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Copy pathGlobal SA simulation in chunks file (Human).R
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Global SA simulation in chunks file (Human).R
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phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[1:2500,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO
Vsmax_L_GST<-phys$Vsmax_L_GST
Km_SI_CA<-phys$Km_SI_CA
Km_SI_AO<-phys$Km_SI_AO
Km_SI_OH<-phys$Km_SI_OH
Km_SI_GST<-phys$Km_SI_GST
Km_SI_GST_G<-phys$Km_SI_GST_G
Vsmax_SI_CA<-phys$Vsmax_SI_CA
Vsmax_SI_AO<-phys$Vsmax_SI_AO
Vsmax_SI_OH<-phys$Vsmax_SI_OH
Vsmax_SI_GST<-phys$Vsmax_SI_GST
Volume_exposure_chamber<- phys$Volume_exposure_chamber
S9_scaling_SI<-phys$S9_scaling_SI
S9_scaling_L<-phys$S9_scaling_L
parameters1 <- cbind( P_F,
P_L,
P_SI,
P_RP,
P_SP,
P_B,
P_Pu,
P_OH_F,
P_OH_L,
P_OH_SI,
P_OH_RP,
P_OH_SP,
P_OH_Pu,
V_F,
V_L,
V_SI,
V_A,
V_V,
V_RP,
V_SP,
V_Pu,
Q_C,
Q_Pu,
Q_F,
Q_L,
Q_SI,
Q_RP,
Q_SP,
P_V,
G_SYN_L,
G_SYN_SI,
k_L_GLOS,
k_SI_GLOS,
init_GSH_L,
init_GSH_SI,
k_GSH,
C_PRO_L,
C_PRO_SI,
Ka,
k_L_OH,
Km_L_CA,
Km_L_AO,
Km_L_GST,
Km_L_GST_G,
Vsmax_L_CA,
Vsmax_L_AO,
Vsmax_L_GST,
Km_SI_CA,
Km_SI_AO,
Km_SI_OH,
Km_SI_GST,
Km_SI_GST_G,
Vsmax_SI_CA,
Vsmax_SI_AO,
Vsmax_SI_OH,
Vsmax_SI_GST,
Volume_exposure_chamber,
S9_scaling_SI,
S9_scaling_L)
#exposure
amount.units <-"umol"
time.units <-"h"
nbr.doses <-1 #number of doses
time.0 <-0 #time start dosing
time.end <-8 #time end of simulation
time.frame <-0.1 #time steps of simulation
MW <-132.16 #The molecular weight of Cinnamaldehyde
Inhalation_Dose_in_mg_bw <-0 #The inhaled dose in mg/kg-bw
Oral_Dose_in_mg_bw <-250 #Dose in mg/kg-bw
#generating a bodyweight data frame for adjusting the dose
Data_BW<-as.data.frame(c(1:2500))
for(i in 1:2500){
Data_BW[i,2]<- sum(phys[i,c(16,17,18,19,20,21,22,23)])
}
colnames(Data_BW)<- c("id","BW")
ex1 <- eventTable(amount.units = amount.units, time.units = time.units) %>%
et(id=1:2500,seq(from = time.0, to = time.end, by = time.frame))%>%
et(id=1:2500,amt=(Oral_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.01, cmt="A_GI", nbr.doses=nbr.doses)%>%
et(id=1:2500,amt=(Inhalation_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.08, cmt="A_inhalation_Dose", nbr.doses=nbr.doses)%>%
et(id=1:2500,amt=phys$init_GSH_SI, dur=0.01, cmt="AM_SIc_GSH", nbr.doses=1)%>%
et(id=1:2500,amt=phys$init_GSH_L, dur=0.01, cmt="AM_Lc_GSH", nbr.doses=1)
inits <- c("A_GI" =0,
"A_Exhalation" =0,
"A_inhalation_Dose" =0,
"A_OH_Pu" =0,
"A_V" =0,
"A_OH_V" =0,
"A_F" =0,
"A_OH_F" =0,
"AM_L_CA" =0,
"AM_L_AO" =0,
"AM_L_AG_GST" =0,
"AM_L_AG_CHEM" =0,
"AM_L_AP" =0,
"A_OH_M_L_C_A" =0,
"A_OH_L" =0,
"A_L" =0,
"AM_Lc_GSH" =0,
"AM_SI_CA" =0,
"AM_SI_AO" =0,
"AM_SI_AG_GST" =0,
"AM_SI_AG_CHEM"=0,
"AM_SI_AP" =0,
"A_OH_M_SI_C_A"=0,
"A_OH_SI" =0,
"A_SI" =0,
"AM_SIc_GSH" =0,
"A_RP" =0,
"A_OH_RP" =0,
"A_SP" =0,
"A_OH_SP" =0
);
#Run the model after assigning the sobol dataset to the variables
solve.pbk_nonpop1 <- solve(PBK_Cinnamaldehyde, parameters1, events = ex1, inits) #Solve the PBPK model
write.csv(solve.pbk_nonpop1,"D:/PBK/Cinnamaldehyde-pbk\\\\solve.pbk_nonpop1", row.names = TRUE)
phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[2501:5000,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO
Vsmax_L_GST<-phys$Vsmax_L_GST
Km_SI_CA<-phys$Km_SI_CA
Km_SI_AO<-phys$Km_SI_AO
Km_SI_OH<-phys$Km_SI_OH
Km_SI_GST<-phys$Km_SI_GST
Km_SI_GST_G<-phys$Km_SI_GST_G
Vsmax_SI_CA<-phys$Vsmax_SI_CA
Vsmax_SI_AO<-phys$Vsmax_SI_AO
Vsmax_SI_OH<-phys$Vsmax_SI_OH
Vsmax_SI_GST<-phys$Vsmax_SI_GST
Volume_exposure_chamber<- phys$Volume_exposure_chamber
S9_scaling_SI<-phys$S9_scaling_SI
S9_scaling_L<-phys$S9_scaling_L
parameters1 <- cbind( P_F,
P_L,
P_SI,
P_RP,
P_SP,
P_B,
P_Pu,
P_OH_F,
P_OH_L,
P_OH_SI,
P_OH_RP,
P_OH_SP,
P_OH_Pu,
V_F,
V_L,
V_SI,
V_A,
V_V,
V_RP,
V_SP,
V_Pu,
Q_C,
Q_Pu,
Q_F,
Q_L,
Q_SI,
Q_RP,
Q_SP,
P_V,
G_SYN_L,
G_SYN_SI,
k_L_GLOS,
k_SI_GLOS,
init_GSH_L,
init_GSH_SI,
k_GSH,
C_PRO_L,
C_PRO_SI,
Ka,
k_L_OH,
Km_L_CA,
Km_L_AO,
Km_L_GST,
Km_L_GST_G,
Vsmax_L_CA,
Vsmax_L_AO,
Vsmax_L_GST,
Km_SI_CA,
Km_SI_AO,
Km_SI_OH,
Km_SI_GST,
Km_SI_GST_G,
Vsmax_SI_CA,
Vsmax_SI_AO,
Vsmax_SI_OH,
Vsmax_SI_GST,
Volume_exposure_chamber,
S9_scaling_SI,
S9_scaling_L)
#exposure
amount.units <-"umol"
time.units <-"h"
nbr.doses <-1 #number of doses
time.0 <-0 #time start dosing
time.end <-8 #time end of simulation
time.frame <-0.1 #time steps of simulation
MW <-132.16 #The molecular weight of Cinnamaldehyde
Inhalation_Dose_in_mg_bw <-0 #The inhaled dose in mg/kg-bw
Oral_Dose_in_mg_bw <-250 #Dose in mg/kg-bw
#generating a bodyweight data frame for adjusting the dose
Data_BW<-as.data.frame(c(2501:5000))
for(i in 1:2500){
Data_BW[i,2]<- sum(phys[i,c(16,17,18,19,20,21,22,23)])
}
colnames(Data_BW)<- c("id","BW")
ex1 <- eventTable(amount.units = amount.units, time.units = time.units) %>%
et(id=2501:5000,seq(from = time.0, to = time.end, by = time.frame))%>%
et(id=2501:5000,amt=(Oral_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.01, cmt="A_GI", nbr.doses=nbr.doses)%>%
et(id=2501:5000,amt=(Inhalation_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.08, cmt="A_inhalation_Dose", nbr.doses=nbr.doses)%>%
et(id=2501:5000,amt=phys$init_GSH_SI, dur=0.01, cmt="AM_SIc_GSH", nbr.doses=1)%>%
et(id=2501:5000,amt=phys$init_GSH_L, dur=0.01, cmt="AM_Lc_GSH", nbr.doses=1)
inits <- c("A_GI" =0,
"A_Exhalation" =0,
"A_inhalation_Dose" =0,
"A_OH_Pu" =0,
"A_V" =0,
"A_OH_V" =0,
"A_F" =0,
"A_OH_F" =0,
"AM_L_CA" =0,
"AM_L_AO" =0,
"AM_L_AG_GST" =0,
"AM_L_AG_CHEM" =0,
"AM_L_AP" =0,
"A_OH_M_L_C_A" =0,
"A_OH_L" =0,
"A_L" =0,
"AM_Lc_GSH" =0,
"AM_SI_CA" =0,
"AM_SI_AO" =0,
"AM_SI_AG_GST" =0,
"AM_SI_AG_CHEM"=0,
"AM_SI_AP" =0,
"A_OH_M_SI_C_A"=0,
"A_OH_SI" =0,
"A_SI" =0,
"AM_SIc_GSH" =0,
"A_RP" =0,
"A_OH_RP" =0,
"A_SP" =0,
"A_OH_SP" =0
);
#Run the model after assigning the sobol dataset to the variables
solve.pbk_nonpop1 <- solve(PBK_Cinnamaldehyde, parameters1, events = ex1, inits) #Solve the PBPK model
write.csv(solve.pbk_nonpop1,"D:/PBK/Cinnamaldehyde-pbk\\\\solve.pbk_nonpop2", row.names = TRUE)
phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[5001:7500,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO
Vsmax_L_GST<-phys$Vsmax_L_GST
Km_SI_CA<-phys$Km_SI_CA
Km_SI_AO<-phys$Km_SI_AO
Km_SI_OH<-phys$Km_SI_OH
Km_SI_GST<-phys$Km_SI_GST
Km_SI_GST_G<-phys$Km_SI_GST_G
Vsmax_SI_CA<-phys$Vsmax_SI_CA
Vsmax_SI_AO<-phys$Vsmax_SI_AO
Vsmax_SI_OH<-phys$Vsmax_SI_OH
Vsmax_SI_GST<-phys$Vsmax_SI_GST
Volume_exposure_chamber<- phys$Volume_exposure_chamber
S9_scaling_SI<-phys$S9_scaling_SI
S9_scaling_L<-phys$S9_scaling_L
parameters1 <- cbind( P_F,
P_L,
P_SI,
P_RP,
P_SP,
P_B,
P_Pu,
P_OH_F,
P_OH_L,
P_OH_SI,
P_OH_RP,
P_OH_SP,
P_OH_Pu,
V_F,
V_L,
V_SI,
V_A,
V_V,
V_RP,
V_SP,
V_Pu,
Q_C,
Q_Pu,
Q_F,
Q_L,
Q_SI,
Q_RP,
Q_SP,
P_V,
G_SYN_L,
G_SYN_SI,
k_L_GLOS,
k_SI_GLOS,
init_GSH_L,
init_GSH_SI,
k_GSH,
C_PRO_L,
C_PRO_SI,
Ka,
k_L_OH,
Km_L_CA,
Km_L_AO,
Km_L_GST,
Km_L_GST_G,
Vsmax_L_CA,
Vsmax_L_AO,
Vsmax_L_GST,
Km_SI_CA,
Km_SI_AO,
Km_SI_OH,
Km_SI_GST,
Km_SI_GST_G,
Vsmax_SI_CA,
Vsmax_SI_AO,
Vsmax_SI_OH,
Vsmax_SI_GST,
Volume_exposure_chamber,
S9_scaling_SI,
S9_scaling_L)
#exposure
amount.units <-"umol"
time.units <-"h"
nbr.doses <-1 #number of doses
time.0 <-0 #time start dosing
time.end <-8 #time end of simulation
time.frame <-0.1 #time steps of simulation
MW <-132.16 #The molecular weight of Cinnamaldehyde
Inhalation_Dose_in_mg_bw <-0 #The inhaled dose in mg/kg-bw
Oral_Dose_in_mg_bw <-250 #Dose in mg/kg-bw
#generating a bodyweight data frame for adjusting the dose
Data_BW<-as.data.frame(c(5001:7500))
for(i in 1:2500){
Data_BW[i,2]<- sum(phys[i,c(16,17,18,19,20,21,22,23)])
}
colnames(Data_BW)<- c("id","BW")
ex1 <- eventTable(amount.units = amount.units, time.units = time.units) %>%
et(id=5001:7500,seq(from = time.0, to = time.end, by = time.frame))%>%
et(id=5001:7500,amt=(Oral_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.01, cmt="A_GI", nbr.doses=nbr.doses)%>%
et(id=5001:7500,amt=(Inhalation_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.08, cmt="A_inhalation_Dose", nbr.doses=nbr.doses)%>%
et(id=5001:7500,amt=phys$init_GSH_SI, dur=0.01, cmt="AM_SIc_GSH", nbr.doses=1)%>%
et(id=5001:7500,amt=phys$init_GSH_L, dur=0.01, cmt="AM_Lc_GSH", nbr.doses=1)
inits <- c("A_GI" =0,
"A_Exhalation" =0,
"A_inhalation_Dose" =0,
"A_OH_Pu" =0,
"A_V" =0,
"A_OH_V" =0,
"A_F" =0,
"A_OH_F" =0,
"AM_L_CA" =0,
"AM_L_AO" =0,
"AM_L_AG_GST" =0,
"AM_L_AG_CHEM" =0,
"AM_L_AP" =0,
"A_OH_M_L_C_A" =0,
"A_OH_L" =0,
"A_L" =0,
"AM_Lc_GSH" =0,
"AM_SI_CA" =0,
"AM_SI_AO" =0,
"AM_SI_AG_GST" =0,
"AM_SI_AG_CHEM"=0,
"AM_SI_AP" =0,
"A_OH_M_SI_C_A"=0,
"A_OH_SI" =0,
"A_SI" =0,
"AM_SIc_GSH" =0,
"A_RP" =0,
"A_OH_RP" =0,
"A_SP" =0,
"A_OH_SP" =0
);
#Run the model after assigning the sobol dataset to the variables
solve.pbk_nonpop1 <- solve(PBK_Cinnamaldehyde, parameters1, events = ex1, inits) #Solve the PBPK model
write.csv(solve.pbk_nonpop1,"D:/PBK/Cinnamaldehyde-pbk\\solve.pbk_nonpop3", row.names = TRUE)
phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[7501:10000,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO
Vsmax_L_GST<-phys$Vsmax_L_GST
Km_SI_CA<-phys$Km_SI_CA
Km_SI_AO<-phys$Km_SI_AO
Km_SI_OH<-phys$Km_SI_OH
Km_SI_GST<-phys$Km_SI_GST
Km_SI_GST_G<-phys$Km_SI_GST_G
Vsmax_SI_CA<-phys$Vsmax_SI_CA
Vsmax_SI_AO<-phys$Vsmax_SI_AO
Vsmax_SI_OH<-phys$Vsmax_SI_OH
Vsmax_SI_GST<-phys$Vsmax_SI_GST
Volume_exposure_chamber<- phys$Volume_exposure_chamber
S9_scaling_SI<-phys$S9_scaling_SI
S9_scaling_L<-phys$S9_scaling_L
parameters1 <- cbind( P_F,
P_L,
P_SI,
P_RP,
P_SP,
P_B,
P_Pu,
P_OH_F,
P_OH_L,
P_OH_SI,
P_OH_RP,
P_OH_SP,
P_OH_Pu,
V_F,
V_L,
V_SI,
V_A,
V_V,
V_RP,
V_SP,
V_Pu,
Q_C,
Q_Pu,
Q_F,
Q_L,
Q_SI,
Q_RP,
Q_SP,
P_V,
G_SYN_L,
G_SYN_SI,
k_L_GLOS,
k_SI_GLOS,
init_GSH_L,
init_GSH_SI,
k_GSH,
C_PRO_L,
C_PRO_SI,
Ka,
k_L_OH,
Km_L_CA,
Km_L_AO,
Km_L_GST,
Km_L_GST_G,
Vsmax_L_CA,
Vsmax_L_AO,
Vsmax_L_GST,
Km_SI_CA,
Km_SI_AO,
Km_SI_OH,
Km_SI_GST,
Km_SI_GST_G,
Vsmax_SI_CA,
Vsmax_SI_AO,
Vsmax_SI_OH,
Vsmax_SI_GST,
Volume_exposure_chamber,
S9_scaling_SI,
S9_scaling_L)
#exposure
amount.units <-"umol"
time.units <-"h"
nbr.doses <-1 #number of doses
time.0 <-0 #time start dosing
time.end <-8 #time end of simulation
time.frame <-0.1 #time steps of simulation
MW <-132.16 #The molecular weight of Cinnamaldehyde
Inhalation_Dose_in_mg_bw <-0 #The inhaled dose in mg/kg-bw
Oral_Dose_in_mg_bw <-250 #Dose in mg/kg-bw
#generating a bodyweight data frame for adjusting the dose
Data_BW<-as.data.frame(c(7501:10000))
for(i in 1:2500){
Data_BW[i,2]<- sum(phys[i,c(16,17,18,19,20,21,22,23)])
}
colnames(Data_BW)<- c("id","BW")
ex1 <- eventTable(amount.units = amount.units, time.units = time.units) %>%
et(id=7501:10000,seq(from = time.0, to = time.end, by = time.frame))%>%
et(id=7501:10000,amt=(Oral_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.01, cmt="A_GI", nbr.doses=nbr.doses)%>%
et(id=7501:10000,amt=(Inhalation_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.08, cmt="A_inhalation_Dose", nbr.doses=nbr.doses)%>%
et(id=7501:10000,amt=phys$init_GSH_SI, dur=0.01, cmt="AM_SIc_GSH", nbr.doses=1)%>%
et(id=7501:10000,amt=phys$init_GSH_L, dur=0.01, cmt="AM_Lc_GSH", nbr.doses=1)
inits <- c("A_GI" =0,
"A_Exhalation" =0,
"A_inhalation_Dose" =0,
"A_OH_Pu" =0,
"A_V" =0,
"A_OH_V" =0,
"A_F" =0,
"A_OH_F" =0,
"AM_L_CA" =0,
"AM_L_AO" =0,
"AM_L_AG_GST" =0,
"AM_L_AG_CHEM" =0,
"AM_L_AP" =0,
"A_OH_M_L_C_A" =0,
"A_OH_L" =0,
"A_L" =0,
"AM_Lc_GSH" =0,
"AM_SI_CA" =0,
"AM_SI_AO" =0,
"AM_SI_AG_GST" =0,
"AM_SI_AG_CHEM"=0,
"AM_SI_AP" =0,
"A_OH_M_SI_C_A"=0,
"A_OH_SI" =0,
"A_SI" =0,
"AM_SIc_GSH" =0,
"A_RP" =0,
"A_OH_RP" =0,
"A_SP" =0,
"A_OH_SP" =0
);
#Run the model after assigning the sobol dataset to the variables
solve.pbk_nonpop1 <- solve(PBK_Cinnamaldehyde, parameters1, events = ex1, inits) #Solve the PBPK model
write.csv(solve.pbk_nonpop1,"D:/PBK/Cinnamaldehyde-pbk\\solve.pbk_nonpop4", row.names = TRUE)
phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[10001:12500,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO
Vsmax_L_GST<-phys$Vsmax_L_GST
Km_SI_CA<-phys$Km_SI_CA
Km_SI_AO<-phys$Km_SI_AO
Km_SI_OH<-phys$Km_SI_OH
Km_SI_GST<-phys$Km_SI_GST
Km_SI_GST_G<-phys$Km_SI_GST_G
Vsmax_SI_CA<-phys$Vsmax_SI_CA
Vsmax_SI_AO<-phys$Vsmax_SI_AO
Vsmax_SI_OH<-phys$Vsmax_SI_OH
Vsmax_SI_GST<-phys$Vsmax_SI_GST
Volume_exposure_chamber<- phys$Volume_exposure_chamber
S9_scaling_SI<-phys$S9_scaling_SI
S9_scaling_L<-phys$S9_scaling_L
parameters1 <- cbind( P_F,
P_L,
P_SI,
P_RP,
P_SP,
P_B,
P_Pu,
P_OH_F,
P_OH_L,
P_OH_SI,
P_OH_RP,
P_OH_SP,
P_OH_Pu,
V_F,
V_L,
V_SI,
V_A,
V_V,
V_RP,
V_SP,
V_Pu,
Q_C,
Q_Pu,
Q_F,
Q_L,
Q_SI,
Q_RP,
Q_SP,
P_V,
G_SYN_L,
G_SYN_SI,
k_L_GLOS,
k_SI_GLOS,
init_GSH_L,
init_GSH_SI,
k_GSH,
C_PRO_L,
C_PRO_SI,
Ka,
k_L_OH,
Km_L_CA,
Km_L_AO,
Km_L_GST,
Km_L_GST_G,
Vsmax_L_CA,
Vsmax_L_AO,
Vsmax_L_GST,
Km_SI_CA,
Km_SI_AO,
Km_SI_OH,
Km_SI_GST,
Km_SI_GST_G,
Vsmax_SI_CA,
Vsmax_SI_AO,
Vsmax_SI_OH,
Vsmax_SI_GST,
Volume_exposure_chamber,
S9_scaling_SI,
S9_scaling_L)
#exposure
amount.units <-"umol"
time.units <-"h"
nbr.doses <-1 #number of doses
time.0 <-0 #time start dosing
time.end <-8 #time end of simulation
time.frame <-0.1 #time steps of simulation
MW <-132.16 #The molecular weight of Cinnamaldehyde
Inhalation_Dose_in_mg_bw <-0 #The inhaled dose in mg/kg-bw
Oral_Dose_in_mg_bw <-250 #Dose in mg/kg-bw
#generating a bodyweight data frame for adjusting the dose
Data_BW<-as.data.frame(c(10001:12500))
for(i in 1:2500){
Data_BW[i,2]<- sum(phys[i,c(16,17,18,19,20,21,22,23)])
}
colnames(Data_BW)<- c("id","BW")
ex1 <- eventTable(amount.units = amount.units, time.units = time.units) %>%
et(id=10001:12500,seq(from = time.0, to = time.end, by = time.frame))%>%
et(id=10001:12500,amt=(Oral_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.01, cmt="A_GI", nbr.doses=nbr.doses)%>%
et(id=10001:12500,amt=(Inhalation_Dose_in_mg_bw) * Data_BW$BW/ MW * 1e+3 , dur=0.08, cmt="A_inhalation_Dose", nbr.doses=nbr.doses)%>%
et(id=10001:12500,amt=phys$init_GSH_SI, dur=0.01, cmt="AM_SIc_GSH", nbr.doses=1)%>%
et(id=10001:12500,amt=phys$init_GSH_L, dur=0.01, cmt="AM_Lc_GSH", nbr.doses=1)
inits <- c("A_GI" =0,
"A_Exhalation" =0,
"A_inhalation_Dose" =0,
"A_OH_Pu" =0,
"A_V" =0,
"A_OH_V" =0,
"A_F" =0,
"A_OH_F" =0,
"AM_L_CA" =0,
"AM_L_AO" =0,
"AM_L_AG_GST" =0,
"AM_L_AG_CHEM" =0,
"AM_L_AP" =0,
"A_OH_M_L_C_A" =0,
"A_OH_L" =0,
"A_L" =0,
"AM_Lc_GSH" =0,
"AM_SI_CA" =0,
"AM_SI_AO" =0,
"AM_SI_AG_GST" =0,
"AM_SI_AG_CHEM"=0,
"AM_SI_AP" =0,
"A_OH_M_SI_C_A"=0,
"A_OH_SI" =0,
"A_SI" =0,
"AM_SIc_GSH" =0,
"A_RP" =0,
"A_OH_RP" =0,
"A_SP" =0,
"A_OH_SP" =0
);
#Run the model after assigning the sobol dataset to the variables
solve.pbk_nonpop1 <- solve(PBK_Cinnamaldehyde, parameters1, events = ex1, inits) #Solve the PBPK model
write.csv(solve.pbk_nonpop1,"D:/PBK/Cinnamaldehyde-pbk\\solve.pbk_nonpop5", row.names = TRUE)
phys <- read_csv("GSA_phys_human_250mg")
#Running the Global SA directly takes to much memory so it is necessary to split up the data set in parts
phys<-phys[12501:15000,]
P_F<-phys$P_F
P_L<-phys$P_L
P_SI<-phys$P_SI
P_RP<-phys$P_RP
P_B<-phys$P_B
P_SP<-phys$P_SP
P_Pu<-phys$P_Pu
P_OH_F<-phys$P_OH_F
P_OH_L<-phys$P_OH_L
P_OH_SI<-phys$P_OH_SI
P_OH_RP<-phys$P_OH_RP
P_OH_SP<-phys$P_OH_SP
P_OH_Pu<-phys$P_OH_Pu
V_L<-phys$V_L
V_F<-phys$V_F
V_A<-phys$V_A
V_V<-phys$V_V
V_SI<-phys$V_SI
V_Pu<-phys$V_Pu
V_RP<-phys$V_RP
V_SP<-phys$V_SP
Q_C<-phys$Q_C
Q_SI<-phys$Q_SI
Q_F<-phys$Q_F
Q_L<-phys$Q_L
Q_Pu<-phys$Q_Pu
Q_RP<-phys$Q_RP
Q_SP<-phys$Q_SP
P_V<-phys$P_V
G_SYN_L<-phys$G_SYN_L
G_SYN_SI<-phys$G_SYN_SI
k_L_GLOS<-phys$k_L_GLOS
k_SI_GLOS<-phys$k_SI_GLOS
init_GSH_L<-phys$init_GSH_L
init_GSH_SI<-phys$init_GSH_SI
k_GSH<-phys$k_GSH
C_PRO_L<-phys$C_PRO_L
C_PRO_SI<-phys$C_PRO_SI
Ka<-phys$Ka
k_L_OH <- phys$k_L_OH
Km_L_CA<-phys$Km_L_CA
Km_L_AO<-phys$Km_L_AO
Km_L_GST<-phys$Km_L_GST
Km_L_GST_G<-phys$Km_L_GST_G
Vsmax_L_CA<-phys$Vsmax_L_CA
Vsmax_L_AO<-phys$Vsmax_L_AO