Case study

Automating antibody discovery: from data to development

The Challenge

The company aimed to perform a comprehensive analysis of camelid germline immunoglobulin V gene repertoires and assess homology to human counterparts. Manual analysis of thousands of sequences is time-consuming and prone to error.

Our Approach

We mined camelid genomes, analysed homology with humans and streamlined immunoinformatics to accelerate antibody discovery.

    • Genome mining & annotation: Used Antibody-extractor® to mine camel and alpaca genomes, identifying new V-gene repertoires and annotating them against human counterparts.
    • Homology analysis & structural prediction: Analysed homology and predicted structures between camelid and human repertoires, confirmed with X-ray crystallography.
    • Efficiency & simplification: Reduced analysis time from days to minutes and enabled discovery of two antibodies that advanced to Phase 1b studies.

The Outcome

    • Simplified and accelerated antibody discovery, yielding novel biological insights.
    • Revealed counterparts of all human V-gene families across camel, alpaca and llama species.
    • Improved processing efficiency and produced antibodies now in clinical development.

Why It Matters

Purpose-built computational tools reduce the barriers to antibody discovery. Automating sequence analysis accelerates therapeutic development and opens new avenues in immuno-biotech.

Let’s discuss how we can turn your data into real scientific impact.

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The Business of AI in Biotech: Why biotech is moving beyond the chatbox

The Business of AI in Biotech: Why biotech is moving beyond the chatbox

AI has quickly become part of everyday biotech research, helping scientists navigate information, write code and make sense of complex data. But chat-based tools have their limits. They can provide answers and guidance, but they cannot always do the actual scientific work. As biotech moves beyond the chatbox, the next step is putting AI to work directly within research workflows.