Protein aggregation hinders industrial protein production, downstream processing, and formulation. Aggrescan3D (A3D) became a widely used structure-based predictor to analyse and redesign aggregation-prone globular proteins. Here, we introduce Aggrescan4D (A4D), a major extension of A3D that integrates AI-driven structure prediction, disorder discrimination, pH-dependent aggregation modelling, and an evolutionarily informed automatic mutation protocol, providing a comprehensive platform for engineering protein solubility and enhancing bioprocessing outcomes.

Fixed-dose combination (FDC) products can offer benefits such as enhanced efficacy and improved patient compliance and convenience. However, optimising co-formulations presents a significant challenge. By introducing a semi-automated workflow for screening injectable FDC formulations of large synthetic molecules, including automated weighing, dispensing, and dilution of solutions, and pH measurement, we have markedly expanded the range of compositions that can be evaluated and substantially increased analytical throughput, accelerating formulation development.

Optimising sandwich ELISA performance necessitates the selection of compatible mAb pairs as capture and detection antibodies. In this study, we investigated the antibody-antibody interactions that contribute to diminished signal and elevated background during therapeutic antibody detection in sandwich ELISA. We identified signal loss due to overlapping binding sites, and elevated background from cross-reactivity between antibodies. Additionally, interactions at distinct epitopes were found to induce varying steric hindrance, affecting oligomerisation.

In silico computations play an increasing role in drug development, but platforms combining multiple models and comprehensively evaluating therapeutic proteins' developability are currently lacking. This presentation covers this gap, showing how different models, spanning structure prediction, bioinformatics, machine learning, and molecular dynamics, can be combined within an automated platform to speed-up and de-risk candidate selection, lead characterisation, and formulation development. Relevant case studies will be presented.

Developability Assessment (DAS) is vital in drug development, enabling early risk identification in lead candidates. With growing biotherapeutic diversity and advanced protein engineering, DAS must adapt case-by-case. Integrating automation, data management, and machine learning reduces this burden and opens new pathways for efficient assessment—key themes to be explored in the upcoming presentation.

Highly potent novel biologics require extensive characterisation and formulation approaches to optimise their stability. This presentation will highlight approaches taken in formulation development to optimise the stability of such molecules. Another unique challenge of highly potent molecules is their administration. We will present an approach that demonstrates effective dosing of these highly sensitive molecules.

Overcoming the biophysical characterisation challenges of IgG2 antibodies is critical for therapeutic development. This presentation covers advanced analytical methods including native cation exchange chromatography-mass spectrometry (CEX-MS) combined with F(ab')2 middle-up analysis to identify IgG2 disulfide isoforms. We demonstrate accurate disulfide linkage elucidation, as confirmed by optimised peptide mapping strategies, that enable rigorous structural characterisation critical for developability assessment and bioprocess optimisation.

Monitoring product quality attributes (PQAs) during biopharmaceutical development is essential to ensure safety, stability and efficacy. Liquid chromatography-mass spectrometry (LC-MS) offers unmatched specificity and sensitivity for PQA identification and quantification. However, reducing analysis time is a priority to enable high-throughput and rapid decision making. This talk will explore cyclic ion mobility-mass spectrometry (cIMS) potential as an ultrafast PQA monitoring method in complex mAb digests to inform bioprocess developers.

The complexity of cell and gene therapies necessitates robust quality control throughout the product lifecycle, from raw and starting materials to final drug product. Stakeholders have identified a need for fit-for-purpose standards to support raw material qualification, impurity profiling, and assessment of critical quality attributes. This presentation will provide an overview of USP resources that can facilitate risk-based decision-making and robust characterisation strategies across upstream and downstream bioprocessing.

Reference standards are essential for Gene Therapy QC, yet their implementation is often challenging due to high material costs, limited batch sizes, time constraints, and evolving product knowledge. A robust strategy is crucial to ensure proper identification, implementation, monitoring, and replacement of reference materials, safeguarding product quality and regulatory compliance.

This study focuses on investigating the reversible self-association (RSA) of a bispecific therapeutic using a range of biophysical techniques, including SAXS, AUC, and dynamic and static light scattering. These methods provide detailed insight into the thermodynamic and hydrodynamic contributions underlying protein–protein interactions. By combining orthogonal scattering data with monomer loss studies, we establish a direct link between reversible self-association and aggregation propensity, demonstrating how RSA impacts long-term stability.