Semenov Institute of Chemical Physics RAS, Moscow, Russia.
Analyst. 2011 Nov 21;136(22):4830-8. doi: 10.1039/c0an01033b. Epub 2011 Oct 3.
A new method for the prediction of the drug release profiles during a running pellet coating process from in-line near infrared (NIR) measurements has been developed. The NIR spectra were acquired during a manufacturing process through an immersion probe. These spectra reflect the coating thickness that is inherently connected with the drug release. Pellets sampled at nine process time points from thirteen designed laboratory-scale coating batches were subjected to the dissolution testing. In the case of the pH-sensitive Acryl-EZE coating the drug release kinetics for the acidic medium has a sigmoid form with a pronounced induction period that tends to grow along with the coating thickness. In this work the autocatalytic model adopted from the chemical kinetics has been successfully applied to describe the drug release. A generalized interpretation of the kinetic constants in terms of the process and product parameters has been suggested. A combination of the kinetic model with the multivariate Partial Least Squares (PLS) regression enabled prediction of the release profiles from the process NIR data. The method can be used to monitor the final pellet quality in the course of a coating process.
已经开发出一种从在线近红外（NIR）测量中预测运行丸剂包衣过程中药物释放曲线的新方法。通过浸入式探头在制造过程中获取 NIR 光谱。这些光谱反映了涂层厚度，而涂层厚度与药物释放密切相关。从 13 个设计的实验室规模包衣批中，在九个过程时间点采集丸剂样本，并进行溶出度测试。对于 pH 敏感的 Acryl-EZE 涂层，酸性介质中的药物释放动力学呈 S 型曲线，具有明显的诱导期，该诱导期随着涂层厚度的增加而延长。在这项工作中，已经成功地将从化学动力学中采用的自催化模型应用于描述药物释放。根据过程和产品参数对动力学常数进行了广义解释。将动力学模型与多元偏最小二乘（PLS）回归相结合，可以根据过程 NIR 数据预测释放曲线。该方法可用于在包衣过程中监测最终丸剂质量。
