Individual model to simualte other events
Martin-Umpierrez opened this issue · 5 comments
Dear Cyril,
First of all, thank you for this amazing package.
I couldn't find a place to post Q&A, so I am reaching out here.
After using the function poso_estim_map(), an object with ETAs, the model, and the event is generated. We can then perform optimization for that ID using poso_dose_conc(), which I assume utilizes the Individual Model with the estimated ETAs. My question is whether there is a possibility to use or extract the Individual Model from the generated object to simulate not only the optimized dose but also other scenarios. I tried some manipulation but couldn't achieve this.
Maybe you can help me!
Thanks again.
Dear Martin,
Thank you for the kind words.
The model you want to use is directly accessible from the output of poso_estim_map() as you assumed. You can store the result of the estimation in an object and access the model:
myEstimate <- poso_estim_map(patient_data,mod_run001)The full output is a list
myEstimate
$eta
ETA_Cl ETA_Vc ETA_Ka
0.6019034 -0.4291750 0.1278449
$model
── Solved rxode2 object ──
── Parameters ($params): ──
THETA_Cl THETA_Vc THETA_Ka ETA_Cl ETA_Vc ETA_Ka
4.0000000 70.0000000 1.0000000 0.6019034 -0.4291750 0.1278449
── Initial Conditions ($inits): ──
depot centr AUC
0 0 0
── First part of data (object): ──
# A tibble: 151 × 12
time TVCl TVVc TVKa Cl Vc Ka K20 Cc depot centr AUC
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 4 70 1 7.30 45.6 1.14 0.160 0 0 0 0
2 0.1 4 70 1 7.30 45.6 1.14 0.160 0.478 378. 21.8 0.0161
3 0.2 4 70 1 7.30 45.6 1.14 0.160 1.83 716. 83.5 0.125
4 0.3 4 70 1 7.30 45.6 1.14 0.160 3.95 1017. 180. 0.408
5 0.4 4 70 1 7.30 45.6 1.14 0.160 6.75 1286. 307. 0.938
6 0.5 4 70 1 7.30 45.6 1.14 0.160 10.1 1526. 461. 1.78
# ℹ 145 more rows
# ℹ Use `print(n = ...)` to see more rows
$event
id time amt evid dur
<int> <num> <num> <int> <num>
1: 1 0.0 NA 0 NA
2: 1 0.0 2000 1 0.5
3: 1 0.1 NA 0 NA
4: 1 0.2 NA 0 NA
5: 1 0.3 NA 0 NA
---
148: 1 14.6 NA 0 NA
149: 1 14.7 NA 0 NA
150: 1 14.8 NA 0 NA
151: 1 14.9 NA 0 NA
152: 1 15.0 NA 0 NA$model is an element of this list
myEstimate$model
── Solved rxode2 object ──
── Parameters ($params): ──
THETA_Cl THETA_Vc THETA_Ka ETA_Cl ETA_Vc ETA_Ka
4.0000000 70.0000000 1.0000000 0.6019034 -0.4291750 0.1278449
── Initial Conditions ($inits): ──
depot centr AUC
0 0 0
── First part of data (object): ──
# A tibble: 151 × 12
time TVCl TVVc TVKa Cl Vc Ka K20 Cc depot centr AUC
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 4 70 1 7.30 45.6 1.14 0.160 0 0 0 0
2 0.1 4 70 1 7.30 45.6 1.14 0.160 0.478 378. 21.8 0.0161
3 0.2 4 70 1 7.30 45.6 1.14 0.160 1.83 716. 83.5 0.125
4 0.3 4 70 1 7.30 45.6 1.14 0.160 3.95 1017. 180. 0.408
5 0.4 4 70 1 7.30 45.6 1.14 0.160 6.75 1286. 307. 0.938
6 0.5 4 70 1 7.30 45.6 1.14 0.160 10.1 1526. 461. 1.78
# ℹ 145 more rows
# ℹ Use `print(n = ...)` to see more rows
It's a model built by rxode2, with the values of the individual parameters. The flexibility of rxode2's event tables means that almost any scenario can be simulated. See for reference: https://nlmixr2.github.io/rxode2/articles/rxode2-event-table.html
Please don't hesitate to get in touch with me if you have a problem with a particular case.
Dear Cyril :
I was trying some coding prior to your answer and it actually worked well, but i ll see if it is easier the way you are showing me here
What I did was the following:
TacrolimusIOV<- list(
ppk_model = rxode2::rxode({
depot(0) = 0;
centr(0) = 0;
#### TV VALUES###
#An RxODE model specification (this line is a comment).
if (CYP3A5==1) {
beta_polimorf = 0.91;
} else if (CYP3A5==2) {
beta_polimorf = 0.63;
} else {
beta_polimorf = 0;
}
TVKa = THETA_Ka ;
TVCl = THETA_Cl *exp(beta_polimorf) * (HCT/33.5)^(-1.0) * (LBW/60)^(0.75);
TVVc = THETA_Vc * (LBW/60) ;
TVVp = THETA_Vp * (LBW/60) ;
TVQ = THETA_Q * (LBW/60)^(0.75);
Ka = TVKa *exp(ETA_Ka + KAPPA_Ka);
Cl = TVCl *exp(ETA_Cl + KAPPA_Cl);
Vc = TVVc *exp(ETA_Vc) ;
Q = TVQ *exp(ETA_Q) ;
Vp = TVVp *exp(ETA_Vp) ;
ke = Cl/Vc;
k12 = Q/Vc;
k21 = Q/Vp;
Cc = centr/Vc;
d/dt(depot) = - Ka*depot ;
d/dt(centr) = + Ka*depot- ke*centr - k12*centr + k21*periph;
d/dt(periph) = + k12*centr - k21*periph;
d/dt(AUC) = Cc;
}),
error_model = function(f,sigma){
g <- sigma[1] + sigma[2]*f
return(g)
},
theta = c(THETA_Ka= 2.49, THETA_Cl=19.84, THETA_Vc=330.4, THETA_Vp=118.2,THETA_Q=35.44),
omega = lotri::lotri({ETA_Ka + ETA_Cl + ETA_Vc + ETA_Q + ETA_Vp ~
c(0.820,
0 , 0.360,
0 , 0, 1.190,
0 , 0, 0, 0.580,
0 , 0, 0, 0, 0)}),
pi_matrix = lotri::lotri({KAPPA_Cl + KAPPA_Ka~
c(0.260,
0 , 0.860)}),
covariates = c("CYP3A5","HCT","LBW"),
sigma = c(additive_a = 0, proportional_b = 0.227))
tdm_tacro <- data.frame(ID=1,
TIME=c(0,12.0,24.0,36.0,48.0, 60.0,72),
DV=c(NA,NA,NA,NA,NA,NA,8.0),
AMT=c(3000,3000,3000,3000,3000,3000,0),
OCC= 1 ,
EVID=c(1,1,1,1,1,1,0),
CYP3A5=3,LBW=60,HCT=40)
## MAP Estimation###
patA_map <- poso_estim_map(dat=tdm_tacro,
prior_model=TacrolimusIOV)
covar <- utils::tail(tdm_tacro[,TacrolimusIOV$covariates],1)
### here extended_et comes from some time values that i need###
dfIOV= data.frame(KAPPA_Cl= extended_et$KAPPA_Cl[1],
KAPPA_Ka= extended_et$KAPPA_Ka[1])
indiv_param <- cbind(patA_map$model$params,
covar,
dfIOV)
new_scenario<- as.et(extended_et) %>% add.dosing(dose=1000,
start.time = from,
nbr.doses= 12,
dosing.interval=12)
runscenario_ppk_model <- rxode2::rxSolve(object=TacrolimusIOV$ppk_model,
params=indiv_param,
event= new_scenario,
nDisplayProgress=200)Thx for your answer again!
Regards, Martin
Hi Martin,
IOV and covariates do complicate things a bit, that's true. Your approach seems right to me. If you want to retrieve the latest KAPPA and covariate values, you can also get them directly from the model produced after MAP-BE:
> tail(patA_map$model[,c(‘KAPPA_Cl’, ‘KAPPA_Ka’, ‘CYP3A5’, ‘HCT’, ‘LBW’)],1)
KAPPA_Cl KAPPA_Ka CYP3A5 HCT LBW
731 0.1181386 0.006395748 3 40 60Please note: in posologyr dose selection functions, IOV values are not used (KAPPA is set to 0 for all parameters). See #32
Hi Martin,
There is now an easier way to simulate other scenarios. The new function poso_replace_et() [1] enables updating a model with events from a new rxode2 event table, while accounting for and interpolating any covariates or inter-occasion variability.
It is now possible to do this:
# convert your dataset to an event table
et_tacro <- rxode2::as.et(tdm_tacro)
# change it to your needs
new_scenario<- et_tacro$add.dosing(dose=1000,
start.time = 72,
nbr.doses= 12,
dosing.interval=12)
# update the model from poso_estim_map() using this new scenario
poso_replace_et(target_model=patA_map$model,
prior_model=TacrolimusIOV,
event_table = new_scenario)[1] https://levenc.github.io/posologyr/reference/poso_replace_et.html