Determine individual pharmacokinetic (and
pharmacokinetic-pharmacodynamic) profiles and use them to personalise
drug regimens. You provide the data (observations from therapeutic drug
monitoring, or TDM) and a population pharmacokinetic model,
posologyr provides the individual a posteriori estimate and
allows you to determine the optimal dosing.
posologyr provides the following functions for dosage
optimization:
poso_dose_conc() estimates the optimal dose to reach a
target concentration at any given timeposo_dose_auc() estimates the optimal dose to reach a
target area under the concentration/time curve (AUC)poso_time_cmin() estimates the time needed to reach a
target trough concentration (Cmin)poso_inter_cmin() estimates the optimal inter-dose
interval to reliably achieve a target Cmin between each
administrationIndividual pharmacokinetic (PK) profiles can be estimated with or without data from therapeutic drug monitoring (TDM):
poso_estim_map() computes the Maximum A Posteriori
(MAP), aka Empirical Bayes Estimates (EBE), of individual PK parameters
from the results of TDMposo_estim_sir() estimates the full posterior
distributions of individual PK parameters by Sequential Importance
Resampling (SIR) from the results of TDMposo_simu_pop() samples from the the a priori
distributions of PK parametersposologyr takes advantage of the simulation framework
provided by the rxode2
package.
You can install the development version of posologyr
from GitHub with:
# install.packages("remotes")
remotes::install_github("levenc/posologyr")posologyr allows the adaptation of dosage from two
elements: a data set, and a prior population PK model.
library("posologyr")Data for input into posologyr is the same type of data
input for rxode2, see vignette("patient_data_input") for
minimal examples.
patient_data <- data.frame(ID=1,
TIME=c(0.0,1.0,14.0),
DV=c(NA,25.0,5.5),
AMT=c(2000,0,0),
DUR=c(0.5,NA,NA),
EVID=c(1,0,0),
CLCREAT=80,
WT=65)A posologyr prior ppk model is a named R list. Its
structure is described in
vignette("posologyr_user_defined_models").
mod_run001 <- list(
ppk_model = rxode2::rxode({
TVCl = THETA_Cl;
TVVc = THETA_Vc;
TVKa = THETA_Ka;
Cl = TVCl*exp(ETA_Cl);
Vc = TVVc*exp(ETA_Vc);
Ka = TVKa*exp(ETA_Ka);
K20 = Cl/Vc;
Cc = centr/Vc;
d/dt(depot) = -Ka*depot;
d/dt(centr) = Ka*depot - K20*centr;
d/dt(AUC) = Cc;
}),
error_model = function(f,sigma) {
dv <- cbind(f,1)
g <- diag(dv%*%sigma%*%t(dv))
return(sqrt(g))
},
theta = c(THETA_Cl=4.0, THETA_Vc=70.0, THETA_Ka=1.0),
omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Ka ~
c(0.2,
0, 0.2,
0, 0, 0.2)}),
sigma = lotri::lotri({prop + add ~ c(0.05,0.0,0.00)}))With these two elements, one can estimate the patient’s MAP-EBE PK parameters, and the individual concentrations over time.
poso_estim_map(patient_data,mod_run001)
#> $eta
#> ETA_Cl ETA_Vc ETA_Ka
#> 0.6019038 -0.4291736 0.1278476
#>
#> $model
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> THETA_Cl THETA_Vc THETA_Ka ETA_Cl ETA_Vc ETA_Ka
#> 4.0000000 70.0000000 1.0000000 0.6019038 -0.4291736 0.1278476
#> ── 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
#>
#> $event
#> id time amt evid dur
#> 1: 1 0.0 NA 0 NA
#> 2: 1 0.0 2000 1 0.5
#> 3: 1 0.1 NA 0 NA
#> 4: 1 0.2 NA 0 NA
#> 5: 1 0.3 NA 0 NA
#> ---
#> 148: 1 14.6 NA 0 NA
#> 149: 1 14.7 NA 0 NA
#> 150: 1 14.8 NA 0 NA
#> 151: 1 14.9 NA 0 NA
#> 152: 1 15.0 NA 0 NAThe individual profile can be plotted easily
patient_001 <- poso_estim_map(patient_data,mod_run001)
plot(patient_001$model,Cc)
Using ggplot2 the observed data points can be added to
the plot
# get the observations from the data
indiv_obs <- patient_data[,c("DV","TIME")]
# set the names to match the names in the rxode2 model
names(indiv_obs) <- c("value","time")
# call ggplot2
plot(patient_001$model,Cc) +
ggplot2::geom_point(data=indiv_obs, size= 3, na.rm=TRUE)
posologyr showed comparable performance to NONMEM MAP
estimation with option MAXEVAL=0: