Statistical Methods and Approaches to Avoid Stability Failures of Drug Product During Shelf-Life

Abstract

Generation of stability data is an important part of a drug product’s life cycle. Various statistical approaches can be used to identify patterns and predict the behavior of the drug products. The ICH guidance Q1E for industry provides nonbinding recommendations on evaluation of the stability data. This chapter compliments those guidelines and describes specific statistical methods to assess the long-term stability data. The primary objective of this chapter is to demonstrate statistical approaches to predict the likelihood of drug product failure during its shelf-life. The key methodologies discussed are estimation of release limits using stability data, predicting batch failure rate and assessment of process performance statistics like CpK and PpK. The chapter describes the regression modeling techniques, application of empirical cumulative distribution function, and estimation of defects per million opportunity using process performance indices. To avoid erroneous statistical estimations, the diagnostic techniques to verify the validity of statistical methods used are also discussed. The ultimate application of the outcomes of these statistical approaches is to support sound business decisions. Failure of drug products during their shelf-life can lead to market recalls, which not only affects the profitability of a company but also impacts its reputation and credibility. These statistical approaches help in backing the decisions on prioritization of process improvement projects and tightening the batch release strategies.