Transcriptomics as a predictor of biopharmaceutically favourable glycan profiles

Area of Science:

• Biotechnology
• Biopharmaceutical Development
• Glycobiology

Background:

• N-glycosylation is critical for biologic drug efficacy and properties.
• Glycosylation variability poses challenges for consistent therapeutic protein production.
• Understanding glycan structures impacts pharmacokinetics, immunogenicity, and stability.

Purpose of the Study:

• To leverage omics technologies for predicting optimal cell lines for therapeutic protein glycosylation.
• To identify transcriptomic markers predictive of glycosylation profiles in monoclonal antibodies (mAbs).
• To assess the utility of early-stage transcriptomics in streamlining cell line development for biologics.

Main Methods:

• Utilized omics technologies, specifically bulk RNA-sequencing, to analyze cell lines.
• Correlated transcriptomic data with known functional impacts of glycan structures on therapeutic antibodies.
• Identified specific genes (e.g., Alg5) and transporters as predictive markers for glycosylation outcomes.

Main Results:

• Bulk RNA-sequencing data demonstrates predictive power for mAb glycosylation.
• Alg5 expression levels predict higher Man5 levels in mAbs, suggesting competition for glycosylation enzymes.
• Elevated UDP-Gal transporter levels correlate with mAbs having a single galactose residue.

Conclusions:

• Early-stage transcriptomics can guide cell line selection and development for consistent glycosylation.
• Transcriptomic data offers valuable insights but does not capture all critical factors like enzyme localization.
• Further research into intracellular dynamics is essential for optimizing biologics production and glycosylation consistency.