ICH M13A: Changes to Step 2 [BE/BA News]

posted by Helmut Homepage – Vienna, Austria, 2024-07-31 16:19 (133 d 13:55 ago) – Posting: # 24115
Views: 4,628

Dear all,

some more changes I [sic] think being relevant.
That’s a more permissive wording and would help to deal with recruitment issues in some areas (e.g., the Middle East, India).The correction is much appreciated. Another well-established design (i.e., a higher-order crossover with comparators from multiple regions) was already mentioned in the draft Section 2.2.5.1. Replicate designs for ABE in order to reduce the sample size are not mentioned in the GL. Reference-scaling for HVD(P)s and approaches for NTIDs are not covered in the guideline; details will be given in M13C.This sentence is new. See Section 2.2.2.2 why.
I like the subtle difference. However, I hoped for more details for non-statisticians. The old Note for Guideline of the EMEA, as well as the current guidelines of the WHO and Health Canada are more specific.Less starving of volunteers.
I guess the 800 kcal in the draft were introduced by someone numerically handicapped.
Posture control is applied for ages anyhow.
The wording of the new paragraph follows essentially the EMA’s GL.
tmax instead of Tmax is appreciated (t is the SI abbreviation of time and T the one of absolute temperature).
The requirement of \(\small{AUC_{{0-}\text{t}}\ge 80\%\,AUC_{0-\infty}}\) appeared out of blue skies in the APV guide­line 37 (‼) years ago without any justification.1 Copy & paste in guidelines (EMA, WHO, Health Canada, ANVISA, Japan, and this one)? It was never required by the FDA.
This requirement is questionable because at \(\small{2-4\times t_\text{max}}\) ab­sor­p­tion is practically complete3,4 (depending on the half life we have at \(\small{2\times t_\text{max}\text{:}\approx97.5\%}\) absorbed, at \(\small{3\times t_\text{max}\text{:}\approx99.6\%}\), and at \(\small{4\times t_\text{max}\text{:}\approx99.9\%}\)). After that we see only elimination (and distribution in a two compartment model), which is (are) drug-specific and thus, simply not relevant for the comparison of formulations. It can be shown that the ≥80% requirement translates to \(\small{>4\times t_\text{max}\to\,>99.99\%}\), which is extremely conservative, and, IMHO, not justified for IR products.
Example: Absorption t½ 1 h, elimination t½ 4 h, sampling according to the guideline four times the elimination half life.

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In a nutshell: A »reliable estimate of the extent of exposure« could readily be »ensured« if the sampling would end (much) earlier. For a given number of sampling times points it would be better to have more around tmaxDoes that mean the complete data set is primary and the one with subject(s) excluded \(\small{\tfrac{\textsf{should}}{\textsf{could}}}\) presented as a sensitivity analysis? What if results contradict? I like discussions…
Unchanged, i.e., AUC(0–72h) instead of AUC(0–t) only for drugs with a half life ≥ 24 hours. This is a step backwards from other guidelines (EMA, WHO), where AUC(0–72) can be used independent from the half life. The FDA, Health Canada, ANVISA, and Japan prevailed.
Once implemented, the FDA will have to remove AUC(0–∞), which was a primary PK metric since 1992. A step forwards becausePartial AUC was a metric of early exposure for the FDA and Health Canada, although the method of selecting the cut-off time differed (at the median tmax of the reference and the tmax of the reference for each subject). Basing the cut-off on PD was introduced in the FDA’s NDA/IND guidance of 2022. However, if a clear PK/PD relationship is lacking, the selection of the cut-off time is challenging at least.4
tmax was required by e.g., the EMA, the WHO, and in Australia.
Same content, changed order. Fine with me.
Regrettably unchanged. Some comparators (e.g., of dasatinib) are lousy and regularly more than one subject show low exposure. This is well known from the literature and should not question »the reliability of dose administration«. Limiting the exclusion to only one subject (irrespective of the sample size) is not helpful.
Good luck for the log-linear graphs if (according to Section Section 2.2.2.2) »concentration reported as below the lower limit of quantification (LLOQ) should be treated as zero«. BTW, which mean? The arithmetic mean is nonsense for concentrations. It implies a normal distribution with a certain probability of negative values because the domain of \(\small{\mathcal{N}(\mu;\sigma^2)}\) is \(\small{[-\infty<x+\infty]}\) for \(\small{x\in \mathbb{R}}\). I hope that the geometric mean is meant because concentrations follow a lognormal distribution for \(\small{x\in \mathbb{R}^{\color{Red}{\textbf{+}}}}\). PK software (e.g., Phoe­nix Win­Non­lin, PK­analix, [image] PK­NCA) automatically exclude values with a non-numeric code like ‘LLOQ’ but obviously cannot deal with \(\small{\log_e(0)}\).
For my thinking about \(\small{\frac{AUC_{0-\text{t}}}{AUC_{0-\infty}}\,\ge 80\%\,AUC_{0-\infty}}\) see above. Possibly we will see ‘regulatory creep’, i.e., »may need to be discussed«  »has to be discussed«.
The method of calculating \(\small{AUC_{0-\infty}}\) is nowhere given. Should it be the simple \(\small{AUC_{0-\text{t}}+C_\text{t}/\lambda_\text{z}}\) or can it be based on the estimated last con­centration, i.e., \(\small{AUC_{0-\text{t}}+\widehat{C_\text{t}}/\lambda_\text{z}}\) – as recommended in the Canadian guidance, publications, and textbooks (see this article)? IMHO, it should unambiguously stated in the protocol.
What’s the purpose of reporting the arithmetic mean for PK metrics (Cmax, AUC, pAUC) following a lognormal distribution?
At least the linear trapezoidal method is only given as an example. The linear-up logarithmic-down trapezoidal method is less biased, especially if there are deviations from the sampling schedule and/or concentrations are missing (see this article). IMHO, the method should not only be reported but already stated in the protocol. If in a subject tlast is not the same after all treatments, the T/R-ratio of AUC(0–t) will unavoidably be biased. Alas, an unbiased approach5 did not make it to the GL.
The swing \(\small{100\frac{C_\text{max}-C_\text{min}}{C_\text{min}}}\)    is a terrible PK metric with extreme variability (esp. in case of low accumulation).6 Given, only to be reported. But for what purpose?
»[…] applicants should […] demonstrate the attainment of steady-state.« Regrettably it is not stated how that should be done. For the problems see this article.
Concentrations < LLOQ 0. I beg your pardon?

After a dose we know only one thing for sure: The concentration is not zero.7

»Values below the LLOQ are to be omitted from the calculation of kel and t1/2.« What else? Try a log-linear regression with a ‘zero concentration’. Good luck.
Is it reasonable to assume that multiple groups can effect the response variable? SCNR. See also the previous post.
Why is »non-replicate« stated here again? Already given in the the title of Section 2.2. The mixed model likely was a concession made to the FDA and Health Canada, the only agencies currently requiring it.
Testing for the effects is ridiculous. AFAIK, currently required only by Health Canada – including an ‘explanation’ of significant ones. The outcome of a comparative BA study is dichotomous. Either it passed (BE) or not… The sequence and formulation effects are not relevant and the period effects cancel out.
What is discussed in this respect in Section 2.2.3.2? Am I blind?
Since there is a »primary statistical analysis«, may I ask: What is the secondary one?
I miss a statement that equal variances must not be assumed (i.e., that the confidence interval has to be calculated by the Welch-test instead of by the t-test). In case of unequal variances and/or group sizes the latter is liberal (anticonservative).

To be continued… Feel free to chime in.


  1. Junginger H. Studies on Bioavailability and Bioequivalence – APV Guideline. Drugs Made in Germany. 1987; 30: 161–6.
  2. Midha KK, Hubbard JW, Rawson MJ. Retrospective evaluation of relative extent of absorption by the use of partial areas under plasma concentration versus time curves in bioequivalence studies on conventional release products. Eur J Pharm Sci. 1996; 4(6): 381–4. doi:10.1016/0928-0987(95)00166-2.
  3. Scheerans C, Derendorf H, Kloft C. Proposal for a Standardised Identification of the Mono-Exponential Ter­mi­nal Phase for Oral­ly Ad­mi­­ni­stered Drugs. Biopharm Drug Dispos. 2008; 29(3): 145–57. doi:10.1002/bdd.596.
  4. Yu LX, Li BV, editors. FDA Bioequivalence Standards. New York: Springer; 2014. ISBN 978-1-4939-1251-0. p. 16.
  5. Fisher D, Kramer W, Burmeister Getz E. Evaluation of a Scenario in Which Estimates of Bioequivalence Are Biased and a Proposed Solution: tlast (Common). J Clin Pharm. 2016; 56(7): 794–800. doi:10.1002/jcph.663.
  6. Endrényi L, Tóthfalusi L. Metrics for the Evaluation of Bioequivalence of Modified-Release Formulations. AAPS J. 2012; 14(2): 813–9. doi:10.1208/12248-012-9396-8.
  7. Boxenbaum H. at: AAPS, FDA, FIP, HPB, AOAC. Analytical Methods Validation: Bioavailability, Bio­equi­va­lence and Phar­ma­co­ki­netic Studies. (Crystal City I). Arlington, VA. December 3–5, 1990.

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