## EMA: Full replicate study [Regulatives / Guidelines]

❝ **Question: To address the challenges related to recruitment of cancer patients in PK BE study, we want to plan a full replicate design study. Will the EMA agency accept this approach even if we are not suspecting the drug to be highly variable?**

❝ As per EMA guidance, *“If an applicant suspects that a drug product can be considered as highly variable in its rate and/or extent of absorption, a replicate cross-over design study can be carried out.”*

**4.1.1 Study design**:

**Standard design**

If two formulations are compared, a randomised, two-period, two-sequence single dose crossover design is recommended.

**Alternative designs**

Under certain circumstances, provided the study design and the statistical analyses are scientifically sound, alternative well-established designs could be considered such as […] replicate designs e.g. for substances with highly variable pharmacokinetic characteristics.

❝ As per USFDA guidance *“A replicate crossover study design (either partial or fully replicate) is appropriate for drugs whether the reference product is a highly variable drug or not. A replicate design can have the advantage of using fewer subjects compared to a non-replicate design, although each subject in a replicate design study would receive more treatments”*.

`PowerTOST`

:`library(PowerTOST)`

CV <- 0.25 # assumed within-subject CV

theta0 <- 0.95 # assumed T/R-ratio

target <- 0.80 # target (desired) power

design <- c("2x2x2", "2x2x4", "2x3x3") # guess…

n.per <- as.integer(substr(design, 5, 5))

expl <- data.frame(design = design, n = NA, treatments = NA)

for (j in seq_along(design)) {

expl[j, 2] <- sampleN.TOST(CV = CV, theta0 = theta0, design = design[j],

targetpower = target, print = FALSE)[["Sample size"]]

expl[j, 3] <- sprintf("%.0f ", expl[j, 2] * n.per[j])

}

fmt <- paste0("Sample sizes and number of treatments for assumed CV = %.3g ",

"and T/R-ratio = %.3g,\npowered for at least %.0f%% ",

"and evaluation for Average Bioequivalence (ABE).\n")

cat(sprintf(fmt, CV, theta0, 100 * target)); print(expl, row.names = FALSE)

`Sample sizes and number of treatments for assumed CV = 0.25 and T/R-ratio = 0.95,`

powered for at least 80% and evaluation for Average Bioequivalence (ABE).

design n treatments

2x2x2 28 56

2x2x4 14 56

2x3x3 21 63

❝ We understand that widening criteria will be applied only to Cmax if we observe Swr > 0.294 or else conventional BE criteria will be applied.

*not*based on \(\small{s_\text{wR}}\) (like for the FDA) but on \(\small{CV_\text{wR}}\), where for reference-scaling \(\small{s_\text{wR}=\sqrt{\log_e(CV_\text{wR}+1)}}\).

- If > 30%:
- Reference-scaling by Average Bioequivalence with Expanding Limits (ABEL)

- Upper cap of scaling at \(\small{CV_\text{wR}=50\%}\) (maximum expansion 69.84–143.19%)

- 90% CI within expanded limits \(\small{\left\{L,U\right\}=\exp(\mp 0.76\cdot s_\text{wR})}\)

- Point estimate within 80.00–125.00%

- Reference-scaling by Average Bioequivalence with Expanding Limits (ABEL)
- If ≤ 30%:
- ABE

- 90% CI within 80.00–125.00%

- ABE

*“a wider difference in C*

_{max}

*is considered clinically irrelevant based on a sound clinical justification can be assessed with a widened acceptance range”*. I have some doubts whether that can be provided for an anticancer drug.

*Dif-tor heh smusma*🖖🏼 Довге життя Україна!

_{}

Helmut Schütz

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

Science Quotes

### Complete thread:

- full replicate study for drug not highly variable - EMA perspective Ankit Parikh 2024-07-11 04:40 [Regulatives / Guidelines]
- EMA: Full replicate studyHelmut 2024-07-15 09:13