Determining the Best Estimate of Probability of Passing Multiple Stage Tests: Part 1–Content Uniformity
This article introduces a practical approach to determining the best estimate for probability of passing uniformity of dosage units (content uniformity) using probability vs. lot coverage charts and tables constructed using simulated probability and lot coverage (LC1) data.
xyz+ - Stock.adobe.com
Peer-Reviewed
Submitted: August 24, 2020
Accepted: October 12, 2020
Abstract
This article introduces a practical approach to determining the best estimate for probability of passing uniformity of dosage units (content uniformity) using probability vs. lot coverage charts and tables constructed using simulated probability and lot coverage (LC1) data. The best estimation concept is to demonstrate that the probability estimates, especially for those approaching 100%, are slightly different (deviated) from the exact calculated values by not more than (NMT) the statistically acceptable deviation limits.
Click here for a PDF of this articlePeer-reviewed research
Submitted: Aug. 24, 2020
Accepted: Oct. 12, 2020
About the author
Pramote Cholayudth is an industrial pharmacist with more than 40 years of experiences. He is a guest speaker on Process Validation to industrial pharmaceutical scientists organized by Thailand’s FDA. He is the founder and manager of PM Consult and can be contacted via cpramote2000@yahoo.com.
Article details
Pharmaceutical Technology
Vol. 45, No. 5
May 2021
Pages: 36-43
Citation
When referring to this article, please cite it as P. Cholayudth, “Determining the Best Estimate of Probability of Passing Multiple Stage Tests: Part 1–Content Uniformity,” Pharmaceutical Technology 45 (5) 2021.
A Novel, Enhanced, and Sustainable Approach to Audit Trail Review
July 4th 2025Eli Lilly and Company developed an innovative and sustainable approach to audit trail review (ATR) aimed at reducing the ATR burden while adhering to regulatory expectations and data integrity (DI) principles. The process has transformed employees' understanding of ATR and complemented the DI by design approach, leading to better system designs that meet expected controls and reduce non-value-added data reviews.
