【摘要】：目的:將球坐標變換、主成分二次推斷函數(shù)建模方法及改進非劣分類遺傳算法應用于混料設計緩控釋制劑處方優(yōu)化,解決緩控釋制劑混料數(shù)據(jù)的定和約束、共線性及評價指標間的重復測量的問題獲得藥物累積釋放度達到最佳釋放目標時的最優(yōu)處方配比,評價整套優(yōu)化方案的效果,為混料設計緩控釋制劑的建模與優(yōu)化提供一套合理、可行的方案。方法:對處方配比含零與不含零兩個實例進行探索性研究,以各時點累積釋放度為因變量,以各混料組分為自變量,利用球坐標變換消除自變量間的定和約束,主成分方法解決變量間的共線性問題,運用二次推斷函數(shù)方法,分別基于可交換相關矩陣、一階自相關矩陣、無結構相關矩陣建立模型,根據(jù)評價指標AIC,BIC選擇較優(yōu)模型,運用改進非劣分類遺傳算法(NSGA-Ⅱ)進行優(yōu)化,最后球坐標反變換獲得處方配比,與原文獻的優(yōu)化結果比較。結果:在尼莫地平骨架片處方配比不含零的處方優(yōu)化中,混料組分球坐標變換后,采用主成分分析提取了7個主成分,可解釋原信息的99.98%,運用二次推斷函數(shù)建模,以可交換相關矩陣建立的模型有統(tǒng)計學意義(Q=4.35,P=0.82),效果較好(AIC=20.3451,BIC=25.4575)。采用改進非劣分類遺傳算法優(yōu)化得到Pareto最優(yōu)解集:所有目標均在處方篩選范圍之內,其中有多個方案12h累積釋放度(Q12)在99%以上,能達到較好釋放。當HPMC、乳糖、海藻酸鈉的比例分別為:0.2649、0.6464、0.0887時,Q12可達到99.60%,3h、6h、9h的累積釋放度分別為:21.21%、50.66%,77.60%,均在處方篩選范圍內。原文獻通過構建Scheffé多項式模型,用等高線圖法從圖形中主觀挑選了5個解構成解方案集,其中有兩個方案9小時累積釋放度不在規(guī)定解范圍內。所有方案中12小時累積釋放度沒有99%以上的解。當HPMC、乳糖、海藻酸鈉的比例分別為:0.3458、0.4715、0.1627時,Q12最高才達到98.65%,比本文優(yōu)化結果低了0.95%。在甲硝唑緩釋片處方配比含零的處方優(yōu)化中,混料組分球坐標變換后,采用主成分分析提取了10個主成分,可解釋原信息的99.97%,運用二次推斷函數(shù)建模,以可交換相關矩陣建立的模型有統(tǒng)計學意義(Q=4.67,P=0.95),效果較好(AIC=26.6661,BIC=37.6192)。采用改進非劣分類遺傳算法優(yōu)化得到Pareto最優(yōu)解集:多個方案1~4天預測累積釋放度與釋放目標的差距不超過2%,5天累積釋放度均在90%以上。當甲硝唑、PCL、HPMC、GMS比例分別為0.044、0.817、0.115、0.023時,1~5天累積釋放度分別為40.32%、55.31%、70.9%、85.08%、92.13%。原文通過建立Scheffé多項式模型尋找到7個最優(yōu)方案,當?shù)?天和第4天預測累積釋放度都達到釋放標準時,其他時點預測累積釋放度與目標相差很大,不能同時接近最佳釋放目標。結論:采用球坐標變換消除混料組分的定和約束,主成分二次推斷函數(shù)解決共線性及累積釋放度間的相關性,將二者結合應用于緩控釋制劑混料數(shù)據(jù)建模,并采用NSGA-Ⅱ多目標遺傳算法進行組分配比優(yōu)化,再進行反變換獲得符合需要的最佳配方配比,這一整套方案應用于混料設計緩控釋制劑的優(yōu)化研究,是可行且合理的。對于有效解決混料設計緩控釋制劑的多目標優(yōu)化問題,有一定應用價值。
[Abstract]:OBJECTIVE: To apply spherical coordinate transformation, principal component quadratic inference function modeling method and improved non-inferior classification genetic algorithm to formulation optimization of sustained and controlled release preparations for mixture design, solve the problem of data constraints, collinearity and repeated measurements among evaluation indexes, and obtain the optimal cumulative drug release rate. METHODS: Two cases of zero and zero-free formulations were studied. The cumulative release rate at each time point was taken as a dependent variable, and the components of each mixture as an independent variable. Scalar transformation eliminates the definite sum constraints between independent variables and principal component analysis solves the problem of collinearity among variables. Using quadratic inference function method, the model is established based on commutative correlation matrix, first-order autocorrelation matrix and unstructured correlation matrix respectively. According to the evaluation index AIC and BIC, the better model is selected and the improved non-inferior classification genetic algorithm (NSGA-BIC) is used. Results: In the formulation optimization of nimodipine skeleton tablets without zero, seven principal components were extracted by principal component analysis after spherical coordinate transformation, which could explain 99.98% of the original information. The model based on interchangeable correlation matrix was statistically significant (Q = 4.35, P = 0.82), and the effect was better (AIC = 20.3451, BIC = 25.4575). The Pareto optimal solution set was obtained by using improved non-inferior classification genetic algorithm. All the targets were within the prescription selection range, and the cumulative release rate (Q12) of several schemes was above 99% in 12 hours, which could achieve better release. When the ratios of HPMC, lactose and sodium alginate were 0.2649, 0.6464 and 0.0887 respectively, the cumulative release rates of Q12 were 99.60%, 21.21%, 50.66% and 77.60% for 3, 6, and 9 hours respectively, which were all within the prescription selection range. The cumulative release rate of Q12 reached 98.65% at the ratios of HPMC, lactose and sodium alginate 0.3458, 0.4715 and 0.1627 respectively, which was 0.95% lower than the optimized results in this paper. In the optimization, 10 principal components were extracted by principal component analysis after spherical coordinate transformation, which could explain 99.97% of the original information. The model established by quadratic inference function was statistically significant (Q = 4.67, P = 0.95) and the effect was better (AIC = 26.6661, BIC = 37.6192). The optimization was carried out by improved non-inferior classification genetic algorithm. The Pareto optimal solution set was obtained: the difference between the predicted cumulative release rate and the release target in 1-4 days was less than 2%, and the cumulative release rate in 5 days was more than 90%. When the ratios of metronidazole, PCL, HPMC and GMS were 0.044, 0.817, 0.115, 0.023, the cumulative release rates in 1-5 days were 40.32%, 55.31%, 70.9%, 85.08% and 92.13% respectively. When the cumulative release rate reached the release standard on the first day and the fourth day, the cumulative release rate at other time points was very different from the target and could not approach the optimal release target at the same time. And the correlation between cumulative release rate, the combination of the two methods was applied to modeling the data of sustained and controlled release formulations, and NSGA-II multi-objective genetic algorithm was used to optimize the composition ratio, and then inverse transformation was carried out to obtain the optimal formulation ratio. This whole set of schemes was applied to the optimization research of sustained and controlled release formulations. It is of certain application value to solve the multi-objective optimization problem of slow release controlled release mixture design effectively.
【學位授予單位】：山西醫(yī)科大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：R943

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