The standard approach for approval of generic drugs is to run a bioequivalence study to demonstrate that a generic product is comparable to an approved (i.e., reference) drug in their rate and extent of absorption. The rate and extent of drug absorption are determined from the pharmacokinetic parameters: peak concentration (Cmax) and the area under the concentration-time curve (AUC) respectively. The approach is referred to as average bioequivalence (ABE) where the 90% confidence interval for the ratio of the average geometric means (test/reference) for AUC and Cmax must fall between preset regulatory bioequivalence limits from 80% to 125%.
Drugs with a narrow therapeutic index (NTIDs) have a narrow range between therapeutic and toxic dose levels. Traditional ABE methodology may be unacceptable for NTIDs because small differences in drug exposure may lead to serious therapeutic failures and/or adverse drug reactions. The usual average ABE limits are not considered sufficient for NTIDs, and several regulatory agencies have narrowed the limits for bioequivalence. For NTIDs, a fully replicated clinical trial design must be used, and the test formulation must pass the following three criteria:
- RSABE, scaled to the reference variability, eg, 90% CI within limits of 90.00-111.11% for reference formulations with CVWR = 10%,
- Unscaled ABE, within 90% CI limits of 80.00-125.00%,
- The upper bound of the 90% CI of the ratio σWT/σWR must be ≤ 2.5.
The US FDA guidance for warfarin sodium (2012) proposed a new bioequivalence methodology for NTIDs as an extension of reference-scaled average bioequivalence (RSABE) to scale bioequivalence limits to the within-subject variability of the reference product and to compare within-subject variabilities of test and reference products.
A Phoenix template workflow was created to analyze narrow NTIDs per the FDA’s Warfarin Guidance criteria. Attend this webinar to learn how RSABE for NTIDs can be performed in Phoenix WinNonlin using this reusable template. Because the template projects require minimal user input to be used with any input dataset from a replicated 4-period crossover design, they can provide significant time savings and increased efficiency to users!
Who should attend?
- Generic drug developers
- Formulation scientists
About Our Speakers
Christopher Mehl is the Customer Support Manager, and is a software trainer at Certara since 2003. His educational background is a BS in Molecular Biology from the Ohio State University, and an MS in Pharmacology from the University of Wisconsin. He has conducted over 200 training courses with desktop products such as Phoenix WinNonlin, IVIVC, NLME, PKS, and Trial Simulator. These include workshops at the US Food and Drug Administration, universities, customer sites, and courses open to the public.
Ana Henry has extensive experience in a variety of roles in the pharmaceutical industry. Most recently she acted as product manager for the complete suite of Certara desktop products, personally leading the development of Phoenix, the industry’s premier PK/PD software platform. Currently, she is with Certara University’s Scientific Training and Education Department, tasked with training and content development of E-learning courses. Ana has extensive experience in software demonstration and training, and is adept at offering technical expertise and evaluation of software products. She has trained and provided support for Phoenix WinNonlin, Phoenix Connect, Phoenix NLME, Phoenix Knowledgebase Suite, AutoPilot, PKS Reporter, Trial Simulator, and PK/PD methodology courses. Prior to Certara, Ana worked in the pharmaceutical industry as a biostatistician and a pharmacokineticist, designing, analyzing, and reporting on clinical studies. Ana is also a regular guest speaker in the graduate PK/PD course at the University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences.
Linda Hughes is a Principal Software Engineer with Certara. She has worked in software development for the pharmaceutical industry for 18 years. At Certara, she has worked on development of the tools for non-compartmental analysis, bioequivalence, non-linear mixed effects modeling, descriptive statistics, IVIVC, convolution, and deconvolution. She holds an MS degree in Electrical Engineering and BA degree in Mathematics, both from the University of Maryland.