Evaluation of stability using one versus three tubes for each quality control concentration in matrix-based bioanalysis.

Aim: Contract research organizations and pharmaceutical firms have performed stability testing using one of two methods: storing in the freezer a single tube of matrix for each quality control concentration (Method 1), followed by aliquoting and analysis; and storing three tubes for each quality control concentration, followed by analysis (Method 2). This research project was conducted to determine if there were detectable differences between Method 1 and Method 2. Methodology: Five model drugs were selected: teriflunomide (stable compound) and acetyl salicylic acid, simvastatin, tenofovir alafenamide and valganciclovir (stability concerns). Samples were stored at -80°C for 1, 3 and 12 months and then analyzed. Samples were also placed at different locations within the freezer. Results: For the drugs tested, the results suggest that there is no significant difference in the outcome of stability testing, regardless whether Method 1 or Method 2 was followed.

Reply to "On the Effect of Common Excipients on the Oral Absorption of Class 3 Drugs".

We previously concluded that 12 common excipients need not be qualitatively the same and quantitatively very similar to reference for Biopharmaceutics Classification System-based biowaivers. This conclusion for regulatory relief is based upon a series of bioequivalence studies in humans involving cimetidine and acyclovir. Limitations were also discussed. We understand the major concern of García-Arieta et al. is that "results obtained by Vaithianathan et al. should not be extrapolated to other drugs." We understand that individuals conducting their own risk/benefit analysis may reach that conclusion, and we reply to the concerns of García-Arieta et al. We continue to conclude that the 12 common excipients need not be qualitatively the same nor quantitatively very similar to reference, but rather, simply be not more than the quantities studied in our manuscript for cimetidine and acyclovir, and potentially other class 3 drugs with similar properties.

Effect of Common Excipients on the Oral Drug Absorption of Biopharmaceutics Classification System Class 3 Drugs Cimetidine and Acyclovir.

The objective was to assess the impact of larger than conventional amounts of 14 commonly used excipients on Biopharmaceutics Classification System (BCS) class 3 drug absorption in humans. Cimetidine and acyclovir were used as model class 3 drugs across three separate four-way crossover bioequivalence (BE) studies (n = 24 each) in healthy human volunteers, denoted as study 1A, 1B, and 2. In study 1A and 1B, three capsule formulations of each drug were manufactured, collectively involving 14 common excipients. Capsule formulations that incorporated hydroxypropyl methylcellulose (HPMC) or magnesium stearate exhibited lower absorption. The cimetidine commercial solution contained sorbitol and also resulted in lower absorption. Hence, in study 2, two capsule formulations with lower amounts of HPMC and magnesium stearate, the sorbitol-containing commercial solution, and a sorbitol-free solution were assessed for BE. Overall, 12 common excipients were found in large amounts to not impact BCS class 3 drug absorption in humans, such that these excipients need not be qualitatively the same nor quantitatively very similar to reference, but rather simply be not more than the quantities studied here. Meanwhile, for each HPMC and microcrystalline cellulose, BCS class 3 biowaivers require these two excipients to be qualitatively the same and quantitatively very similar to the reference.

Workshop report: Crystal City V--quantitative bioanalytical method validation and implementation: the 2013 revised FDA guidance.

In September 2013, the FDA released a draft revision of the Bioanalytical Method Validation (BMV) Guidance, which included a number of changes to the expectations for bioanalysis, most notably the inclusion of biomarker assays and data. To provide a forum for an open, inclusive discussion of the revised draft BMV Guidance, the AAPS and FDA once again collaborated to convene a two-and-a-half day workshop during early December 2013 in Baltimore, MD, USA. The resulting format embodied extensive open discussion and each thematic session included only brief, concise descriptions by Agency and industry representatives prior to opening the floor discussion. The Workshop was built around four thematic sessions (Common Topics, Chromatographic, Ligand-Binding Assays, and Biomarkers) and a final session with international regulators, concluding with a review of the outcomes and recommendations from the thematic sessions. This Workshop report summarizes the outcomes and includes topics of agreement, those where the FDA will consider the Industry's perspective, and those where the workshop provided a first open dialogue. This article will be available to the bioanalytical community at http://www.aaps.org/BMV13 .

Common reasons for "for-cause" inspections in bioequivalence studies submitted to the Food and Drug Administration.

"For-cause" inspections are initiated during the review of bioequivalence (BE) data submitted to Abbreviated New Drug Applications when possible scientific misconduct and study irregularities are discovered. We investigated the common reasons for initiating "for-cause" inspections related to the clinical, analytical, and dissolution study sites associated with BE studies. This information may help the pharmaceutical industry to understand the root causes of compliance failures in BE studies and help them to improve compliance with FDA's regulations, thereby facilitating more rapid approval of safe and effective generic drugs.

Implementation of a reference-scaled average bioequivalence approach for highly variable generic drug products by the US Food and Drug Administration.

Highly variable (HV) drugs are defined as those for which within-subject variability (%CV) in bioequivalence (BE) measures is 30% or greater. Because of this high variability, studies designed to show whether generic HV drugs are bioequivalent to their corresponding HV reference drugs may need to enroll large numbers of subjects even when the products have no significant mean differences. To avoid unnecessary human testing, the US Food and Drug Administration's Office of Generic Drugs developed a reference-scaled average bioequivalence (RSABE) approach, whereby the BE acceptance limits are scaled to the variability of the reference product. For an acceptable RSABE study, an HV generic drug product must meet the scaled BE limit and a point estimate constraint. The approach has been implemented successfully. To date, the RSABE approach has supported four full approvals and one tentative approval of HV generic drug products.

Comparing generic and innovator drugs: a review of 12 years of bioequivalence data from the United States Food and Drug Administration.

BACKGROUND: In the US, manufacturers seeking approval to market a generic drug product must submit data demonstrating that the generic formulation provides the same rate and extent of absorption as (ie, is bioequivalent to) the innovator drug product. Thus, most orally administered generic drug products in the US are approved based on results of one or more clinical bioequivalence studies. OBJECTIVE: To evaluate how well the bioequivalence measures of generic drugs approved in the US over a 12-year period compare with those of their corresponding innovator counterparts. METHODS: This retrospective analysis compared the generic and innovator bioequivalence measures from 2070 single-dose clinical bioequivalence studies of orally administered generic drug products approved by the Food and Drug Administration (FDA) from 1996 to 2007 (12 y). Bioequivalence measures evaluated were drug peak plasma concentration (C(max)) and area under the plasma drug concentration versus time curve (AUC), representing drug rate and extent of absorption, respectively. The generic/innovator C(max) and AUC geometric mean ratios (GMRs) were determined from each of the bioequivalence studies, which used from 12 to 170 subjects. The GMRs from the 2070 studies were averaged. In addition, the distribution of differences between generic means and innovator means was determined for both C(max) and AUC. RESULTS: The mean +/- SD of the GMRs from the 2070 studies was 1.00 +/- 0.06 for C(max) and 1.00 +/- 0.04 for AUC. The average difference in C(max) and AUC between generic and innovator products was 4.35% and 3.56%, respectively. In addition, in nearly 98% of the bioequivalence studies conducted during this period, the generic product AUC differed from that of the innovator product by less than 10%. CONCLUSIONS: The criteria used to evaluate generic drug bioequivalence studies support the FDA's objective of approving generic drug formulations that are therapeutically equivalent to their innovator counterparts.

Evaluation of a scaling approach for the bioequivalence of highly variable drugs.

Various approaches for evaluating the bioequivalence (BE) of highly variable drugs (CV > or = 30%) have been debated for many years. More recently, the FDA conducted research to evaluate one such approach: scaled average BE. A main objective of this study was to determine the impact of scaled average BE on study power, and compare it to the method commonly applied currently (average BE). Three-sequence, three period, two treatment partially replicated cross-over BE studies were simulated in S-Plus. Average BE criteria, using 80-125% limits on the 90% confidence intervals for C (max) and AUC geometric mean ratios, as well as scaled average BE were applied to the results. The percent of studies passing BE was determined under different conditions. Variables tested included within subject variability, point estimate constraint, and different values for sigma(w0), which is a constant set by the regulatory agency. The simulation results demonstrated higher study power with scaled average BE, compared to average BE, as within subject variability increased. At 60% CV, study power was more than 90% for scaled average BE, compared with about 22% for average BE. A sigma(w0) value of 0.25 appears to work best. The results of this research project suggest that scaled average BE, using a partial replicate design, is a good approach for the evaluation of BE of highly variable drugs.

Highly variable drugs: observations from bioequivalence data submitted to the FDA for new generic drug applications.

INTRODUCTION: It is widely believed that acceptable bioequivalence studies of drugs with high within-subject pharmacokinetic variability must enroll higher numbers of subjects than studies of drugs with lower variability. We studied the scope of this issue within US generic drug regulatory submissions. MATERIALS AND METHODS: We collected data from all in vivo bioequivalence studies reviewed at FDA's Office of Generic Drugs (OGD) from 2003-2005. We used the ANOVA root mean square error (RMSE) from bioequivalence statistical analyses to estimate within-subject variability. A drug was considered highly variable if its RMSE for C (max) and/or AUC was > or =0.3. To identify factors contributing to high variability, we evaluated drug substance pharmacokinetic characteristics and drug product dissolution performance. RESULTS AND DISCUSSION: In 2003-2005, the OGD reviewed 1,010 acceptable bioequivalence studies of 180 different drugs, of which 31% (57/180) were highly variable. Of these highly variable drugs, 51%, 10%, and 39% were either consistently, borderline, or inconsistently highly variable, respectively. We observed that most of the consistent and borderline highly variable drugs underwent extensive first pass metabolism. Drug product dissolution variability was high for about half of the inconsistently highly variable drugs. We could not identify factors causing variability for the other half. Studies of highly variable drugs generally used more subjects than studies of lower variability drugs. CONCLUSION: About 60% of the highly variable drugs we surveyed were highly variable due to drug substance pharmacokinetic characteristics. For about 20% of the highly variable drugs, it appeared that formulation performance contributed to the high variability.

Bioequivalence approaches for highly variable drugs and drug products.

Over the past decade, concerns have been expressed increasingly regarding the difficulty for highly variable drugs and drug products (%CV greater than 30) to meet the standard bioequivalence (BE) criteria using a reasonable number of study subjects. The topic has been discussed on numerous occasions at national and international meetings. Despite the lack of a universally accepted solution for the issue, regulatory agencies generally agree that an adjustment of the traditional BE limits for these drugs or products may be warranted to alleviate the resource burden of studying relatively large numbers of subjects in bioequivalence trials. This report summarizes a careful examination of all the statistical methods available and extensive simulations for BE assessment of highly variable drugs/products. Herein, the authors present an approach of scaling an average BE criterion to the within-subject variability of the reference product in a crossover BE study, together with a point-estimate constraint imposed on the geometric mean ratio between the test and reference products. The use of a reference-scaling approach involves the determination of variability of the reference product, which requires replication of the reference treatment in each individual. A partial replicated-treatment design with this new data analysis methodology will thus provide a more efficient design for BE studies with highly variable drugs and drug products.

Modeling the pharmacokinetics and pharmacodynamics of a unique oral hypoglycemic agent using neural networks.

PURPOSE: To develop a predictive population pharmacokinetic/ pharmacodynamic (PK/PD) model for repaglinide (REP), an oral hypoglycemic agent, using artificial neural networks (ANNs). METHODS: REP, glucose concentrations, and demographic data from a dose ranging Phase 2 trial were divided into a training set (70%) and a test set (30%). NeuroShell Predictor was used to create predictive PK and PK/PD models using population covariates: evaluate the relative significance of different covariates; and simulate the effect of covariates on the PK/PD of REP. Predictive performance was evaluated by calculating root mean square error and mean error for the training and test sets. These values were compared to naive averaging (NA) and randomly generated numbers (RN). RESULTS: Covariates found to have an influence on PK of REP include dose, gender. race, age, and weight. Covariates affecting the glucose response included dose, gender, and weight. These differences are not expected to be clinically significant. CONCLUSIONS: We came to the following three conclusions: 1) ANNs are more precise than NA and RN for both PK and PD; 2) the bias was acceptable for ANNs as compared with NA and RN; and 3) neural networks offer a quick and simple method for predicting, for identifying significant covariates, and for generating hypotheses.