Attribute Analytics Performance Metrics from the MAM Consortium Interlaboratory Study

Abstract

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.

What is the role of current mass spectrometry in pharmaceutical analysis?

“…Moreover, scientific and regulatory questions need to be addressed before the final stages of MAM implementation. We refer interested readers to respective literature discussing the principles, benefits, and limitations of the MAM methodology (Mouchahoir et al, 2022; Rogers et al, 2017; Rogstad et al, 2019; Sokolowska et al, 2020).…”

“…MAM has already been applied to several early‐stage clinical programs with various classes of protein therapeutics and compared to conventional practices, such as IEX, HILIC, and CE‐SDS. However, the capability of implementing MAM and completely replacing conventional methods is still under discussion (Liu et al, 2019; Mouchahoir et al, 2022; Rogers et al, 2017; Rogstad et al, 2019; Wang, Chu, et al, 2017; Xu et al, 2017). The main challenges include the high instrument‐to‐instrument variability or the need for compliant and fully automated software (Rogers et al, 2017; Sokolowska et al, 2020; Zhang & Guo, 2017; Zhang et al, 2019).…”

Accurate prediction of antibody deamidations by combining high-throughput automated peptide mapping and protein language model-based deep learning

“…The peptide mapping-rooted approach, recently dubbed as multi-attribute method (MAM), employs mass spectrometry detection for simultaneous identification and quantitation of many protein quality attributes including deamidations. 13,14,38,39 Although peptide mapping is ideal in providing site-specific modification readouts, the throughput of peptide mapping has long been a bottleneck limiting its practicality especially in drug discovery phase where sample numbers are large and expected throughputs are high. To address this, we developed an automated peptide mapping sample preparation protocol using Lynx liquid handling system.…”

“…12 The LC-MS/MS based peptide mapping method, which enzymatically dissociates the protein into smaller peptide pieces, spatially separates those peptides followed by high-resolution MS detection, on the other hand, can confidently detect, quantify, and localize the deamidations, providing site-specific deamidation information. 13,14 Nevertheless, peptide mapping is intrinsically labor-intensive in both sample preparation and data processing. In addition, to accommodate for forced degradation followed by peptide mapping sample preparations, the amount of purified antibody to initiate this task can be quite high.…”

A Robust Purity Method for Biotherapeutics Using New Peak Detection in an LC–MS-Based Multi-Attribute Method

“…One recent advancement in the biotherapeutic community has been the introduction of the multi-attribute method (MAM), an ultra-high-performance liquid chromatography–mass spectrometry (UHPLC–MS) method, as a means to monitor multiple site-specific PQAs in a single method. − MAM is able to assess certain PQAs on the control system of mAbs that are traditionally obtained by several methods including clipped size variant analysis, glycan analysis, charge variant analysis, oxidation analysis, and identity tests. − As a result, MAM can replace several of these traditional methods to improve and simplify the control system. MAM has evolved from the MS peptide maps commonly used for characterization activities in research and development into a method composed of two parts: targeted monitoring and non-targeted new peak detection (NPD). − …”