poped - infusion time as optimization variable "a"

[TheoPapath]

Not an issue per se - unsure on how to code this

Can we have infusion time coded as an "a" poped variable?

I am trying some code structure similar to this - maybe different coding should be used?

Code example:
https://github.com/nlmixr2/babelmixr2/blob/poped-examples-tp/inst/poped/ex.10.PKPD.HCV.babelmixr2.R

[mattfidler]

For ODEs, you can if you code infusion time as a duration, ie

....
dur(central) = Tinf
...

Then in your design table you would have to use RATE=-2 as well as your amount.

[TheoPapath]

Redid the codes with rate=-2 and the dose defined in the event table. Predictions now look good.

How could we define the dose as an optimization variable when we define it in the event table?

Also receiving an error when evaluating the design (line 107)
**

**

[mattfidler]

How could we define the dose as an optimization variable when we define it in the event table?

You would use amt=1 and then use f(depot)=DOSE or whatever compartment you dose to...

Also receiving an error when evaluating the design (line 107)

I don't know how to reproduce your issue, so I don't know why. I do know I don't use underscores, so this comes from PopED not babelmixr2.

[mattfidler]

Just looked at the code, since you are looking at an infusion, you can't really optimize over dose.

[mattfidler]

The v_tmp error comes from an evaluation of gradfg, which gives NAs in it:

gradfg(x, a, bpop, b_ind, bocc_ind, poped.db)
      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
 [1,]  0.0 0.00    0 0.00    0  0.0    0
 [2,]  0.8 0.00    0 0.00    0  0.0    0
 [3,]  0.0 0.15    0 0.00    0  0.0    0
 [4,]  0.0 0.00  100 0.00    0  0.0    0
 [5,]  0.0 0.00    0 0.12    0  0.0    0
 [6,]  0.0 0.00    0 0.00    2  0.0    0
 [7,]  0.0 0.00    0 0.00    0  0.2    0
 [8,]  0.0 0.00    0 0.00    0  0.0    7
 [9,]   NA   NA   NA   NA   NA   NA   NA

[mattfidler]

It seems to be getting the NA value for the TINF covariate, so I guess it cannot be optimized over at this time.

Without it you can get what you need:

library(babelmixr2)
#> Loading required package: nlmixr2
#> Loading required package: nlmixr2data
library(PopED)

f <- function() {
      ini({

            tp     <- fix(100)
            td     <- fix(0.001)
            te     <- fix(1e-7)
            ts     <- fix(20000)

            tKA <- log(0.8)
            tKE <- log(0.15)
            tVD <- log(100) #VD=log(100000),
            tEC50 <- log(0.12) #EC50=log(0.00012),
            tn <- log(2)
            tdelta <- log(0.2)
            tc <- log(7)

            eta.KA ~ 0.25
            eta.KE ~ 0.25
            eta.VD ~ 0.25
            eta.EC50 ~0.25
            eta.n ~ 0.25
            eta.delta ~ 0.25
            eta.c ~ 0.25

            add.sd.pk <- sqrt(0.04) # nlmixr2 uses sd
            add.sd.pd <- sqrt(0.04)
      })
      model({

            p <- tp
            d <- td
            e <- te
            s <- ts
            KA <- exp(tKA + eta.KA)
            KE <- exp(tKE + eta.KE)
            VD <- exp(tVD + eta.VD)
            EC50 <- exp(tEC50 + eta.EC50)
            n <- exp(tn + eta.n)
            delta <- exp(tdelta + eta.delta)
            c <- exp(tc + eta.c)

            # # Apply the conditional logic -
            # # This does not work right now - could be addressed in the future
            # if ( (time - (floor(time / TAU) * TAU)) < TINF) {
            #       In <- DOSE / TINF  # Infusion rate
            # } else {
            #       In <- 0  # No infusion
            # }

            d/dt(depot)   = -KA*depot #  + In
            d/dt(central) =  KA*depot - KE*central
            d/dt(TC) = s - TC*(e*VP+d)    # target cells (TC)
            d/dt(IC) = e*TC*VP-delta*IC # productively infected cells (IC)
            d/dt(VP) = p*(1-(pow(central/VD,n)/(pow(central/VD,n)+pow(EC50,n))))*IC-c*VP # viral particles (VP)

            # Initial conditions
            dur(depot) = 1
            # depot(0) <- DOSE

            TC(0) =c*delta/(p*e)
            IC(0) =(s*e*p-d*c*delta)/(p*delta*e)
            VP(0) = (s*e*p-d*c*delta)/(c*delta*e)

            # Concentration and effect are calculated
            conc = central/VD
            eff  = log10(VP)
            ## And both are assumed to follow additive error
            conc ~ add(add.sd.pk)
            eff ~ add(add.sd.pd)
      })
}

rxClean()
f <- f()

TAU = 7
e1 <- et(c( 0,0.25,0.5,1,2,3,4,7,10,14,21,28)) %>%
      add.dosing(dose=180, nbr.doses=4, dosing.interval=TAU) %>%
      as.data.frame() %>%
      dplyr::mutate(dvid=1)

e2 <- et(c( 0,0.25,0.5,1,2,3,4,7,10,14,21,28)) %>%
      add.dosing(dose=0, nbr.doses=4, dosing.interval=TAU) %>%
      as.data.frame() %>%
      dplyr::mutate(dvid=2) %>%
      dplyr::filter(is.na(ii))

e <- rbind(e1, e2)

babel.db <- nlmixr2(f, e, "poped",
                    popedControl(
                          groupsize=30,
                          #a=list(c(TINF=1))))
                          # a=list(c(DOSE=180,TINF=1,TAU=7))
                     ))
#> ℹ ordering data by dvid and time
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
#> 
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’

##  create plot of model without variability
plot_model_prediction(babel.db, facet_scales = "free")
#> rxode2 3.0.1.9000 using 8 threads (see ?getRxThreads)
#>   no cache: create with `rxCreateCache()`

evaluate_design(babel.db)
#> $ofv
#> [1] 88.05631
#> 
#> $fim
#>                           tKA         tKE         tVD       tEC50         tn
#> tKA                85.8596242  17.8246985 -14.0568779  -0.1678529  -2.315434
#> tKE                17.8246985 103.6929320  11.7609721   0.2768431   2.567008
#> tVD               -14.0568779  11.7609721 109.9254711   0.3285378  -2.178854
#> tEC50              -0.1678529   0.2768431   0.3285378  41.3594004 -29.456807
#> tn                 -2.3154336   2.5670081  -2.1788544 -29.4568068 105.587877
#> tdelta              0.7321587  -3.6388776   2.3495185  -0.6219348   2.953830
#> tc                  7.5425097  -1.0455137   1.0248459 -20.3366826  -5.574666
#> d_eta.KA            0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.KE            0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.VD            0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.EC50          0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.n             0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.delta         0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> d_eta.c             0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> sig_var_add.sd.pk   0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#> sig_var_add.sd.pd   0.0000000   0.0000000   0.0000000   0.0000000   0.000000
#>                        tdelta         tc     d_eta.KA     d_eta.KE     d_eta.VD
#> tKA                 0.7321587   7.542510 0.000000e+00 0.000000e+00   0.00000000
#> tKE                -3.6388776  -1.045514 0.000000e+00 0.000000e+00   0.00000000
#> tVD                 2.3495185   1.024846 0.000000e+00 0.000000e+00   0.00000000
#> tEC50              -0.6219348 -20.336683 0.000000e+00 0.000000e+00   0.00000000
#> tn                  2.9538303  -5.574666 0.000000e+00 0.000000e+00   0.00000000
#> tdelta            114.3630827   1.560629 0.000000e+00 0.000000e+00   0.00000000
#> tc                  1.5606288 101.909897 0.000000e+00 0.000000e+00   0.00000000
#> d_eta.KA            0.0000000   0.000000 1.228646e+02 5.295331e+00   3.29326567
#> d_eta.KE            0.0000000   0.000000 5.295331e+00 1.792037e+02   2.30534602
#> d_eta.VD            0.0000000   0.000000 3.293266e+00 2.305346e+00 201.39340916
#> d_eta.EC50          0.0000000   0.000000 4.695781e-04 1.277368e-03   0.00179905
#> d_eta.n             0.0000000   0.000000 8.935389e-02 1.098255e-01   0.07912350
#> d_eta.delta         0.0000000   0.000000 8.934279e-03 2.206905e-01   0.09200264
#> d_eta.c             0.0000000   0.000000 9.481578e-01 1.821833e-02   0.01750552
#> sig_var_add.sd.pk   0.0000000   0.000000 1.782866e+02 1.122728e+02  70.25054514
#> sig_var_add.sd.pd   0.0000000   0.000000 6.683309e+01 1.417364e+01   8.92725686
#>                     d_eta.EC50      d_eta.n  d_eta.delta      d_eta.c
#> tKA               0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tKE               0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tVD               0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tEC50             0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tn                0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tdelta            0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> tc                0.000000e+00   0.00000000 0.000000e+00   0.00000000
#> d_eta.KA          4.695781e-04   0.08935389 8.934279e-03   0.94815778
#> d_eta.KE          1.277368e-03   0.10982551 2.206905e-01   0.01821833
#> d_eta.VD          1.799050e-03   0.07912350 9.200264e-02   0.01750552
#> d_eta.EC50        2.851000e+01  14.46172406 6.446730e-03   6.89301052
#> d_eta.n           1.446172e+01 185.81333118 1.454185e-01   0.51794849
#> d_eta.delta       6.446730e-03   0.14541851 2.179819e+02   0.04059285
#> d_eta.c           6.893011e+00   0.51794849 4.059285e-02 173.09377536
#> sig_var_add.sd.pk 2.276454e+00   4.33466660 1.610358e+01  24.09061842
#> sig_var_add.sd.pd 2.029990e+02  57.90926983 4.783497e+01 115.22550284
#>                   sig_var_add.sd.pk sig_var_add.sd.pd
#> tKA                        0.000000          0.000000
#> tKE                        0.000000          0.000000
#> tVD                        0.000000          0.000000
#> tEC50                      0.000000          0.000000
#> tn                         0.000000          0.000000
#> tdelta                     0.000000          0.000000
#> tc                         0.000000          0.000000
#> d_eta.KA                 178.286607         66.833089
#> d_eta.KE                 112.272850         14.173638
#> d_eta.VD                  70.250545          8.927257
#> d_eta.EC50                 2.276454        202.999006
#> d_eta.n                    4.334667         57.909270
#> d_eta.delta               16.103582         47.834967
#> d_eta.c                   24.090618        115.225503
#> sig_var_add.sd.pk      87821.881319        959.139934
#> sig_var_add.sd.pd        959.139934      77727.049239
#> 
#> $rse
#>               tKA               tKE               tVD             tEC50 
#>         50.199535          5.334518          2.116734          8.848459 
#>                tn            tdelta                tc          d_eta.KA 
#>         16.127344          5.819421          5.525224         36.176782 
#>          d_eta.KE          d_eta.VD        d_eta.EC50           d_eta.n 
#>         29.911831         28.197607         77.515557         29.947637 
#>       d_eta.delta           d_eta.c sig_var_add.sd.pk sig_var_add.sd.pd 
#>         27.094601         30.561106          8.453072          9.057133

^{Created on 2024-10-18 with reprex v2.1.1}