Implementing Multi-Angle Light Scattering for Biomolecular Characterization: Practical Strategies from Standalone to LC-Coupled Workflows

Transform Your Biomolecular Characterization with MALS

Multi-angle light scattering (MALS) determines essential biophysical attributes of complex biotherapeutics. This webinar explores how MALS is applied in discovery and development, on-line, at-line or coupled offline to analytical separations, the challenges and benefits of these methods, and presents representative case studies where MALS supports candidate selection, stability studies and process development.

As biotherapeutic modalities continue to increase in size, complexity, and structural heterogeneity, robust analytical workflows are needed to generate reliable, fit-for-purpose molecular characterization. Multi-angle light scattering (MALS) provides an absolute, calibration-independent approach for assessing molar mass, size, aggregation, oligomeric state, and sample heterogeneity across a broad range of biomolecular systems.

This webinar will focus on the practical implementation of light scattering-based methods in discovery and development settings, with emphasis on experimental design, sample handling, method selection, and data interpretation. In-process, real-time (RT) MALS approaches, including on-line and at-line measurements, will be discussed as rapid tools for evaluating molecular size, assembly state, and solution behavior under near-native conditions. LC-coupled MALS workflows will also be highlighted for species-resolved characterization, where chromatographic separation enables accurate assessment of aggregates, fragments, conjugation variants, and other heterogeneous species prior to light scattering detection.

Through representative case studies involving proteins and bioconjugates, the presentation will illustrate how MALS can be implemented to support candidate selection, aggregation assessment, stability characterization, and comparability across the pharmaceutical development cycle. Key criteria for selecting RT- versus LC-coupled MALS workflows will be discussed, including method-specific strengths, limitations, and troubleshooting strategies to support robust biomolecular characterization and real-time analytical testing.

Key takeaways

• How in-process, real-time MALS monitors biophysical attributes of vaccine modalities such as VLPs or protein-polysaccharide conjugates in real time, inline or online with downstream processes.
• How offline LC-MALS provide absolute characterization of these species and their biophysical attributes.

Who should attend

• Scientists and Lab Managers in biopharmaceutical or vaccine discovery, process development, PAT or MSAT involved in downstream unit operations.
• CDMO, CRO and other organizations that are interested in expanding their productivity and analytical capabilities.

Presenter: Daniel Some, Ph. D. (Sr. Principal Product Manager, Waters Corporation)

Dr. Daniel Some is Sr. Principal Product Manager at Waters | Wyatt Technology, where he leads product and application development for light-scattering based, real-time process analytical technology.

Previously he worked in Wyatt’s R&D and marketing departments.

Dan obtained his Ph.D. in physics from Brown University, then carried out postdoctoral research at LANL and Weizmann Institute. Prior to Wyatt he was involved in defense technology and development of processed wafer inspection tools for the semiconductor industry.

Presenter: Gunjan Dixit (Sr. Scientist, Merck & Co.)

Gunjan Dixit is a Senior Scientist in the Discovery Interface team at Merck, leading separations-based efforts to support pre-clinical candidate selection. She spearheads novel separation technologies, integrating analytical method development from early process through clinical manufacturing.

Gunjan holds a Ph.D. in Cell, Molecular, and Structural Biology from Miami University and completed postdoctoral training at the University of Chicago. Prior to Merck, she was a Principal Scientific Researcher at Genentech supporting early-stage protein-based drug candidates.