Statistical validation of reproducibility of HPLC peptide mapping for the identity of an investigational drug compound based on principal component analysis

Peptide mapping is a key analytical method for studying the primary structu re of proteins. The sensitivity of the peptide map to even the smallest cha nge in the covalent structure of the protein makes it a valuable "fingerpri nt" for identity testing and process monitoring. We recently conducted a fu ll method validation study of an optimized reverse-phase high-performance l iquid chromatography (RP-HPLC) tryptic map of a therapeutic anti-CD4 monocl onal antibody. We have used this method routinely for over a year to test p roduction lots for clinical trials and to support bioprocess development. O ne of the difficulties in the validation of the peptide mapping method is t he lack of proper quantitative measures of its reproducibility. A reproduci bility study may include method and system precision study, ruggedness stud y, and robustness study. In this paper, we discuss the use of principal com ponent analysis (PCA) to quantitate peptide maps properly using its project ed scores on the reduced dimensions. This approach allowed us not only to s ummarize the reproducibility study properly, but also to use the method as a diagnostic tool to investigate any troubles in the reproducibility valida tion process.