Implementing the additional strength biowaiver for generics: EMA recommended approaches and challenges for a US-FDA submission.

This review describes the EMA requirements on biowaivers for additional strengths of immediate release and modified release oral solid dosage forms focused on generic applications and highlights the challenges for a simultaneous EMA and FDA submission. Some specificities of the current EMA guidelines are compared with the current FDA Guidance for Industry, with a special focus on the strength to be investigated in vivo, formulation suitability for biowaiver, and optimizing dissolution studies for additional strength biowaivers. In Europe, the same principles applied for generics may be considered for deriving the biowaivers for innovator products. Several case studies are presented to illustrate the challenges of applying for additional strength biowaivers in EMA and FDA simultaneously.

Implementing the Biopharmaceutics Classification System in Drug Development: Reconciling Similarities, Differences, and Shared Challenges in the EMA and US-FDA-Recommended Approaches.

The US-FDA recently posted a draft guideline for industry recommending procedures necessary to obtain a biowaiver for immediate-release oral dosage forms based on the Biopharmaceutics Classification System (BCS). This review compares the present FDA BCS biowaiver approach, with the existing European Medicines Agency (EMA) approach, with an emphasis on similarities, difficulties, and shared challenges. Some specifics of the current EMA BCS guideline are compared with those in the recently published draft US-FDA BCS guideline. In particular, similarities and differences in the EMA versus US-FDA approaches to establishing drug solubility, permeability, dissolution, and formulation suitability for BCS biowaiver are critically reviewed. Several case studies are presented to illustrate the (i) challenges of applying for BCS biowaivers for global registration in the face of differences in the EMA and US-FDA BCS biowaiver criteria, as well as (ii) challenges inherent in applying for BCS class I or III designation and common to both jurisdictions.

Novel bioequivalence approach for narrow therapeutic index drugs.

Narrow therapeutic index drugs are defined as those drugs where small differences in dose or blood concentration may lead to serious therapeutic failures and/or adverse drug reactions that are life-threatening or result in persistent or significant disability or incapacity. The US Food and Drug Administration proposes that the bioequivalence of narrow therapeutic index drugs be determined using a scaling approach with a four-way, fully replicated, crossover design study in healthy subjects that permits the simultaneous equivalence comparison of the mean and within-subject variability of the test and reference products. The proposed bioequivalence limits for narrow therapeutic index drugs of 90.00%-111.11% would be scaled based on the within-subject variability of the reference product. The proposed study design and data analysis should provide greater assurance of therapeutic equivalence of narrow therapeutic index drug products.

Impact of data base structure in a successful in vitro-in vivo correlation for pharmaceutical products.

The in vitro-in vivo correlation (IVIVC) (Food and Drug Administration 1997) aims to predict performances in vivo of a pharmaceutical formulation based on its in vitro characteristics. It is a complex process that (i) incorporates in a gradual and incremental way a large amount of information and (ii) requires information from different properties (formulation, analytical, clinical) and associated dedicated treatments (statistics, modeling, simulation). These results in many studies that are initiated and integrated into the specifications (quality target product profile, QTPP). This latter defines the appropriate experimental designs (quality by design, QbD) (Food and Drug Administration 2011, 2012) whose main objectives are determination (i) of key factors of development and manufacturing (critical process parameters, CPPs) and (ii) of critical points of physicochemical nature relating to active ingredients (API) and critical quality attribute (CQA) which may have implications in terms of efficiency, safety, and inoffensiveness for the patient, due to their non-inclusion. These processes generate a very large amount of data that is necessary to structure. In this context, the storage of information in a database (DB) and the management of this database (database management system, DBMS) become an important issue for the management of projects and IVIVC and more generally for development of new pharmaceutical forms. This article describes the implementation of a prototype object-oriented database (OODB) considered as a tool, which is helpful for decision taking, responding in a structured and consistent way to the issues of project management of IVIVC (including bioequivalence and bioavailability) (Food and Drug Administration 2003) necessary for the implementation of QTPP.

In vitro- in vivo correlation's dissolution limits setting.

PURPOSE: In vitro in vivo correlation (IVIVC) is a biopharmaceutical tool recommended for use in formulation development. When validated, IVIVC can be used to set dissolution limits and, based on the dissolution limits, as a surrogate for an in vivo study. The purpose of this paper is to study the various methods used to fix dissolution limits. METHODS: Fixing dissolution limits is not a straightforward process; various approaches exist. The classical ±10% of dissolution limits was compared to the recommended ±10% of Cmax and AUC and to an innovative back calculation of the 90% CI. Based on simulated values the influence of the calculation method as well as of the variability of the results and pharmacokinetic processes was investigated. RESULTS: Depending upon the method, the results are different and their comparison leads to possible rules. It appears that the usage of a back calculation of a 90% CI is an accurate and advantageous method when intra-individual variability associated with the drug is low. Those findings are in accordance with the current practice of IVIVC, which is not recommended for highly variable drugs. CONCLUSIONS: The approach of using a 90% CI allows the intra-subject variability to be taken into account and fixes limits that ensure a greater chance to show acceptable BE, in case of reasonable intra-subject variability, leading to setting broader in vitro dissolution limits compared to classical solutions.

In vitro-in vivo correlations: tricks and traps.

In vitro-in vivo correlation (IVIVC) is a biopharmaceutical tool recommended to be used in development of formulation. When validated, it can speed up development of formulation, be used to fix dissolution limits and also as surrogate of in vivo study. However, as do all tools, it presents limitations and traps. The aim of the present paper is to investigate five common traps which could limit either the setting or use of IVIVC (1) using mean or individual values; (2) correction of absolute bioavailability; (3) correction of lag time and time scaling; (4) flip-flop model; and (5) predictability corrections.

FDA evaluations using in vitro metabolism to predict and interpret in vivo metabolic drug-drug interactions: impact on labeling.

Recent advances in in vitro metabolism methods have led to an improved ability to predict clinically relevant metabolic drug-drug interactions. To address the relationships of in vitro metabolism data and in vivo metabolism outcomes, the Office of Clinical Pharmacology and Biopharmaceutics in the Center for Drug Evaluation and Research, Food and Drug Administration, evaluated a number of recently approved new drug applications. The goal of these evaluations was to determine the contribution of in vitro metabolism data in (1) predicting in vivo drug-drug interactions, (2) determining the need to conduct an in vivo drug-drug interaction study, and (3) incorporating findings into drug product labeling. Ten cases are presented in this article. They fall into two major groups: (1) in vitro data were predictive of in vivo results, and (2) in vitro data were not predictive of in vivo results. Discussion of these cases highlights factors limiting predictability of in vivo metabolic interactions from in vitro metabolism data. The integration of these findings into drug product labeling is also discussed.