Abstract

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.

Highlights

  • California, 94608, USA. 2 KJC Statistics Limited, Cheshire, UK. 3 Merck & Co., Inc., West Point, Pennsylvania, USA. 4 Inhaled Product Development, AstraZeneca, Durham, North Carolina, USA. 5 Biometrics-Global Clinical Operations, Viatris/Mylan Pharma UKLtd., Sandwich, Kent, UK. 6 CMC-Quality Control & Product Development, Chiesi FarmaceuticiS.p.A., Parma, Italy. 7 Research & Development, Kindeva Drug Delivery, St Paul, Minnesota, USA. 8 Formulation Development, Kindeva Drug Delivery, Loughborough, UK. 9 Manufacturing Science & Engineering, GlaxoSmithKline, Zebulon, North Carolina, USA. Pharmaceutical Consortia Management Team, Faegre DrinkerBiddle & Reath LLP, Washington, DC, USA. To whom correspondence should be addressed

  • Sample size calculations are routinely performed to estimate the number of clinical study subjects required for a study, but consideration is rarely given to the number of drug product samples to be used and, in particular, whether variability between product samples is an additional, separate source of sampling variability

  • With zero between-batch PK variability (Fig. 1, blue line), the single-batch two-way crossover PK BE study delivers a high probability of concluding BE for truly equivalent products, a low probability of concluding BE for truly non-equivalent products, and a steep transition in success rate as the true T/R product ratio deviates from 1.00 over the BE window

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Summary

Introduction

California, 94608, USA. 2 KJC Statistics Limited, Cheshire, UK. 3 Merck & Co., Inc., West Point, Pennsylvania, USA. 4 Inhaled Product Development, AstraZeneca, Durham, North Carolina, USA. 5 Biometrics-Global Clinical Operations, Viatris/Mylan Pharma UKLtd., Sandwich, Kent, UK. 6 CMC-Quality Control & Product Development, Chiesi FarmaceuticiS.p.A., Parma, Italy. 7 Research & Development, Kindeva Drug Delivery, St Paul, Minnesota, USA. 8 Formulation Development, Kindeva Drug Delivery, Loughborough, UK. 9 Manufacturing Science & Engineering, GlaxoSmithKline, Zebulon, North Carolina, USA. Pharmaceutical Consortia Management Team, Faegre DrinkerBiddle & Reath LLP, Washington, DC, USA. To whom correspondence should be addressed. 4 Inhaled Product Development, AstraZeneca, Durham, North Carolina, USA. 7 Research & Development, Kindeva Drug Delivery, St Paul, Minnesota, USA. 9 Manufacturing Science & Engineering, GlaxoSmithKline, Zebulon, North Carolina, USA. (e–mail: Sampling variability is an important consideration in clinical study design and interpretation. Investigators account for sampling variability when drawing conclusions based on the observed sample, and often reduce sampling error by increasing the size of the sample. Sample size calculations are routinely performed to estimate the number of clinical study subjects required for a study, but consideration is rarely given to the number of drug product samples (i.e., manufacturing batches) to be used and, in particular, whether variability between product samples is an additional, separate source of sampling variability. This may not be a problem because of negligible in vivo variability from batch to batch.

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