Unifying Bioprocessing Data to Transform Biomanufacturing with AI

Artificial intelligence (AI) is edging closer to the bioreactor, yet its advancement is hindered by a significant challenge: the disarray of bioprocessing data. Phil Mounteney, vice president of science & technology at Dotmatics, emphasizes that the industry’s primary obstacle lies not in algorithm sophistication but in the fractured information ecosystem that AI must navigate.

Bioprocessing data is frequently dispersed across various platforms, including electronic laboratory notebooks (ELNs), laboratory information management systems (LIMS), and legacy systems that lack interoperability. Mounteney points out that critical real-time analytics from bioreactors—such as pH levels and dissolved oxygen—are often siloed from batch records and offline assays, preventing AI models from learning reliable patterns.

Moreover, the absence of a continuous digital lineage complicates matters. Without a cohesive link between real-time bioreactor data and corresponding batches or analytical results, even sophisticated algorithms struggle to identify successful runs versus those at risk of failure. Mounteney advocates for treating bioprocess information as a vital shared asset, proposing a unified data layer that integrates signals from multiple sources to create an AI-ready model.

This integration requires robust connections to bioreactors and inline analytical systems, alongside metadata frameworks to capture essential operational details. The potential benefits are substantial: unified, high-quality data could enhance model accuracy, streamline design-of-experiments cycles, and improve scale-up processes by better anticipating risks. Furthermore, regulatory interactions would be strengthened through enhanced traceability, allowing for a coherent, data-driven narrative.

Looking ahead, Mounteney envisions AI transitioning from offline analytics to embedded process intelligence, enabling real-time inference of critical quality attributes. As organizations invest in integrated data infrastructures, the adoption of these innovations is expected to accelerate, ultimately transforming biomanufacturing.