Custom PK models and TCI algorithms using the tci package

Introduction

The tci package provides closed-form solutions for 1-, 2-, 3-, and 3-compartment with effect-site PK models based on solutions described and code provided in Abuhelwa, Foster, and Upton (2015). It does not, however, include an ordinary differential equation (ODE) solver as many other R packages do, including mrgsolve, PKPDsim, and RxODE. tci package functions can nonetheless be applied to models from these packages through the creation of user-defined PK functions.

Custom PK models

Custom PK models based on ODEs or analytical solutions can be passed to pkmod objects through the pkfn argument. To illustrate this functionality, we consider the PK of the analgesic remifentanil. Remifentanil is an opioid derivative that is often administered intravenously to induce analgesia alongside propofol. Here, we consider the three-compartment PK model proposed by . Remifentanil is infused into a central compartment, representing the blood supply, and then circulated to two peripheral compartments, representing highly-perfused and scarcely-perfused organs and tissues. Remifentanil is then removed from all three-compartments with separate clearance rates. A diagram of the three-compartment model, reproduced from , is displayed in figure . The differential equations describing the remifentanyl model are given in equations .

To implement the ODE system in mrgsolve we initialize a model using mrgsolve::mcode with default parameter values.

library(tci)
library(mrgsolve) # implement ODE equation
library(xtable)   # printing tables
library(ggplot2)  # plotting results
library(reshape2) # melt function

form <- '
$PARAM V1 = 7.88, V2=23.9, V3=13.8, CL1=5, CL2=0.828, CL3=0.0784,
k10 = 0.172, k12=0.373, k21=0.103, k13=0.0367, k31=0.0124
$CMT A1 A2 A3
$ODE
dxdt_A1 = k21*A2 + k31*A3 - (k12+k13+k10)*A1 - CL1/V1*A1;
dxdt_A2 = k12*A1 - k21*A2 - CL2/V2*A2;
dxdt_A3 = k13*A1 - k31*A3 - CL3/V3*A3;
'
mrg_mod_remif <- mcode("remifentanil", form)

For a custom model to be compatible with PK, it must take as arguments 1) a vector of time points, tm, 2) an numeric value describing a constant infusion rate, kR, 3) a vector of PK parameter values, pars, and 4) initial starting concentrations, init. Notably, init should be created with default values, as pkmod will use the initial values to determine the number of compartments in the model.

pk_remif <- function(tm, kR, pars, init = c(0,0,0)){

  # allow lowercase names
  names(pars) <- toupper(names(pars))
  # store volume
  vols <- pars[c("V1","V2","V3")]
  A0 <- init*vols # initial amounts
  names(A0) <- c("A1","A2","A3") # names required by mrgsolve
  
  # pass parameters as list
  pars <- sapply(pars, as.list)

  # update parameters and initial values (as amounts)
  mrg_mod_remif <- update(mrg_mod_remif, param = pars, init = A0)

  # dosing regimen - mrgsolve function in terms of amount infused
  event <- ev(amt =  kR*max(tm), time = 0, tinf = max(tm))

  # simulate responses (skip tm=0 unless specified)
  dat <- mrgsim_q(x = mrg_mod_remif, # pk model
                  data = event, # dosing event
                  stime = tm) # evaluation times

  # skip tm=0 unless specified in tm
  dat <- dat@data[-1,]

  # return concentrations with compartments in rows and times in columns
  cons <- t(dat[,c("A1","A2","A3")]) / vols

  rownames(cons) <- colnames(cons) <- NULL
  return(cons)
}

We can now evaluate the remifentanil PK model as we would any of the internal PK functions. The optimized parameter values identified by , which we will use as an example, are reproduced in Table .

% latex table generated in R 4.4.2 by xtable 1.8-4 package 
dose_remi <- inf_manual(inf_tms = 0, inf_rate = 60, duration = 20)
pars_remif <- c(V1 = 7.88, V2=23.9, V3=13.8, CL1=5, CL2=0.828, CL3=0.0784,
                k10 = 0.172, k12=0.373, k21=0.103, k13=0.0367, k31=0.0124)
mod_remif <- pkmod(pkfn = pk_remif, pars_pk = pars_remif)
p1 <- predict(mod_remif, inf = dose_remi, tms = 0:80)
ggplot(melt(data.frame(time = 0:80, p1), id = "time"), 
       aes(x = time, y = value, color = variable)) +
  geom_line()

Custom TCI algorithms

The tci package implements the Jacobs and Shafer algorithms plasma- and effect-site targeting algorithms, respectively (Jacobs 1990; Shafer and Gregg 1992). These algorithms aim to reach the target concentrations as quickly as possible without overshooting the target. There may, however, be situations in which the speed of target attainment is not the only goal. In these cases, a user may wish to specify a different TCI algorithm.

An example of this is the algorithm proposed by Van Poucke, Bravo, and Shafer (2004) that limits the maximum percentage overshoot of the target in the central compartment. The motivation for this is that there may exist cases in which excessive plasma concentrations are associated with toxicity to the patient. Here, we construct a similar algorithm that limits the absolute, rather than the percentage, overshoot in the central compartment.

In this example algorithm the user specifies a permissible amount of overshoot in the central compartment, , beyond the nominal target. At each step, the example TCI algorithm defines a maximum plasma concentration to equal the target effect-site concentration plus the permissible overshoot: . It then calculates the infusion required to reach or maintain over the subsequent ten seconds, . It then calculates the maximum effect-site concentration if is given, . If is less than the target concentration, can be administered without overshoot. If is greater than the target concentration, then targeting the effect-site directly will result in a maximum plasma concentration less than and the effect-site targeting algorithm is applied.

The required arguments for a custom algorithm are 1) a single numeric value specifying the target concentration, Ct, 2) a pkmod object created by pkmod(), 3) a single numeric value specifying the infusion duration, dtm, and 4) additional arguments that are passed to update.pkmod at the beginning of the algorithm. Argument (4) is used to update starting concentrations when the algorithm is iteratively applied in inf_tci.

tci_plasma_lim <- function(Ct, pkmod, dtm = 1/6, maxrt = 1200,
                          lim_amt = 0.5, ecmpt = NULL, tmax_search = 20, 
                          cetol = 0.05, cptol = 0.1, ...){

  pkmod <- update(pkmod,...)
  
  # if effect-site concentration is close to target,
  # switch to plasma targeting
  if(with(pkmod,(Ct - init[ecmpt]) / Ct <  cetol &
     (Ct - init[pcmpt])/Ct <= cptol))
    return(tci_plasma(Ct, pkmod = pkmod, dtm = dtm, maxrt = maxrt))
  
  # maximum tolerable plasma concentration
  Cp_max <- Ct + lim_amt
  
  # infusion required to reach Cp_max
  pinf <- tci_plasma(Ct = Cp_max, pkmod = pkmod, dtm = dtm, maxrt = maxrt)
  
  # Administer dtm-minute infusion
  unit_inf <- inf_manual(inf_tms = 0, inf_rate = pinf, duration = dtm)
  
  # Calculate maximum effect-site concentration
  CeP <- function(tm) predict(pkmod, inf = unit_inf, tms = tm)[,pkmod$ecmpt]
  Ce_max <- optimize(CeP, c(0,20), maximum = TRUE)$objective
  
  # if max Ce < Ct administer infusion to reach maximum target
  if(Ce_max <= Ct + cetol*Ct) 
    infrt <- pinf
  else
    infrt <- tci_effect_only(Ct, pkmod, dtm, maxrt = maxrt)

  return(infrt)
}

We can now apply the algorithm directly to a pkmod object to calculate a single infusion rate.

mod3ecpt <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50, ke0 = 1.2))
tci_plasma_lim(Ct = 2, pkmod = mod3ecpt, lim_amt = 0.25)
#> [1] 240.0374

More usefully, however, we can pass the algorithm to inf_tci through the custom_alg argument and use it to calculate infusion rates required to reach a series of targets.

# tci target concentrations
tci_targets <- cbind(value = c(1,2,2.5,2.5), time = c(0,3,7,10))
# calculate infusion schedule using plasma-limiting algorithm
plim_inf <- inf_tci(target_vals = c(1,2,2.5,2.5),
                    target_tms = c(0,3,7,10),
                    pkmod = mod3ecpt, 
                    custom_alg = tci_plasma_lim, 
                    lim_amt = 0.25)
head(plim_inf)
#>        begin     end  inf_rate Ct c1_start    c2_start   c3_start  c4_start
#> [1,] 0.00000 0.16667 133.35413  1     0.00 0.000000000 0.00000000 0.0000000
#> [2,] 0.16667 0.33333  38.27734  1     1.25 0.007317989 0.04308491 0.1236035
#> [3,] 0.33333 0.50000  36.74880  1     1.25 0.021033000 0.12082833 0.3275202
#> [4,] 0.50000 0.66667  35.31726  1     1.25 0.034597490 0.19356382 0.4944899
#> [5,] 0.66667 0.83333  33.97649  1     1.25 0.048013056 0.26161398 0.6312089
#> [6,] 0.83333 1.00000  32.72067  1     1.25 0.061281283 0.32528061 0.7431596
#>      c1_end      c2_end     c3_end    c4_end
#> [1,]   1.25 0.007317989 0.04308491 0.1236035
#> [2,]   1.25 0.021033000 0.12082833 0.3275202
#> [3,]   1.25 0.034597490 0.19356382 0.4944899
#> [4,]   1.25 0.048013056 0.26161398 0.6312089
#> [5,]   1.25 0.061281283 0.32528061 0.7431596
#> [6,]   1.25 0.074403741 0.38484609 0.8348308

For comparison, we calculate the infusion schedule associated with direct effect-site targeting.

# effect-site targeting
eff_inf <- inf_tci(target_vals = c(1,2,2.5,2.5),
                    target_tms = c(0,3,7,10), 
                   pkmod = mod3ecpt, 
                   type = "effect")

We now can use the infusion schedule in predict.pkmod or plot.pkmod methods

# predict responses
tms_pred <- seq(0,10,0.1)
plim_pred <- predict(mod3ecpt, plim_inf, tms_pred)
eff_pred <- predict(mod3ecpt, eff_inf, tms_pred)

# plot results
dat <- data.frame(time = tms_pred, 
                  `plasma (custom)` = plim_pred[,"c1"], 
                  `effect (custom)` = plim_pred[,"c4"],
                  `plasma (effect)` = eff_pred[,"c1"], 
                  `effect (effect)` = eff_pred[,"c4"],
                  check.names = FALSE)
datm <- melt(dat, id = "time")
datm$algorithm <- ifelse(datm$variable %in% c("plasma (custom)","effect (custom)"),
                         "Plasma-limiting", "Effect-site")
ggplot(datm, aes(x = time, y = value, color = variable, linetype = algorithm)) + 
  geom_line() + 
  xlab("Minutes") + 
  ylab("Concentration (mg/L)") +
  ggtitle(label = "Plasma-limiting effect-site TCI algorithm")

References

Abuhelwa, Ahmad Y., David J R Foster, and Richard N. Upton. 2015. “ADVAN-style analytical solutions for common pharmacokinetic models.” Journal of Pharmacological and Toxicological Methods 73: 42–48. https://doi.org/10.1016/j.vascn.2015.03.004.
Jacobs, James R. 1990. “Algorithm for Optimal Linear Model-Based Control with Application to Pharmacokinetic Model-Driven Drug Delivery.” IEEE Transactions on Biomedical Engineering 37 (1): 107–9. https://doi.org/10.1109/10.43622.
Shafer, Steven L., and Keith M. Gregg. 1992. “Algorithms to rapidly achieve and maintain stable drug concentrations at the site of drug effect with a computer-controlled infusion pump.” Journal of Pharmacokinetics and Biopharmaceutics 20 (2): 147–69. https://doi.org/10.1007/BF01070999.
Van Poucke, Guido E., Louis J Brandon Bravo, and Steven L. Shafer. 2004. “Target controlled infusions: Targeting the effect site while limiting peak plasma concentration.” IEEE Transactions on Biomedical Engineering 51 (11): 1869–75. https://doi.org/10.1109/TBME.2004.827935.