Method for detecting rare differences between two LC-MS runs

LC-MS based multi-attribute methods (MAM) have drawn substantial attention due to their capability of simultaneously monitoring a large number of quality attributes of a biopharmaceutical product. For successful implementation of MAM, it is usually considered a requirement that the method is capable of detecting any new or missing peaks in the sample when compared to a control. Comparing a sample to a control for rare differences is also commonly practiced in many fields for investigational purpose. Because MS signal variability differs greatly between signals of different intensities, this type of comparison is often challenging, especially when the comparison is made without enough replicates. In this report we describe a statistical method for detecting rare differences between two very similar samples without replicate analyses. The method assumes that an overwhelming majority of components have equivalent abundance between the two samples, and signals with similar intensities have similar relative variability. By analyzing several monoclonal antibody peptide mapping datasets, we demonstrated that the method is suitable for new-peak detection for MAM as well as for other applications when rare differences between two samples need to be detected. The method greatly reduced false positive rate without a significant increase of false negative rate.

Automated summary

LC-MS based multi-attribute methods (MAM) are highly valued for their ability to simultaneously monitor numerous quality attributes of biopharmaceutical products. To successfully implement MAM, it is crucial for the method to detect new or missing peaks in the sample compared to a control. This report presents a statistical method for detecting rare differences between two similar samples without replicate analyses, which greatly reduces false positive rate without a significant increase of false negative rate.