$$ \newcommand{\esp}[1]{\mathbb{E}\left(#1\right)} \newcommand{\var}[1]{\mbox{Var}\left(#1\right)} \newcommand{\deriv}[1]{\dot{#1}(t)} \newcommand{\prob}[1]{ \mathbb{P}\!(#1)} \newcommand{\eqdef}{\mathop{=}\limits^{\mathrm{def}}} \newcommand{\by}{\boldsymbol{y}} \newcommand{\bc}{\boldsymbol{c}} \newcommand{\bpsi}{\boldsymbol{\psi}} \def\pmacro{\texttt{p}} \def\like{{\cal L}} \def\llike{{\cal LL}} \def\logit{{\rm logit}} \def\probit{{\rm probit}} \def\one{{\rm 1\!I}} \def\iid{\mathop{\sim}_{\rm i.i.d.}} \def\simh0{\mathop{\sim}_{H_0}} \def\df{\texttt{df}} \def\res{e} \def\xomega{x} \newcommand{\argmin}[1]{{\rm arg}\min_{#1}} \newcommand{\argmax}[1]{{\rm arg}\max_{#1}} \newcommand{\Rset}{\mbox{$\mathbb{R}$}} \def\param{\theta} \def\setparam{\Theta} \def\xnew{x_{\rm new}} \def\fnew{f_{\rm new}} \def\ynew{y_{\rm new}} \def\nnew{n_{\rm new}} \def\enew{e_{\rm new}} \def\Xnew{X_{\rm new}} \def\hfnew{\widehat{\fnew}} \def\degree{m} \def\nbeta{d} \newcommand{\limite}[1]{\mathop{\longrightarrow}\limits_{#1}} \def\ka{k{\scriptstyle a}} \def\ska{k{\scriptscriptstyle a}} \def\kel{k{\scriptstyle e}} \def\skel{k{\scriptscriptstyle e}} \def\cl{C{\small l}} \def\Tlag{T\hspace{-0.1em}{\scriptstyle lag}} \def\sTlag{T\hspace{-0.07em}{\scriptscriptstyle lag}} \def\Tk{T\hspace{-0.1em}{\scriptstyle k0}} \def\sTk{T\hspace{-0.07em}{\scriptscriptstyle k0}} \def\thalf{t{\scriptstyle 1/2}} \newcommand{\Dphi}[1]{\partial_\pphi #1} \def\asigma{a} \def\pphi{\psi} \newcommand{\stheta}{{\theta^\star}} \newcommand{\htheta}{{\widehat{\theta}}}$$

Delbene

Get some information about this model from the DDMoRe model repository

Description of the mathematical model and Shiny application here


model.pharmml <- 'pharmML/DelBene_2009_oncology_in_vitro_v1.xml'

d = read.csv('data/delbene2009_data.csv',skip=1,na='.')
head(d)
##   ID TIME      DV CONC EVID
## 1  1    0      NA    0    2
## 2  1    4 2330.56    0    0
## 3  1    8 3164.31    0    0
## 4  1   12 2814.66    0    0
## 5  1   24 5468.12    0    0
## 6  1   48 8707.01    0    0
N=length(unique(d$ID))
conc <- d$CONC[!duplicated(d$ID)]

p1 <- c(k1=0.0743, k2=0.0745, lambda0=0.0292, N0=2147.3, CV=0.1)
p2 <- list( name     = 'CONC',
            colNames = c('id', 'CONC'),
            value    = cbind(1:N, conc));

out  <- list( name = c('Nt','y'), time = unique(d$TIME[d$EVID!=1]))

res <- simulx( model     = model.pharmml,
               parameter = list(p1,p2),
               output    = out,
               settings  = list(seed=12345));

print(ggplot() + geom_line(data=res$Nt, aes(x=time, y=Nt, colour=id)) + 
                 geom_point(data=res$y, aes(x=time, y=y,colour=id)))

Del Bene F, Germani M, De Nicolao G, Magni P, Re CE, Ballinari D, Rocchetti M A model-based approach to the in vitro evaluation of anticancer activity, Cancer chemotherapy and pharmacology, 4/2009, Volume 63, Issue 5, pages: 827-836


Simeoni

Get some information about this model from the DDMoRe model repository

Description of the mathematical model and Shiny application here


model.pharmml <- "pharmML/Simeoni_2004_oncology_TGI_v2.xml"

d = read.csv('data/simeoni2004_data.csv',skip=1,na='.')
head(d)
##   ID TIME      DV AMT EVID CMT
## 1  1    0      NA  NA    2   3
## 2  1    8 0.47809  NA    0   3
## 3  1   10 0.95340  NA    0   3
## 4  1   13 2.14540  NA    0   3
## 5  1   15 3.41620  NA    0   3
## 6  1   18 4.35640  NA    0   3
p <- c(V1=0.81,  k1=0.968, k2=0.629,  
       k10=0.868*24, k12=0.006*24, k2=0.629, k21=0.0838*24,
       lambda0=0.273, lambda1=0.814, psi=20, CV=0.1, w0=0.055)

adm2 <- list( time  = d$TIME[d$EVID==1], 
              amount = d$AMT[d$EVID==1], 
              target = 'Q1')

f1 <- list( name='Wtot', time=seq(0,30,by=0.5))
f2 <- list( name='Wtot', time=seq(0,45,by=0.5))
y1 <- list( name = 'y', time = d$TIME[d$EVID!=1&d$ID==1])
y2 <- list( name = 'y', time = d$TIME[d$EVID!=1&d$ID==2])

g1 <- list( output = list(y1, f1))
g2 <- list( treatment = adm2, output = list(y2, f2))

res <- simulx( model     = model.pharmml,
               parameter = p,
               group     = list(g1,g2),
               settings  = list(seed=12345) )

print(ggplot() + geom_line(data=res$Wtot, aes(x=time, y=Wtot, colour=id)) + 
                 geom_point(data=res$y, aes(x=time, y=y,colour=id)))


Rocchetti

Get some information about this model from the DDMoRe model repository

Description of the mathematical model and Shiny application here


model.pharmml <- 'pharmML/Rocchetti_2013_oncology_TGI_antiangiogenic_combo_v1.xml'

d = read.csv('data/rocchetti2013_data.csv',skip=1,na='.')
head(d)
##   ID TIME     DV        AMT CMT EVID
## 1  1    0     NA         NA   1    2
## 2  1    7 0.2594         NA   1    0
## 3  1    8     NA  0.1342282   1    1
## 4  1    9     NA 86.0420650   3    1
## 5  1    9 0.2111         NA   1    0
## 6  1   10     NA 86.0420650   3    1
adm1 <- list( time  = d$TIME[d$EVID==1&d$CMT==1],
             amount = d$AMT[d$EVID==1&d$CMT==1],
             target = 'Q0_A')

adm2 <- list( time   = d$TIME[d$EVID==1&d$CMT==3],
              amount = d$AMT[d$EVID==1&d$CMT==3],
              target = 'Q1_B')

p <- c(Emax=1, FV1_A=1/0.119, FV1_B=1/2.13, IC50=3.6, IC50combo=2.02,
       k1=3.54, k12=141.1, k2=0.221, k21=10.4,
       ka_A=24*log(2)/6.19, ka_B=18.8, ke_A=log(2)/6.05, ke_B=49.2,
       lambda0=0.14, lambda1=0.129, psi=20, CV=0.1, w0=0.062)

out   <- list( name = c('Wtot','y'), time = d$TIME[d$EVID!=1])

res <- simulx( model     = model.pharmml,
               parameter = p,
               treatment = list(adm1, adm2),
               output    = out,
               settings  = list(seed=12345))

print(ggplot() + geom_line(data=res$Wtot, aes(x=time, y=Wtot), colour="black") + 
                 geom_point(data=res$y, aes(x=time, y=y), colour="red"))