Literature DB >> 28948584

Evaluating Current Practices in Shelf Life Estimation.

Robert Capen1,2, David Christopher3, Patrick Forenzo4, Kim Huynh-Ba5, David LeBlond6, Oscar Liu7, John O'Neill8, Nate Patterson9, Michelle Quinlan10, Radhika Rajagopalan11, James Schwenke12, Walter Stroup13.   

Abstract

The current International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) methods for determining the supported shelf life of a drug product, described in ICH guidance documents Q1A and Q1E, are evaluated in this paper. To support this evaluation, an industry data set is used which is comprised of 26 individual stability batches of a common drug product where most batches are measured over a 24 month storage period. Using randomly sampled sets of 3 or 6 batches from the industry data set, the current ICH methods are assessed from three perspectives. First, the distributional properties of the supported shelf lives are summarized and compared to the distributional properties of the true shelf lives associated with the industry data set, assuming the industry data set represents a finite population of drug product batches for discussion purposes. Second, the results of the ICH "poolability" tests for model selection are summarized and the separate shelf life distributions from the possible alternative models are compared. Finally, the ICH methods are evaluated in terms of their ability to manage risk. Shelf life estimates that are too long result in an unacceptable percentage of nonconforming batches at expiry while those that are too short put the manufacturer at risk of possibly having to prematurely discard safe and efficacious drug product. Based on the analysis of the industry data set, the ICH-recommended approach did not produce supported shelf lives that effectively managed risk. Alternative approaches are required.

Entities:  

Keywords:  FDA; ICH Q1A/Q1E; managing risk; shelf life estimation; stability

Mesh:

Year:  2017        PMID: 28948584     DOI: 10.1208/s12249-017-0880-4

Source DB:  PubMed          Journal:  AAPS PharmSciTech        ISSN: 1530-9932            Impact factor:   3.246


  2 in total

1.  Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling.

Authors:  Andreas Evers; Didier Clénet; Stefania Pfeiffer-Marek
Journal:  Pharmaceutics       Date:  2022-02-08       Impact factor: 6.321

2.  Simple and non-invasive screening method for diabetes based on myoinositol levels in urine samples collected at home.

Authors:  Misaki Takakado; Yasunori Takata; Fumio Yamagata; Michiko Yaguchi; Go Hiasa; Sumiko Sato; Jun-Ichi Funada; Shoji Kawazu; Haruhiko Osawa
Journal:  BMJ Open Diabetes Res Care       Date:  2020-02
  2 in total

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