Bioequivalence and Bioavailability Forum • Conclusion & beyond

Conclusion & beyond [NCA / SHAM]

posted by Helmut – Vienna, Austria, 2019-04-17 19:05 (2099 d 09:20 ago) – Posting: # 20185
Views: 9,280

Hi mittyri,

❝ ❝ I corrected a typo in your original post

❝

❝ forgot to execute it on clean environment, sorry!

Welcome to the club! BTW, THX for implementing the lin-up/log-down trapezoidal. :-D

❝ ❝ So what do you conclude?

❝

❝ pAUC is more sensitive, but the sensitivity is still low (nothing new...)

Yep. Actually this story reaches too far (for an IR formulation crossing flip-flop PK; no regulator would buy that regardless what is written in a guideline) and not far enough: Setting the cut-off for pAUC at the individual t_max-values of the reference is Canadian tradition (termed AUC_Reftmax) but even in Canada history. If pAUCs are used, the cut-off has to be pre-specified. Currently pAUCs are not required for IR. OK, if the first pAUC performs better than C_max, great.
I think that limiting \(\small{k_\text{a(T)}/k_\text{a(R)}}\) to 0.5–2 is sufficient.
For the cut-off \(\small{2\times t_\text{max}=2\log_{e}\Big{(}\frac{k_\text{a}}{k_\text{b}\,(k_\text{a}-k_\text{b})}\Big{)}}\), what else?

Edit: No wonder you hated this line of your code. Shouldn’t it be:

SubjectsDFstack <-

  reshape(SubjectsDF[, -c(2,3,4,6,7,9,11)],

    direction = 'long', varying = c(3:5), v.names = "ratio",

    timevar = "metric", times = names(SubjectsDF)[c(7,9,11)])

My sim’s: In the input section t.cut <- 2*log(ka/(CL/Vd)/(ka-(CL/Vd)))
Then (relevant lines only):

AbsorptionDF <- function(D, ka, Vd, CL,t,ratio,t.cut){

  # Reference

  ke       <- CL/Vd

  C        <- C.sd(D=D, Vd=Vd, ka=ka, ke=ke, t=t)

  tmax     <- t[C == max(C)][1]


  Cmax     <- C.sd(D=D, Vd=Vd, ka=ka, ke=ke, t=tmax)

  AUC.t    <- AUCcalc(t, C)

  t.1      <- t[which(t <= t.cut)]


  C.1      <- C[which(t <= t.cut)]


  pAUC     <- AUCcalc(t.1, C.1)

  Cmax.AUC <- Cmax/AUC.t



  DF.sub <- cbind(Subject = isub, V = Vd.sub, CL = CL.sub,

                  AbsorptionDF(D, ka.sub, Vd.sub, CL.sub, t, ratio, t.cut))



sp1 <- ggplot(SubjectsDFstack[SubjectsDFstack$metric == "Cmax", ],

              aes(x=kaT_kaR, y=ratio, color=factor(metric)))

sp1 + theme_bw() +

  geom_point(size=.3) +

  geom_smooth(method = 'loess', se = FALSE) +

  stat_density_2d(data = SubjectsDFstack[SubjectsDFstack$metric == "Cmax", ],

    geom = "raster", aes(alpha = ..density..), fill = "#F8766D",

    contour = FALSE) +

  scale_alpha(range = c(0, 0.7)) +

  scale_x_continuous(trans='log2') +

  scale_y_continuous(limits=c(0.5,2), trans='log2')

sp2 <- ggplot(SubjectsDFstack[SubjectsDFstack$metric == "pAUC", ],

              aes(x=kaT_kaR, y=ratio, color=factor(metric)))

sp2 + theme_bw() +

  geom_point(size=.3) +

  geom_smooth(method = 'loess', se = FALSE) +

  stat_density_2d(data = SubjectsDFstack[SubjectsDFstack$metric == "pAUC", ],

    geom = "raster", aes(alpha = ..density..), fill = "#6DAAF8",

    contour = FALSE) +

  scale_alpha(range = c(0, 0.7)) +

  scale_x_continuous(trans='log2') +

  scale_y_continuous(limits=c(0.5,2), trans='log2')

sp3 <- ggplot(SubjectsDFstack[SubjectsDFstack$metric == "Cmax_AUC", ],

              aes(x=kaT_kaR, y=ratio, color=factor(metric)))

sp3 + theme_bw() +

  geom_point(size=.3) +

  geom_smooth(method = 'loess', se = FALSE) +

  stat_density_2d(data = SubjectsDFstack[SubjectsDFstack$metric == "Cmax_AUC", ],

    geom = "raster", aes(alpha = ..density..), fill = "#6DF876",

    contour = FALSE) +

  scale_alpha(range = c(0, 0.7)) +

  scale_x_continuous(trans='log2') +

  scale_y_continuous(limits=c(0.5,2), trans='log2')

Based on loess:

   metric kaT_kaR predicted sensitivity

 Cmax_AUC     0.5 0.8082842  0.54181715

     Cmax     0.5 0.8075275  0.54298163

     pAUC     0.5 0.7143879  0.76337666

     pAUC     2.0 1.2181904  0.08875421

 Cmax_AUC     2.0 1.2012353  0.10671423

     Cmax     2.0 1.2028693  0.10745301

Again, pAUC is the one-eyed leading the blind ones but only if k_aT < k_aR.

—
Dif-tor heh smusma 🖖🏼 Довге життя Україна!
Helmut Schütz

The quality of responses received is directly proportional to the quality of the question asked. 🚮
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Complete thread:

Metrics for absorption Helmut 2019-04-15 12:08 [NCA / SHAM]
- Metrics for absorption nobody 2019-04-15 12:15
  - Metrics for absorption ElMaestro 2019-04-15 12:23
    - Metrics for absorption nobody 2019-04-15 12:39
  - Metrics for absorption Helmut 2019-04-15 13:02
    - Metrics for absorption nobody 2019-04-15 13:48
      - Metrics for absorption Helmut 2019-04-15 19:31
        
        Metrics for absorption nobody 2019-04-16 09:34
        
        Metrics for absorption nobody 2019-04-16 15:15
        
        Metrics for absorption Helmut 2019-04-16 17:01
        
        Metrics for absorption nobody 2019-04-17 11:15
        
        Simulation framework mittyri 2019-04-17 01:21
        
        Simulation framework Helmut 2019-04-17 13:31
        
        Conclusion mittyri 2019-04-17 15:42
        
        Conclusion & beyondHelmut 2019-04-17 17:05