Dear Sir/ Madam,

Kindly confirm the quantity of retention quantity required for retention for SFDA BA/ BE studies.(SAUDI FDA regulations)]]>

Assuming you are in steady-state on day 15 (and linear PK/no induction, of course), the AUC over the dosing interval on Day 15 should equal the AUC0-oo of a single dose.

To estimate the Cmax following single-dose you would need to sample on Day 1 imho...]]>

Hi Helmut

AUC is truncated with an interval of reception (in the morning and in the evening - 12 hours).

what 24 hours? if after the first dose - it is also 12 hours. Perhaps, the AUCt / AUCi criterion will be less than 80%.

Thank you]]>

Hi GM,

As El Maestro explained, when your test hypothesis is a ratio (multiplicative model) then you must work with ln transformed data. The guidance is clear on that as it also defines the acceptance range as 0.8 to 1.25 (multiplicative model). If in some guideline they state the hypothesis as treatment differences (H0: u1-u2=0) and a symmetric equivalence range (0.8 to 1.2) then they would be suggesting an analysis on untransformed data. This is true regardless of the type of your endpoint as long as it is a continuous endpoint. As simple as that ;-)

Regards,

David]]>

Hello El-Maestro,

Sorry I am understanding the guidance (see Clindamycin OGD)wrongly, as I already told "am new to endpoint studies".

As per the OGD section #6, the primary end point is,

As per the OGD section #20, Equivalence Analysis

H0: µT / µR ≤ θ1 or µT / µR ≥ θ2 versus HA : θ1 < µT / µR < θ2

Where µT = mean of test treatment, and µR = mean of reference treatment

Typically, we reject H0 with a type I error α = 0.05 (two 1-sided tests), if the 90% confidence interval for the ratio of means between test and reference products (µT / µR) is contained within the interval [θ1, θ2], where θ1 = 0.80 and θ2 = 1.25.

Here as per my understanding, the primary endpoint is untransformed data. That's why I had this confusion:confused:. and why 90% CI interval given as 00.80-1.25?

if the primary endpoint is ln-transformed data then there is no problem to calculate ratio of means or difference of means as you said earlier.

Sorry if I am confusing everybody. Kindly give me some idea on this.

Thanks in advance,

GM]]>

We are trying to decide how we should think about the new PKPlus™ by S+. Any thoughts on efficacy / pros and cons / pricing? Thank you in advance.]]>

Hi GM,

Why is it that you want the endpoint to be untransformed?

Statistically normal distributions are wonderful because any two normal distributions added (= subtracted) will yield a new normal distrution. But try and divide them and you are facing a mathematical challenge that is in no way straightforward to deal with.

If you want to do a parametric CI on a ratio then the only way forward that I know of is a transformation one way or another so that the endpoint can reasonably be assumed normal.

So I am sorry that I cannot answer your question in the way you have asked it. Having said this I am of the impression that you don't really need a 90% CI for a ratio of

Hello El-Maestro,

Thankyou for the reply.

How can we will calculate 90% CI for ratio of means of untransformed continuous variable using PROC GLM in SAS.

Please help me in this regards.

Thankyou

GM.]]>

Hi Jeewaka,

Not easy. You could only perform modeling assuming (!) linear PK (clearance will not change over time). Given your sampling schedule I guess it will be very difficult to model anything beyond a simple one compartment model (

The combination of some data and an aching desire

for an answer does not ensure that a reasonable answer

can be extracted from a given body of data. John W. Tukey

- You can come up with a couple of models assuming something (
*i.e.*, to avoid over-parameterisation). Very difficult to decide which one describes the PK “best”.

?]]>

1) Do you know the half-life of this drug?

2) Was there drug accumulation?

3) Is the elimination kinetic complicated (i.e, biphasic elimination)

J]]>

Hi

I am conducting a PK study where multiple doses are given morning and night for 14 days and blood concentrations are measured at pre-dose, 0.5,1,2,3,6,12,24 hours on day 15. The last dose is given on day 15 morning.

The researcher wants me to estimate the PK parameters of the single dose from the blood concentrations of multiple dosing.

is this possible?

Thank you in advance.

Edit: Category changed; see also this post #1 --> 16205. [Helmut]]]>

Hi GM,

the tradition is to use 80.00%-125.00% for a ratio of continuous variables. This makes extremely good sense under a certain set of assumptions.

You refer to section 21 of the guidance, but this does not relate to continuous variables. It applies to dichotomous variables instead.

Rule of thumb: For a difference, the acceptance range is symmetric. For a ratio the lower acceptance limit equals the reciprocal upper limit, naturally. Most BE issues you will come across have elements of both if logs are involved.]]>

Hello All,

As I am new to the endpoint studies, how can we calculate 90% CI for continuous variable in clinical endpoint studies?

As per my understanding, there are two ways. One is ±20 rule for the difference of means and 80-120% rule for ratio of means of un-transformed data. Based on this reference

But as per OGD, the 90%CI should lies in 80-125% for un-transformed data. Please see the point no.21 in the OGD (see Clindamycin Phosphate).

This is :confused: me a lot. Please clarify.

Edit: URL corrected. Please don’t link to a Google-India search term. [Helmut]]]>

Hi Ananth,

See this post #3 --> 16205.

I’m pretty sure an official English translation does not exist. Get INVIMA’s Resolución 1124 of 2016 and hire a qualified translator.]]>

Dear luvblooms,

Can you just mentioned the few probable reasons for not accepting the published literature information? If the Clinical Pharmacology and biopharmaceutics data of Innovator clearly demonstrated a validated permeability study conducted using all the standards (low permeable, moderate permeable, high permeable, zero permeable) will it be acceptable in your opinion? :confused:

Thank You in advance! :-)

Maitri

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 --> 16205! [Helmut]]]>

Dear Sir/Madam,

can you please provide Colombia guidelines in english and report format for study submission

thanks and regards

ananth prabhu 1]]>

Dear Helmut, dear All,

5 minutes are gone :PCchaos:.

See Power2Stage on Github. Function

`power.tsd.2m()`

.It's pure Potvin B, abbreviated with implicit power monitoring, i.e. via sample size estimation, which gives n2=0 in case of 'enough' power.

What's missing at the moment is some sort of correlation between the PK metrics analogous to what is possible in

`power.2TOST()`

with the argument `rho`

. Here I don't know at the moment at which place it comes into play. Suggestions are welcome.The function is not public at the moment. Not in the mood to document :sleeping:.

Example:

# install from GitHub via

# devtools::install_github("Detlew/Power2Stage")

`library(Power2Stage)`

# Cmax not BE, but AUC assumed BE

Power2Stage:::power.tsd.2m(CV=c(0.3, 0.2), theta0=c(1.25, 0.95), n1=28)$pBE

[1] 0.043354

# Cmax assumed BE, but AUC not BE

Power2Stage:::power.tsd.2m(CV=c(0.3, 0.2), theta0=c(0.95, 1.25), n1=28)$pBE

[1] 0.032048

# Cmax not BE, AUC also not BE

Power2Stage:::power.tsd.2m(CV=c(0.3, 0.2), theta0=c(1.25, 1.25), n1=28)$pBE

[1] 0.001495

Take the results with a grain of salt. Don't know how to validate. Suggestions here also welcome.

But I think the results look plausible.

Edited numbers after removing a little bug.]]>

Dear Helmut,

Meanwhile prepare a decision scheme with two metrics. So that I can see if my implementation is correct. My brain gets crowded if I think about the decision scheme. So many "What if". And here you are trained as scuba diver I was told from somebody ;-).]]>

Thumbs up for El Maestro post! :clap:

Although I may understand the cost issue, I can't understand why pharmaceutical companies that outsource most of their R&D and manufacturing activities don't think and behave as you've described. They should be as interested as the regulatory authorities in assuring that the highest standards are followed by their vendors. When something goes wrong, and as the "vigilance" decreases the probability of lack of compliance increases, it's their brands and products that feel the consequences, as well as the market as a whole. Unless they all believe that there isn't such thing as bad publicity.]]>