Out of Trend in Stability Studies

2 Replies

Out of Trend in Stability Studies

Posted by Siddharth Sanghvi on Sep 12, 2018 5:56 am

Hello Members,

Stability studies is an integral part of any pharmaceutical manufacturing system. Monitoring this data across the life cycle of the product is equally important. One way to monitor is to check for any out of trend data in stability studies that are currently being performed. I wish to check the trend of data at a given time point against historical trend of data at that same time point. Is there is way to accomplish this? Can someone please guide me?

Thank you all for your time.

With Regards,

Re: Out of Trend in Stability Studies

Posted by Jeremiah Genest on Sep 28, 2018 4:51 pm


Are you asking what statistical tools should be part of a stability study?

The stability program should cover statistical analysis, and including Qualitative stability data, and those attributes tested annually or those with an insufficient amount of data. Data should be evaluated for conformance to specifications and the significant change criteria as described in ICH Q1A(R2) (Stability Testing of New Drug Substances and Products) and ICH Q1E, as applicable to the product/material.

Start with analyzing for linear data. In the case of non-linear data you need a statistician. In these cases, techniques used to analyze the data should always been explained and justified.

I'd be happy to answer specific questions. I strongly recommend reading Q1A and Q1E

Re: Out of Trend in Stability Studies

Posted by Siddharth Sanghvi on Oct 8, 2018 11:27 pm

Thank you Jeremiah for your response. My question as you have correctly stated is identifying tools to use for stability studies. The ICH recommends to use regression analysis and this is basically for predicting the shelf life of the product under study. I was looking for tools that I can use to check if results of analysis of the batch under observation are within trend or out of trend with respect to results generated for the same time point on many older batches (of course having the same formula and manufacturing procedure).  

Siddharth Sanghvi