Bioequivalence and Bioavailability Forum

Hi Relaxation,

THX for noticing us.

❝ From a quick once over interesting topics included .

Indeed.

• 3-period 2-sequence full replicate as an example (is the bloody partial replicate design finally landing in the dustbin of history where it belongs?)
  
• Adaptive designs (yes, also simulation-based; EMA, are you reading this?)
  
• Sparse sampling – wow! I hope by ‘mean’ the FDA means the geometric mean. 
  
• Sensitivity of sample size estimation. THX, now in line with ICH E9 (of 1998!)
  
• The dreadful estimands enter the scene.
  
• ‘for a replicated PK BE study, if reference-scaled average BE is used, the applicant should ensure that the calculated intra-subject variability is not inflated due to extreme values or situations.’ That’s new but inline with all others (except ANVISA, still not requiring it)
  ’In the Random statement, TYPE=FA0(2) could possibly be replaced by TYPE=CSH or UNR.’ THX for CSH. Why not TYPE=FA0(1)?
  Instead of Satterthwaite’s degrees of freedom Kenward-Roger could be used (what does ‘possibly’ mean here?)
  
• Sorry folks, the CI inclusion approach ≠ TOST. Only the conclusions are the same.
  
• What’s the purpose of reporting arithmetic means additionally to geometric means?
  
• Covariates in parallel designs. THX!
  
• Details about studies in multiple groups or sites. Why equal sizes / group?
  
• How to deal in Higher-Order Crossover Designs with references from two regions (i.e., Two at a Time).
  
• Does anybody understand why carryover rose from the grave (Appendix A)? Any treatment-interaction in the model leads to an treatment effect which cannot be interpreted!

Hi Relaxation & all,

❝ From a quick once over interesting topics included .

Since I’m working on a paper on group-effects in BE (still collecting data; see this post) I had a closer look at Section III.D.

The relevant parts:

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[…] sponsors should minimize the group effect in a PK BE study as recommended below:

1. Dose all groups at the same clinic unless multiple clinics are needed to enroll a sufficient number of subjects.
   
2. Recruit subjects from the same enrollment pool to achieve similar demographics among groups.
   
3. Recruit all subjects, and randomly assign them to group and treatment arm, at study outset.
   
4. Follow the same protocol criteria and procedures for all groups.
   
5. When feasible (e.g., when healthy volunteers are enrolled), assign an equal sample size to each group.

Bioequivalence should be determined based on the overall treatment effect in the whole study population. In general, the assessment of BE in the whole study population should be done without including the treatment and group interaction(s) term in the model, but applicants may also use other pre-specified models, as appropriate. The assessment of interaction between the treatment and group(s) is important, especially if any of the first four study design criteria recommended above are not met and the PK BE data are considered pivotal information for drug approval. If the interaction term of group and treatment is significant, heterogeneity of treatment effect across groups should be carefully examined and interpreted with care. If the observed treatment effect of the products varies greatly among the groups, vigorous attempts should be made to find an explanation for the heterogeneity in terms of other features of trial management or subject characteristics, which may suggest appropriate further analysis and interpretation.

It is important that statistical methods and models for the primary BE analysis are fully pre-specified in the protocol or SAP (e.g., in an ANDA study, the applicant should pre-specify detailed statistical criteria and models to be used if the interaction term of group and treatment is applicable). In addition, the statistical model should reflect the multigroup nature of the study. For example, if subjects are dosed in two groups in a crossover BE study, the model should reflect the fact that the periods for the first group are different from the periods for the second group, i.e., the period effect should be nested within the group effect.

(my emphases)

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The terrible ‘model (I)’ $$\eqalign{Y&|\;\text{Group},\,\text{Sequence},\,\text{Treatment},\\
&\phantom{|}\;\text{Subject}(\text{Group}\times \text{Sequence}),\,\text{Period}(\text{Group}),\\
&\phantom{|}\;\text{Group}\times \text{Sequence},\,\text{Group}\times \text{Treatment}}\tag{I}$$ as a mandatory pre-test – which was not stated in any of the previous guidances but in deficiency letters – is gone with the wind. Very good, because it inflated the Type I Error and compromised power.
Now the FDA recommends ‘model (II)’ $$\eqalign{Y&|\;\text{F},\,\;\text{Sequence},\,\text{Subject}(\text{F}\times \text{Sequence}),\\
&\phantom{|}\;\text{Period}(\text{F}),\;\text{F}\times \text{Sequence},\,\text{Treatment}\small{\textsf{,}}}\tag{II}$$ where \(\small{\text{F}}\) is the code for \(\small{\text{Group}}\) or \(\small{\text{Site}}\), respectively. Whilst important in a parallel design, I fail to understand why ‘similar demographics’ are of any importance in a crossover design. Equally sized groups are recommended but not necessary.
If any of the first four conditions are not met, ‘model (I)’ should be used to assess the Group-by-Treatment interaction. If \(\small{p(G\times T)<0.1}\), ‘vigorous [sic] attempts’ should be made to find an explanation. It is an open question what to do if treatment effects vary between groups. Base the BE assessment on the largest one or on the one with smallest variability?

See also the updated article and doi:10.1208/s12248-024-00921-x.

Hi all,

the new draft is interesting and I am still digesting it.

I see that the SxF interaction is given some consideration here, but I am not sure what is meant now. One thing is the outlier aspect and that is ok (Line 387 etc). But it enters the scene again in Line 478 and that part is not so clear. As you read it, do they recommend estimation of SxF whenever the design allows? What do we do with the info?

Muchas gracias.

Dear All

The FDA Generic Drugs Advisory Committee recommended in 1991 that the primary comparison of interest in a BE study is the ratio, rather than the difference, between average PK parameter data from the T and R formulations. Using logarithmic transformation, the general linear statistical model employed in the analysis of BE data allows inferences about the difference between the two means on the log scale, which can then be retransformed into inferences about the ratio of the two averages (geometric means) on the original scale. Logarithmic transformation thus achieves a general comparison based on the ratio rather than the differences.

Under lognormal data for PK paramertes ratio of geometric means the same as ratio of medians

However, situations with different within-subject variances of drugs may often occur in practice. BE tests cannot be easily transformed back from the log scale to the original scale under unequal within-subject variances.

Good morning all

FDA is organizing a webinar: A Deep Dive: FDA Draft Guidance on Statistical Approaches to Establishing Bioequivalence

Agenda

March 14, 2023
10:00 AM - 12:00 PM US ET

TOPICS COVERED
In Vitro Bioequivalence assessment
Statistical Methods for Narrow Therapeutic Index and Highly Variable Drug Products
Comparative Clinical Endpoint Bioequivalence Studies
Studies in Multiple Groups
Bioequivalence Statistics for Adhesion and Irritation Studies
Dose Scale for Bioequivalence Assessment