Issues with covariates in the simulation

[1811julian]

Hello everyone,
I´m doing simulation studies with neratinib in different patients for my Master´s thesis and I got issues with the age as a covariate. The cmax is increasing with the age when I do the simulation with PKPDsim, but decreases when I do the simulation with NONMEM. Because it has an influence on KA, CL and Vc(V2), I can´t say for sure which relationship is the right one. When I compared the other covariates, the results from NONMEM and PKPDsim matched. My team checked the NONMEM code, but I am unfortunately the only one who works with PKPDsim. Maybe one of you can see my mistake.
Thanks :)
(This is my first time posting on github, so please excuse the bad format)

`library(PKPDsim)

DOSE <- 240 # dose in mg
parameters <- list(KA = 0.391, # values from the published model in
CL = 195, # the FDA multi discipline review of
V = 4430, # neratinib (Nerlinx)
Q = 52,
V2 = 1440)

n_ind <- 100
ids <- 1:n_ind
cov_table1 <- data.frame("ID" = 1:n_ind, # values for WT, ALT, BILI are population means
"WT" = 70, # so they don´t have influence on the simulation
"AGE" = 20, # (centered power model)
"ALT" = 20,
"BILI" = 10,
"CAPE" = 0, # Comedikation with capecitabin, ketoconazol and
"KETO" = 0, # trastuzumab (0=no, 1=yes)
"TRAS" = 0)

neratinib_model_001 <- new_ode_model(
code = "
KAi = KA * pow((AGE/53), 1.42)
CLi = CL * pow((WT/70), 0.381) * pow((AGE/53), -0.102) * pow((ALT/20), 0.0559) * pow((BILI/10), -0.00805) * pow(0.986, CAPE) * pow(0.172, KETO) * pow(0.882, TRAS)
Vi = V * pow((WT/70), 0.461) * pow((AGE/53), 1.67)
dAdt[1] = -KAiA[1]
dAdt[2] = KAiA[1] - (CLi/Vi)*A[2] + (Q/V2)*A[3] - (Q/Vi)*A[2]
dAdt[3] = -(Q/V2)*A[3] + (Q/Vi)*A[2]
",
declare_variables = c("KAi", "CLi", "Vi"),
covariates = c("WT", "AGE", "ALT", "BILI", "CAPE", "KETO", "TRAS"),
dose = list(cmt = 1),
obs = list(cmt = 2, scale = "V/1000"),
lagtime = 0.727
) # influences of the covariates are taken from the model of the FDA report

regimen1 <- new_regimen(amt = DOSE, # daily one dose of 240 mg for 1 month
interval = 24,
n = 31)

omega <- c(0.717, # IIV KA # values from the FDA report
0, 0.19, # IIV CL
0, 0, 0.595) # IIV Vc

times <- c(0,2,4,8,12,16,24,26,28,32,36,40,48,50,52,56,60,64,72,74,76,80,84,88,96,98,100,
104,108,112,120,122,124,128,132,136,144,146,148,152,156,160,168,170,172,176,180,
184,192,194,196,200,204,208,216,218,220,224,228,232,240,242,244,248,252,256,264,
266,268,272,276,280,288,290,292,296,300,304,312,314,316,320,324,328,336,338,340,
344,348,352,360,362,364,368,372,376,384,386,388,392,396,400,408,410,412,416,420,
424,432,434,436,440,444,448,456,458,460,464,468,472,480,482,484,488,492,496,504,
506,508,512,516,520,528,530,532,536,540,544,552,554,556,560,564,568,576,578,580,
584,588,592,600,602,604,608,612,616,624,626,628,632,636,640,648,650,652,656,660,
664,672,674,676,680,684,688,696,698,700,704,708,712,720,722,724,728,732,736,744)

pat_001 <- sim(ode = neratinib_model_001,
parameters = parameters,
t_obs= times,
n_ind = n_ind,
regimen = regimen1,
omega = omega,
res_var = list(prop = 0.104, add = 2.7),
seed = 12345,
lagtime = 0.727,
covariates_table = cov_table1,
only_obs = T,
output_include = list(covariates = TRUE))
`