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Full body model_V1.2.R
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Full body model_V1.2.R
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# Whole-body PBPK model for Doxorubicin
#DNA content: 2.15 ug
rm(list=ls(all=TRUE))
#REQUIRED PACKAGES:
require(distr)
require(data.table)
require(dplyr)
require(ggplot2)
require(deSolve)
require(plotly)
{
t_end <- 45 #[h] time of the end of simulation
times <- seq(0, t_end, by = 0.1) #time of simulation
age <- 25 #the age of chosen sample
gender <- 'male'
weight <- 70 #[kg]
height <- 180 #[cm]
BSA <- weight ^ 0.425 * height ^ 0.725 * 0.007184 #[m2] Body surface area according to [DuBois-DuBois 1916]
oral_dose <- 0 #[mg] oral bolus dose
inf_dose <- 50 * BSA #[mg] infusion dose according to Pfizer guidance : https://www.pfizermedicalinformation.com/en-us/doxorubicin/dosage-admin
inf_time <- 2 #[h] infusion time
CO <- 1.1 * BSA - 0.05 * age + 5.5 #[L/min] cardiac output
CO <- CO * 60 #[L/h] cardiac output units change from [L/min] to [L/h]
#SEX DEPENDENT BLOOD FLOWS for healthy population according to [Simycp Simulator v.16]
# BLOOD FLOWS [L/h] -------------------------------------------------------
Qbr <- CO * 0.12 #Brain
Qhe <- CO * 0.04 #Heart
Qsk <- CO * 0.05 #Skin
Qmu <- CO * 0.17 #Muscle
Qad <- CO * 0.05 #Adipose
Qsp <- CO * 0.02 #Spleen
Qgu <- CO * 0.16 #Gut
Qki <- CO * 0.19 #Kidney
Qha <- CO * 0.065#Hepatic artery
Qbo <- CO * 0.05 #Bone
Qre <- CO * 0.085#Rest
Qlu <- CO#lung
Qli <- Qha + Qsp + Qgu#Liver
# ORGAN VOLUMES [L] -----------------------------------------------------------
#BW fraction (according to Simcyp Simulator) * random BW / tissue density
Vad <- (0.259 * weight) / 0.923
Vbo <- (0.090 * weight) / 1.850
Vbr <- (0.017 * weight) / 1.04
Vgu <- (0.016 * weight) / 1.04
Vhe <- (0.005 * weight) / 1.04
Vki <- (0.004 * weight) / 1.05
Vli <- (0.022 * weight) / 1.08
Vlu <- (0.007 * weight) / 1.05
Vmu <- (0.403 * weight) / 1.04
Vsk <- (0.043 * weight) / 1.1
Vsp <- (0.002 * weight) / 1.06
Vre <- (0.057 * weight) / 1.05
Vpl <- (0.044 * weight) / 1.025
Vrb <- (0.031 * weight) / 1.125
Vbl <- Vpl + Vrb
# CARDIOMYOCYTE VOLUME AND SURFACE AREA according to [Polak 2012] -------------------------------------------------------
MV <- exp(age * 0.04551 + 7.36346) #[cm^3]
MSA <-exp(sqrt(0.102 ^ 2 + (log(MV)) ^ 2 * 0.002939 ^ 2)) #[cm^2]
}
{
# MODEL -------------------------------------------------------------------
#Arguments of the function are the model parameters that vary
ModelVar <- function (BW,
CO,
MPPGL,
Vad,
Vbl,
Vrb,
Vbo,
Vbr,
Vgu,
Vheart,
Vki,
Vli,
Vlu,
Vpl,
Vmu,
Vsk,
Vsp,
Vre,
Qre,
Qad,
Qbo,
Qbr,
Qgu,
Qheart,
Qki,
Qh,
Qlu,
Qmu,
Qsk,
Qsp,
CYP3A4,
tlag,
Fabs,
t_end,
fup,
BP,
MV,
MSA)
times <- seq(0, t_end, by = 0.1)
# PHYSICO-CHEMICAL PARAMETERS OF DOX -------------------------------------------------------
MW <- 543.52 #[g/mol] Molecular weight
pKa <- 8.22 # [Zahra 2020]
HBD <- 6 #number of hydrogen bond donors: [PubChem Compound Database; CID = 31703];
PSA <- 206 #Polar Surface Area: [PubChem Compound Database; CID = 31703]
logD74 <- 0.02 #octanol/water distribution coefficient at pH 7.4 [Alves 2017]
# PHYSIOLOGICAL PARAMETERS -------------------------------------------------------
liver_density <- 1080 #[g/L]
heart_density <- 1055 #[g/L] [Alexandra 2019]
#Tissue volumes [L] -------------------------------------------------------
Vendo = 0.5 * Vhe #endocardial
Vmid = 0.3 * Vhe #midmyocardial
Vepi = 0.2 * Vhe #epicardial
Vendo_ic = 87.5 / 100 * Vendo #intracellular space of endocardial [Sjogaard 1982] -> data for skeletal muscle; calculated the proportion of extracellular water to total water in skeletal muscle
Vepi_ic = 87.5 / 100 * Vepi #intracellular space of epicardial
Vmid_ic = 87.5 / 100 * Vmid #intracellular space of midmyocardial
Vhe_ec = 12.5 / 100 * Vhe #extracellular space of heart tissue
Vve = (2 / 3) * Vbl #venous blood; assumed 2/3 of total blood according to volmues published in CPT. Regarding the distribution of blood volume within the circulation, the greatest volume resides in the venous vasculature, where 70-80% of the blood volume is found. -> http://www.cvphysiology.com/Blood%20Pressure/BP019
Var = Vbl - Vve #arterial blood
Vplas_ven = Vpl * (Vve / (Vve + Var)) #venous plasma
Vplas_art = Vpl * (Var / (Vve + Var)) #arterial plasma
#myocyte volume -------------------------------------------------------
ML <- 134 #[mcm] myocyte length [Tracy 2011, Gerdes 1995]
MB <- ML / 7 #=2r [mcm] myocyte breadth ML:MB = 7:1 [Tracy 2011, Gerdes 1995]
MVol <- MV * (10 ^ -15) #[L] random age dependent myocate volume in cm3 -> changing to liters
#cells amounts -------------------------------------------------------
cell_amount_epi <- Vepi_ic / MVol #epicardial
cell_amount_mid <- Vmid_ic / MVol #midmyocardial
cell_amount_endo <- Vendo_ic / MVol #endocardial
#cells concentration -------------------------------------------------------
cell_concentration_epi <- cell_amount_epi / Vhe #epicardial
cell_concentration_mid <- cell_amount_mid / Vhe #midmyocardial
cell_concentration_endo <- cell_amount_endo / Vhe#endocardial
#heart DNA: 90.6 ng/ul = 90.6 mg/l from literature -------------------------------------------------------
DNA_epi <- 2.15 * 0.001 / Vhe
DNA_mid <- 2.15 * 0.001 / Vhe
DNA_endo <- 2.15 * 0.001 / Vhe
mtDNA_epi <- 2.15 * 0.001 * 0.01 / Vhe
mtDNA_mid <- 2.15 * 0.001 * 0.01 / Vhe
mtDNA_endo <- 2.15 * 0.001 * 0.01 / Vhe
#DNA concentration (6pg = 6 * 10^-9 mg DNA per cell) # DNA and mtDNA concentration, assume epi:mid:endo = 0.2 : 0.3 : 0.5 [DOI:10.6000/1927-5129.2017.13.35]
#DNA_epi <- cell_concentration_epi * 6 * 10 ^ -9
#DNA_mid <- cell_concentration_mid * 6 * 10 ^ -9
#DNA_endo <- cell_concentration_endo * 6 * 10 ^ -9
#mtDNA_epi <- DNA_epi * 0.01
#mtDNA_mid <- DNA_mid * 0.01
#mtDNA_endo <- DNA_endo * 0.01
#PER：cytoplasmic membrane permeability coefficient
PER <- 0.0756 # cm/h （huahe Unpublished）
#surface area -------------------------------------------------------
SA_epi <- (cell_amount_epi * MSA) / (10 ^ 8) #[cm^2]
SA_mid <- (cell_amount_mid * MSA) / (10 ^ 8) #[cm^2]
SA_endo <- (cell_amount_endo * MSA) / (10 ^ 8) #[cm^2]
SA_bloodcell <- 25800 #[dm2] [Huahe paper]
# PARAMETERS FOR ICF and ECF in heart tissue -------------------------------------------------------
pH_ic <- 7.2 #[Vaugha-Jones 2009, Zheng 2005]
pH_ec <- 7.4 #[Vaugha-Jones 2009, Zheng 2005]
#Henderson_Hasselbalch equation for base compound -> fraction of un-ionized base in heart compartments: -------------------------------------------------------
funionized_ic <- 1 / (1 + 10 ^ (pKa - pH_ic))
funionized_ec <- 1 / (1 + 10 ^ (pKa - pH_ec))
Kpp <- (1 + 10 ^ (pKa - pH_ic)) / (1 + 10 ^ (pKa - pH_ec))
#IV INFUSION RATE
r = inf_dose #[mg]
t = inf_time #time of infusion [h]
inf = r / t #infusion rate [mg/h]
#Absorption: -------------------------------------------------------
PAMPA <- 1 / 3600 #[cm/s] [Eikenberry 2009]
Pdiff_dox <- PAMPA #[cm/s]
#DISTRIBUTION -------------------------------------------------------
#Drug binding
fup <- 0.26 #fraction unbound in plasma (Huahe)
fu_ec <- 1 #fraction unbound in extracellular fluid is assumed
fu_heart <- 1 #fraction unbound in heart is assumed
fuha <- 0.1230352 #fraction unbound in hepatic arterial
# TISSUE TO PLASMA PARTITION COEFFICIENT -------------------------------------------------------
# E/P values gets from Simcyp Simulator (ratio); Equations to calculate Kpec for the heart
# other Kpvalues using KP values with KP scalar = 4; Vss = 26.248 L/kg; Compound type = Ampholyte; KP heart = 14.713 (KP scalar = 1)
ratio <- 0.157
Kpec <- (1- ratio) * fup + ratio
Kpad <- 7.7431
Kpbo <- 13.553
Kpbr <- 7.504
Kpgu <- 54.531
Kpki <- 37.792
Kpli <- 96.797
Kplu <- 18.689
Kpmu <- 48.424
Kpsk <- 25.527
Kpsp <- 54.269
Kpre <- 7.7431
Kphe <- 14.713
BP <- 1.15 #blood to plasma ratio from Simcyp
# Kp, partition coefficient due to nonspecific protein binding is optimized.
Kp_endo <- 45.63
Kp_mid <- 45.63
Kp_epi<- 45.63
# Passive permeability surface area product in heart tissue -------------------------------------------------------
PSA_epi = Pdiff_dox * SA_epi * (10 ^ -3) * 60 * 60 #[L/h] passive permeability surface area product
PSA_mid = Pdiff_dox * SA_mid * (10 ^ -3) * 60 * 60 #[L/h] passive permeability surface area product
PSA_endo = Pdiff_dox * SA_endo * (10 ^ -3) * 60 * 60 #[L/h] passive permeability surface area product
Kd_DNA <- 0.4 # nM to Molar
Kd_mtDNA <- 5.6 # nM to Molar
# CL clearance [Leandro 2019] -------------------------------------------------------
CL_renal <- 0.66 #[L/ h] renal clearance
CL_hepatic <- 29.97 #[L/ h] hepatic clearance
CLint_heart <- 0 #[L/ h] heart clearance CYP3A4 was not detected in heart tissue
# MODEL -------------------------------------------------------------------
parameters <- c(
BP = BP,
Kpec = Kpec,
Kplu = Kplu,
Kpli = Kpli,
Kpad = Kpad,
Kpbo = Kpbo,
Kpbr = Kpbr,
Kpgu = Kpgu,
Kpki = Kpki,
Kpmu = Kpmu,
Kpsk = Kpsk,
Kpsp = Kpsp,
Kpre = Kpre,
Kphe = Kphe,
Vendo = Vendo,
Vmid = Vmid,
Vepi = Vepi,
fup = fup,
funionized_ic = funionized_ic,
funionized_ec = funionized_ec,
fu_ec = fu_ec,
PSA_epi = PSA_epi,
PSA_mid = PSA_mid,
PSA_endo = PSA_endo,
CL_renal = CL_renal,
CL_hepatic = CL_hepatic
)
# State variables -------------------------------------------------------
state <- c(
INFUSION = r,
Aad = 0,
Abo = 0,
Abr = 0,
Agu = 0,
Aki = 0,
Ali = 0,
Alu = 0,
Amu = 0,
Ask = 0,
Asp = 0,
Ave = 0,
Aar = 0,
Are = 0,
Aheart_ec = 0,
Aepi_ict = 0,
Amid_ict = 0,
Aendo_ict = 0,
Abloodcell = 0,
Cendo_ic = 0,
Cmid_ic = 0,
Cepi_ic = 0)
###Differential equations - mg/h -------------------------------------------------------
PBPKModel = function(times, state, parameters) {
with(as.list(c(state, parameters)), {
inf <- ifelse(times <= t, inf, 0)
# if (times <= t)
# inf
# else
# 0
#DOX concentrations:
Cadipose <- Aad / Vad #adipose
Cbone <- Abo / Vbo #bone
Cbrain <- Abr / Vbr #brain
Cgut <- Agu / Vgu #gut
Ckidney <- Aki / Vki #kidney
Cliver <- Ali / Vli #liver
Cliverfree <- Cliver * (fup / BP) #liver free concentration
Ckidneyfree <- Ckidney * (fup / BP) #kidney free concentration
Clung <- Alu / Vlu #lung
Cmuscle <- Amu / Vmu #muscle
Cskin <- Ask / Vsk #skin
Cspleen <- Asp / Vsp #spleen
Crest <- Are / Vre #rest of body
Cvenous <- Ave / Vve #venous blood
Carterial <- Aar / Var #arterial blood
Cplasmavenous <- Cvenous / BP #venous plasma concentration
Cbloodcell <- Abloodcell / Vrb#blood cell concentration
Cheart_ec <- Aheart_ec / Vhe_ec #heart extracellular fluid
Cendo_ict <- Aendo_ict / Vendo_ic
Cmid_ict <- Amid_ict / Vmid_ic
Cepi_ict <- Aepi_ict / Vepi_ic
# Nuclear sub-compartment
Cendo_ic <- 0.5 * (sqrt((Cendo_ict + DNA_endo + Kd_DNA )^2 - 4* DNA_endo *Cendo_ict) + sqrt((Cendo_ict + mtDNA_endo + Kd_mtDNA )^2 - 4* mtDNA_endo *Cendo_ict) - (DNA_endo + Kd_DNA + mtDNA_endo + Kd_mtDNA) )
Cmid_ic <- 0.5 * (sqrt((Cmid_ict + DNA_mid + Kd_DNA )^2 - 4* DNA_mid *Cmid_ict) + sqrt((Cmid_ict + mtDNA_mid + Kd_mtDNA )^2 - 4* mtDNA_mid *Cmid_ict) - (DNA_mid + Kd_DNA + mtDNA_mid + Kd_mtDNA) )
Cepi_ic <- 0.5 * (sqrt((Cepi_ict + DNA_epi + Kd_DNA )^2 - 4* DNA_epi *Cepi_ict) + sqrt((Cepi_ict + mtDNA_epi + Kd_mtDNA )^2 - 4* mtDNA_epi *Cepi_ict) - (DNA_epi + Kd_DNA + mtDNA_epi + Kd_mtDNA) )
## rates of changes
dINFUSION <- -inf
dAad <- Qad * (Carterial - Cadipose / Kpad * BP) #adipose
dAbo <- Qbo * (Carterial - Cbone / Kpbo * BP) #bone
dAbr <- Qbr * (Carterial - Cbrain / Kpbr * BP) #brain
dAgu <- Qgu * (Carterial - Cgut / Kpgu * BP) #gut
dAki <- Qki * (Carterial - Ckidney / Kpki * BP) - CL_renal * Ckidney * (fup / BP) #kidney
dAli <- Qha * Carterial + Qgu * (Cgut / Kpgu * BP) + Qsp * (Cspleen / Kpsp * BP) - Qli * (Cliver / Kpli * BP) - Cliver * (fup / BP) * CL_hepatic #liver
dAlu <- Qlu * Cvenous - Qlu * (Clung / Kplu * BP) #lung
dAmu <- Qmu * (Carterial - Cmuscle / Kpmu * BP) #muscle
dAsk <- Qsk * (Carterial - Cskin / Kpsk * BP) #skin
dAsp <- Qsp * (Carterial - Cspleen / Kpsp * BP) #spleen
dAre <- Qre * (Carterial - Crest / Kpre * BP) #rest of body
dAve <- inf + Qad * (Cadipose / Kpad * BP) + Qbo * (Cbone / Kpbo * BP) + Qbr * (Cbrain / Kpbr * BP) + Qgu * (Cgut / Kpgu * BP) + Qki* (Ckidney / Kpki * BP) + Qli * (Cliver / Kpli * BP) + Qmu* (Cmuscle / Kpmu * BP) + Qsk* (Cskin / Kpsk * BP)+ Qhe * (Cheart_ec / Kpec * BP) + Qre*(Crest / Kpre * BP) - Qlu * Cvenous - PER * SA_bloodcell * 0.1 * (Cvenous - Cbloodcell) #venous blood
dAar <- Qlu * (Clung / Kplu * BP) - Qad * Carterial - Qbo * Carterial - Qbr * Carterial - Qgu * Carterial- Qki * Carterial- Qha * Carterial- Qmu * Carterial- Qsk * Carterial- Qsp * Carterial- Qre * Carterial #arterial blood
dAbloodcell <- PER * SA_bloodcell * 0.1 * (Cvenous - Cbloodcell) + PER * SA_bloodcell * 0.1 * (Carterial - Cbloodcell)
# Heart extracellular sub-compartment
dAheart_ec <- Qhe * (Carterial - (Cheart_ec/Kpec) * BP) - 0.001 * PER * SA_endo * (Cheart_ec * fu_ec * funionized_ec - Cendo_ic/(Kp_endo * Kpp) * funionized_ic ) -0.001 * PER * SA_mid * (Cheart_ec * fu_ec * funionized_ec - Cmid_ic/(Kp_mid * Kpp) * funionized_ic ) - 0.001 *PER * SA_epi * (Cheart_ec * fu_ec * funionized_ec - Cepi_ic/(Kp_epi * Kpp) * funionized_ic )
dAendo_ict <- 0.001 * PER * SA_endo * (Cheart_ec * fu_ec * funionized_ec - Cendo_ic/(Kp_endo * Kpp) * funionized_ic ) - Cendo_ict * fu_heart * CLint_heart * (Vendo_ic/Vhe)
dAmid_ict <- 0.001 * PER * SA_mid * (Cheart_ec * fu_ec * funionized_ec - Cmid_ic/(Kp_mid * Kpp) * funionized_ic ) - Cepi_ict * fu_heart * CLint_heart * (Vmid_ic/Vhe)
dAepi_ict <- 0.001 * PER * SA_epi * (Cheart_ec * fu_ec * funionized_ec - Cepi_ic/(Kp_epi * Kpp) * funionized_ic ) - Cepi_ict * fu_heart * CLint_heart * (Vepi_ic/Vhe)
#return the rate of changes
list(
c(dINFUSION,
dAad,
dAbo,
dAbr,
dAgu,
dAki,
dAli,
dAlu,
dAmu,
dAsk,
dAsp,
dAve,
dAar,
dAre,
dAheart_ec,
dAepi_ict,
dAmid_ict,
dAendo_ict,
dAbloodcell,
Cendo_ic,
Cmid_ic,
Cepi_ic),
Cadipose = Aad / Vad,
Cbone = Abo / Vbo ,
Cbrain = Abr / Vbr,
Cgut = Agu / Vgu,
Ckidney = Aki / Vki,
Cliver = Ali / Vli,
Clung = Alu / Vlu,
Cmuscle = Amu / Vmu,
Cskin = Ask / Vsk,
Cspleen = Asp / Vsp,
Crest = Are / Vre,
MID = (Amid_ict / Vmid_ic),
ENDO = (Aendo_ict) / Vendo_ic,
EPI = (Aepi_ict) / Vepi_ic,
EC = Cheart_ec,
logHT = log10((Aheart_ec + Aepi_ict + Amid_ict + Aendo_ict) / Vhe),
logBL = log10(Cplasmavenous),
BL = Cplasmavenous,
BLCELL = Cbloodcell,
HT = (Aheart_ec + Aepi_ict + Amid_ict + Aendo_ict) / Vhe
)
})
}
}
out <-
ode(
y = state,
times = times,
func = PBPKModel,
parm = parameters
)
results <- data.frame(out)
plot(
results$time,
results$BL,
type = "l",
col = "blue",
xlab = "Time [h]",
ylab = "Blood Concentration [mg/L]"
)
plot(
results$time,
results$BLCELL,
type = "l",
col = "blue",
xlab = "Time [h]",
ylab = "Blood cells Concentration [mg/L]"
)
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00",
"#CC79A7")
plot1 <-ggplot(data=data.frame(results), aes(x=time))+
geom_line(aes(y=HT,col="HT"),lty=1,size=1.5)+
geom_line(aes(y=MID,col= "MID"),lty=1,size=1.5)+
geom_line(aes(y=ENDO,col="ENDO"),lty=1,size=1.5)+
geom_line(aes(y=EPI,col="EPI"),lty=1,size=1.5)+
ylab(" Concentration (mg/L)")+ xlab("Time (hour)")+
scale_fill_manual( breaks = c("HT","MID","ENDO","EPI"),
values = c("#000000", "#E69F00", "#56B4E9","#009E73"),
labels = c("HT","MID","ENDO","EPI"))+
theme_bw()+theme(text=element_text(size=15),plot.margin = unit(c(5,5,5,5),"mm"))+#changed all plot margins from 5 to 10
labs(title="Concentration of sub-compartment layers", size=1)
plot1
plot2 <-ggplot(data=data.frame(results), aes(x=time))+
geom_line(aes(y=MID,col= "MID"),lty=1,size=1.5)+
geom_line(aes(y=ENDO,col="ENDO"),lty=1,size=1.5)+
geom_line(aes(y=EPI,col="EPI"),lty=1,size=1.5)+
ylab(" Concentration (mg/L)")+ xlab("Time (hour)")+
scale_fill_manual( breaks = c("MID","ENDO","EPI"),
values = c( "#E69F00", "#56B4E9","#009E73"),
labels = c("MID","ENDO","EPI"))+
theme_bw()+theme(text=element_text(size=15),plot.margin = unit(c(5,5,5,5),"mm"))+#changed all plot margins from 5 to 10
labs(title="Concentration of sub-compartment layers", size=1)
plot2
ggplotly(plot2)
plot3 <-ggplot(data=data.frame(results), aes(x=time))+
geom_line(aes(y=HT,col="Mean heart"),lty=1,size=1.5)+
geom_line(aes(y=MID,col= "Midmyocardial"),lty=1,size=1.5)+
geom_line(aes(y=BL,col= "Blood"),lty=1,size=1.5)+
ylab(" Concentration (mg/L)")+ xlab("Time (hour)")+
scale_x_continuous(limits = c(0, 45))+
scale_fill_manual( breaks = c("Mean heart","Midmyocardial","Blood"),
values = c("#000000", "#E69F00", "#56B4E9"),
labels = c("Mean heart","Midmyocardial","Blood"))+
theme_bw()+theme(text=element_text(size=15),plot.margin = unit(c(5,5,5,5),"mm"))+#changed all plot margins from 5 to 10
labs(title="Concentration of sub-compartment layers", size=1)
plot3
plot4 <-ggplot(data=data.frame(results), aes(x=time))+
geom_line(aes(y=Cendo_ic,col="Cendo_ic"),lty=1,size=1.5)+
geom_line(aes(y=Cmid_ic,col= "Cmid_ic"),lty=1,size=1.5)+
geom_line(aes(y=Cepi_ic,col= "Cepi_ic"),lty=1,size=1.5)+
ylab(" Concentration (mg/L)")+ xlab("Time (hour)")+
scale_x_continuous(limits = c(0, 45))+
scale_fill_manual( breaks = c("Cendo_ic","Cmid_ic","Cepi_ic"),
values = c("#000000", "#E69F00", "#56B4E9"),
labels = c("Cendo_ic","Cmid_ic","Cepi_ic"))+
theme_bw()+theme(text=element_text(size=15),plot.margin = unit(c(5,5,5,5),"mm"))+#changed all plot margins from 5 to 10
labs(title="Concentration of layers", size=1)
plot4
plot5 <-ggplot(data=data.frame(results), aes(x=time))+
geom_line(aes(y=Cadipose,col="Cadipose"),lty=1,size=1.5)+
geom_line(aes(y=Cbone,col= "Cbone"),lty=1,size=1.5)+
geom_line(aes(y=Cbrain,col= "Cbrain"),lty=1,size=1.5)+
geom_line(aes(y=Cgut,col= "Cgut"),lty=1,size=1.5)+
geom_line(aes(y=Ckidney,col= "Ckidney"),lty=1,size=1.5)+
geom_line(aes(y=Cliver,col= "Cliver"),lty=1,size=1.5)+
geom_line(aes(y=Clung,col= "Clung"),lty=1,size=1.5)+
geom_line(aes(y=Cmuscle,col= "Cmuscle"),lty=1,size=1.5)+
geom_line(aes(y=Cskin,col= "Cskin"),lty=1,size=1.5)+
geom_line(aes(y=Crest,col= "Crest"),lty=1,size=1.5)+
ylab(" Concentration (mg/L)")+ xlab("Time (hour)")+
scale_x_continuous(limits = c(0, 45))+
scale_fill_manual( breaks = c("Cadipose","Cbone","Cbrain","Cgut","Ckidney","Cliver","Clung","Cmuscle","Cskin","Crest"),
values = c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00","#CC79A7"),
labels = c("Cadipose","Cbone","Cbrain","Cgut","Ckidney","Cliver","Clung","Cmuscle","Cskin","Crest"))+
theme_bw()+theme(text=element_text(size=15),plot.margin = unit(c(5,5,5,5),"mm"))+#changed all plot margins from 5 to 10
labs(title="Concentration of organs", size=1)
plot5
ggplotly(plot5)
# MPPGL [mg/g] according to [Barter 2008]
MPPGL <- 10 ^ (1.407 + 0.0158 * age - 0.00038 * (age ^ 2) + 0.0000024 *(age ^ 3))
# the content of CYP1A2 and CYP3A4 in liver [pmol/mg protein]
CYP1A2_L <- 52
CYP3A4_L <- 137
#Metabolism (hydroxylation)
#LIVER (L)
#Vmax for DOX after [pmol/min/pmol CYP]
#Km for DOX [mcM]
#1.CYP1A2
V_1A2 <- 1.79 * MW * 10 ^ -9 #[mg/min/pmol CYP]
K_1A2 <- 63.5 * MW * 10 ^ -3 #[mg/L]
CLint_1A2 <- (ISEF1A2 * (V_1A2 / (K_1A2 + Cliver)) * CYP1A2_L) / fumic #[L/min/mg of microsomal protein]
#2.CYP3A4
V_3A4 <- 3.37 * MW * 10 ^ -9 #[mg/min/pmol CYP]#[Ghahramani 1997]
K_3A4 <- 213.8 * MW * 10 ^ -3 #[mg/L]
CLint_3A4 <- (ISEF3A4 * (V_3A4 / (K_3A4 + Cliver)) * CYP3A4_L) / fumic #[L/min/mg of microsomal protein]
#Hepatic intrinsic clearance:
CLint_L <- (CLint_1A2 + CLint_3A4) * MPPGL * Vli * liver_density #[L/h]
#HEART (H) CYP3A4 was not detected in heart tissue: assumed no metabolism of DOX in heart tissue
# ELIMINATION
#1) LIVER
#ISEF
#values from Simcyp. rCYP system: Lymph B
ISEF1A2 = 11.1
ISEF3A4 = 3.92
#fumic
fumic <- 0.82# [Venkatakrishnan 2001]
CYP3A4_H <- 0 #CYP 3A4 abundance in average human heart [pmol/mg tissue][Thum 2000]