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Sample Size for Tablet Compression and Capsule Filling Events During Process Validation.

Authors
  • Charoo, Naseem Ahmad1
  • Durivage, Mark2
  • Rahman, Ziyaur3
  • Ayad, Mohamad Haitham4
  • 1 Zeno Therapeutics FZ LLC, Alpha Towers, Dubai Internet City, Dubai, UAE. Electronic address: [email protected]
  • 2 Quality Systems Compliance LLC, Lambertville, Michigan 48144.
  • 3 Irma Lerma Rangel College of Pharmacy, Texas A&M Health Science Center, College Station, Texas 77843.
  • 4 Johnson & Johnson, Dubai Health Care City, Dubai, UAE.
Type
Published Article
Journal
Journal of Pharmaceutical Sciences
Publisher
Elsevier
Publication Date
Dec 01, 2017
Volume
106
Issue
12
Pages
3533–3538
Identifiers
DOI: 10.1016/j.xphs.2017.07.021
PMID: 28780393
Source
Medline
Keywords
License
Unknown

Abstract

During solid dosage form manufacturing, the uniformity of dosage units (UDU) is ensured by testing samples at 2 stages, that is, blend stage and tablet compression or capsule/powder filling stage. The aim of this work is to propose a sample size selection approach based on quality risk management principles for process performance qualification (PPQ) and continued process verification (CPV) stages by linking UDU to potential formulation and process risk factors. Bayes success run theorem appeared to be the most appropriate approach among various methods considered in this work for computing sample size for PPQ. The sample sizes for high-risk (reliability level of 99%), medium-risk (reliability level of 95%), and low-risk factors (reliability level of 90%) were estimated to be 299, 59, and 29, respectively. Risk-based assignment of reliability levels was supported by the fact that at low defect rate, the confidence to detect out-of-specification units would decrease which must be supplemented with an increase in sample size to enhance the confidence in estimation. Based on level of knowledge acquired during PPQ and the level of knowledge further required to comprehend process, sample size for CPV was calculated using Bayesian statistics to accomplish reduced sampling design for CPV.

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