R语言缓释制剂QBD解决方案之二
药物层优化研究
在药物层工艺中水溶剂蒸发起到重要的作用。湿的环境会使丸子聚集，而干的环境影响药物与MCC的粘合。输入变量如气流量，喷雾速率，雾化压力，和产品温度对MCC沉着和包衣溶剂蒸发的平衡有影响。进行了带3个中心点的24-1分式析因实验。表57是设计总结和响应的可接受标准。药物溶液喷雾完成后，产品干燥直到55℃，控制湿度在2.0%以下。研究的响应包括细粒，聚集，含量。其它特征包括丸的表面粗糙度，包衣膜厚度，丸的粒径分布。表58是实验结果。
影响细粒形成的显著因子
如图30的半正态图所示，影响细粒的显著因子是D，A。这两个因子有强的交互作用。如图31的等值线图所示，细粒%随产品温 度和气流量增加而增加。
影响聚集的显著因子
如图32的半正态图所示，影响聚集的显著因子为D，A，B。其它项没有显著影响。产品温度和气流量对聚集的影响见图33。聚集随温度和气流量增加而减少。
影响含量的显著因子
如图34的半正态图所示，影响含量的显著因子为D，A。这两个因子有显著的交互作用。
library(FrF2)
study2<-FrF2(nruns=8, nfactors=4, generator=c("ABC"), ncenter=3, replications=1,randomize=FALSE)
y1=c(0.4,6.3,2.8,0.8,3.5,0.9,0.5,5.5,2.2,1.8,2.5)
y2=c(8.0,0.5,3.9,6.4,1.9,3.4,11.0,0.8,4.1,3.8,4.4)
y3=c(99.7,94.5,97.9,99.4,97.5,99.4,99.6,96.0,98.4,98.7,98.2)
study2 <-add.response(study2, y1, replace=FALSE)
study2 <-add.response(study2, y2, replace=FALSE)
study2 <-add.response(study2, y3, replace=FALSE)
print( study2, std.order=TRUE)
A.num <-study2$A
levels(A.num) <- c(80,120)
B.num <- study2$B
levels(B.num) <- c(25,45)
C.num <- study2$C
levels(C.num) <- c(1.2,2.0)
D.num <- study2$D
levels(D.num) <- c(42,50)
A.num <- as.numeric(as.character(A.num))
B.num <- as.numeric(as.character(B.num))
C.num <- as.numeric(as.character(C.num))
D.num <- as.numeric(as.character(D.num))
mod1<-lm(y1 ~ A*B*C*D, data=study2)
anova(mod1)
library(daewr)
fullnormal(coef(mod1)[-1], alpha=.025)
library(BsMD)
LenthPlot(mod1, main = "Lenth Plot of Effects")
effects <-coef(mod1)
effects <-effects[2:4]
effects <-effects[ !is.na(effects) ]
halfnorm(effects, names(effects), alpha=.25)
mod1<-lm(y1 ~ A.num*D.num, data=study2)
library(rsm)
contour(mod1, ~ D.num +A.num)
persp(mod1, ~ A.num +D.num, zlab=" y1", contours=list(z="bottom"))
mod2<-lm(y2 ~ A*B*C*D, data=study2)
anova(mod2)
> anova(mod2)
Analysis of Variance Table
Response: y2
Df Sum Sq Mean Sq F value Pr(>F)
A 1 23.461 23.461 260.6806 0.003814 **
B 1 8.611 8.611 95.6806 0.010290 *
C 1 0.361 0.361 4.0139 0.183032
D 1 58.861 58.861 654.0139 0.001526 **
A:B 1 0.361 0.361 4.0139 0.183032
A:C 1 1.711 1.711 19.0139 0.048777 *
B:C 1 2.761 2.761 30.6806 0.031082 *
A:B:C:D 1 0.328 0.328 3.6402 0.196632
Residuals 2 0.180 0.090
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Mod2<-lm(y2 ~ A.num* B.num *C.num* D.num, data=study2)
library(rsm)
contour(mod2, ~ D.num +A.num)
persp(mod2, ~ A.num +D.num, zlab=" y2", contours=list(z="bottom"))
mod3<-lm(y3 ~ A*B*C*D, data=study2)
anova(mod3)
> anova(mod3)
Analysis of Variance Table
Response: y3
Df Sum Sq Mean Sq F value Pr(>F)
A 1 3.6450 3.6450 57.5526 0.016935 *
B 1 0.4050 0.4050 6.3947 0.127214
C 1 0.1250 0.1250 1.9737 0.295239
D 1 18.6050 18.6050 293.7632 0.003387 **
A:B 1 0.1800 0.1800 2.8421 0.233869
A:C 1 0.5000 0.5000 7.8947 0.106763
B:C 1 2.4200 2.4200 38.2105 0.025186 *
A:B:C:D 1 0.4097 0.4097 6.4689 0.126020
Residuals 2 0.1267 0.0633
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
mod3<-lm(y3 ~ A.num*D.num, data=study2)
library(rsm)
contour(mod3, ~ D.num +A.num)
persp(mod3, ~ A.num +D.num, zlab=" y3", contours=list(z="bottom"))