Vangelis Karalis1, Mira Symillides, Panos Macheras. 1. Laboratory of Biopharmaceutics-Pharmacokinetics Faculty of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Athens, 15771, Greece. vkaralis@pharm.uoa.gr
Abstract
PURPOSE: To explore the comparative performance of the recently proposed bioequivalence (BE) approaches, FDA(s) and EMA(s), by the FDA working group on highly variable drugs and the EMA, respectively; to compare the impact of the GMR-constraint on the two approaches; and to provide representative plots of % BE acceptance as a function of geometric mean ratio, sample size and variability. METHODS: Simulated BE studies and extreme GMR versus CV plots were used. Three sequence, three period crossover studies with two treatments were simulated using four levels of within-subject variability. RESULTS: The FDA(s) and EMA(s) approaches were identical when variability was <30%. In all other cases, the FDA(s) method was more permissive than EMA(s). The major discrepancy was observed for variability values >50%. The GMR-constraint was necessary for FDA(s), especially for drugs with high variabilities. For EMA(s), the GMR-constraint only became effective when sample size was large and variability was close to 50%. CONCLUSIONS: A significant discrepancy in the performances of FDA(s) and EMA(s) was observed for high variability values. The GMR-constraint was essential for FDA(s), but it was of minor importance in case of the EMA(s).
PURPOSE: To explore the comparative performance of the recently proposed bioequivalence (BE) approaches, FDA(s) and EMA(s), by the FDA working group on highly variable drugs and the EMA, respectively; to compare the impact of the GMR-constraint on the two approaches; and to provide representative plots of % BE acceptance as a function of geometric mean ratio, sample size and variability. METHODS: Simulated BE studies and extreme GMR versus CV plots were used. Three sequence, three period crossover studies with two treatments were simulated using four levels of within-subject variability. RESULTS: The FDA(s) and EMA(s) approaches were identical when variability was <30%. In all other cases, the FDA(s) method was more permissive than EMA(s). The major discrepancy was observed for variability values >50%. The GMR-constraint was necessary for FDA(s), especially for drugs with high variabilities. For EMA(s), the GMR-constraint only became effective when sample size was large and variability was close to 50%. CONCLUSIONS: A significant discrepancy in the performances of FDA(s) and EMA(s) was observed for high variability values. The GMR-constraint was essential for FDA(s), but it was of minor importance in case of the EMA(s).
Authors: Sam H Haidar; Barbara Davit; Mei-Ling Chen; Dale Conner; LaiMing Lee; Qian H Li; Robert Lionberger; Fairouz Makhlouf; Devvrat Patel; Donald J Schuirmann; Lawrence X Yu Journal: Pharm Res Date: 2007-09-22 Impact factor: 4.200
Authors: Sam H Haidar; Fairouz Makhlouf; Donald J Schuirmann; Terry Hyslop; Barbara Davit; Dale Conner; Lawrence X Yu Journal: AAPS J Date: 2008-08-26 Impact factor: 4.009
Authors: Barbara M Davit; Mei-Ling Chen; Dale P Conner; Sam H Haidar; Stephanie Kim; Christina H Lee; Robert A Lionberger; Fairouz T Makhlouf; Patrick E Nwakama; Devvrat T Patel; Donald J Schuirmann; Lawrence X Yu Journal: AAPS J Date: 2012-09-13 Impact factor: 4.009
Authors: J Garcés-Eisele; A Ruiz-Argüelles; Larisa Estrada-Marín; Virginia Reyes-Núñez; R Vázquez-Pérez; Olga Guzmán-García; R Coutiño-Medina; Leticia Acosta-Sandria; Beatriz Cedillo-Carvallo Journal: Indian J Pharm Sci Date: 2014-07 Impact factor: 0.975