Proposing some innovative study design features to regulatory agencies (ema and fda) in bioequivalence trials. Reference scaled average bioequivalence, and two-stage adaptive designs
- Autores: Eduard Molins Lleonart
- Directores de la Tesis: Jordi Ocaña i Rebull (dir. tes.)
- Lectura: En la Universitat Politècnica de Catalunya (UPC) ( España ) en 2021
- Idioma: español
- Materias:
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- Resumen
In applications for generic medicinal products the concept of bioequivalence is fundamental. Two medicinal products, i.e. a test and a reference drugs containing the same active substance are considered bioequivalent if their bioavailability (rate and extent of absorption of an active substance that is absorbed from a drug product and becomes available at the site of action) after the administration of both products produce a similar therapeutic effect. The assessment of bioequivalence is based upon 90% confidence intervals for the ratio of the population geometric means (test/reference) for the parameters under consideration which should be contained within the limits 80%-125%. It is recommended using randomized, two-period, two-sequence, single dose crossover designs (2x2 crossover designs) The number of subjects to be included should be based on an appropriate sample size calculation, though the number of evaluable subjects should not be less than 12.
Sometimes, there are drugs whose rate and extent of absorption is highly variable dose to dose within the same subject. The main problem with highly variable drugs is that to declare bioequivalence it requires a study with an unacceptably larger sample size. In this case, the usual approach to determine bioequivalence is ‘Reference Scaled Average Bioequivalence’ (RSABE), which is based on expanding the limits as a function of the within-subject variability in the reference formulation. But, using 2x2 crossover designs, it is not possible to estimate separately the test and reference variabilities, and thus it requires using more complex designs like replicated or semi-replicated crossover designs.
On the other hand, regulations also allow using common 2×2 crossover designs based on two-stage adaptive designs (TSD) with sample size re-estimation at an interim analysis. At an interim look (stage 1), if average bioequivalence is not declared with an initial sample size, they allow to increase it based on the intra-subject estimated variability and to enroll additional subjects at a stage 2, or to stop for futility in case of poor likelihood of bioequivalence. This is crucial because both parameters must clearly be pre-specified in protocols, and the strategy agreed with regulatory agencies in advance with emphasis on controlling the overall type I error.
Using Monte Carlo simulations, we show that RSABE and TSD methodologies achieve comparable statistical power, though the scaled method usually requires less sample size, but at the expense of each subject being exposed more times to the treatments. With an adequate initial sample size (not too low, e.g., 24 subjects), TSDs are a flexible and efficient option to consider: They have enough power (e.g., 80%) at the stage 1 for non-highly variable drugs and, if otherwise, they provide the opportunity to step up to a stage 2 that includes additional subjects.
Based on TSDs, we also present an iterative method to adjust the significance levels at each stage which preserves the overall type I error for a wide set of scenarios which should include the true unknown variability value, and which provides a power of at least 80%. TSDs work particularly well for coefficients of variation below 0.3 which are especially useful due to the balance between the power and the percentage of studies proceeding to stage 2. We present an R package to adjust the significance levels at each stage in order to control the overall type I error.
