Bioequivalence and Bioavailability Forum

Dear all,

We are collecting data to assess a ‘Sex-by-Formulation interaction’ because in Section 2.1.1 of the draft ICH M13A we find:

Subjects should be at least 18 years of age and preferably have a Body Mass Index between 18.5 and 30.0 kg/m². If a drug product is intended for use in both sexes, it is recommended the study include male and female subjects. […] Subjects should preferably be non-nicotine users.

Obviously there are no concerns about extrapolating results to patients < 18 years, to obese patients (in the US a whopping 42% of the adult population), or to 23% of adults smoking tobacco. However, this sentence is similar to what the FDA recently recommended but is not stated in any other of the global guidelines. Where does this come from and why? Science or rather (gender)politics?^1,2 We have only few anecdotal reports and small meta studies with contradicting results. Nevertheless, authors of the largest one concluded:³

There is no evidence to require studies in both sex groups, combined or separately.

In the past – at least in Europe – the majority of studies were performed in males only. We know that pharmacovigilance is not very sen­sitive. However, in 23.8 million drug switches in The Netherlands only 1,348 ADRs were reported (i.e., in 0.006% of switches)…⁴

Recently I gave presentations on the topic.^5,6 In the meantime I have more data (235 mixed-sex datasets, 190 passing BE for AUC; 3,970 males, 3,302 females):

• Similar within-subject CV in males (x̃ 18.68%) and females (x̃ 17.12%). Therefore, I could not confirm the hearsay of CV_w being larger in females than in males.
  
• No evidence that medians of Point Estimates of subgroups differ (notches of box plots overlap).
  
• Difference in PEs of males and females > ±20% in 3.16% of datasets.
  
• Difference in PEs of males and females ≤ ±10% in 78.4% of datasets.
  
• p(S × F) uniformly distributed (Kol­mo­go­rov–Smirnov p 0.8823).
  
• Significant (p < 0.1) S × F interaction in 11.6% of datasets; below the upper 95% significance limit of the binomial test (0.1433).
  Some authors discussed an even higher significance limit of 0.2 (being aware of the increased false positive rate).⁷ What do we observe here? 19.5% (<0.2538)…
  
• Discordant Qualitative Interaction⁸ in 1.58% of datasets.

I had only limited information about the subjects’ bodyweight. However, if the AUC was adjusted by bodyweight, the apparent difference between sexes mainly disappeared.

Any type of comparative BA (assessing BE, food-effect, DDI) is welcome.
Of course, data will be treated strictly confidential and not published . The preferred data format is CSV (though xls(x), ODS, SAS XPT or Phoenix project files would serve as well).

Columns (any order is fine):

1. Company or individual (text)
   
2. Study code (text)
   
3. Analyte (text) if you don’t want to give this information, use not spec. X, where X is an integer 1 … number of analytes
   
4. Design (2x2x2, 3x6x3, 3x3, 4x4, 2x2x4, 2x2x3, 2x3x3)
   Simple crossover, 6-sequence 3-period Williams’ design, 3-period Latin Squares, 4-sequence 4-period Williams’ design or 4-period Latin Squares, 2-sequence 4-period full replicate, 2-sequence 3-period full replicate, partial replicate; no parallel design
   
5. Drug (integer) 1 … number of analytes
   
6. Subject (integer or text) min(n) … max(n); missings due to dropouts not a problem
   
7. Group or Site (integer) 1 … number of groups / sites
   
8. Sequence (character or integer), e.g., TR, RT or AB, BA or 1, 2 (simple crossover), e.g., TRTR, RTRT or TRT, RTR (full re­pli­cate designs), TRR, RTR, RRR (partial re­pli­cate design), ABC, BCA, CAB (Latin Squares), ABC, ACB, BAC, BCA, CAB, CBA (Williams’ design)
   Essentially any kind of coding is possible, as long as it is unambiguous.
   
9. Formulation (character) mandatory T or R (not A or B)
   
10. Period (integer) 1 … number of periods
    
11. AUC (numeric); for single dose AUC_0–t or AUC_0–72, for multiple dose AUC_0–τ.
    Missing values should be coded with NA (preferred) or Missing.
    
12. Sex (character) f or m
    
13. Bodyweight (numeric; optional)

If you want to participate, please send data to [email protected].
No cherry-picking, otherwise we will fall into the trap of selection bias and the outcome will be useless. Hence, if you decide to provide data, please do so irrespective of whether you ‘detected’ a significant Sex-by-Formulation interaction or not.
We are primarily working on 2×2×2 crossover designs. How­ever, if you have data of replicate designs, fine as well. In Higher-Order crossover designs indicate which of the treatments is the test and the reference.
If possible, give the analyte. Once we have enough data sets, we will perform sub-group analyses.

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1. Criado Perez C. Invisible Women: Exposing data bias in a world designed for men. New York: Random House; 2019.
   
2. Senn S. Randomisation Versus Random Sampling: Clinical Trials and the Representation Fallacy. Presentation at the Center for Me­di­cal Data Science of the Medical University of Vienna. 20 October 2023.
   
3. González-Rojano E, Marcotegui J, Ochoa D, Román M, Álvarez C, Gordon J, Abad-Santos F, García-Arieta A. Investigation on the Existence of Sex-By-Formulation Interaction in Bioequivalence Trials. Clin Pharm Ther. 2019; 106(5): 1099–112. doi:10.1002/cpt.1539.
   
4. Glerum PJ, Neef C, Burger DM, Yu Y, Maliepaard M. Pharmacokinetics and Generic Drug Switching: A Regulator’s View. Clin Phar­ma­co­kin. 2020; 59: 1065–9. doi:10.1007/s40262-020-00909-8.
   
5. Schütz H. Statistical challenges and opportunities in ICH M13A. Presentation at: 2^nd Bioequivalence Workshop, Brussels. 26 April 2023. Online.
   
6. Schütz H. Sex– and group–related problems in BE. A delusion. Presentation at: BioBridges, Prague. 21 September 2023. Online.
   
7. Alosh M, Fritsch K, Huque M, Mahjoob K, Pennello G, Rothmann M, Russek-Chen E, Smith E, Wilson S, Yiu L. Sta­tis­tical Con­si­de­ra­tions on Subgroup Analysis in Clinical Trials. Stat Pharm Res. 2015; 7(4): 286–304. doi:10.1080/19466315.2015.1077726.
   
8. Sun W, Schuirmann D, Grosser S. Qualitative versus Quantitative Treatment-by-Subgroup Interaction in Equivalence Studies with Mul­­ti­ple Subgroups. Stat Pharm Res. 2023; 15(4): 737–47. doi:10.1080/19466315.2022.2123385.