Apply a TCI algorithm to a 'pkmod' object

Description

Apply a TCI algorithm to a set of targets and a 'pkmod' object to calculate infusion rates.

Usage

apply_tci(
  pkmod,
  target_vals,
  target_tms,
  type = c("plasma", "effect"),
  dtm = NULL,
  custom_alg = NULL,
  inittm = 0,
  ignore_pd = FALSE,
  ...
)

Arguments

Examples

# 3-compartment model with effect-site
my_mod <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963, cl = 1.382,
q2 = 0.919, q3 = 0.609, ke0 = 1.289))
# plasma targeting
apply_tci(my_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "plasma")
# effect-site targeting
apply_tci(my_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "effect")
# incorporate  PD model
my_mod_pd <- update(my_mod, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv)
apply_tci(my_mod_pd, target_vals = c(70,60,50,50), target_tms = c(0,2,3,10), "effect")

Set default PK parameter values Set default PK parameter values for a pkmod object.

Description

Set default PK parameter values Set default PK parameter values for a pkmod object.

Usage

assign_pars(pkmod, pars)

Arguments

Value

pkmod object

Update PK-PD model parameters using observed data values

Description

Function will update parameters of 'pkmod' object based on available data using a Laplace approximation to the posterior. Parameters and "Omega" values from 'pkmod' are used as prior point estimates and variance terms, respectively. Parameters fixed within the Omega matrix are assumed to be fixed and are not updated.

Usage

bayes_update(lpars, pkmod, inf, tms, obs, update_init = FALSE, ...)

Arguments

Value

'pkmod' object with PK/PK-PD/sigma parameters updated based on minimizing negative logged posterior.

Examples

# evaluate negative log posterior at a new set of parameters
lpars = log(c(cl=11,q2=3,q3=25,v=20,v2=40,v3=80,ke0=1.15,sigma_add=0.3))
prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.610,0.565,0.597,0.702,0.463)),
8,8, dimnames = list(NULL,names(lpars)))
my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50, ke0 = 1.2),
sigma_add = 0.2, log_response = TRUE, Omega = prior_vcov)
inf <- inf_manual(inf_tms = 0, inf_rate = 80, duration = 2)
tms <- c(1,2,4,8,12)
obs <- simulate(my_mod, inf = inf, tms = tms)
bayes_update(lpars, my_mod, inf, tms, obs)

# evaluate log-prior for subset of parameters (remove volume parameters)
lpars_sub = log(c(cl=11,q2=3,q3=25,ke0=1.15,sigma_add=0.15))
my_mod <- update(my_mod, Omega = matrix(diag(c(0.265,0.346,0.209,0.702,0.463)),5,5,
 dimnames = list(NULL,names(lpars_sub))))
bayes_update(lpars_sub, my_mod, inf, tms, obs)

# add a pd response and replace multiplicative error with additive error
my_mod_pd <- update(my_mod, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, ecmpt = 4, sigma_mult = 0, sigma_add = 4)
# simulate observations
obs_pd <- simulate(my_mod_pd, inf = inf, tms = seq(0,12,0.5))
# evaluate likelihood at new parameters
lpars_pd_eval = log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_add=4,c50=5,gamma=1))
prior_vcov_pd <- matrix(diag(c(0.265,0.209,0.610,0.702,0.230,0.242,0.1)),7,7,
dimnames = list(NULL,names(lpars_pd_eval)))
my_mod_pd <- update(my_mod_pd, Omega = prior_vcov_pd)
bayes_update(lpars_pd_eval, my_mod_pd, inf, tms, obs_pd)

Simulate closed-loop control

Description

Simulate closed-loop control using Bayesian updates. Infusion rates are calculated using 'pkmod_prior' to reach 'target_vals' at 'target_tms'. Data values are simulated using 'pkmod_true' at 'obs_tms'. 'pkmod_prior' and 'pkmod_true' do not need to have the same structure. Model parameters are updated at each update time using all available simulated observations. Processing delays can be added through the 'delay' argument, such that observations aren't made available to the update mechanism until 'update_tms >= obs_tms + delay'.

Usage

clc(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  update_tms,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  delay = 0,
  seed = NULL
)

Arguments

Examples

prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.610,0.565,0.597,0.702,0.463)),
8,8, dimnames = list(NULL,c('cl','q2','q3','v','v2','v3','ke0','sigma_add')))
pkmod_prior <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50, ke0 = 1.2),
sigma_add = 0.2, log_response = TRUE, Omega = prior_vcov)
pkmod_true  <- pkmod(pars_pk = c(cl = 16, q2 = 4, q3 =10, v = 20, v2 = 20, v3 = 80, ke0 = 0.8),
sigma_add = 0.1, log_response = TRUE)
target_vals <- c(2,3,4,3,3)
target_tms <- c(0,5,10,36,60)
obs_tms <- c(1,2,4,8,12,16,24,36,48)
update_tms <- c(5,15,25,40,50)
sim <- clc(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms, update_tms, seed = 200)
len <- 500
tms <- seq(0,60,length.out = len)
# true, prior, posterior plasma concentrations
df <- data.frame(time = rep(tms,3),
                 value = c(predict(pkmod_true, sim$inf,tms)[,1],
                 predict(pkmod_prior, sim$inf,tms)[,1],
                 predict(sim$pkmod_post, sim$inf, tms)[,1]),
                 type = c(rep("true",len),rep("prior",len),rep("posterior",len)))
library(ggplot2)
ggplot(df, aes(x = time, y = value, color = type)) +
  geom_line() +
  geom_point(data = sim$obs, aes(x = time, y = obs), inherit.aes = FALSE) +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE)


# PK-PD example with observation delay (30 sec)
prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.702,0.242,0.230)),6,6,
dimnames = list(NULL,c('cl','q2','q3','ke0','c50','sigma_add')))
pkpdmod_prior <- update(pkmod_prior, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, sigma_add = 4, log_response = FALSE, Omega = prior_vcov)
pkpdmod_true <- update(pkmod_true, pars_pd = c(c50 = 3.4, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, sigma_add = 3, log_response = FALSE)
target_vals <- c(75,60,50,50)
target_tms <- c(0,3,6,10)
obs_tms <- seq(1/6,10,1/6)
update_tms <- seq(1,10,0.5)
## Not run: 
sim_pkpd <- clc(pkpdmod_prior, pkpdmod_true, target_vals, target_tms, obs_tms,
update_tms, seed = 201, delay = 0.5)
# plot results
tms <- seq(0,10,length.out = len)
df <- data.frame(time = rep(tms,3),
                 value = c(predict(pkpdmod_true, sim_pkpd$inf,tms)[,"pdresp"],
                 predict(pkpdmod_prior, sim_pkpd$inf,tms)[,"pdresp"],
                 predict(sim_pkpd$pkmod_post, sim_pkpd$inf, tms)[,"pdresp"]),
                 type = c(rep("true",len),rep("prior",len),rep("posterior",len)))
library(ggplot2)
ggplot(df, aes(x = time, y = value, color = type)) +
  geom_line() +
  geom_point(data = sim_pkpd$obs, aes(x = time, y = obs), inherit.aes = FALSE) +
  labs(x = "Hours", y = "Bispectral Index") + theme_bw() +
  geom_vline(xintercept = update_tms, linetype = "dotted", alpha = 0.6) +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE)

## End(Not run)

Eleveld et al. pharmacodynamic data

Description

Empirical Bayes (EB) estimates of PD parameters made by the Eleveld et al (2018) PK-PD model. EB estimates were calculated using the PK-PD datasets and NONMEM files provided by Eleveled et al. (2018). The original datasets were obtained through the Open TCI Initiative website (opentci.org) and based on contributions from a number of researchers who made their datasets publically available.

Usage

data(eleveld_pd)

Format

A data frame with 122 rows and 15 variables:

ID

Patient ID

E50

EB estimate of effect-site concentration required to achieve 50 percent response

KE0

EB estimate of elimination rate from effect-site compartment

EMAX

EB estimate of baseline bispectral index (BIS) with no drug administered

GAM

EB estimate of Hill parameter when the effect-site concentration is less than E50

GAM1

EB estimate of Hill parameter when the effect-site concentration is greater than than E50

RESD

EB estimate of residual error term

ALAG1

Estimated time lag in BIS measurements due to patient age (fixed-effects only)

AGE

Patient's age (years)

WGT

Patient's weight (kg)

HGT

Patient's height (cm)

M1F2

Patient's sex: male = 1, female = 2

A1V2

Sampling site: arterial sampling = 1, venous sampling = 2

PMA

Patient's post-menstrual age. Assumed to be age + 40 weeks if not provided

TECH

Presence of concomitant anaesthetic techniques (Local anesthetic = 1, Opioids = 2)

References

Eleveld et al. (2018) British Journal of Anesthesia Vol. 120, 5:942-959 (BJA)

Eleveld et al. pharmacokinetic data

Description

Empirical Bayes (EB) estimates of PK parameters for the Eleveld et al. (2018) PK-PD model. EB estimates were calculated using the PK-PD datasets and NONMEM files provided by Eleveled et al. (2018). The original datasets were obtained through the Open TCI Initiative website (opentci.org) and based on contributions from a number of researchers who made their datasets publically available.

Usage

data(eleveld_pk)

Format

A data frame with 1033 rows and 16 variables:

ID

Patient ID

V1

EB estimate of first compartment volume

V2

EB estimate of second compartment volume

V3

EB estimate of third compartment volume

CL

EB estimate of clearance for the first compartment

Q2

EB estimate of inter-compartmental clearance for second compartment

Q3

EB estimate of inter-compartmental clearance for third compartment

AGE

Patient's age (years)

WGT

Patient's weight (kg)

HGT

Patient's height (cm)

M1F2

Patient's sex: male = 1, female = 2

PMA

Patient's post-menstrual age. Assumed to be age + 40 weeks if not provided

TECH

Presence of concomitant anaesthetic techniques (Local anesthetic = 1, Opioids = 2)

BMI

Patient's BMI

FFM

Patient's fat-free mass (FFM)

A1V2

Sampling site: arterial sampling = 1, venous sampling = 2

References

Eleveld et al. (2018) British Journal of Anesthesia Vol. 120, 5:942-959 (BJA)

Get logged parameters updated in Eleveld model

Description

Extract the logged parameter values to be updated within the Eleveld model from a data frame of patient PK-PD values.

Usage

elvdlpars(x, pd = TRUE)

Arguments

Value

List of parameters used by Eleveld PK-PD model.

Emax function

Description

Emax function. c50 is the concentration eliciting a 50 identifying the slope of the Emax curve at c50, E0 is the response value with no drug present, Emx is the maximum effect size.

Usage

emax(ce, pars)

Arguments

Value

Numeric vector of same length as ce.

Examples

pars_emax <- c(c50 = 1.5, gamma = 1.47, e0 = 100, emx = 100)
ce_seq <- seq(0,4,0.1)
plot(ce_seq, emax(ce_seq, pars_emax), type = "l",
xlab = "Effect-site concentrtion (ug/mL)", ylab = "BIS")

Emax function for Eleveld (2018) model.

Description

The parameter gamma takes one of two values depending on whether ce <= c50.

Usage

emax_eleveld(ce, pars)

Arguments

Value

Numeric vector of same length as ce.

Examples

pars_emax_eleveld <- c(c50 = 1.5, e0 = 100, gamma = 1.47, gamma2 = 1.89)
ce_seq <- seq(0,4,0.1)
plot(ce_seq, emax_eleveld(ce_seq, pars_emax_eleveld), type = "l",
xlab = "Effect-site concentrtion (ug/mL)", ylab = "BIS")

Inverse Emax function

Description

Inverse Emax function to return effect-site concentrations required to reach target effect.

Usage

emax_inv(pdresp, pars)

Arguments

Value

Numeric vector of same length as pdresp.

Examples

pars_emax <- c(c50 = 1.5, gamma = 4, e0 = 100, emx = 100)
ce_seq <- seq(0,4,0.1)
all.equal(emax_inv(emax(ce_seq, pars_emax), pars_emax), ce_seq)

Inverse Emax function

Description

Inverse of Emax function used by Eleveld population PK model.

Usage

emax_inv_eleveld(pdresp, pars)

Arguments

Value

Numeric vector of same length as pdresp.

Examples

pars_emax_eleveld <- c(c50 = 1.5, e0 = 100, gamma = 1.47, gamma2 = 1.89)
ce_seq <- seq(0,4,0.1)
all.equal(emax_inv_eleveld(emax_eleveld(ce_seq, pars_emax_eleveld), pars_emax_eleveld), ce_seq)

Inverse Emax function implemented by Eleveld remifentanil model

Description

Inverse Emax function to return effect-site concentrations required to reach target effect.

Usage

emax_inv_remi(pdresp, pars)

Arguments

Value

Numeric vector of same length as pdresp.

Examples

pars_emax <- c(e0 = 19, emx = 5.6, c50 = 12.7, gamma = 2.87)
emax_inv_remi(emax_remi(10, pars_emax), pars_emax)

Emax function implemented by Eleveld remifentanil model

Description

Emax function. c50 is the concentration eliciting a 50 identifying the slope of the Emax curve at c50, E0 is the response value with no drug present, Emx is the maximum effect size.

Usage

emax_remi(ce, pars)

Arguments

Value

Numeric vector of same length as ce.

Examples

pars_emax <- c(e0 = 19, emx = 5.6, c50 = 12.7, gamma = 2.87)
ce_seq <- seq(0,60,0.1)
plot(ce_seq, emax_remi(ce_seq, pars_emax), type = "l",
xlab = "Effect-site concentrtion (ug/mL)", ylab = "Spectral Edge Frequency (HZ)")

Format parameters for use in Rcpp functions Order parameters for 1-4 compartment models to be used in Rcpp functions in predict_pkmod method.

Description

Format parameters for use in Rcpp functions

Order parameters for 1-4 compartment models to be used in Rcpp functions in predict_pkmod method.

Usage

format_pars(pars, ncmpt = 3)

Arguments

Value

Numeric vector of transformed parameter values.

Examples

format_pars(c(V1 = 8.9, CL = 1.4, q2 = 0.9, v2 = 18), ncmpt = 2)
format_pars(c(V1 = 8.9, CL = 1.4, q2 = 0.9, v2 = 18, cl2 = 3), ncmpt = 2)

infusion schedule

Description

Returns a data frame describing a set of infusions to be administered. Output can be used as argument "inf" in predict_pkmod.

Usage

inf_manual(inf_tms, inf_rate, duration = NULL)

Arguments

Value

Matrix of infusion rates, start and end times.

Examples

# specify start and end times, as well as infusion rates (including 0).
inf_manual(inf_tms = c(0,0.5,4,4.5,10), inf_rate = c(100,0,80,0,0))
# specify start times, single infusion rate and single duration
inf_manual(inf_tms = c(0,4), inf_rate = 100, duration = 0.5)
# multiple infusion rates, single duration
inf_manual(inf_tms = c(0,4), inf_rate = c(100,80), duration = 0.5)
# multiple sequential infusion rates
inf_manual(inf_tms = seq(0,1,1/6), inf_rate = 100, duration = 1/6)
# single infusion rate, multiple durations
inf_manual(inf_tms = c(0,4), inf_rate = 100, duration = c(0.5,1))
# multiple infusion rates, multiple durations
inf_manual(inf_tms = c(0,4), inf_rate = c(100,80), duration = c(0.5,1))

Target-controlled infusion

Description

Apply a TCI algorithm to a set of targets and a 'pkmod' or 'poppkmod' object to calculate infusion rates.

Usage

inf_tci(
  pkmod,
  target_vals,
  target_tms,
  type = c("plasma", "effect"),
  dtm = NULL,
  custom_alg = NULL,
  inittm = 0,
  ignore_pd = FALSE,
  ...
)

Arguments

Examples

# 3-compartment model with effect-site
my_mod <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963, cl = 1.382,
q2 = 0.919, q3 = 0.609, ke0 = 1.289))
# plasma targeting
inf_tci(my_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "plasma")
# effect-site targeting
inf_tci(my_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "effect")
# poppkmod object
data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
elvd_mod <- poppkmod(data, drug = "ppf", model = "eleveld")
inf_tci(elvd_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "effect")

Identify pkfn from parameter names

Description

Identify structural PK model function (i.e., 'pkfn') from parameter names. Models available are 1-, 2-, and 3-compartment mammillary models, or 3-compartment with an effect site, corresponding to functions 'pkmod1cpt', 'pkmod2cpt', 'pkmod3cpt', and 'pkmod3cptm', respectively.

Usage

infer_pkfn(parnms)

Arguments

Value

Returns one of the following functions: 'pkmod1cpt', 'pkmod2cpt', 'pkmod3cpt', or 'pkmod3cptm' based on the parameter names entered.

Examples

# 1-compartment
infer_pkfn(c("CL","V"))
infer_pkfn(c("Cl","v1"))
# 2-compartment
infer_pkfn(c("CL","v","v2","q"))
# 3-compartment
infer_pkfn(c("CL","v","v2","q","Q2","V3"))
# 3-compartment with effect-site
infer_pkfn(c("CL","v","v2","q","Q2","V3","ke0"))

Create an object with class "pkmod"

Description

Create an object with class "pkmod"

Usage

init_pkmod(
  pars_pk = NULL,
  init = NULL,
  pkfn = NULL,
  pars_pd = NULL,
  pdfn = NULL,
  pdinv = NULL,
  pcmpt = NULL,
  ecmpt = NULL,
  sigma_add = NULL,
  sigma_mult = NULL,
  log_response = NULL,
  Omega = NULL
)

Arguments

Value

A list with class pkmod for which print, plot, predict, and simulate methods exist.

Examples

# create a pkmod object for a one compartment model with plasma targeting
init_pkmod()

Initialize a 'poppkmod' object.

Description

Generate a 'poppkmod' object from an existing population PK model for propofol or remifentanil using patient covariates. Available models for propofol are the Marsh, Schnider, and Eleveld models. Available models for remifentanil are the Minto, Kim, and Eleveld models. Input is a data frame with rows corresponding to individuals and columns recording patient covariates.

Usage

init_poppkmod(data = NULL, drug = NULL, model = NULL, sample = NULL, PD = NULL)

Arguments

Value

'poppkmod' object

Examples

data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
init_poppkmod(data, drug = "ppf", model = "eleveld")
init_poppkmod(data, drug = "remi", model = "kim")

Identify pkfn from parameter names

Description

Identify structural PK model function (i.e., 'pkfn') from parameter names. Models available are 1-, 2-, and 3-compartment mammillary models, or 3-compartment with an effect site, corresponding to functions 'pkmod1cpt', 'pkmod2cpt', 'pkmod3cpt', and 'pkmod3cptm', respectively.

Usage

list_parnms()

Value

Returns one of the following functions: 'pkmod1cpt', 'pkmod2cpt', 'pkmod3cpt', or 'pkmod3cptm' based on the parameter names entered.

Examples

list_parnms()

Print population PK models available in 'tci'

Description

Print population PK models available in 'tci' for propofol (Marsh, Schnider, Eleveld) and remifentanil (Minto, Kim, Eleveld).

Usage

list_pkmods()

Value

Prints function names, model types, and required covariates for each model.

Examples

list_pkmods()

Evaluate the log likelihood of a vector of parameter values

Description

Evaluate the log liklihood of parameters given observed data. Can be applied to PK or PK-PD models.

Usage

log_likelihood(lpars, pkmod, inf, tms, obs)

Arguments

Value

Numeric value of length 1

Examples

my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50,
 ke0 = 1.2), sigma_mult = 0.2)
inf <- inf_manual(inf_tms = 0, inf_rate = 80, duration = 2)
tms <- c(1,2,4,8,12)
obs <- simulate(my_mod, inf = inf, tms = tms)
# evaluate log-likelihood at a new set of parameters
lpars = log(c(cl=11,q2=3,q3=25,v=15,v2=30,v3=50,ke0=1.15,sigma_mult=0.3))
log_likelihood(lpars, my_mod, inf, tms, obs)

# estimate for a subset of parameters (exclude q2, v2, v3)
lpars_sub = log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_mult=0.3))
log_likelihood(lpars_sub, my_mod, inf, tms, obs)

# add a pd response and replace multiplicative error with additive error
my_mod_pd <- update(my_mod, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93,
emx = 93), pdfn = emax, pdinv = emax_inv, ecmpt = 4, sigma_mult = 0, sigma_add = 4)
# simulate observations
obs_pd <- simulate(my_mod_pd, inf = inf, tms = seq(0,12,0.5))
# evaluate likelihood at new parameters
lpars_pd <- log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_add=4,c50=5,gamma=1))
log_likelihood(lpars_pd, my_mod_pd, inf, tms = seq(0,12,0.5), obs_pd)

Evaluate the negative log posterior value of a parameter vector

Description

Evaluate the negative log posterior value of a parameter vector given a set of observations and prior distribution for log-normally distributed PK or PK-PD parameters.

Usage

log_posterior_neg(lpars, pkmod, inf, tms, obs)

Arguments

Value

Numeric value of length 1

Examples

# evaluate negative log posterior at a new set of parameters
lpars = log(c(cl=11,q2=3,q3=25,v=20,v2=40,v3=80,ke0=1.15,sigma_add=0.3))
prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.610,0.565,0.597,0.702,0.463)),
8,8, dimnames = list(NULL,names(lpars)))
my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50,
ke0 = 1.2), sigma_add = 0.2, log_response = TRUE, Omega = prior_vcov)
inf <- inf_manual(inf_tms = 0, inf_rate = 80, duration = 2)
tms <- c(1,2,4,8,12)
obs <- simulate(my_mod, inf = inf, tms = tms)
log_posterior_neg(lpars, my_mod, inf, tms, obs)

# evaluate log-prior for subset of parameters (remove volume parameters)
lpars_sub = log(c(cl=11,q2=3,q3=25,ke0=1.15,sigma_add=0.15))
my_mod <- update(my_mod, Omega = matrix(diag(c(0.265,0.346,0.209,0.702,0.463)),
5,5, dimnames = list(NULL,names(lpars_sub))))
log_posterior_neg(lpars_sub, my_mod, inf, tms, obs)

# add a pd response and replace multiplicative error with additive error
# evaluate likelihood at new parameters
lpars_pd_eval = log(c(cl=11,q3=25,v=15,ke0=1.15,sigma_add=4,c50=5,gamma=1))
prior_vcov_pd <- matrix(diag(c(0.265,0.209,0.610,0.702,0.230,0.242,0.1)),7,7,
dimnames = list(NULL,names(lpars_pd_eval)))
my_mod_pd <- update(my_mod, pars_pd = c(c50 = 2.8, gamma = 1.47, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, ecmpt = 4, sigma_mult = 0, sigma_add = 4,
Omega = prior_vcov_pd)
# simulate observations
obs_pd <- simulate(my_mod_pd, inf = inf, tms = seq(0,12,0.5))
log_posterior_neg(lpars_pd_eval, my_mod_pd, inf, tms, obs_pd)

Calculate logged-prior probability for a set of parameters

Description

Calculate logged-prior probability for a set of parameters, assuming that parameter values are log-normally distributed. Mean values are set as the logged parameter values in the 'pkmod' object. Variances are given by the diagonal elements of 'prior_vcov'.

Usage

log_prior(lpars, pkmod)

Arguments

Value

Numeric value of length 1

Examples

# evaluate log-prior for pk parameters + residual
lpars = log(c(cl=11,q2=3,q3=25,v=15,v2=30,v3=50,ke0=1.15,sigma_add=0.15))
prior_vcov <- matrix(diag(c(0.265,0.346,0.209,0.610,0.565,0.597,0.702,0.463)), 8,8,
dimnames = list(NULL,names(lpars)))
my_mod <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50, ke0 = 1.2),
sigma_add = 0.2, log_response = TRUE, Omega = prior_vcov)
log_prior(lpars, my_mod)

# evaluate log-prior for subset of parameters (remove volume parameters)
lpars_sub = log(c(cl=11,q2=3,q3=25,ke0=1.15,sigma_add=0.15))
prior_vcov_sub <- matrix(diag(c(0.265,0.346,0.209,0.702,0.463)), 5,5,
dimnames = list(NULL,names(lpars_sub)))
my_mod <- update(my_mod, Omega = prior_vcov_sub)
log_prior(lpars_sub, my_mod)

Simulate open-loop control

Description

Simulate open-loop control with target-controlled infusion for a 'pkmod' object. Infusion rates are calculated using 'pkmod_prior' to reach 'target_vals' at 'target_tms'. Data values are simulated using 'pkmod_true' at 'obs_tms'. 'pkmod_prior' and 'pkmod_true' do not need to have the same structure.

Usage

olc(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  seed = NULL
)

Arguments

Examples

pkmod_prior <- pkmod(pars_pk = c(cl = 10, q2 = 2, q3 =20, v = 15, v2 = 30, v3 = 50, ke0 = 1.2))
pkmod_true  <- pkmod(pars_pk = c(cl = 16, q2 = 4, q3 =10, v = 20, v2 = 20, v3 = 80, ke0 = 0.8),
sigma_add = 0.1, log_response = TRUE)
target_vals <- c(2,3,4,3,3)
target_tms <- c(0,5,10,36,60)
obs_tms <- c(1,2,4,8,12,16,24,36,48)
sim <- olc(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms)
len <- 500
tms <- seq(0,60,length.out = len)
df <- data.frame(time = rep(tms,2),
                 value = c(predict(pkmod_true, sim$inf,tms)[,1],
                 predict(pkmod_prior, sim$inf,tms)[,1]),
                 type = c(rep("true",len),rep("prior",len)))
library(ggplot2)
ggplot(df, aes(x = time, y = value, color = type)) +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE) +
  geom_line() +
  geom_point(data = sim$obs, aes(x = time, y = obs), inherit.aes = FALSE)

Create a pkmod object

Description

User function to create pkmod objects with validation checks. Multiple rows are permitted for arguments 'pars_pk', 'pars_pd', 'init'

Usage

pkmod(
  pars_pk = NULL,
  init = NULL,
  pkfn = NULL,
  pars_pd = NULL,
  pdfn = NULL,
  pdinv = NULL,
  pcmpt = NULL,
  ecmpt = NULL,
  sigma_add = 0,
  sigma_mult = 0,
  log_response = FALSE,
  Omega = NULL
)

Arguments

Value

Returns a list with class "pkmod" if validation checks are passed. Returns an error if not.

Examples

# 1-compartment model
pkmod(pars_pk = c(CL = 10, V1 = 10))
# 2-compartment model
pkmod(pars_pk = c(CL = 10, V1 = 10, Q = 3, v2 = 20))
# 2-compartment model with random effects matrix
Omega <- matrix(diag(c(0.3,0.2,0,0.4)), 4,4, dimnames = list(NULL,c("CL","V1","Q","v2")))
pkmod(pars_pk = c(CL = 10, V1 = 10, Q = 3, v2 = 20), Omega = Omega)

One compartment IV infusion with first-order elimination.

Description

One compartment IV infusion with first-order elimination.

Usage

pkmod1cpt(tm, kR, pars, init = 0)

Arguments

Value

Numeric vector of concentrations for a constant infusion rate

Examples

pkmod1cpt(1,1,c(k10 = 0.5, v1 = 1))
pkmod1cpt(1,1,c(KE = 0.5, v1 = 1))
pkmod1cpt(1,1,c(CL = 0.5, v1 = 1))

Two compartment IV infusion with first-order elimination.

Description

Two compartment IV infusion with first-order elimination. Elimination from peripheral compartment is assumed to be zero unless 'k20' is specified.

Usage

pkmod2cpt(tm, kR, pars, init = c(0, 0))

Arguments

Value

Numeric matrix of concentrations for a constant infusion rate

Examples

pkmod2cpt(1,1,c(CL = 15, V1 = 10, Q2 = 10, V2 = 20))
pkmod2cpt(1,1,c(CL = 15, v1 = 10, Q2 = 10, V2 = 20, cl2 = 4))

Three compartment IV infusion with first-order elimination.

Description

Three compartment IV infusion with first-order elimination. Elimination is assumed to occur only from central compartment if 'k20', 'k30' are not specified.

Usage

pkmod3cpt(tm, kR, pars, init = c(0, 0, 0))

Arguments

Value

Numeric matrix of concentrations for a constant infusion rate

Examples

pkmod3cpt(1,1,c(CL = 15, Q2 = 10, Q3 = 5, V1 = 10, V2 = 20, V3 = 50))

Solution to three-compartment IV model with effect-site

Description

3 compartment IV infusion with first-order absorption between compartments and with an additional effect-site compartment. The analytical solutions implemented in this function are provided in "ADVAN-style analytical solutions for common pharmacokinetic models" by Abuhelwa et al. 2015.

Usage

pkmod3cptm(tm, kR, pars, init = c(0, 0, 0, 0))

Arguments

Details

This function takes in arguments for each of the absorption and elimination rate constants of a three-compartment model as well as initial concentrations, c0. ke0 gives the rate of elimination from the effect-site compartment into the central compartment (i.e. k41). The rate of absorption into the effect-site compartment is set at 1/10,000 the value of ke0. The function returns a set of functions that calculate the concentration in each of the four compartments as a function of time.

Value

Numeric matrix of concentrations for a constant infusion rate

Examples

pars_3cpt <- c(k10=1.5,k12=0.15,k21=0.09,k13=0.8,k31=0.8,v1=10,v2=15,v3=100,ke0=1)
pkmod3cptm(1,1,pars_3cpt)

Eleveld population PK model for propofol

Description

Function takes patient covariate values required for the Eleveld PK or PK-PD model for propofol and returns a 'pkmod' object with the appropriate model parameters.

Usage

pkmod_eleveld_ppf(
  AGE,
  TBW,
  HGT,
  MALE,
  OPIATE = TRUE,
  ARTERIAL = TRUE,
  PMA = NULL,
  PD = TRUE,
  ...
)

Arguments

Value

'pkmod' object with Eleveld propofol population PK or PK-PD parameters

Examples

pkmod_eleveld_ppf(AGE = 40,TBW = 56,HGT=150,MALE = TRUE)

Eleveld population PK model for remifentanil

Description

Function takes patient covariate values required for the Eleveld PK or PK-PD model for propofol and returns a 'pkmod' object with the appropriate model parameters.

Usage

pkmod_eleveld_remi(AGE, MALE, TBW, HGT = NULL, BMI = NULL, PD = TRUE, ...)

Arguments

Value

'pkmod' object with Eleveld remifentanil population PK or PK-PD parameters

Examples

pkmod_eleveld_remi(AGE = 40,TBW = 56,HGT=150,MALE = TRUE)

Kim population PK model for remifentanil

Description

Evaluate Kim population PK model at patient covariate values. Published in Kim et al. (2017). "Disposition of Remifentanil in Obesity: A New Pharmacokinetic Model Incorporating the Influence of Body Mass" Anesthesiology Vol. 126, 1019–1032. doi: https://doi.org/10.1097/ALN.0000000000001635

Usage

pkmod_kim(AGE, TBW, HGT = NULL, BMI = NULL, MALE = NULL, FFM = NULL, ...)

Arguments

Value

'pkmod' object with Schnider population PK parameters

Examples

pkmod_kim(AGE = 40,TBW = 75, BMI = 30, MALE = TRUE)
pkmod_kim(AGE = 40,TBW = 75, FFM = 52.83)

—————————————————————————– Population PK and PK-PD functions ——————————————- —————————————————————————– Marsh population PK model for propofol

Description

Evaluates the Marsh propofol model at patient covariates (total body mass) and returns a 'pkmod' object. KE0 parameter set to 1.2 in accordance with recommendations from Absalom et al., 2009 "Pharmacokinetic models for propofol- Defining and illuminating the devil in the detail."

Usage

pkmod_marsh(TBW, ...)

Arguments

Value

'pkmod' object with Marsh population PK parameters

Examples

pkmod_marsh(TBW = 50)

Minto population PK model for remifentanil

Description

Evaluate Minto population PK model at patient covariate values. Published in Minto et al. (1997). "Influence of Age and Gender on the Pharmacokinetics and Pharmacodynamics of Remifentanil: I. Model Development." Anesthesiology 86:10-23 doi: https://doi.org/10.1097/00000542-199701000-00004. Residual error standard deviations are taken from Eleveld et al. (2017). "An Allometric Model of Remifentanil Pharmacokinetics and Pharmacodynamics." Anesthesiology Vol. 126, 1005–1018 doi: https://doi.org/10.1097/ALN.0000000000001634.

Usage

pkmod_minto(
  AGE,
  HGT = NULL,
  TBW = NULL,
  MALE = NULL,
  LBM = NULL,
  PD = TRUE,
  ...
)

Arguments

Value

'pkmod' object with Schnider population PK parameters

Examples

pkmod_minto(AGE = 40,HGT=170,LBM = 43.9)
pkmod_minto(AGE = 40,HGT=170,TBW=50,MALE=TRUE)

Schnider population PK model for propofol

Description

Evaluate Schnider population PK model at patient covariate values. Published in Schnider et al. (1998). "The influence of method of administration and covariates on the pharmacokinetics of propofol in adult volunteers." Anesthesiology 88 (5):1170-82.

Usage

pkmod_schnider(AGE, HGT, LBM = NULL, TBW = NULL, MALE = NULL, ...)

Arguments

Value

'pkmod' object with Schnider population PK parameters

Examples

pkmod_schnider(AGE = 40,HGT=170,LBM = 43.9)
pkmod_schnider(AGE = 40,HGT=170,TBW=50,MALE=TRUE)

Plot method for sim_tci class

Description

Plot object with class "sim_tci" created by 'simulate_tci()'.

Usage

## S3 method for class 'sim_tci'
plot(
  x,
  ...,
  yvar = NULL,
  id = NULL,
  type = c("true", "prior", "posterior"),
  show_inf = FALSE,
  show_data = FALSE,
  show_updates = FALSE,
  wrap_id = FALSE
)

Arguments

Value

Plots simulation results

Examples

data <- data.frame(ID = 1:2, AGE = c(30,40), TBW = c(70,80),
HGT = c(160,170), MALE = c(FALSE,TRUE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)

# open-loop simulation (without update_tms)
sim_ol <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
seed = 200)
plot(sim_ol, id = 1, type = "true")
plot(sim_ol, yvar = "c4", type = "true")
plot(sim_ol, yvar = "c4", type = "true", wrap_id = TRUE, show_inf = TRUE)

# closed-loop simulation (with update_tms)
## Not run: 
sim_cl <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
update_tms = c(2,4,6), seed = 200)
plot(sim_cl, type = "posterior", id = 1, show_inf = TRUE)
plot(sim_cl, type = "posterior", wrap_id = TRUE, show_data = TRUE)
plot(sim_cl, yvar = "c4", wrap_id = TRUE)

## End(Not run)

Implement a population pharmacokinetic/pharmacodynamic model.

Description

Create a 'poppkmod' object using an existing population PK model for propofol or remifentanil using patient covariates. Available models for propofol are the Marsh, Schnider, and Eleveld models. Available models for remifentanil are the Minto, Kim, and Eleveld models. Input is a data frame with rows corresponding to individuals and columns recording patient covariates. An ID column is optional, but will be generated as 1:nrow(data) if not supplied. Covariates required by each model are

Propofol

• Marsh: TBW

• Schnider: (AGE, HGT, TBW, MALE) or (AGE, HGT, LBW)

• Eleveld: AGE, TBW, HGT, MALE

Remifentanil

• Minto: (AGE, HGT, TBW, MALE) or (AGE, HGT, LBW)

• Kim: (AGE, TBW, BMI, HGT) or (AGE, TBW, FFM)

• Eleveld: (AGE, MALE, TBW, HGT) or (AGE, MALE, TBW, BMI)

Abbreviations

• TBW = Total body weight (kg)

• LBW = Lean body weight (kg)

• FFM = Fat-free mass (kg)

• AGE = Age (years)

• HGT = Height (cm)

• MALE = Male (1/0, TRUE/FALSE)

• BMI = Body mass index (kg/m^2)

Usage

poppkmod(
  data,
  drug = c("ppf", "remi"),
  model = c("marsh", "schnider", "eleveld", "minto", "kim"),
  sample = FALSE,
  PD = TRUE,
  ...
)

Arguments

Value

'poppkmod' object

Examples

data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
poppkmod(data, drug = "ppf", model = "eleveld")
poppkmod(data, drug = "remi", model = "kim")

Predict method for pkmod objects

Description

Predict concentrations from a pkmod object - can be a user defined function

Usage

## S3 method for class 'pkmod'
predict(object, inf, tms, return_times = FALSE, ...)

Arguments

Value

Matrix of predicted concentrations associated with a pkmod object and and infusion schedule.

Examples

# dosing schedule
dose <- inf_manual(inf_tms = c(0,0.5,4,4.5,10), inf_rate = c(100,0,80,0,0))
# pkmod object
my_mod <- pkmod(pars_pk = c(CL = 15, V1 = 10, Q2 = 10, V2 = 20))
# predict at specific times
predict(my_mod, inf = dose, tms = c(1.5,2.5,3))
# predict with an Emax PD function
my_mod_pd <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963, cl = 1.382,
q2 = 0.919, q3 = 0.609, ke0 = 1.289),
pars_pd = c(c50 = 2.8, gamma = 1.47, gamma2 = 1.89, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, ecmpt = 4)
predict(my_mod_pd, inf = dose, tms = c(1.5,2.5,3))
# predict with a subset of new PK-PD parameters
predict(my_mod_pd, inf = dose, tms = c(1.5,2.5,3), pars_pk = c(ke0 = 0.8),
pars_pd = c(c50 = 2, e0 = 100))

Predict method for pkmod objects

Description

Predict concentrations from a pkmod object - can be a user defined function

Usage

## S3 method for class 'poppkmod'
predict(object, inf, tms, ...)

Arguments

Value

Matrix of predicted concentrations associated with a poppkmod object and and infusion schedule.

Examples

# dosing schedule
dose <- inf_manual(inf_tms = c(0,0.5,4,4.5,10), inf_rate = c(100,0,80,0,0))
# poppkmod object
data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
elvd_mod <- poppkmod(data, drug = "ppf", model = "eleveld")
predict(elvd_mod, inf = dose, tms = c(1.5,2.5,3))

Print pkmod

Description

Print method for pkmod objects

Usage

## S3 method for class 'pkmod'
print(x, ..., digits = 3)

Arguments

Value

Prints description of pkmod

Examples

# 1-parameter model
pkmod(pars_pk = c(CL = 10, V1 = 10))

Print poppkmod

Description

Print method for poppkmod objects

Usage

## S3 method for class 'poppkmod'
print(x, ..., digits = 3)

Arguments

Value

Prints description of pkmod

Examples

data <- data.frame(ID = 1:5,
AGE = seq(20,60,by=10),
TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10),
MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
poppkmod(data, drug = "ppf", model = "eleveld")
poppkmod(data, drug = "remi", model = "kim")

Print method for sim_tci class

Description

Print object with class "sim_tci" created by 'simulate_tci()'.

Usage

## S3 method for class 'sim_tci'
print(x, ...)

Arguments

Value

Prints a description of the simulation.

Examples

data <- data.frame(ID = 1:3, AGE = c(20,30,40), TBW = c(60,70,80),
HGT = c(150,160,170), MALE = c(TRUE,FALSE,TRUE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)

# open-loop simulation (without update_tms)
sim_ol <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
seed = 200)

# closed-loop simulation (with update_tms)
## Not run: 
sim_cl <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
update_tms = c(2,4,6,8), seed = 200)

## End(Not run)

Sample PK or PK-PD parameters from the distribution of inter- or intra-individual variability

Description

Sample PK or PK-PD parameters from the distribution of random effects for a 'pkmod' or 'poppkmod' object, as described by the Omega matrix (see ?pkmod).

Usage

sample_iiv(mod, log_normal = TRUE, ...)

Arguments

Value

Returns model passed to "mod" argument with randomly sampled PK or PK-PD parameters.

Examples

# sample from single PK model
sample_iiv(pkmod_schnider(AGE = 40,HGT=170,TBW=50,MALE=TRUE))
# sample from `poppkmod`
data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
sample_iiv(poppkmod(data = data, drug = "ppf", model = "eleveld"))

Sample parameters from a 'pkmod' object

Description

Sample parameters from a 'pkmod' object

Usage

sample_pkmod(pkmod, log_normal = TRUE, ...)

Arguments

Value

'pkmod' object with updated parameters.

Examples

sample_pkmod(pkmod_schnider(AGE = 40,HGT=170,TBW=50,MALE=TRUE))
sample_pkmod(pkmod_eleveld_ppf(AGE = 40,TBW = 56,HGT=150,MALE = TRUE, PD = FALSE))

Simulate method for pkmod objects

Description

Simulate observations from a pkmod object

Usage

## S3 method for class 'pkmod'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  ...,
  inf,
  tms,
  obs_cmpt = 1,
  resp_bounds = NULL
)

Arguments

Examples

# simulate data from a 2 compartment model with multiplicative error
dose <- inf_manual(inf_tms = c(0,0.5,4,4.5,10), inf_rate = c(100,0,80,0,0))
my_mod <- pkmod(pars_pk = c(CL = 10, V1 = 10, Q2 = 4, V2 = 30))
inf <- inf_tci(my_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "plasma")
simulate(my_mod, nsim = 3, inf = inf, tms = c(1,2,4,6,10), sigma_mult = 0.2)
# simulate with PD model
my_mod_pd <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963, cl = 1.382,
q2 = 0.919, q3 = 0.609, ke0 = 1.289),
pars_pd = c(c50 = 2.8, gamma = 1.47, gamma2 = 1.89, e0 = 93, emx = 93),
pdfn = emax, pdinv = emax_inv, ecmpt = 4)
simulate(my_mod_pd, inf = dose, tms = c(1.5,2.5,3))

Summary method for 'poppkmod' objects

Description

Summarize parameter distribution Simulate method for poppkmod objects

Usage

## S3 method for class 'poppkmod'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  ...,
  inf,
  tms,
  obs_cmpt = 1,
  resp_bounds = NULL
)

Arguments

Details

Simulate observations from a poppkmod object

Examples

# simulate data from a 2 compartment model with multiplicative error
dose <- inf_manual(inf_tms = c(0,0.5,4,4.5,10), inf_rate = c(100,0,80,0,0))
# poppkmod object
data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
 HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
elvd_mod <- poppkmod(data, drug = "ppf", model = "eleveld")
inf <- inf_tci(elvd_mod, target_vals = c(2,3,4,4), target_tms = c(0,2,3,10), "plasma")
simulate(elvd_mod, nsim = 3, inf = inf, tms = c(1,2,4,6,10))

Simulate closed-loop control using Bayesian updates

Description

Simulate closed-loop control using Bayesian updates for 'pkmod' or 'poppkmod' objects. Infusion rates are calculated using 'pkmod_prior' to reach 'target_vals' at 'target_tms'. Data values are simulated using 'pkmod_true' at 'obs_tms'. 'pkmod_prior' and 'pkmod_true' do not need to have the same structure. Model parameters are updated at each update time using all available simulated observations. Processing delays can be added through the 'delay' argument, such that observations aren't made available to the update mechanism until 'update_tms >= obs_tms + delay'.

Usage

simulate_clc(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  update_tms,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  delay = 0,
  seed = NULL,
  verbose = TRUE
)

Arguments

Examples

data <- data.frame(ID = 1:3, AGE = c(20,30,40), TBW = c(60,70,80),
HGT = c(150,160,170), MALE = c(TRUE,FALSE,TRUE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
update_tms <- c(2,4,6,8)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)
## Not run: 
sim <- simulate_clc(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
update_tms, seed = 200)
len <- 500
tms <- seq(0,10,length.out = len)
resp <- data.frame(rbind(predict(pkmod_true, sim$inf, tms),
predict(pkmod_prior, sim$inf, tms),
predict(sim$pkmod_post, sim$inf, tms)))
resp$type = c(rep("true",len*3),rep("prior",len*3),rep("posterior",len*3))
library(ggplot2)
ggplot(resp) + geom_line(aes(x = time, y = pdresp, color = factor(id))) +
facet_wrap(~type) + labs(x = "Hours", y = "Bispectral Index") +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE)

## End(Not run)

Simulate open-loop control using TCI

Description

Simulate open-loop control using TCI for 'pkmod' or 'poppkmod' objects. Infusion rates are calculated using 'pkmod_prior' to reach 'target_vals' at 'target_tms'. Data values are simulated using 'pkmod_true' at 'obs_tms'. 'pkmod_prior' and 'pkmod_true' do not need to have the same structure, but are required to have the same number of IDs (i.e., N) if 'poppkmod' objects are used.

Usage

simulate_olc(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  seed = NULL
)

Arguments

Examples

data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)
sim <- simulate_olc(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms)
len <- 500
tms <- seq(0,10,length.out = len)
resp <- data.frame(rbind(predict(pkmod_true, sim$inf, tms),
predict(pkmod_prior, sim$inf, tms)))
resp$type = c(rep("true",len*5),rep("prior",len*5))
library(ggplot2)
ggplot(resp) + geom_line(aes(x = time, y = pdresp, color = factor(id))) + facet_wrap(~type) +
  labs(x = "Hours", y = "Bispectral Index") + theme_bw() +
  geom_step(data = data.frame(time = target_tms, value = target_vals),
  aes(x = time, y = value), inherit.aes = FALSE)

Simulate open- or closed-loop control

Description

Simulate responses from a 'pkmod' or 'poppkmod' object using TCI control. Infusion rates are calculated to reach targets using 'pkmod_prior'. Data values are simulated using 'pkmod_true'. 'pkmod_prior' and 'pkmod_true' do not need to have the same parameters or structure and should be different when simulating responses under model misspecification. If update times (argument 'update_tms') are provided then the function 'simulate_clc' is called for "closed-loop" control and model parameters will be updated via Bayes' rule using available data. Only parameters with values in the Omega matrix will be updated. A data processing delay can be added through the argument 'delay'. See ?bayes_update? for more details. If update times are not specified, then the function 'simulate_olc' will be called to implement "open-loop" control and model parameters will not be updated. Simulation results have class 'tci_sim' and can be plotted using 'plot.tci_sim()'.

Usage

simulate_tci(
  pkmod_prior,
  pkmod_true,
  target_vals,
  target_tms,
  obs_tms,
  update_tms = NULL,
  type = c("effect", "plasma"),
  custom_alg = NULL,
  resp_bounds = NULL,
  delay = 0,
  seed = NULL,
  verbose = TRUE
)

Arguments

Examples

data <- data.frame(ID = 1:3, AGE = c(20,30,40), TBW = c(60,70,80),
HGT = c(150,160,170), MALE = c(TRUE,FALSE,TRUE))
pkmod_prior <- poppkmod(data, drug = "ppf", model = "eleveld")
pkmod_true  <- poppkmod(data, drug = "ppf", model = "eleveld", sample = TRUE)
obs_tms <- seq(1/6,10,1/6)
target_vals = c(75,60,50,50)
target_tms = c(0,3,6,10)

# open-loop simulation (without updates)
sim_ol <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
seed = 200)
plot(sim_ol)

# closed-loop simulation (with updates)
## Not run: 
sim_cl <- simulate_tci(pkmod_prior, pkmod_true, target_vals, target_tms, obs_tms,
update_tms = c(2,4,6,8), seed = 200)
plot(sim_cl, wrap_id = TRUE, show_inf = TRUE, show_data = TRUE)

## End(Not run)

tci_documentation

Description

Functions to implement target-controlled infusion algorithms

Details

tci package documentation

This package contains functions to implement target-controlled infusion (TCI) algorithms for compartmental PK models under intravenous administration. TCI algorithms for plasma or effect-site targeting are included and can be extended to pharmacodynamic responses. Custom PK-PD models and custom TCI algorithms can be specified. Functions are provided to simulate responses from PK/PK-PD models under open- or closed-loop control.

Effect-site TCI algorithm with plasma targeting within small range of target

Description

Modified effect-site TCI algorithm that switches to plasma-targeting when the plasma concentration is within 20% of the target and the effect-site concentration is within 0.5% of the target. The modification decreases computation time and prevents oscillatory behavior in the effect-site concentrations.

Usage

tci_effect(Ct, pkmod, dtm = 1/6, cptol = 0.2, cetol = 0.05, ...)

Arguments

Value

Numeric value

Examples

my_mod <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963, cl = 1.382,
q2 = 0.919, q3 = 0.609, ke0 = 1.289))
tci_effect(Ct = 2, pkmod = my_mod)
# update parameters
tci_effect(Ct = 2, pkmod = my_mod, pars_pk = c(v1 = 12, cl = 2))

TCI algorithm for effect-site targeting

Description

Function for calculating a TCI infusion schedule corresponding to a set of target concentrations. This function makes use of formulas described by Shafer and Gregg (1992) in "Algorithms to rapidly achieve and maintain stable drug concentrations at the site of drug effect with a computer-controlled infusion pump"

Usage

tci_effect_only(
  Ct,
  pkmod,
  dtm = 1/6,
  tmax_search = 10,
  maxrt = 1200,
  grid_len = 1200,
  ...
)

Arguments

Value

Numeric value

Examples

# three compartment model with effect site
my_mod <- pkmod(pars_pk = c(v1 = 8.995, v2 = 17.297, v3 = 120.963,
cl = 1.382, q2 = 0.919, q3 = 0.609, ke0 = 1.289))
tci_effect_only(Ct = 2, pkmod = my_mod)
# update parameters
tci_effect_only(Ct = 2, pkmod = my_mod, pars_pk = c(v1 = 12, cl = 2))

TCI algorithm for plasma targeting

Description

TCI algorithm based on the algorithm described by Jacobs (1990).

Usage

tci_plasma(Ct, pkmod, dtm = 1/6, maxrt = 1200, ...)

Arguments

Value

Numeric value

Examples

# plasma targeting
my_mod <- pkmod(pars_pk = c(CL = 10, V1 = 10))
tci_plasma(Ct = 2, my_mod)
# update CL parameter
tci_plasma(Ct = 2, my_mod, pars_pk = c(CL = 15))

Update method for pkmod

Description

Update parameters or initial values of a pkmod object

Usage

## S3 method for class 'pkmod'
update(object, ...)

Arguments

Value

Returns the pkmod object with elements replaced

Examples

# initial pkmod object
(my_mod <- pkmod(pars_pk = c(CL = 10, V1 = 10)))
# update a subset of parameters and initial values
update(my_mod, pars_pk = c(CL = 20, V1 = 2), init = 3, sigma_add = 1, log_response = TRUE)

pkmod validation checks

Description

Function to provide validation checks for a pkmod object

Usage

validate_pkmod(x)

Arguments

Value

Returns a list with class "pkmod" if validation checks are passed. Returns an error if not.

Examples

validate_pkmod(init_pkmod(pars_pk = c(CL = 10, V1 = 10,Q2 = 4,V2=20)))

Perform validation checks on a 'poppkmod' object

Description

Perform validation checks on a 'poppkmod' object created by 'init_poppkmod'.

Usage

validate_poppkmod(x)

Arguments

Value

'poppkmod' object or error

Examples

data <- data.frame(ID = 1:5, AGE = seq(20,60,by=10), TBW = seq(60,80,by=5),
HGT = seq(150,190,by=10), MALE = c(TRUE,TRUE,FALSE,FALSE,FALSE))
validate_poppkmod(init_poppkmod(data, drug = "ppf", model = "eleveld"))