藥物層優化研究
在藥物層工藝中水溶劑蒸發起到重要的作用。濕的環境會使丸子聚集，而干的環境影響藥物與MCC的粘合。輸入變量如氣流量，噴霧速率，霧化壓力，和產品溫度對MCC沉著和包衣溶劑蒸發的平衡有影響。進行了帶3個中心點的24-1分式析因實驗。表57是設計總結和響應的可接受標準。藥物溶液噴霧完成后，產品干燥直到55℃，控制濕度在2.0%以下。研究的響應包括細粒，聚集，含量。其它特征包括丸的表面粗糙度，包衣膜厚度，丸的粒徑分布。表58是實驗結果。
影響細粒形成的顯著因子
如圖30的半正態圖所示，影響細粒的顯著因子是D，A。這兩個因子有強的交互作用。如圖31的等值線圖所示，細粒%隨產品溫 度和氣流量增加而增加。
影響聚集的顯著因子
如圖32的半正態圖所示，影響聚集的顯著因子為D，A，B。其它項沒有顯著影響。產品溫度和氣流量對聚集的影響見圖33。聚集隨溫度和氣流量增加而減少。
影響含量的顯著因子
如圖34的半正態圖所示，影響含量的顯著因子為D，A。這兩個因子有顯著的交互作用。
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"))
