Multiple End Points Error

[Dan-Bending]

Hello,

I've noticed a few other people have this problem and the one offered solution I found: #373
didn't seem to resolve my problem. Specifically I can fit my model perfectly fine:

`model_structure <- function() {
  ini({
    # Providing initial parameters for the model
    # Must be given as log values.
    log_v_1 <- log(3800) #
    log_cl <- log(32) # 
    log_q <- log(26) #
    log_v_2 <- log(1800) # 
    log_k_dec <- log(0.000005) # 
    log_tox_k_el <- log(0.08) # 
    log_tox_v_1 <- log(20000) # 
    
    # Define initial IIVs
    eta.v_1  ~ 0.25
    eta.cl  ~ 0.3
    eta.k_dec ~ 0.25
    eta.tox_v_1 ~ 0.5
    
    # Define error model for 3 observations
    # Initial exploratory fitting suggests constant
    # constant and proportional
    const.err_payload <- 0.3
    
    const.err_adc <- 1000
    prop.err_adc <- 0.05
    
    const.err_mab <- 600
    prop.err_mab <- 0.01
    
  })
  model({
    
    # Define the parameters based on initial value and initial IIV
    v_1 <- exp(log_v_1 + eta.v_1)
    cl <- exp(log_cl + eta.cl)
    q <- exp(log_q)
    v_2 <- exp(log_v_2)
    k_dec <- exp(log_k_dec + eta.k_dec)
    tox_k_el <- exp(log_tox_k_el)
    tox_v_1 <- exp(log_tox_v_1 + eta.tox_v_1)
    
    # Define initial conditions for species in model, notably central and 
    # peripheral quantity for each DAR level
    DAR8c(0) <- 0
    DAR8p(0) <- 0
    
    DAR7c(0) <- 0
    DAR7p(0) <- 0
    
    DAR6c(0) <- 0
    DAR6p(0) <- 0
    
    DAR5c(0) <- 0
    DAR5p(0) <- 0
    
    DAR4c(0) <- 0
    DAR4p(0) <- 0
    
    DAR3c(0) <- 0
    DAR3p(0) <- 0
    
    DAR2c(0) <- 0
    DAR2p(0) <- 0
    
    DAR1c(0) <- 0
    DAR1p(0) <- 0
    
    DAR0c(0) <- 0
    DAR0p(0) <- 0
    
    Pay(0) <- 0
    
    #Define derived parameters
    k_el = cl/v_1 #
    k_12 = q/v_1 # 
    k_21 = q/v_2 # 
    
    # Define the Ordinary Differential Equations (ODEs)
    
    d/dt(DAR8c) = (- k_12 * DAR8c + k_21 * DAR8p - k_el * DAR8c - 8 * k_dec * DAR8c)
    d/dt(DAR8p) = (+ k_12 * DAR8c - k_21 * DAR8p)
    
    
    d/dt(DAR7c) = (- k_12 * DAR7c + k_21 * DAR7p - k_el * DAR7c + 8 * k_dec * DAR8c 
                                                                - 7 * k_dec * DAR7c)
    d/dt(DAR7p) = (+ k_12 * DAR7c - k_21 * DAR7p)
    
    
    d/dt(DAR6c) = (- k_12 * DAR6c + k_21 * DAR6p - k_el * DAR6c + 7 * k_dec * DAR7c
                                                                - 6 * k_dec * DAR6c)
    d/dt(DAR6p) = (+ k_12 * DAR6c - k_21 * DAR6p)
    
    
    d/dt(DAR5c) = (- k_12 * DAR5c + k_21 * DAR5p - k_el * DAR5c + 6 * k_dec * DAR6c 
                                                                - 5 * k_dec * DAR5c)
    d/dt(DAR5p) = (+ k_12 * DAR5c - k_21 * DAR5p)
    
    
    d/dt(DAR4c) = (- k_12 * DAR4c + k_21 * DAR4p - k_el * DAR4c + 5 * k_dec * DAR5c 
                                                                - 4 * k_dec * DAR4c)
    d/dt(DAR4p) = (+ k_12 * DAR4c - k_21 * DAR4p)
    
    
    d/dt(DAR3c) = (- k_12 * DAR3c + k_21 * DAR3p - k_el * DAR3c + 4 * k_dec * DAR4c
                                                                - 3 * k_dec * DAR3c)
    d/dt(DAR3p) = (+ k_12 * DAR3c - k_21 * DAR3p)
    
    
    d/dt(DAR2c) = (- k_12 * DAR2c + k_21 * DAR2p - k_el * DAR2c + 3 * k_dec * DAR3c 
                                                                - 2 * k_dec * DAR2c)
    d/dt(DAR2p) = (+ k_12 * DAR2c - k_21 * DAR2p)
    
    
    d/dt(DAR1c) = (- k_12 * DAR1c + k_21 * DAR1p - k_el * DAR1c + 2 * k_dec * DAR2c
                                                                - 1 * k_dec * DAR1c)
    d/dt(DAR1p) = (+ k_12 * DAR1c - k_21 * DAR1p)
    
    
    d/dt(DAR0c) = (- k_12 * DAR0c + k_21 * DAR0p - k_el * DAR0c + 1 * k_dec * DAR1c)
    
    d/dt(DAR0p) = (+ k_12 * DAR0c - k_21 * DAR0p)
    
    
    d/dt(Pay) = (+ 8 * k_dec * DAR8c + 7 * k_dec * DAR7c + 6 * k_dec * DAR6c
                 + 5 * k_dec * DAR5c + 4 * k_dec * DAR4c + 3 * k_dec * DAR3c 
                 + 2 * k_dec * DAR2c + 1 * k_dec * DAR1c - tox_k_el * Pay)
    
    Free_Pay = (Pay/(tox_v_1))
    Tot_ADC = ((DAR8c + DAR7c + DAR6c + DAR5c + DAR4c + DAR3c + DAR2c + DAR1c)/(v_1))
    Tot_mab = ((DAR8c + DAR7c + DAR6c + DAR5c + DAR4c + DAR3c + DAR2c + DAR1c + DAR0c)/(v_1))
    
    Free_Pay ~ add(const.err_payload) | pay_cmt
    Tot_ADC ~ add(const.err_adc) + prop(prop.err_adc) | adc_cmt
    Tot_mab ~ add(const.err_mab) + prop(prop.err_mab) | mab_cmt

      })
}`

The solution being to remove the DVID column and use | central in their variance definitions. However, they were using the name of a specific ode (i.e. central) however my observations are defined as an aggregate of a number of other compartments so I've given them their own names. Which, as with all my previous attempts works completely fine for fitting, but throws the following error:

'DVID'/'CMT' translation: dvid modeled cmt 1 1 20 2 2 21 3 3 22 'CMT': 1 Error in rxSolveSEXP(object, .ctl, .nms, .xtra, params, events, inits, : 'dvid'->'cmt' or 'cmt' on observation record on a undefined compartment (use 'cmt()' 'dvid()') id: 1 row: 0 Error: 'dvid'->'cmt' or 'cmt' on observation record on a undefined compartment (use 'cmt()' 'dvid()') id: 1 row: 0

When I try and simulate the model with some arbitrary dose from:

ev <- eventTable(amount.units='ng', time.units='hours') %>%
  add.dosing(dose = 600000, start.time = 10, cmt = "DAR8c") %>%
  add.sampling(0:480)

sim <- simulate(fit, events=ev, nSub = 500)

The final bit of information I can obviously provide is the following:

unique(data[, "cmt"])
[1] "pay_cmt" "adc_cmt" "mab_cmt" "DAR8c" 

Where DAR8c is where the input is allocated.

Thank you for your help!

[Dan-Bending]

As a small update, options(RxODE.combine.dvid=FALSE did not seem to do anything either.

[Dan-Bending]

Problem solved:

ev <- eventTable(amount.units='ng', time.units='hours') %>%
  add.dosing(dose = 600000, start.time = 10, cmt = 1)

for (x in c(20,21,22)) {
  ev <- ev %>%
    et(seq(0,tend,length.out = tend+1), cmt = x)
}

To simulate multiple end points you need a 'blank' sample in your event table for each end point, here these are 20, 21 and 22.