Automated multi-attribute method sample preparation using high-throughput buffer exchange tips

Fully Unattended Online Protein Digestion and LC-MS Peptide Mapping

LC-MS based peptide mapping, i.e., proteolytic digestion followed by LC-MS/MS analysis, is the method of choice for protein primary structural characterization. Manual proteolytic digestion is usually a labor-intensive procedure. In this work, a novel method was developed for fully automated online protein digestion and LC-MS peptide mapping. The method generates LC-MS data from undigested protein samples without user intervention by utilizing the same HPLC system that performs the chromatographic separation with some additional modules. Each sample is rapidly digested immediately prior to its LC-MS analysis, minimizing artifacts that can grow over longer digestion times or digest storage times as in manual or automated offline digestion methods. In this report, we implemented the method on an Agilent 1290 Infinity II LC system equipped with a Multisampler. The system performs a complete digestion workflow including denaturation, disulfide reduction, cysteine alkylation, buffer exchange, and tryptic digestion. We demonstrated that the system is capable of digesting monoclonal antibodies and other proteins with excellent efficiency and is robust and reproducible and produces fewer artifacts than manually prepared digests. In addition, it consumes only a few micrograms of material as most of the digested sample protein is subjected to LC-MS analysis.

A novel filter-assisted protein precipitation (FAPP) based sample pre-treatment method for LC-MS peptide mapping for biosimilar characterization

Establishing analytical and functional comparability serves as the foundation of biosimilar development. A critical part of this exercise is sequence similarity search and categorization of post-translational modifications (PTMs), often by peptide mapping using liquid chromatography-mass spectrometry (LC-MS). When performing bottom-up proteomic sample preparation, efficient digestion of the protein and extraction of peptides for subsequent mass spectrometric analysis can be a challenge. Conventional sample preparation strategies face the risk of allowing interference of chemicals which are essential for extraction but are likely to interfere with digestion, resulting in complex chromatographic profiles due to semi-cleavages, insufficient peptide cleavages, and other unwanted reactions. Further, peptide cleanup through commonly used immobilized C-18 pipette tips can cause significant peptide loss as well as variability in individual peptide yields, thereby causing artifacts of various product-related modifications. In this study, we proposed a simple enzymatic digestion technique by incorporating different molecular weight filters and protein precipitation, with the objective to minimize interference of denaturing, reducing, and alkylating agents throughout overnight digestion. As a result, the need for peptide cleanup is significantly reduced and results in higher peptide yield. The proposed FAPP approach outperformed the conventional method across multiple metrics including, 30% more peptides, 8.19% more fully digested peptides, 14% higher sequence coverage rate, and 11.82% more site-specific alterations. Quantitative and qualitative repeatability of the proposed approach have been demonstrated. It can be concluded that the filter-assisted protein precipitation (FAPP) protocol proposed in this study offers an effective substitute for the traditional approach.

Incorporation of automated buffer exchange empowers high-throughput protein and plasmid purification for downstream uses

The continued acceleration of time-to-market product development and rising demand for biotherapeutics have hastened the need for higher throughput within the biopharmaceutical industry. Automated liquid handlers (ALH) are increasingly popular due to flexible programming that enables processing of multiple samples with an array of functions. This flexibility is useful in streamlining research that requires chromatographic procedures to achieve product purity for downstream analysis. However, purification of biologics often requires additional off-deck buffer exchange steps due to undesirable elution conditions such as high acid or high salt content. Expanding the capability of ALHs to perform purification in sequence with buffer exchange would, therefore, increase workflow efficiency by eliminating the need for manual intervention, thus expediting sample preparation. Here we demonstrate two different automated purifications using pipet-based dispersive solid-phase extraction (dSPE). The first is an affinity purification of His-tagged proteins from bacterial lysate. The second is an anion-exchange purification of plasmid DNA. Both methods are followed by buffer exchange performed by an ALH. Percent recoveries for the three purified recombinant proteins ranged from 51 ± 1.2 to 86 ± 10%. The yields were inversely correlated to starting sample load and protein molecular weight. Yields for plasmid purification ranged between 11.4 ± 0.8 and 13.7 ± 0.9 µg, with the largest plasmid providing the highest yield. Both programs were rapid, with protein purification taking <80 min and plasmid purification <60 min. Our results demonstrate that high-quality, ready-to-use biologics can be obtained rapidly from a crude sample after two separate chromatographic processes without manual intervention.

Technical considerations for the implementation of the Multi-Attribute-Method by mass spectrometry in a Quality Control laboratory

Multi-attribute methods employing mass spectrometry are applied throughout the biopharmaceutical industry for product and process characterization purposes but are not yet widely accepted as a method for batch release and stability testing under good manufacturing practice (GMP) due to limited experience and level of comfort with the technical, compliance and regulatory aspects of its implementation at quality control (QC) laboratories. Here, current literature related to the development and application of the multi-attribute method by peptide mapping liquid chromatography mass spectrometry (MAM) is compiled with the aim of providing guidance for the implementation of MAM in a QC laboratory. This article, focusing on technical considerations, is the first part of a two-tiered publication, whereby the second part will focus on GMP compliance and regulatory aspects. This publication has been prepared by a group of industry experts representing 14 globally acting major biotechnology companies under the umbrella of the European Federation of Pharmaceutical Industries and Associations (EFPIA) Manufacturing & Quality Expert Group (MQEG).

Interlaboratory Evaluation of a User-Friendly Benchtop Mass Spectrometer for Multiple-Attribute Monitoring Studies of a Monoclonal Antibody

In the quest to market increasingly safer and more potent biotherapeutic proteins, the concept of the multi-attribute method (MAM) has emerged from biopharmaceutical companies to boost the quality-by-design process development. MAM strategies rely on state-of-the-art analytical workflows based on liquid chromatography coupled to mass spectrometry (LC-MS) to identify and quantify a selected series of critical quality attributes (CQA) in a single assay. Here, we aimed at evaluating the repeatability and robustness of a benchtop LC-MS platform along with bioinformatics data treatment pipelines for peptide mapping-based MAM studies using standardized LC-MS methods, with the objective to benchmark MAM methods across laboratories, taking nivolumab as a case study. Our results evidence strong interlaboratory consistency across LC-MS platforms for all CQAs (i.e., deamidation, oxidation, lysine clipping and glycosylation). In addition, our work uniquely highlights the crucial role of bioinformatics postprocessing in MAM studies, especially for low-abundant species quantification. Altogether, we believe that MAM has fostered the development of routine, robust, easy-to-use LC-MS platforms for high-throughput determination of major CQAs in a regulated environment.

Optimized Multi-Attribute Method Workflow Addressing Missed Cleavages and Chromatographic Tailing/Carry-Over of Hydrophobic Peptides

Peptide mapping by liquid chromatography mass spectrometry (LC-MS) and the related multi-attribute method (MAM) are well-established analytical tools for verification of the primary structure and mapping/quantitation of co- and post-translational modifications (PTMs) or product quality attributes in biopharmaceutical development. Proteolytic digestion is a key step in peptide mapping workflows, which traditionally is labor-intensive, involving multiple manual steps. Recently, simple high-temperature workflows with automatic digestion were introduced, which facilitate robustness and reproducibility across laboratories. Here, a modified workflow with an automatic digestion step is presented, which includes a two-step digestion at high and low temperatures, as opposed to the original one-step digestion at a high temperature. The new automatic digestion workflow significantly reduces the number of missed cleavages, obtaining a more complete digestion profile. In addition, we describe how chromatographic peak tailing and carry-over is dramatically reduced for hydrophobic peptides by switching from the traditional C18 reversed-phase (RP) column chemistry used for peptide mapping to a less retentive C4 column chemistry. No negative impact is observed on MS/MS-derived sequence coverage when switching to a C4 column chemistry. Overall, the new peptide mapping workflow significantly reduces the number of missed cleavages, yielding more robust and simple data interpretation, while providing dramatically reduced tailing and carry-over of hydrophobic peptides.

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.