Methods for Identifying Out-of-Trend Results in Ongoing Stability Data - Pharmaceutical Technology

Latest Issue
PharmTech

Latest Issue
PharmTech Europe

Methods for Identifying Out-of-Trend Results in Ongoing Stability Data
The authors discuss three methods for identification of out-of-trend (OOT) results and further compare the z-score method and the tolerance interval in OOT analysis for stability studies.


Pharmaceutical Technology
Volume 37, Issue 6, pp. 46-51,59

Conclusion

The pharmaceutical industry still lacks having a proper guideline for the identification of present OOT results among ongoing stability data. As a result, many pharmaceutical companies are not harmonized in the way they conduct this type of analysis.

In this study, three methods for identification of OOT results in ongoing stability data were proposed: the regression-control-chart method, the by-time-point method, and the slope-control-chart method. To obtain more accurate identification of existing OOT results, simultaneous use of all three methods is advised, which will result in getting a visual image of the results of the analyzed batches. The use of the z-score method for defining the limits for the OOT results is preferred. Lastly, the study highlighted the necessity of issuing a regulatory confirmed guideline for identification of OOT results within ongoing stability data.

Adrijana Torbovska* is an analyst in the Quality Control Department of ReplekPharm, Kozle 188, 1000 Skopje, Macedonia,
Suzana Trajkovic-Jolevska, PhD, is a professor in the Drug Quality Control Department, Faculty of Pharmacy, Ss Cyril and Methodius University, Skopje, Macedonia.

* To whom all correspondence should be addressed.

References

1. MHRA, Guidance for Out Of Secification Investigation, online presentation, (2010), http://www.mhra.gov.uk/home/groups/comms-con/documents/websiteresources/con088215.pdf, accessed May 13, 2013.

2. FDA, Guidance for Industry: Investigating Out-Of-Specification (OOS) Test Results for Pharmaceutical Production (Rockville, MD, 2006).

3. PhRMA CMC Statistics and Stability Expert Teams, Pharm. Technol. 27 (4), 38-52 (2003).

4. ICH, Q1A (R2) Stability Testing of New Drug Substances and Products (Feb. 2003).

5. P.Rowe, Essential Statistics for the Pharmaceutical Sciences (John Wiley & Sons, West Sussex, UK, 2007), pp. 169-194.

6. W.W. Daniel, Biostatistics- A Foundation for Analysis in the Health Sciences (John Wiley & Sons, Hoboken, NJ, 9th ed., 2009), pp. 93-131, 215-304, and 409-484.

7. S. Bolton and C. Bon, Pharmaceutical Statistics–Practical and Clinical Applications (Marcel Dekker Inc., Monticello, NY, Vol. 135, 4th ed., 2004), pp. 96-150.


ADVERTISEMENT

blog comments powered by Disqus
LCGC E-mail Newsletters

Subscribe: Click to learn more about the newsletter
| Weekly
| Monthly
|Monthly
| Weekly

Survey
FDASIA was signed into law two years ago. Where has the most progress been made in implementation?
Reducing drug shortages
Breakthrough designations
Protecting the supply chain
Expedited reviews of drug submissions
More stakeholder involvement
Reducing drug shortages
35%
Breakthrough designations
12%
Protecting the supply chain
35%
Expedited reviews of drug submissions
12%
More stakeholder involvement
6%
View Results
Jim Miller Outsourcing Outlook Jim Miller Health Systems Raise the Bar on Reimbursing New Drugs
Cynthia Challener, PhD Ingredients Insider Cynthia ChallenerThe Mainstreaming of Continuous Flow API Synthesis
Jill Wechsler Regulatory Watch Jill Wechsler Industry Seeks Clearer Standards for Track and Trace
Siegfried Schmitt Ask the Expert Siegfried SchmittData Integrity
NIH Translational Research Partnership Yields Promising Therapy
Clusters set to benefit from improved funding climate but IP rights are even more critical
Supplier Audit Program Marks Progress
FDA, Drug Companies Struggle with Compassionate Use Requests
USP Faces New Challenges
Source: Pharmaceutical Technology,
Click here