Bioequivalence and Bioavailability Forum

Dear all,

M13A Bioequivalence for Immediate-Release Solid Oral Dosage Forms

was adopted on 23 July 2024 and is thus, in Step 4 (final). It was published today. The draft (Step 2 of 20 December 2022) is no more linked on the ICH’s website but is – as of today – still available.

I suggest that we discuss what has changed (the supporting Q&A document might give hints why) and the impact on future studies.

I start with my favorite, the dreadful Group-by-Treatment interaction.

• Draft (2.2.3.5 Multi-Group Design Studies, page 16)
  BE 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 Group by Treatment interaction term in the model, but applicants may also use other pre-specified models, as appropriate. However, the appropriateness of the statistical model should be evaluated to account for the multi-group nature of the BE study. Applicants should evaluate potential for heterogeneity of treatment effect across groups, i.e., Group by Treatment interaction. If the Group by Treatment interaction is significant, this should be reported and the root cause of the Group by Treatment interaction should be investigated to the extent possible. Substantial differences in the treatment effect for PK parameters across groups should be evaluated. Further analysis and interpretation may be warranted in case heterogeneity across groups is observed.
  
• Final (page 13)
  BE should be determined based on the overall treatment effect in the whole study population. The statistical model should take into account the multi-group nature of the BE study, e.g., by using a model including terms for group, sequence, sequence × group, subject within sequence × group, period within group and formulation. The group × treatment interaction term should not be included in the model. However, applicants should evaluate potential for heterogeneity of treatment effect across groups and discuss its potential impact on the study data, e.g., by investigation of group × treatment interaction in a supportive analysis and calculation of descriptive statistics by group.

In the Questions and Answers we find:

In a single-site study, dosing subjects in groups may be unavoidable for logistic reasons. The following measures should be considered to minimize group effects:

1. Start dosing all groups at the same clinic over a specific time span, e.g., within a few weeks.
   
2. Follow the same protocol requirements and procedures for all groups, and recruit subjects from the same enrollment pool thereby achieving similar demographics among groups.
   
3. Randomly assign subjects to group and treatment arm (or treatment sequence) at the study outset.

Assign an equal sample size to each group when feasible, e.g., when healthy subjects are enrolled.

A clear improvement. However, in a meta-analysis of more than 320 studies we found an average loss of ≈6% power by using this model compared to the conventional one (without group-terms).* In the meantime I collected data of more studies (see this post).
Although I’m not happy with the last sentence of this section, we will have to live with it. I’m not sure what is meant by »calculation of descriptive statistics by group«. Geometric means of PK metrics (irrespective of treatment), separate for treatments, or point estimates by the conventional model?
Post hoc analyses regularly lead to endless and – quite often fruitless – discussions. Be prepared for them in ≈5% of your studies (i.e., at the level \(\small{\alpha}\) of the \(\small{G\times T}\)-test; see this article).

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• Schütz H , Burger DA , Cobo E , Dubins DD , Farkás T, Labes D , Lang L, Ocaña J, Ring A , Shitova A , Stus V, Tomashevskiy M. Group-by-Treatment Interaction Effects in Comparative Bioavailability Studies. AAPS J. 2024; 26(3): 50. doi:10.1208/s12248-024-00921-x. Open Access. Supplementary Material.