NIRS法用于药物共晶质量控制的研究

目的 建立一种基于近红外光谱（NIRS）的药物共晶质量控制方法，以大黄酸赖氨酸（大赖酸）共晶为例进行研究。方法 采用积分球漫反射附件采集大赖酸共晶光谱，结合偏最小二乘回归（PLS）算法，建立NIRS定量分析模型，用于考察在不同环境因素以及湿法制粒过程中共晶降解状况。结果 大赖酸共晶校正模型的相关系数（r）、校正集均方差（RMSEC）、预测均方差（RMSEP）、交互验证均方差（RMSECV）分别为0.999 5、0.009 3、0.011 0、0.012 0。高温和光照情况下，大赖酸共晶质量分数变化（＜1%）基本稳定；在高湿和制粒过程，质量分数发生了一定的变化（＜3%），但变化不显著。结论 建立的近红外定量分析模型在质量控制方面快速、无损、简便，结果准确可靠；大赖酸共晶在不同环境因素和模拟制粒过程中能基本保持稳定。

Study on quality control of pharmaceutical cocrystal by NIRS

Objective To establish a method for the quality control of pharmaceutical cocrystal based on NIRS, using rhein-lysine (lysirein) cocrystal as a case study. Methods Integrating sphere diffuse reflectance accessory combining partial least squares regression algorithm to establish an analytical method for the content of rhein-lysine cocrystal, and the model was used to study environmental factors, as well as wet mixing and granulating process eutectic degradation conditions. Results The correlation coefficients (r), root mean square error of calibration (RMSEC), root mean square error of prediction (RMSEP), and root-mean-squares error of cross-validation (RMSECV) of the proposed models were 0.999 5, 0.009 3, 0.011 0, and 0.012 0; Under high temperature and light conditions, the content of rhein-lysine cocrystal remained roughly constant (content change < 1%). Under high humidity and wet granulation process, there were some changes in cocrystal concentration, content change < 3%, but it was not significant. Conclusion The proposed method is fast, non-destructive, simple, and accurate; Rhein-lysine cocrystal in environmental factors and modeling granulation process remains stable.