mittyri
★★  

Russia,
2018-06-09 22:39
(1480 d 18:52 ago)

Posting: # 18878
Views: 6,013
 

 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
(1477 d 14:51 ago)

@ mittyri
Posting: # 18894
Views: 5,044
 

 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
(1349 d 16:14 ago)

@ mittyri
Posting: # 19474
Views: 4,574
 

 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
Área de Farmacometría
Universidad de Guadalajara, México lindo y querido...
mittyri
★★  

Russia,
2019-06-21 13:50
(1104 d 03:41 ago)

@ mittyri
Posting: # 20356
Views: 3,757
 

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


Kind regards,
Mittyri
Helmut
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Vienna, Austria,
2019-06-22 18:11
(1102 d 23:20 ago)

@ mittyri
Posting: # 20358
Views: 3,772
 

 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.

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Helmut
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Vienna, Austria,
2021-12-23 22:31
(187 d 18:00 ago)

@ mittyri
Posting: # 22714
Views: 1,076
 

 Virtual Bioequivalence: a myth or coming reality?

Hi Mittyri,

sorry for excavating such an old post.

» AFAIK regulatories are also interested and funding some of them.

See here and the back-story by authors of the FDA.*

» What do you think?

In my CRO I had the displeasure to deal with topical diclofenac. Low plasma concentrations doable by GC/MS with stable isotope IS but extremely variable. Really ugly profiles. Synovia was as close to a nightmare you can get. How to sample without contamination? We tried a lot of crazy stuff (cleaning the skin with acetone and isopropanol, skin stripping, …). Nothing worked. We ended up in patients scheduled for a total knee arthroplasty. Recruitment not easy “Why should I apply the cream daily under occlusion for three weeks when my knee is cut off anyway?” Turned out that >50% had a so-called ‘dry knee’, i.e., no synovia at all. Bad luck.
Given all that, I’m asking myself on which data the FDA’s model is based upon and how reliable they are. :confused:


  • Tsakalozou E, Babiskin A, Liang Zhao L. Physiologically-based pharmacokinetic modeling to support bioequivalence and approval of generic products: A case for diclofenac sodium topical gel, 1%. CPT Pharmacometrics Syst Pharmacol. 2021; 10(5): 399-411. doi:10.1002/psp4.12600. [image] Open access.

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mittyri
★★  

Russia,
2021-12-24 13:23
(187 d 03:08 ago)

@ Helmut
Posting: # 22715
Views: 1,076
 

 Synovia is a nasty beast

Hi Helmut,

» Turned out that >50% had a so-called ‘dry knee’, i.e., no synovia at all.

Oh Gush! Loss of lubrication is the one of the components of diseases causing arthroplasty.

» Given all that, I’m asking myself on which data the FDA’s model is based upon and how reliable they are. :confused:

Citing the original article:
The lack of agreement between observed data and model predictions on diclofenac amounts in the presumed site of action, the synovial fluid, even after model refinement can be attributed to the differences in physiology between the muscle and the synovial fluid; the muscle compartment was modified to mimic the synovial fluid only in terms of its volume without changes in the physiology. For instance, protein expression, diclofenac partitioning and diffusion, and extent of vascularization may be some of the differences between the muscle and the synovial fluid physiology and its interplay with diclofenac that were not taken into account. Finally, a suboptimal model performance may be attributed to not accounting for the disease (osteoarthritis) pathophysiology—namely, the increase in the synovial fluid volume and local blood flow commonly reported in patients with knee osteoarthritis due to inflammation; model predictions generated in virtual healthy volunteers were compared with observed data collected in patients.

Figure 3e:
[image]

hmmm... Suboptimal? :-D
When you worked hard and the experts were involved, some fails could be named just as suboptimal results :-)

Kind regards,
Mittyri
dshah
★    

India/United Kingdom,
2022-01-10 15:31
(170 d 01:00 ago)

@ mittyri
Posting: # 22728
Views: 868
 

 Synovia is a nasty beast

Dear all!
The applicant wanted a wavier from Clinical end point study and so they have used a PBPK approach to prove that the levels of free drug in fluid are similar to reference product. As per the article- "The applicant conducted the PSG- recommended in vivo BE study with PK end points but did not perform the PSG- recommended comparative clinical end point study. Instead, the applicant developed a dermal PBPK model for a VBE assessment based on drug exposure at the presumed site of action between the reference and the test products."
The applicant choose- “the most accurate, sensitive, and reproducible approach available among those set forth" which is PK endpoint compared to clinical endpoint.
To further support- Q1, Q2 and Q3 (IVRT and IVPT) were compared against RLD.
Based on all the data- it is acceptable to presume that there is no significant difference between reference drug and applicant formulation. On the same basis- FDA has accepted the application.
Regards,
Dshah
Helmut
★★★
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Vienna, Austria,
2022-02-17 12:06
(132 d 04:25 ago)

@ mittyri
Posting: # 22787
Views: 575
 

 Virtual Bioequivalence: a myth or coming reality?

Hi mittyri,

did you see this* one?


  • Bego M, Patel N, Cristofoletti R, Rostami-Hodjegan A. Proof of Concept in Assignment of Within-Subject Vari­ability During Virtual Bioequivalence Studies: Propagation of Intra-Subject Variation in Gastrointestinal Physi­o­logy Using Physiologically Based Pharmacokinetic Modeling. AAPS J. 2022; 24: Article 21. doi:10.1208/s12248-021-00672-z. [image] Open access.

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