Mr.Rao
☆

India,
2022-07-13 15:51
(262 d 15:07 ago)

Posting: # 23138
Views: 1,263

## Drug-drug interaction with two different route of administration [Design Issues]

Dear All,

Please note that my client needs to check the drug-drug interaction with one oral route of administration (tab or cap) with another inhaled medication,

I need to know more details about how the procedure (drug-drug interaction) will follow two different routes of administration.

The detailed statistical procedure also.

Your quick response will help us to do a replay to our client.

Rao.

Mr.Rao
☆

India,
2022-07-14 11:05
(261 d 19:54 ago)

@ Mr.Rao
Posting: # 23146
Views: 1,093

## Drug-drug interaction with two different route of administration

Dear All,

Your quick response will much help full to me

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! ? Relax; see also this post #9. 19 hours  two weeks. [Helmut]
dshah
★★

India/United Kingdom,
2022-07-20 12:27
(255 d 18:31 ago)

@ Mr.Rao
Posting: # 23151
Views: 962

## Drug-drug interaction with two different route of administration

hello Mr. Rao!
I think the information provided is too little. There are guidelines available for DDI in both FDA and EMA.
Regards,
Divyen Shah
Mr.Rao
☆

India,
2022-07-21 13:24
(254 d 17:34 ago)

@ dshah
Posting: # 23153
Views: 934

## Drug-drug interaction with two different route of administration

❝ I think the information provided is too little.

Dear Shah,

Sponsor need to check their X-Drug for Idiopathic pulmonary fibrosis treatment along with nintedanib.

First, we will provide the nintedanib for 8 days and we will collect the PK sample and from days 9-16 we will provide both nintedanib and X-Drug and we will collect PK samples, from that sponsor need to check Drug-drug interactions.

Here X-Drug is inhaler and nintedanib is tab/cap.

So what is the procedure.

Thanks,
Rao

dshah
★★

India/United Kingdom,
2022-07-21 20:51
(254 d 10:07 ago)

@ Mr.Rao
Posting: # 23156
Views: 920

## Drug-drug interaction with two different route of administration

Dear Rao!

Regards,
Divyen Shah
Mr.Rao
☆

India,
2022-07-22 09:23
(253 d 21:35 ago)

@ dshah
Posting: # 23158
Views: 940

## Drug-drug interaction with two different route of administration

Dear Shah,

Thanks for the information.

Regards,
Rao

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! [Ohlbe]
Mr.Rao
☆

India,
2022-07-27 10:31
(248 d 20:27 ago)

@ Mr.Rao
Posting: # 23182
Views: 907

## Drug-drug interaction with two different route of admi­nistration

Dear All,

from below reference link they are concluded there is no clinically relevant drug–drug interaction between CHF 5993 and result of cimetidine co-administration, with total exposure increased by 16 %, and peak concentration by 26 %.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5340825/

I need to know how they concluded.

Looking forward to hear in details.

Thansk,

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! Why didn’t you ignore Ohlbe’s note? [Helmut]
Helmut
★★★

Vienna, Austria,
2022-07-27 11:52
(248 d 19:06 ago)

@ Mr.Rao
Posting: # 23183
Views: 871

## Interpreting results of a DDI study

Hi Mr. Rao,

you confirmed in registration that you have read and will comply with the forum’s policy. Please read the relevant section again. You managed it to full quote in all of your replies. That’s an amazing percentage.
Hence, this is the first warning.

You shouldn’t expect to learn the basics from an internet forum. Suggested reading:
1. Hauschke D, Steinijans V, Pigeot I. Bioequivalence Studies in Drug Development. Methods and Applications. Chichester; Wiley: 2007.
Chapter 8: Analysis of pharmacokinetic interactions.
2. Chow S-C, Liu J-p. Design and Analysis of Bioavailability and Bioequivalence Studies. Boca Raton; Chapman & Hall / CRC Press: 3rd ed. 2009.
Chapter 18.2: Drug Interaction Studies.
3. Patterson S, Jones B. Bioequivalence and Statistics in Clinical Pharmacology. Boca Raton; Chapman & Hall / CRC Press: 2nd ed. 2017.
Chapter 8.4: Food Effect Assessment and Drug-Drug Interactions (DDIs).
4. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. Drug Interaction Studies. M12. 08 June 2022. Online.
If a clinically relevant interaction is found, there’s no reason to panic. It only has to be stated as such in the label / prescribing information.

❝ […] concluded there is no clinically relevant drug–drug interaction between CHF 5993 and result of cimetidine co-administration, with total exposure increased by 16 %, and peak concentration by 26 %.

❝ I need to know how they concluded.

What do you fail to understand?

Methods
This two-period, open-label, crossover study examined the drug–drug interaction of CHF 5993 and cimetidine. In one period, subjects received cimetidine 800 mg twice daily for 6 days; on the fourth day they also received CHF 5993 (BDP/FF/GB 400/24/100 µg). In the other, they received CHF 5993 alone. Primary objective was to compare the area under the plasma concentration–time curve from time 0 to last quantifiable concentration (AUC0–t) of GB, with and without cimetidine. Secondary endpoints included GB AUC0–12h, maximum concentration (Cmax) […]

Results
Twenty-six subjects were randomised; 25 completed. Co-administration of CHF 5993 and cimetidine resulted in small, statistically significant increases in GB AUC0–t, AUC0–12h and Cmax vs CHF 5993 (ratios 1.16, 1.21 and 1.26, respectively) […]

Conclusions
Overall, this study indicates that there is no clinically relevant drug–drug interaction between GB and the other components of the triple combination of GB/FF/BDP that comprise extrafine CHF 5993, and cimetidine […]

Statistically significant = 90% confidence interval of the ratio of adjusted geometric means does not include 1. Unfortunately only three significant digits are given in Table 3: AUC0–t 1.16 (1.07, 1.27), AUC0–12h 1.21 (1.08, 1.36), Cmax 1.26 (1.00, 1.58). Perhaps the lower confidence limit of Cmax in full precision was >1.

Contrary to bioequivalence, where the clinically relevant difference $$\small{\Delta}$$ is commonly set to 20%, leading to the limits $$\small{\{\theta_1,\theta_2\}=\{1-\Delta,\,(1-\Delta)^{-1}\}=\{0.8,\,1.25\}}$$, in DDI-studies a different (while still pre-spe­ci­fied) $$\small{\Delta}$$ can be chosen. The paper does not give $$\small{\Delta}$$. From the sample size section (page 272) one can assume that $$\small{\Delta=0.15}$$, which would be pretty strict. Since the authors claimed that the increase was not clinically relevant, one could recalculate $$\small{\widehat{\Delta}_\text{r}}$$ from the upper confidence limit by $$\small{\widehat{\Delta}_\text{r}=\left|CL_\text{upper}^{-1}-1\right|}$$. For the ‘worst’ PK metric Cmax, we get $$\small{\widehat{\Delta}_\text{r}\approx\left|1.58^{-1}-1\right|\approx0.367}$$. IMHO, that’s a lot. If we consider only the primary PK metric AUC0–t, $$\small{\widehat{\Delta}_\text{r}\approx0.213}$$. The study was extremely underpowered (≈55%) for this PK metric. Interesting that the within-subject CV of Cmax was smaller than the one of AUC0–t. Not impossible but rare.

-script below.

library(PowerTOST) design   <- "2x2x2" n        <- 25 metrics  <- c("AUC0-t", "AUC0-12", "Cmax") ratio    <- c(1.16, 1.12, 1.26) CL.lower <- c(1.07, 1.08, 1.36) CL.upper <- c(1.27, 1.36, 1.58) res      <- data.frame(metric = metrics, ratio = ratio,                        CL.lower = CL.lower, CL.upper = CL.upper,                        Delta.r = NA_real_, CV = NA_real_,                        power = NA_real_) for (j in seq_along(metrics)) {   res$Delta.r[j] <- abs(CL.upper[j]^-1 - 1) res$CV[j]      <- suppressMessages(                       CI2CV(lower = CL.lower[j],                             upper = CL.upper[j],                             design = design, n = n))   res$power[j] <- suppressMessages( power.TOST(CV = res$CV[j], theta0 = ratio[j],                                  theta1 = 1 - res\$Delta.r[j],                                  design = design, n = n)) } res[, 5:7] <- signif(res[, 5:7], 4) print(res, row.names = FALSE, right = FALSE)

Gives:

metric  ratio CL.lower CL.upper Delta.r  CV     power AUC0-t  1.16  1.07     1.27     0.2126   0.1780 0.5453 AUC0-12 1.12  1.08     1.36     0.2647   0.2410 0.8761 Cmax    1.26  1.36     1.58     0.3671   0.1555 0.9996

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

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