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

Unequivocal Identification of Aspartic Acid and isoAspartic Acid by MALDI-TOF/TOF: From Peptide Standards to a Therapeutic Antibody

Aspartic acid (Asp) to isoaspartic acid (isoAsp) isomerization in therapeutic monoclonal antibodies (mAbs) and other biotherapeutics is a critical quality attribute (CQA) that requires careful control and monitoring during the drug discovery and production processes. The unwanted formation of isoAsp within biotherapeutics and resultant structural changes in the peptide backbone may negatively impact the efficacy, potency, and safety of the molecule or become immunogenic, especially if the isomerization occurs within the mAb complementarity determining region (CDR). Herein we describe a MALDI-TOF/TOF mass spectrometry method that affords unequivocal identification of the presence and the exact position of the isoAsp residue(s) in peptide standards ranging in size from a tripeptide to a docosapeptide (22 residues). In general, the peptide bond immediately N-terminal to the isoAsp residue is more susceptible to MALDI-TOF/TOF fragmentation than its unmodified counterpart. In some of the peptides evaluated in this study, fragmentation of the peptide bond C-terminal to the isoAsp residue (the aspartate effect) is also enhanced when compared to the control. Relative quantification by MALDI-TOF/TOF of this chemical modification is dependent upon a successful reversed-phase HPLC (rpHPLC) separation of the control and modified peptides. This method has also been validated on a therapeutic mAb that contains a well-documented isoAsp residue in the heavy chain CDR3 after forced degradation. Moreover, we also demonstrate that higher energy C-trap dissociation of only the singly charged species, and not the multiply charged form, of the isoAsp containing peptide, separated by rpHPLC, results in LC-MS/MS fragmentation that is highly consistent to that of MALDI-TOF/TOF.

Evaluation of novel sample identification approach based on chromatographic fingerprint set correlation homogeneity analysis

Instead of usual rationale for chromatographic fingerprint based sample identification which relies upon visual inspection or principal component analysis of raw or aligned chromatograms novel nonparametric statistical measure of fingerprint set homogeneity is proposed. Randomization test is applied for significance analysis of fingerprint set homogeneity while average maximum crosscorrelation is used as a merit function. Chromatogram sets generated by random selection from standard and unknown sample chromatogram collections are compared with respect to merit function values with set of chromatograms that represents standard and/or unknown sample. In that instance fingerprint homogeneity significance is represented by the fraction of random chromatogram sets that have higher merit values than the standard and/or unknown sample sets. A set of peptide maps corresponding to different haemoglobin variants has been selected for evaluation of proposed test. This approach is compared to chromatogram alignment based on correlation optimized warping coupled with principal component or cluster analysis. Proposed method is simple i.e. straightforward sample identification procedure which reliability has been evaluated here. Impact of this approach on peptide mapping validation and system suitability analysis is discussed.

Data preprocessing by wavelets and genetic algorithms for enhanced multivariate analysis of LC peptide mapping

Peptide mapping by means of liquid chromatography is a powerful technique used for the characterisation and analysis of the primary structure of proteins. Subtle changes in the covalent structure of the protein can be detected by means of the chromatographic profile (fingerprint). Chromatographic methods, however, display variations in the chromatographic profile even at identical instrumental settings and sample conditions. These variations may be due to changes of the chromatographic conditions, e.g. slight shifts in column temperature, and degradation or alterations of the stationary phase or small changes in the trifluoroacetic acid (TFA) concentration. Such variations may result in varying retention times and peak shapes of the analytes and differences in the chromatographic baseline, thereby having a detrimental impact on the results obtained on multivariate analysis of peptide maps. In order to reduce the non-sample-related variations and to be able to more fully extract the information in peptide mapping, approaches for achieving this objective are outlined in the present study. These methods are denoising and data compression of the chromatograms by wavelets, baseline corrections by linear interpolation, and peak shift alignments towards a target chromatogram by means of a genetic algorithm. Visual inspections of preprocessed chromatograms and principal component analysis (PCA) score plots demonstrate the efficiency of the methodology used. Furthermore, deliberately added changes, e.g. insertions of small Gaussian peaks (outliers), are more easily detected by the proposed methods than from the original chromatograms by multivariate analysis.