Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study.

The multi-attribute method (MAM) was conceived as a single assay to potentially replace multiple single-attribute assays that have long been used in process development and quality control (QC) for protein therapeutics. MAM is rooted in traditional peptide mapping methods; it leverages mass spectrometry (MS) detection for confident identification and quantitation of many types of protein attributes that may be targeted for monitoring. While MAM has been widely explored across the industry, it has yet to gain a strong foothold within QC laboratories as a replacement method for established orthogonal platforms. Members of the MAM consortium recently undertook an interlaboratory study to evaluate the industry-wide status of MAM. Here we present the results of this study as they pertain to the targeted attribute analytics component of MAM, including investigation into the sources of variability between laboratories and comparison of MAM data to orthogonal methods. These results are made available with an eye toward aiding the community in further optimizing the method to enable its more frequent use in the QC environment.

Orthogonal Comparison of Analytical Methods by Theoretical Reconstruction from Bottom-up Assay Data.

In the never-ending endeavor to produce stable and efficacious protein therapeutics, biopharmaceutical companies often employ numerous analytical techniques to characterize and quantify a drug candidate's stability. Mass spectrometry, due to the information-rich data it produces, is commonly used in its numerous configurations to ascertain chemical and structural stability. At issue is the comparison of the various configurations utilized, that is, comparing bottom-up methods such as proteolytic digest followed by reversed phase LC-MS with intact LC-MS methods. Similar issues also arise when using capillary isoelectric focusing to see how charge variants change over time, that is, monitoring the progression of charge altering modifications like deamidation. To this end, site-specific degradations as quantified from bottom-up methods like peptide mapping can be used to build reconstructions of both theoretical intact mass spectra as well as theoretical electropherograms. The result can then be superimposed over the experimental data to qualitatively, and perhaps quantitatively, evaluate differences. In theory, if both experimental bottom-up data and intact data are accurate, the theoretical reconstruction produced from the bottom-up data should perfectly overlay with that of the experimental data. Valuable secondary information can also be ascertained from reconstructions, such as whether modifications are stochastic, as well as a detailed view of all possible combinations of modifications and their quantities used in the reconstruction. This comparison is also useful in determining unknown mass differences in deconvoluted intact protein spectra that may be a result of multiple modifications in combination. The comparison of data from alternate sources provides a holistic and more comprehensive view of the molecule under study.

New Peak Detection Performance Metrics from the MAM Consortium Interlaboratory Study.

The Multi-Attribute Method (MAM) Consortium was initially formed as a venue to harmonize best practices, share experiences, and generate innovative methodologies to facilitate widespread integration of the MAM platform, which is an emerging ultra-high-performance liquid chromatography-mass spectrometry application. Successful implementation of MAM as a purity-indicating assay requires new peak detection (NPD) of potential process- and/or product-related impurities. The NPD interlaboratory study described herein was carried out by the MAM Consortium to report on the industry-wide performance of NPD using predigested samples of the NISTmAb Reference Material 8671. Results from 28 participating laboratories show that the NPD parameters being utilized across the industry are representative of high-resolution MS performance capabilities. Certain elements of NPD, including common sources of variability in the number of new peaks detected, that are critical to the performance of the purity function of MAM were identified in this study and are reported here as a means to further refine the methodology and accelerate adoption into manufacturer-specific protein therapeutic product life cycles.