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目的星点设计-效应面法优化美斯地浓聚乳酸纳米粒处方。方法以复乳液中干燥法制备美斯地浓聚乳酸纳米粒,以包封率和载药量为评价指标,在单因素试验的基础上,用星点设计对显著性因素进行优化,并进行二项式方程拟合,以效应面法选取较好的工艺条件进行预测。结果以效应面法优选出的最佳工艺为:美斯地浓投药量为49.20 mg,PLA浓度为3.31%,PVA浓度为3.41%。制备的美斯地浓聚乳酸纳米粒平均包封率和载药量分别为(51.98±1.28)%和(7.01±0.31)%(n=3),与二项式拟合方程预测值相差<2%。结论应用星点设计-效应面法优化美斯地浓聚乳酸纳米粒制备工艺,能够快速、准确的得到最佳制备工艺,预测性良好。
Aim of Star Design - Response Surface Methodology to Optimize the Prescription of Maze Concentrated Lactic Acid Nanoparticles. Methods The mesilastaxel polylactic acid nanoparticles were prepared by double emulsion method and the entrapment efficiency and drug loading were used as the evaluation indexes. Based on the single factor experiment, the conspicuity factors were optimized by using the star design. Binomial equation fitting, effect surface method to select the better process conditions for prediction. Results The optimal process was optimized by response surface methodology. The dosage of mesylate was 49.20 mg, the PLA concentration was 3.31% and the PVA concentration was 3.41%. The average entrapment efficiency and drug loading of the concentrated lactate nanoparticles were (51.98 ± 1.28)% and (7.01 ± 0.31)% (n = 3), respectively, which were different from those of the binomial fitting equation 2%. Conclusion The optimal preparation process of mesalase-rich PLA-lactic acid nanoparticles can be obtained by the method of apical design-response surface method, and the best preparation technology can be obtained quickly and accurately with good predictability.