头孢西丁钠标准物质原料稳定性的预测与选择

Prediction and selection of raw materials for reference standards of cefoxitin sodium

目的:构建一种快速预测原料长期稳定性的方法,用于头孢西丁钠药品标准物质原料的快速筛选。方法:利用湿度校正的Arrhenius方程建立头孢西丁钠药物稳定性预测模型。通过分析头孢西丁钠的杂质谱确定头孢西丁钠温湿度降解反应的指征性杂质。采用HPLC法,对5个加速实验条件下样品的指征性杂质进行定量分析,根据零级动力学确定其化学反应速率常数。利用所得到的速率常数与降解反应条件,使用SPSS 20.0统计软件建立4个生产厂家的长期稳定性预测模型。结果:4个厂家的预测模型方程:A为lnk=26.013/RT+0.004 2×(RH)+29.093;B为lnk=25.940/RT+0.018 6×(RH)+29.074;C为lnk=15.225/RT+0.002 6×(RH)+13.992;D为lnk=25.661/RT+0.011 7×(RH)+28.915,模型R2均大于0.95,P<0.05,方程拟合良好。利用预测模型推测不同厂家头孢西丁钠的长期稳定性状态,在相同条件下贮存,各厂家产品稳定性不同。结论:该模型不仅可以用于头孢西丁钠长期稳定性的预测,还可以用于确定其储存条件、运输条件和质量监测周期,为其在原药的选择上提供依据。在进行标准物质的原料药选择时,可以根据原料药的预测模型,选择预测稳定性时间更长的原料药,以提高对照品的稳定性。

Objective： To develop a rapid method to predict the long stability of bulk cefoxitin sodium and assist the selection of the raw materials for the reference standard of cefoxitin sodium. Methods： The humidity- corrected Arrhenius equation was used to build the stability prediction model of eefoxitin sodium. The impurity as stability indicator was defined by analyzing the impurity profile of cefoxitin sodium. A high performance liquid chromatographic method was used to quantify the content of the indicative impurity under five different accelerated degradation conditions. The rate constants of degradation reaction were determined based on the principles of zero-order kinetics. Software SPSS 20.0 was applied to build the stability prediction models of cefoxitin sodium from four different manufacturing companies. Results： The equations for the four prediction models were as fullows： lnk = 26. 013/RT ＋ 0. 004 2 × （RH） ＋ 29. 093 for manufacturer A, lnk= 25. 940/RT ＋ 0. 018 6 × （RH） ＋ 29. 074 manufacturer B, lnk = 15. 225/RT ＋0. 002 6 × （RH） ＋ 13. 992 for manufacturer C, and lnk =25. 661/RT ＋0. 011 7 ×（RH） ＋ 28. 915 for manufacturer D, all R2 of the four prediction models were no less than 0.95 and all P 〈 0.05, indicating that all the prediction equations had good prediction performance. When the equations were applied to predict the long stability of cefoxitin sodium from four different companies, it was shown that the four products had different stabilities under the same storage condition. Conclusion： The prediction models can be applied not only in the prediction of the long stability of cefoxitin sodium, but also in the determination of storage conditions, transportation conditions and quality supervision circle, providing basis for the selection of raw materials for the reference standard. It was suggested that raw materials with longer stability period be selected based on the prediction model of the raw materials to improve the stability of the reference standard.