The increasing complexity and heterogeneity of advanced therapeutic products is a growing challenge. Currently, very few analytical methods are available to characterize heterogeneous biotherapeutics at the intact level. We show how charge detection mass spectrometry and mass photometry allow to tackle even extremely large and polydisperse samples, including adeno-associated viruses, glycoproteins, and messenger RNAs. These innovative approaches hold great promises to support the development and quality assessment of next-generation biotherapeutics.

While the multi-attribute method (MAM) has potential to improve the efficiency and specificity of analytical testing, comparison to conventional methods is critical for implementation in QC. Through a cooperative agreement with US FDA, we have evaluated the performance of MAM versus conventional methods in detecting differences between thermally degraded therapeutic proteins from multiple sources. This presentation will share results and an implementation roadmap to facilitate broader adoption of MAM.

We demonstrate a data-driven automation system that integrates wet-lab experimentation using standardized databases, robotics, and scheduling software. This system automates sample preparation from cell culture to bioassay plates, with real-time data acquisition, processing, and error-handling capabilities. It significantly impacts the pipeline by enabling quality control, developability assessments, and assay support for UP-SEC, CE-SDS, HIC, nanoDSF, etc. This enhances the interface between AI and multispecific protein screening, improving efficiency and reliability.

One of the key challenges in antibody engineering is the development of analytical screening methods to ensure biologics are suitable for administration. Charge-stabilized self-interaction nanoparticle spectroscopy (CS-SINS), a modified version of the traditionally performed affinity-capture self-interaction nanoparticle spectroscopy (AC-SINS), has been adapted for predicting high-concentration solution behavior at Sanofi. Here, we demonstrate that the assay is compatible with high-throughput screening of therapeutic antibody candidates in early research.

The increasing complexity of next-generation therapeutic drug modalities presents a significant challenge for bioanalytical scientists responsible for quantifying drug exposure during early drug research. In response, we have engineered generic ligand binding assays, computational tools, and fully automated analytical platforms, which increase our analytical capacity and provide data applicable for training ML/AI models. Moreover, we are actively exploring AI-designed protein binders as an alternative to traditional assay antibodies.

The BEAT platform enables 5 or more functional modules to be combined into a single molecule with excellent manufacturability and developability. This has been clinically validated by demonstrating superior efficacy, low immunogenicity in humans and good pharmacokinetics. The biophysical/functional screening and precision engineering required to generate ISB 2001, a first-in-class trispecific BCMA and CD38 T-cell engager advancing in the clinic to treat relapsed/refractory Multiple Myeloma, will be described.

AAV-based gene therapy has become an important therapeutic modality in the recent decade, and one of the key product quality attributes of AAV is the percentage of “empty” vs. “full” particles. Orthogonal technologies have been developed for empty/full capsid measurement and comparing analytical data from various AAV empty/full methods is critical. When making correlations across empty/full methods, key differences between the methods should be considered, such as how the methods account for partials and the units of measure. This presentation reviews data from several empty/full techniques collected for different serotypes and products. Our strategy, correlations, and conclusions will be presented.

• Pre-screen monoclonal arms or final format or both?
• Which are the best HTP developability screens?
• Can in silico methods reliably predict developability and help fix poorly behaved multispecifics without an antibody structure? 
• What are the best late stage developability assays to perform to help Clinical Candidate Selection (CCS)​

• Confirmation of protein primary structure
• Detection of common PTMs and influence by process
• Determination of unexpected covalent variants and root cause​

Antibody sequencing is essential for therapeutic discovery and recombinant production, but current methods have significant limitations. We present a novel pipeline utilizing bottom-up proteomics with optimized protease digestion and AI-guided de novo peptide sequencing. This approach includes high-throughput MS analysis, precise peptide sequence prediction, effective PSM filtering, and a robust protein assembly algorithm. Our work advances de novo protein sequencing for monoclonal and polyclonal antibodies, facilitating efficient therapeutic antibody development.

We commonly use hydrogen exchange/mass spectrometry (HX/MS) to qualitatively assess how molecular perturbations impact protein structure. However, in theory, HX/MS data contain all the information necessary to derive residue-level energies of local unfolding (ΔGop). We introduce PFNet, our method to determine residue-level ΔGop from conventional HX/MS datasets. Benchmarking PFNet against HX/NMR shows it has near-perfect accuracy. We will share examples of how PFNet can reveal mechanisms of dynamic protein behavior.

Polysorbates 20 and 80 are the most widely used surfactants in biologics formulations as protein stabilizer. Polysorbates (PS) are chemically diverse mixtures and may degrade through oxidation and hydrolysis pathways, and negatively impact product quality and stability. Comprehensive characterization of PS and its degradants are essential to enable effective PS analytical control strategies to assure product quality, stability and safety. Case studies describe utilization of LC-MS methods to characterize PS80 subspecies and its degradants, as well as fatty acid distribution.

With the increasing complexity of biologic therapeutics, more sophisticated analytical strategies are required to ensure proper assembly, primary structure, three-dimensional structure, and microheterogeneity. To address these challenges, application of high-resolution mass spectrometry is used to assess the molecular attributes of protein drugs in a holistic approach. In this talk, we will discuss a comprehensive analytical strategy and present case studies which highlight the unexpected challenges which may be encountered.

The CryoEM “resolution revolution” has transformed our ability to visualize molecular mechanisms at atomic detail, advancing our understanding of pathogenic infections, cancer, and cellular processes. Our structural biology team combines X-ray crystallography, AI-enhanced CryoEM, and Alphafold2 modeling to rapidly determine high-resolution 3D structures of antigen-Fab complexes, revealing crucial epitope-paratope interactions and mechanisms of action.I will present how we decipher CryoEM structures of glycosylated antigen-Fab complexes using automated protocols. By integrating this data with complementary methods like HDX, alanine scanning, we enable efficient structure interpretation and epitope mapping from intermediate to high-resolution cryoEM maps, supporting structure-based engineering of therapeutic properties including pH-dependent binding and affinity tuning.

Sedimentation Velocity Analytical Ultracentrifugation (SV-AUC) is used to characterize and quantify size variants throughout mAb therapeutic development, either independently or in conjunction with methods such as SEC. Despite its unique advantages, such as analysis in native matrix and offering a broader dynamic range, SV-AUC is often considered a low-throughput technique. This session will illustrate how the incorporation of newer-generation AUC and data-processing software helps to improve efficiency and precision.