Batch Selection via In Vitro/In Vivo Correlation in Pharmacokinetic Bioequivalence Testing.

Pharmacokinetic differences between manufacturing batches, well established for inhaled drug products, preclude control of patient risk in the customary two-way (single batch) pharmacokinetic bioequivalence crossover design if batches are randomly chosen. European regulators have recommended selecting a "typical" in vitro batch to represent each product in pharmacokinetic bioequivalence testing. We explored the feasibility of this approach to control patient risk (the "false equivalence", or Type I, error rate). The probability of achieving a Test/Reference 90% confidence interval within (0.80, 1.25) for a true (non-equivalent) value of 1.25 was simulated for a two-way crossover design using the median in vitro batch across a range of number of in vitro batches, in vitro/in vivo correlation (IVIVC) quality (correlation coefficient, r, of zero to one), and within-subject between-batch pharmacokinetic variability. Even under extremely optimistic conditions, e.g., r=0.95 and >100 batches per product screened in vitro, patient risk for typical between-batch variability levels remained at least threefold higher than the 5% regulatory expectation for the significance level (the false equivalence error rate) of the pharmacokinetic bioequivalence test. This elevated error rate in bioequivalence decision-making occurs because of incomplete confidence that the true product average has been identified, and, importantly, omission of this uncertainty from the bioequivalence confidence interval.

Performance of Multiple-Batch Approaches to Pharmacokinetic Bioequivalence Testing for Orally Inhaled Drug Products with Batch-to-Batch Variability.

Batch-to-batch pharmacokinetic (PK) variability of orally inhaled drug products has been documented and can render single-batch PK bioequivalence (BE) studies unreliable; results from one batch may not be consistent with a repeated study using a different batch, yet the goal of PK BE is to deliver a product comparison that is interpretable beyond the specific batches used in the study. We characterized four multiple-batch PK BE approaches to improve outcome reliability without increasing the number of clinical study participants. Three approaches include multiple batches directly in the PK BE study with batch identity either excluded from the statistical model ("Superbatch") or included as a fixed or random effect ("Fixed Batch Effect," "Random Batch Effect"). A fourth approach uses a bio-predictive in vitro test to screen candidate batches, bringing the median batch of each product into the PK BE study ("Targeted Batch"). Three of these approaches (Fixed Batch Effect, Superbatch, Targeted Batch) continue the single-batch PK BE convention in which uncertainty in the Test/Reference ratio estimate due to batch sampling is omitted from the Test/Reference confidence interval. All three of these approaches provided higher power to correctly identify true bioequivalence than the standard single-batch approach with no increase in clinical burden. False equivalence (type I) error was inflated above the expected 5% level, but multiple batches controlled type I error better than a single batch. The Random Batch Effect approach restored 5% type I error, but had low power for small (e.g., <8) batch sample sizes using standard [0.8000, 1.2500] bioequivalence limits.

Performance of the Population Bioequivalence (PBE) Statistical Test with Impactor Sized Mass Data.

This article extends previous work studying performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled and nasal drug products. Based on analysis of a metered dose inhaler database for impactor sized mass, a simulation study was designed to compare performance of the recommended PBE approach with several modified or alternative approaches. These included an extended PBE that separately modeled within-batch (can) and between-batch (batch) variability and average bioequivalence (ABE) tests that modeled with or without between-batch variability and with or without log-transformation. This work showed that separately modeling within- and between-batch variability while increasing the number of sampled batches addressed previously identified issues of the PBE approach when between-batch variability was present, namely, (a) increased risk for falsely concluding equivalence and (b) low probability of correctly concluding equivalence. The same modifications were also required of the ABE to achieve expected performance. However, these modifications did not successfully address the issue of equivalence conclusions that depended on the direction of product mean differences (asymmetric performance). This work highlights the importance of understanding decision-making error rates in developing regulatory recommendations to standardize bioequivalence outcomes across products.

Performance of the Population Bioequivalence (PBE) Statistical Test Using an IPAC-RS Database of Delivered Dose from Metered Dose Inhalers.

This article reports performance characteristics of the population bioequivalence (PBE) statistical test recommended by the US Food and Drug Administration (FDA) for orally inhaled products. A PBE Working Group of the International Pharmaceutical Aerosol Consortium on Regulation and Science (IPAC-RS) assembled and considered a database comprising delivered dose measurements from 856 individual batches across 20 metered dose inhaler products submitted by industry. A review of the industry dataset identified variability between batches and a systematic lifestage effect that was not included in the FDA-prescribed model for PBE. A simulation study was designed to understand PBE performance when factors identified in the industry database were present. Neglecting between-batch variability in the PBE model inflated errors in the equivalence conclusion: (i) The probability of incorrectly concluding equivalence (type I error) often exceeded 15% for non-zero between-batch variability, and (ii) the probability of incorrectly rejecting equivalence (type II error) for identical products approached 20% when product and between-batch variabilities were high. Neglecting a systematic through-life increase in the PBE model did not substantially impact PBE performance for the magnitude of lifestage effect considered. Extreme values were present in 80% of the industry products considered, with low-dose extremes having a larger impact on equivalence conclusions. The dataset did not support the need for log-transformation prior to analysis, as requested by FDA. Log-transformation resulted in equivalence conclusions that depended on the direction of product mean differences. These results highlight a need for further refinement of in vitro equivalence methodology.

Evaluation of a Scenario in Which Estimates of Bioequivalence Are Biased and a Proposed Solution: tlast (Common).

In bioequivalence (BE) testing, it is the convention to identify tlast separately for each concentration-vs-time profile. Within-subject differences in tlast between treatments can arise when assay sensitivity is reached during washout, causing profiles to fall below the limit of quantitation (LOQ) at different sampling times. The resulting tlast difference may be systematic, due to true differences in exposure, and/or random, due to measurement noise. The conventional profile-specific tlast approach assumes that concentrations in the terminal phase are sufficiently low that use of different tlast values between treatments within a subject causes negligible bias in the AUC0-t geometric mean ratio (GMR). Here we investigate the validity of this assumption. Using concentration-vs-time data following oral inhalation of 50 µg salmeterol as an example data set, we conducted simulations to evaluate whether use of different test/reference AUC timeframes arising from a systematic difference in exposure causes sufficient AUC0-t GMR bias to influence the determination of BE. To ensure that results would be relevant to BE testing, we considered only test/reference relative systemic exposures within the BE window (80.00%-125.00%). We show that use of conventional profile-specific tlast exaggerates true differences in systemic exposure; the resulting AUC0-t ratios are biased from true relative exposure by an amount large enough to impact the conclusion of BE. Thus, drugs whose concentrations fall below LOQ during washout may fail BE inappropriately using conventional methods. AUC0-t calculated over a common timeframe within each subject (tlast [common]) minimizes this bias and harmonizes the statistical analysis of BE.

Human pharmacokinetics/pharmacodynamics of an interleukin-4 and interleukin-13 dual antagonist in asthma.

Pitrakinra, a 15-kDa recombinant human interleukin-4 mutein, targets allergic Th2 inflammation by competitively binding to interleukin-4 receptor alpha to interfere with interleukin-4 and interleukin-13 action. The authors characterized pitrakinra pharmacokinetics using data from 96 atopic patients, then compared pharmacokinetics with pharmacological response in asthma following subcutaneous versus inhalation dosing. A 1-compartment systemic model with site-specific absorption describes pitrakinra pharmacokinetics following subcutaneous, nebulization, and inhalation powder delivery. Typical CL/F and V/F, referenced to subcutaneous administration, are 15.5 L/h and 67.5 L, yielding a 3.0-hour half-life of plasma decline. Absorption into the blood (half-life