mittyri
★★  

Russia,
2018-06-09 22:39

Posting: # 18878
Views: 2,458
 

 Virtual Bioequivalence: a myth or coming reality? [Dissolution / BCS / IVIVC]

Dear All!

I was looking into the abstracts published after annual PAGE meeting (and had a pleasure to discuss it with some authors).
I collected some of them here for your review:
  1. In vitro-in vivo correlation (IVIVC) population modeling for the in silico bioequivalence of a long-acting release formulation of Progesterone
  2. Automatic framework for bioequivalence studies from In Vitro test to In Vivo study design
  3. Defining level A IVIVC dissolution specifications based on individual in vitro dissolution profiles
  4. Selecting in vitro dissolution tests using population pharmacokinetic modelling to help bioequivalence studies
  5. Bayesian knowledge integration for an in vitro–in vivo correlation (IVIVC) model
The investigators are actively trying to raise the level of the models from in vivo level to in vitro in vivo level.
AFAIK regulatories are also interested and funding some of them.
What do you think? Is time of deconvolution over? Could we see new class A IVIVC using that approach soon?

Kind regards,
Mittyri
jag009
★★★

NJ,
2018-06-13 02:40

@ mittyri
Posting: # 18894
Views: 2,006
 

 Virtual Bioequivalence: a myth or coming reality?

Hi,

» What do you think? Is time of deconvolution over? Could we see new class A IVIVC using that approach soon?

Afraid so (more or less). I spoke w my former boss who is now working in FDA and he told me about it...

John
elba.romero
☆    

Guadalajara, Mexico,
2018-10-19 01:17

@ mittyri
Posting: # 19474
Views: 1,547
 

 Virtual Bioequivalence: a myth or coming reality?

» The investigators are actively trying to raise the level of the models from in vivo level to in vitro in vivo level.
» AFAIK regulatories are also interested and funding some of them.
» What do you think? Is time of deconvolution over? Could we see new class A IVIVC using that approach soon?

Yes!!!! Indeed :-D


Edit: Full quote removed. Please delete everything from the text of the original poster which is not necessary in understanding your answer; see also this post #5! [Helmut]

Regards,

--
Elba Romero
Pharmacometrics Area
University of Guadalajara, Mexico
mittyri
★★  

Russia,
2019-06-21 13:50

@ mittyri
Posting: # 20356
Views: 750
 

 Virtual Bioequivalence: an update from PAGE (+TSD!)


Kind regards,
Mittyri
Helmut
★★★
avatar
Homepage
Vienna, Austria,
2019-06-22 18:11

@ mittyri
Posting: # 20358
Views: 716
 

 FDA not concerned about the type I error?

Hi Mittyri,

»
  1. Model-based approach for group sequential and adaptive designs in parallel and cross-over bioequivalence studies

» AFAIK this one is the most interesting. Authors are INSERM and FDA people. But it looks like their adaptive design cannot control TIE. How come?

The devil is in the details. :blahblah: and then:
→ type I error α1 (stage 1)=α2 (stage 2)=0.0304 to ensure global α 0.05 [7].

Though [7] lamented that Potvin used 0.0294 (which is Pocock’s α for a one-sided test and the right one for equivalence is 0.0304), they erred.
  • 0.0304 is for Pocock’s group-sequential design (fixed N) with one interim at exactly N/2.
  • The fact that 0.0304 ‘works’ for Potvin’s original ‘Method B’ is a coincidence (other settings of the GMR and power require other alphas).
Believing that 0.0304 ‘ensures’ no inflation of the TIE for all TSDs is, well, a deception. Didn’t they read other papers dealing with TSDs? Sim’s at the location of the maximum inflation (CV 0.10–0.80, n1 12–72):

library(Power2Stage)
alpha <- rep(0.0304, 2)
df    <- data.frame(alpha1=alpha[1], alpha2=alpha[2],
                    CV=rep(0.24, 4), n1=c(12, rep(16, 3)),
                    GMR=rep(c(0.95, 0.9), each=2),
                    power=rep(c(0.8, 0.9), 2), TIE=NA, CL.lo=NA, CL.hi=NA)
for (j in 1:nrow(df)) {
  df$TIE[j]  <- power.tsd(alpha=alpha, CV=df$CV[j], n1=df$n1[j],
                          GMR=df$GMR[j], theta0=1.25,
                          targetpower=df$power[j], nsims=1e6)$pBE
  df[j, 8:9] <- binom.test(df$TIE[j]*1e6, 1e6)[["conf.int"]]
}
adj   <- c(0.0302, 0.0286, 0.0273, 0.0269)
ad    <- data.frame(alpha1=adj, alpha2=adj,
                    CV=rep(0.24, 4), n1=c(12, rep(16, 3)),
                    GMR=rep(c(0.95, 0.9), each=2),
                    power=rep(c(0.8, 0.9), 2), TIE=NA,
                    sig.lim=binom.test(0.05*1e6, 1e6,
                                       alternative="less")[["conf.int"]][2])
for (j in 1:nrow(ad)) {
  ad$TIE[j]  <- power.tsd(alpha=rep(adj[j], 2), CV=ad$CV[j], n1=ad$n1[j],
                          GMR=ad$GMR[j], theta0=1.25,
                          targetpower=ad$power[j], nsims=1e6)$pBE
}
cat("\n\u201Cnatural constant\u201D (one size fits all)\n");print(signif(df, 5), row.names=FALSE)
cat("\nadjusted alphas\n");print(signif(ad, 5), row.names=FALSE)

“natural constant” (one size fits all)
 alpha1 alpha2   CV n1  GMR power      TIE    CL.lo    CL.hi
 0.0304 0.0304 0.24 12 0.95   0.8
0.050270 0.049843 0.050700
 0.0304 0.0304 0.24 16 0.95   0.9 0.052329 0.051893 0.052767
 0.0304 0.0304 0.24 16 0.90   0.8 0.055298 0.054851 0.055748
 0.0304 0.0304 0.24 16 0.90   0.9 0.056219 0.055768 0.056672

adjusted alphas
 alpha1 alpha2   CV n1  GMR power      TIE sig.lim

 0.0302 0.0302 0.24 12 0.95   0.8 0.049987 0.05036
 0.0286 0.0286 0.24 16 0.95   0.9 0.049576 0.05036
 0.0273 0.0273 0.24 16 0.90   0.8 0.049789 0.05036
 0.0269 0.0269 0.24 16 0.90   0.9 0.049794 0.05036


Furthermore, they performed 500 (‼) simulations for the type I error, …

Quote:
→ In most cases TSS and TSA type 1 error estimates
are within the 0.05 prediction interval [0.0326-0.0729]

… which is just a joke.

round(as.numeric(binom.test(0.05*500, 500)[["conf.int"]]), 4)
[1] 0.0326 0.0729

Common are 1 mio sim’s and a one-sided test of the TIE:

signif(binom.test(0.05*1e6, 1e6, alternative="less")[["conf.int"]][2], 5)
[1] 0.05036

… as in the second data.frame above.

Cheers,
Helmut Schütz
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