, respectively, showing high accuracy. We established four optimal models that encompassed all combinations of different batches in a workplace environment and verified them by predicting each of the remaining batches.
机译:固体剂型生产中的包衣过程对于优化治疗效果非常重要,因为包衣对于控制药物释放速率至关重要。最通常通过喷涂包含涂料的溶液以形成膜来施加涂料。在这项研究中,从按比例放大制造包衣工艺的四批批次中定期采集核心片剂样品,并获得拉曼光谱来确定包衣片剂的质量增加。为了获得光谱,采用了广域照明方案,用785-nm激光对大面积(28.3 mm2)进行采样。此外,我们使用三批片剂的拉曼光谱建立了校准模型,并通过预测第四批片剂的重量验证了模型的准确性。所有光谱均经过基线校正和归一化预处理。光谱范围为1700至300 cm?1,并进行了偏最小二乘分析,以建立具有完全交叉验证的模型。通过比较使用该模型的预测重量与在作为参考测试方法的分析天平上测得的重量之间的差异来评估准确性。第四批的交叉验证标准误差(SECV)值和预测的标准预测误差(SEP)值分别为0.0023 g和0.0020 g,显示出很高的准确性。我们建立了四个最佳模型,涵盖了工作场所环境中不同批次的所有组合,并通过预测剩余的每个批次对其进行了验证。

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