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PK_2cmt_tdcl_des <- function() {
description <- "Two compartment PK model with time-dependent clearance using differential equations (structured like nivolumab PK model)"
reference <- "C Liu, J Yu, H Li, J Liu, Y Xu, P Song, Q Liu, H Zhao, J Xu, V E Maher, B P Booth, G Kim, A Rahman, Y Wang; Association of time-varying clearance of nivolumab with disease dynamics and its implications on exposure response analysis. Clin Pharmacol Ther May 2017; 101(5): 657-666. https://doi.org/10.1002/cpt.656"
ini({
lcl <- log(0.2) ; label("Time-stationary clearance (CLTS)")
lcltmax <- log(0.22) ; label("Typical value of the maximal change of clearance relative to baseline (Tmax)")
lclgamma <- log(1) ; label("Hill coefficient for time-dependent clearance")
lclt50 <- log(30) ; label("Time for 50% of maximal CL change")
lvc <- log(20) ; label("Central volume of distribution (V)")
lvp <- log(150) ; label("Peripheral volume of distribution (Vp)")
lq <- log(0.75) ; label("Intercompartmental clearance (Q)")
propSd <- 0.5 ; label("Proportional residual error (fraction)")
})
model({
clts <- exp(lcl)
cltmax <- exp(lcltmax)
clgamma <- exp(lclgamma)
clt50 <- exp(lclt50)
vc <- exp(lvc)
vp <- exp(lvp)
q <- exp(lq)
cl <- clts*exp(cltmax*time^clgamma/(clt50^clgamma+time^clgamma))
kel <- cl/vc
k12 <- q/vc
k21 <- q/vp
d/dt(central) <- - kel*central - k12*central + k21*peripheral1
d/dt(peripheral1) <- k12*central - k21*peripheral1
Cc <- central / vc
Cc ~ prop(propSd)
})
}
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