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End-to-End Workflow Integration for Monoclonal and Bispecific Antibody Development

August 25, 2025
Ada Yee

The biopharmaceutical industry is witnessing an unprecedented acceleration in the complexity and volume of data generated throughout R&D, especially as therapeutic focus shifts toward sophisticated biologics such as monoclonal and bispecific antibodies. Handling these massive, multifaceted datasets — ranging from molecular sequence and design to high-throughput screening and manufacturability profiles — has become a defining challenge for innovation-driven firms. Moreover, systematic, large-scale data collection on candidate molecules and their performance lays the foundation for smarter, AI-driven antibody design.1–4 

For global organizations such as Pfizer, geographic dispersion of teams amplifies these data hurdles. Siloed digital systems, inconsistent data standards, and disconnected workflows hinder real-time collaboration and delay critical insights.5,6 These bottlenecks translate directly into inefficiencies, duplicated work, and slower progression from discovery to development.

Key Challenges in Data Management for Antibody Development: Diverse Data and System Integration

Antibody R&D produces varied datasets such as protein and DNA sequences, target binding and functional assay results, and developability attributes.7 Modern antibody platforms, including various multi-specific, fragment, bioconjugate formats, require navigation of even larger combinatorial sequence and design spaces and the management of exponentially more variants and associated metadata.1,3,8 Logistical hurdles in handling these data only grow with project scale.

Fragmented IT infrastructure, lack of interoperability, and the use of outdated tracking tools (such as spreadsheets or isolated LIMS) further intensify these challenges, making it difficult to ensure traceability, data integrity, and fast design and selection cycles. For geographically distributed teams, these problems are further compounded. Implementing interoperable, well-documented, and machine-actionable approaches in line with Findable, Accessible, Interoperable, and Reusable (FAIR) data principles is now an industry-wide mandate.6

The Demand for Centralized Workflow Integration in Biopharma R&D

With ballooning data volumes, the need for sophisticated, centralized R&D workflow platforms is urgent. Ideal solutions consolidate multisite data pipelines, automate sample tracking, and embed quality checks, enabling organizations to move from laborious manual compilation to reliable, scalable digital pipelines. Furthermore, for the biopharma enterprise environment, digital platforms should integrate tightly with existing systems and adapt readily to proprietary workflows or future technological developments in this fast-moving therapeutic space.3

How the Genedata Digital Backbone Increased Antibody Development Efficiency by 10-Fold

The deployment of the Genedata platform at Pfizer helped eliminate fragmented data systems and inefficient workflows by establishing a unified, central repository for all large-molecule discovery data, including screening, molecular biology, protein engineering, expression, purification, and analytics. This not only broke down internal silos but also allowed more than 250 researchers across 15 groups and 6 global R&D sites to collaborate seamlessly and effectively on over 200 discovery projects.

The Genedata platform functions as Pfizer’s core data backbone and process engine for all large-molecule R&D. The system’s out-of-the-box capabilities offer broad process coverage, supporting all major antibody and large molecule discovery technologies (e.g., phage/yeast display, hybridoma, and B-cell approaches), while its open architecture and robust, RESTful APIs allowed adaptation to Pfizer-specific requirements and the integration of proprietary applications. Pfizer leveraged this flexibility to develop custom tools for antibody humanization, clone alignment, and Pfizer-specific standard report generation. At the same time, unlike piecemeal or homegrown solutions, the Genedata solution was implemented at Pfizer within weeks for initial research groups, followed by swift expansion company-wide. As a commercial, validated software, it allowed immediate adoption without lengthy development cycles. 

Seamless integration with laboratory automation tools, such as pipetting robots and assay readers, enabled automated sample handling and high-throughput screening. Intuitive dashboards consolidated and visualized developability and manufacturability metrics in real time, allowing for immediate decisions on candidate progression. Automated batch reporting and comprehensive historical record-keeping enhanced compliance, with Genedata establishing a structured, queryable data foundation that paved the way for the use of artificial intelligence and machine learning across Pfizer’s R&D.

Pfizer’s feedback confirms the magnitude of these benefits: manual data reconciliation and ad hoc spreadsheet processes have become obsolete. Scientists now spend vastly less time searching for and interpreting data; critical information pertaining to protein sequences, developability scores, and manufacturability measures is accessible instantly to every team, at every location.  Overall, this digitalization led to dramatic efficiency improvements — a 10-fold increase in antibody conversion to full IgG per project.

In summary, the Genedata solution has enabled Pfizer to shift from disparate, fragmented data processes to a digital-first, highly automated and analytics-driven R&D paradigm. The outcome is not simply more efficient molecule discovery, but a strategic data platform long-term — future-proofed for ongoing scientific, technological, and regulatory evolution.

Learn more about how Pfizer streamlined their R&D data with Genedata Biologics by downloading the full case study.

 

References

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