Beholder
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Russia,
2019-11-08 07:51
(edited by Beholder on 2019-11-08 11:27)

Posting: # 20764
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 Clarification from Russian expert organization regarding telmisartan BE studies [Regulatives / Guidelines]

Hi to all!

An article which might serve as "guideline" for conducting telmisartan BE studies in Russia written by expert organization representative. Open access and abstract in English is available.

Main points represented in the article:
  1. 7 BE studies performed in Russia were assessed.
  2. Not only males should be enrolled but females as well (50/50).
  3. HPLC FLD is, in some cases, more sensitive than HPLC MS/MS (see study 3) ;-).

Best regards
Beholder
Helmut
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2019-11-08 15:35

@ Beholder
Posting: # 20768
Views: 725
 

 Clarification from Russian expert organization…

Hi Beholder,

» An article which might serve as "guideline" for conducting telmisartan BE studies in Russia written by expert organization representative. Open access and abstract in English is available.

THX; interesting. In Table 3 three studies are give twice each. Could reproduce the pooled CV of Cmax (43.57%). The upper limit 44.84% is based on a 75% CI. I would use a 80% CL. Given.

library(PowerTOST)
design <- "2x2x2"
n      <- c(85, 36, 40, 59, 60, 50, 40)
CV     <- c(43.27, 32.72, 33.67, 48.35, 49.45, 34.12, 55.34)/100
df     <- n - 2
study <- data.frame(design = "2x2x2", n = n,
                    df = df, CV = CV, CV.CL = NA,
                    stringsAsFactors = FALSE)
for (j in seq_along(CV)) {
  study$CV.CL[j] <- signif(CVCL(CV = study$CV[j], df = study$df[j],
                                side = "upper", alpha = 0.25)[["upper CL"]], 4)
}
print(study); print(CVpooled(study, alpha = 0.25), verbose = TRUE)

#   design  n df     CV  CV.CL
# 1  2x2x2 85 83 0.4327 0.4677
# 2  2x2x2 36 34 0.3272 0.3701
# 3  2x2x2 40 38 0.3367 0.3781
# 4  2x2x2 59 57 0.4835 0.5331
# 5  2x2x2 60 58 0.4945 0.5449
# 6  2x2x2 50 48 0.3412 0.3778
# 7  2x2x2 40 38 0.5534 0.6280
# Pooled CV = 0.4357 with 356 degrees of freedom
# Upper 75% confidence limit of CV = 0.4485


The recommended sample of 20 subjects is for a 2×2×4 design and ABEL based on a GMR of 0.95 and CV 0.45). I would not be that optimistic and suggest a GMR of not better than 0.90 (42 subjects).
Not sure whether it makes sense to work with the pooled CV at all (different sampling schedules and bioanalytical methods).

I like that the experts recommend 2-sequence 4- (TRTR|RTRT) and 3-period (TRT|RTR) full replicate designs and not the lousy partial replicate. Kudos!

Cheers,
Helmut Schütz
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mittyri
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Russia,
2019-11-08 22:00

@ Helmut
Posting: # 20772
Views: 695
 

 Questions

Hi Helmut,

» In Table 3 three studies are give twice each. Could reproduce the pooled CV of Cmax (43.57%). The upper limit 44.84% is based on a 75% CI. I would use a 80% CL. Given.

They stated above regarding 90% CI. Don't know what kind of distribution was used for CI calculation.

Questions:
  • what do you think about statement that males and females (50/50) should be enrolled, since RLD says regarding sex differences in PK? 'Otherwise the marketed formulation could be not BE for other gender'
  • what do you think about FLU results (study 3)? There's a comment in the text stated that LC-MSMS should be recommended since higher (!) concentrations were achieved with that method and LLOQ 0.5-3.0

Kind regards,
Mittyri
Helmut
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Vienna, Austria,
2019-11-09 11:36

@ mittyri
Posting: # 20776
Views: 665
 

 Guesses & answers

Hi mittyri,

» They stated above regarding 90% CI. Don't know what kind of distribution was used for CI calculation.

I think that’s wrong. The upper 90% confidence limit of the pooled CV (0.4357) with 356 degrees of freedom is 0.4601.

library(PowerTOST)
alpha <- c(0.25, 0.20, 0.10)
CV    <- 0.4357
df    <- 356
res <- data.frame(alpha = alpha, df = df, CV = CV, upper.CL = NA)
for (j in seq_along(alpha)) {
  res$upper.CL[j] <- signif(CVCL(CV = CV, df = df, side = "upper",
                                 alpha = alpha[j])[["upper CL"]], 4)
}
cat("CV =", CV, "df =", df, "\n"); print(res[, c(1, 4)], row.names = FALSE)

# CV = 0.4357 df = 356
# alpha upper.CL
#  0.25   0.4485
#  0.20   0.4516
#  0.10   0.4601


»   – what do you think about statement that males and females (50/50) should be enrolled, since RLD says regarding sex differences in PK? 'Otherwise the marketed formulation could be not BE for other gender'

From the EPAR:

Plasma concentrations were higher in females than in males, without relevant influence on efficacy.
Differences in plasma concentrations were observed, with Cmax and AUC being approximately 3- and 2-fold higher, respectively, in females compared to males.

That confirms the observation that highly variable drugs have a flat dose-response curve (no relevant influence on efficacy despite significantly higher concentrations). Since the experts recommended males and females, go for it. Bioanalysts will like it.

»   – what do you think about FLU results (study 3)?

I don’t believe it. Look at Figure 1 and Table 1.

» There's a comment in the text stated that LC-MSMS should be recommended since higher (!) concentrations were achieved with that method and LLOQ 0.5-3.0

Agree. The concentration in study 3 are way lower than what is not only seen in the other studies but also in the originator’s. Furthermore, the LLOQ is 14% of the (mean!) Cmax. That’s insufficient.

Cheers,
Helmut Schütz
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mittyri
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Russia,
2019-12-04 08:53

@ Helmut
Posting: # 20913
Views: 99
 

 intergender differences and consequences

Hi Helmut,

» »   – what do you think about statement that males and females (50/50) should be enrolled, since RLD says regarding sex differences in PK? 'Otherwise the marketed formulation could be not BE for other gender'

» Differences in plasma concentrations were observed, with Cmax and AUC being approximately 3- and 2-fold higher, respectively, in females compared to males.[/indent]That confirms the observation that highly variable drugs have a flat dose-response curve (no relevant influence on efficacy despite significantly higher concentrations). Since the experts recommended males and females, go for it. Bioanalysts will like it.

I've found very interesting paper which explains the vision of experts.

The abstract does not show the essence of recommendations given in that article, so I'll try to translate it here:
The factor of intergender differences in crossover BE studies could be evaluated using CVintra and GMR evaluation stratified by gender. The extent of difference for CVintra could be established using F test with alpha=5% (95% significance level). For GMRs the difference is significant if it is more than 20%.
In case of differences discovered the results of BE study call into question since it could be ineffective for more patients than in BE study. [what kind of patients are mentioned within BE study? I don't know, but the key message is clear]
Due to high risk of that IMP administration the patients should be informed (addendum into leaflet)

The last sentence is taken from here:
Nevertheless, analysis according to sex will provide useful information that can be included in the package insert of the generic product warning patients about original by generic substitution in accordance with their sex
But Manuel Ibarra et al. did not call into the question:
The goal of our recommendation is not to restrict the commercialization of generic drugs. Average BE should be maintained as currently performed, based on the total number of subjects.

Do you remember your investigations regarding dosing groups? :-D Looks like another factor starts the game in Russia!

Kind regards,
Mittyri
Helmut
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Vienna, Austria,
2019-12-04 11:23

@ mittyri
Posting: # 20915
Views: 73
 

 intergender differences and no consequences

Hi Mittyri,

» I've found very interesting paper which explains the vision of experts.

IMHO, rubbish.

» Do you remember your investigations regarding dosing groups? :-D Looks like another factor starts the game in Russia!

Please, not opening another construction site (the paper is still on my TODO-list).

Let’s see what my my friend Alfredo* thinks about it (four authors of the Spanish agency and one of Health Canada):


The misunderstanding of the sex effect and the sex-by-formulation interaction
Other authors have suggested the need for recruiting male and females in bioequivalence studies simply because they exhibit different exposures when the reference product is administered. Although obvious, it seems necessary to highlight the fact that the existence of sex-related differences in pharmacokinetics of many drugs is not indicative of a sex-by-formulation interaction because if a drug exhibits different pharmacokinetic parameters between males and females, the same is expected for the test and for the reference product. A sex-by-formulation interaction in bioequivalence means that, hypothetically, a test product might be equivalent to the reference product in one sex group (e.g., males) and bioinequivalent in the other group (e.g., females); or bioinequivalent in both groups, but equivalent in the combined analysis (e.g., if the test product were 30% more bioavailable in males and 30% less bioavailable in females, a balanced study with respect to the sex of the recruited participants would be able to show equivalence).
      It is also important to clarify that even if the variability in females were larger than in males, which may occur in certain cases, this is not a reason to include women as suggested by some authors, because we are not interested in making the demonstration of bioequivalence more difficult, but in detecting differences if they exist. Where variability is larger, more subjects need to be recruited, which can be considered unethical since more subjects are exposed unnecessarily to the risks of the study. Importantly, once a large number of subjects is recruited, the estimation would be the same since the larger variability does not bias the study results (i.e., imprecision is not inaccuracy).

(my emphases)


  • González-Rojano E, Abad-Santos F, Ochoa D, Román M, Marcotegui J, Álvarez C, Gordon J, García-Arieta A. Evaluation of sex-by-formulation interaction in bioequivalence studies of efavirenz tablets. Br J Clin Pharmacol. 2018;84:1729–37. doi:10.1111/bcp.13601.

Cheers,
Helmut Schütz
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Yura
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Belarus,
2019-12-04 12:16

@ mittyri
Posting: # 20919
Views: 67
 

 Questions

Hi everyone!
I see not the first article, they are so "raw" :-|
Articles are written like machine guns ... :-)

Let's talk about slow and fast metabolites ... :cool:
the circus :-D

with respect
Yura
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Belarus,
2019-12-04 14:14

@ Yura
Posting: # 20922
Views: 43
 

 Questions

...from Russia
Helmut
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Vienna, Austria,
2019-12-03 12:19

@ Beholder
Posting: # 20908
Views: 202
 

 Confusion from Russian expert organization

Hi Beholder,

following the previous article another one* was published yesterday.

Regrettably designs of studies are again not given (I thought from the other article that the Telmisartan studies were performed in 2×2×2 crossovers). I guess there is a typo in the paper. I could reproduce the pooled CV only when using CV 55.34% of study #7 (and not with the 31.51% of this paper).
Seems that the studies were performed in 4-period full replicates (Candesartan in different designs?). I think that in some cases naïve pooling (and “officially” recommending a sample size based on the upper CL of the pooled CV) is not a good idea. Do you trust in a CV of Cmax of Losartan of 6.84% (5/27 studies had <10%)? I doubt it.
Similar Valsartan: Two studies with 7.54% and 8.07% (gimme a break), when the median of the 16 others is already 32.8%.

What I like (p. 681, first paragraph):

The factors contributing to the observed variation that were included in the ANOVA were the sequence, subjects, period, and drug.

(my emphasis)
Hey, no stupid nested subject(sequence)! ElMaestro will be happy.

Since the article is behind Springer’s pay-wall see the R-code at the end.

I’m not worried about differences in the 3rd decimal figure. The authors used Excel 2016. Hence, it might be a rounding issue (commercial in Excel and scientific in R).
Perhaps there are other typos? Can one of the Russian friends check the original publication?


  • Romodanovskii DP, Goryachev DV, Khokhlov AL, Miroshnikov AN. Investigation Planning and Bioequivalence evaluation of Angiotensin II Receptor Antagonists. Pharm Chem J. 2019;53(8):680–4. doi:10.1007/s11094-019-02062-4.
    Translated from Khimiko-Farmatsevticheskii Zhurnal. August 2019;53(8):11–5.


There would be mismatch for Candesartan when assuming the same design in all studies. Hence I bootstrapped the design (note: other patterns of designs would work as well).

library(PowerTOST)
# Try to guess the Candesartan-designs. Seemingly different ones were used.
# Note: If a 3-period full replicate ("2x2x3") wouldn't matter because the
# same degrees of freedom like the partial replicate "2x3x3"
# Caution: Not a unique (i.e., the 'true') solution!

design      <- c("2x2x2", "2x3x3", "2x2x4")
CV.reported <- 0.230
CL.reported <- 0.238
CVdata <- data.frame(CV = c(24.91, 16.98,  7.92, 15.85, 23.14, 18.45,
                            22.87, 25.37,  7.24, 30.14)/100,
                     n = c(40, 35, 18, 30, 24, 18, 24, 24, 18, 37))
set.seed(123) # uncomment for full random resampling
repeat {
  CVdata$design <- sample(design, nrow(CVdata), replace = TRUE) # bootstrap designs
  x         <- unlist(CVpooled(CVdata = CVdata, alpha = 0.1))
  CV.pooled <- signif(x[["CV"]], 3)
  CV.CL     <- signif(x[["CVupper"]], 3)
  if (abs(CV.pooled - CV.reported) <= 5e-3 &
      abs(CV.CL - CL.reported) <= 1e-3) {
    break # both the pooled CV and its CL are sufficiently close to the reported ones
  }
}
drug  <- c("Losartan", "Valsartan", "Candesartan", "Telmisartan", "Irbesartan")
study <- c(27, 18, 10, 7, 5)
Cmax  <- data.frame(drug = c(rep(drug[1], study[1]), rep(drug[2], study[2]),
                             rep(drug[3], study[3]), rep(drug[4], study[4]),
                             rep(drug[5], study[5])),
                    CV = c(c( 7.39,  7.28, 46.41, 33.67, 10.16, 47.87,
                             37.67, 31.07, 40.28, 28.11, 33.67, 35.37,
                             41.42, 23.07, 40.44,  7.02, 19.51, 23.38,
                              6.84,  8.59, 23.03, 28.26, 41.80, 39.65,
                             22.71, 36.22, 25.43),
                           c(12.79, 32.61, 35.65, 38.41, 36.04, 24.65,
                             31.51, 44.09,  8.07,  7.54, 24.01, 30.54,
                             34.60, 33.02, 28.83, 33.86, 30.73, 35.79),
                           CVdata$CV*100,
                           c(43.27, 32.72, 33.67, 48.35, 49.45, 34.12,
                             55.34), # last CV given in this paper with 31.51%
                           c(20.20, 24.67, 25.12, 15.38, 15.56))/100,
                    n = c(c(24, 24, 40, 18, 18, 24, 26, 44, 72, 30, 18,
                            56, 36, 24, 48, 24, 18, 24, 24, 18, 24, 24,
                            54, 18, 24, 24, 24),
                          c(18, 70, 39, 56, 53, 38, 44, 36, 40, 40, 18,
                            24, 40, 28, 34, 34, 44, 45),
                          CVdata$n,
                          c(85, 36, 40, 59, 60, 50, 40),
                          c(27, 32, 18, 22, 24)),
                   design = c(rep("2x2x4", study[1]), rep("2x2x4", study[2]),
                              CVdata$design,
                              rep("2x2x4", study[4]), rep("2x2x4", study[5])))
# print(Cmax, row.names = FALSE) # uncomment to see the data
res <- data.frame(drug = drug,
                  CV.reported = c(0.327, 0.314, 0.230, 0.435, 0.207),
                  CL.reported = c(0.334, 0.320, 0.238, 0.448, 0.217),
                  CV.pooled = NA, CV.CL = NA)
for (j in seq_along(drug)) {
  x                <- unlist(CVpooled(CVdata = Cmax[Cmax$drug == drug[j], 2:4],
                                      alpha = 0.1))
  res$CV.pooled[j] <- signif(x[["CV"]], 3)
  res$CV.CL[j]     <- signif(x[["CVupper"]], 3)
}
print(res, row.names = FALSE)

Gives:

        drug CV.reported CL.reported CV.pooled CV.CL
    Losartan       0.327       0.334     0.327 0.334
   Valsartan       0.314       0.320     0.314 0.321
 Candesartan       0.230       0.238     0.229 0.238
 Telmisartan       0.435       0.448     0.436 0.449
  Irbesartan       0.207       0.217     0.207 0.218


Cheers,
Helmut Schütz
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