Over the past few years, the mass spectrometry-based Multi-Attribute Method (MAM) has gained traction due to its potential to streamline lab work and decrease development time. However, several barriers to adoption remain, one of which is evaluating performance of MAM vs. conventional methods.
To evaluate the performance of MAM vs conventional methods, adalimumab and etanercept were procured from multiple sources, and subjected to forced degradation to generate a range of modifications. Samples were analyzed using both conventional and MAM approaches to evaluate size variants, charge variants, glycosylation, sialic acid content, and other molecular variants like oxidation, deamidation, and glycation.
The ability of each technique to detect differences between nominally similar products and forced degradation-induced changes was evaluated. Physiochemical differences were compared to functional and structural changes detected using cell-based assays, binding assays, and circular dichroism. The knowledge base from this study can help lower the barrier to adoption of MAM, enabling greater efficiency and reducing costs.
Presented by
Diane McCarthy Senior Director, Science and Standards, Global Biologics Department
US Pharmacopeia
Dr. McCarthy is Senior Director, Science and Standards in the Global Biologics Department at the US Pharmacopeia. She leads development and maintenance of standards and tools to support quality of medicines and oversees the USP biologics laboratories in the US and India. Her team supports a diverse range of biological therapies, including monoclonal antibodies and other protein therapeutics, vaccines, peptides, and cell and gene therapy. Prior to joining USP, Diane worked for several small CROs that focused on the use of mass spectrometry for characterization of biologics, host cell proteins, and biomarkers. Dr. McCarthy earned her Ph.D. in Biochemistry from the University of Texas at Austin.
The rapid advancement of biotherapeutics, including monoclonal and multi-specific antibodies, Fc-fusion proteins, gene therapies (AAV vectors), mRNA vaccines, recombinant proteins, and antibody-drug conjugates (ADCs), requires analytical platforms that offer both depth and flexibility. This webinar highlights how Pfizer utilizes heightened characterization by liquid chromatography mass spectrometry (LC-MS) and the Multi-Attribute Method (MAM) to drive process and product development across these diverse modalities.
Attendees will discover how LC-MS enables comprehensive structural and functional characterization, delivering detailed insights into critical product quality attributes (PQAs) at every stage of development. The session will showcase MAM’s transformative role as a high-throughput platform for simultaneous monitoring of multiple PQAs, supporting process optimization and comparability assessments.
Real-world case studies will demonstrate how LC-MS and MAM inform process optimization and understanding, analytical comparability assessments, manufacturing investigations, regulatory submissions, and ensure consistent product quality from early research through commercial launch. By incorporating LC-MS and MAM into its analytical packages, Pfizer enhances decision-making, streamlines development, and upholds the highest standards of safety and efficacy for patients. This webinar is ideal for scientists, engineers, and regulatory professionals interested in the future of analytical science in biotherapeutics.
Presented by
Andrew W. Dawdy, Ph.D. Senior Principal Scientist
Pfizer
Andrew William Dawdy, Ph.D., is a Senior Principal Scientist at Pfizer, St. Louis, MO with 16 years of mass spectrometry experience and 12 years in the pharmaceutical industry. He specializes in LC-MS characterization to support biotherapeutic process and product development, leading strategies for over 15 biotherapeutic submissions, including mAbs, ADCs, multi-specifics, and mRNA vaccines. Andrew has authored 35+ scientific publications and presentations, co-developed Pfizer’s Multi-Attribute Method (MAM) platform, and co-authored USP <1060> on MAM with a cross-industry LC-MS team. He continues to drive innovation in analytical characterization, regulatory authoring, project leadership, and cross-functional collaboration.
The multi-attribute method (MAM) offers significant potential to replace certain conventional release and stability assays, yet widespread adoption has been slowed by challenges such as reliable new peak detection (NPD) while minimizing false positives. These hurdles, combined with the need to monitor critical quality attributes (CQAs) and support tradition in-depth characterization activities, often force analysts to rely on multiple software solutions to meet their goals.
In this presentation, we introduce a comprehensive, GxP-ready MAM workflow designed for both CQA monitoring and robust NPD. The workflow integrates advanced features to reduce false positives, including statistical scoring, artifact filtering, and curated known-peak libraries. Users have successfully validated this approach in accordance with ICH Q2 guidelines and applied it across development and MAM applications, achieving sensitive and accurate identification of new peaks with zero false positives. This streamlined solution enables efficient implementation of MAM while maintaining compliance and analytical rigor.
Dr. Michelle English is a Scientific Consultant at Genedata, where she supports global clients in solving complex mass spectrometry challenges. After an early career in programming and systems analysis, she returned to academia to earn a chemistry degree, followed by postgraduate research in Don Hunt’s lab at the University of Virginia. Michelle’s work on advanced mass spectrometry techniques led her to remain as research faculty after graduation. In 2014, Michelle joined Pfizer, contributing to the mass spectrometry characterization group focused on developing innovative analytical methods to support drug development from early to late stages. Prior to joining Genedata in 2024, she worked at mass spectrometry and software vendors, applying her expertise across a wide range of protein therapeutic applications. At Genedata, her recent focus has been on Multi-Attribute Method (MAM) workflows, where she helps clients implement novel new peak detection (NPD) strategies using Genedata Expressionist. Michelle’s unique blend of scientific depth and software experience enables her to bridge technical and practical needs, delivering impactful solutions that improve efficiency and insight in biopharmaceutical analysis.
Followed by a live Question and Answer Session
Sponsored by
Genedata transforms data into intelligence with innovative software solutions that incorporate extensive biopharma R&D domain knowledge. Multinational biopharmaceutical organizations and cutting-edge biotechs around the globe rely on Genedata to digitalize and automate data-rich and complex R&D processes. From early discovery all the way to the clinic, Genedata solutions help maximize the ROI in R&D expenditure. Founded in 1997, Genedata is headquartered in Basel, Switzerland with additional offices in Boston, London, Munich, San Francisco, Singapore, and Tokyo.
