AI and Automation Poised to Reshape Biopharmaceutical Quality Testing

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According to a team of researchers at the University of Porto, scientific publications combining the terms "biopharmaceutical" and "analytical" grew by approximately 171% between 2014 and 2024, rising from 48 to more than 130 annual publications.¹ This trend in the literature reflects how much the biopharmaceutical industry is growing. According to the authors of this review article, a combination of artificial intelligence (AI), automation, and multi-omics technologies have played a key role in this growth.¹

“The convergence of AI, machine learning, and high-throughput screening platforms holds promise for transforming biopharmaceutical analysis by streamlining data interpretation, improving predictive modeling, and accelerating quality control processes,” the authors wrote in their study, which was published in the journal Analytical and Bioanalytical Chemistry.¹

What has led to the rise of biopharmaceutical analysis?

Biopharmaceuticals is a broad category that includes monoclonal antibodies, gene therapies, and biosimilars. It has become one of the fastest-growing segments of the global pharmaceutical industry.¹ That growth has placed mounting pressure on the analytical infrastructure needed to characterize these complex molecules before and after they reach patients.¹

For example, Raman spectroscopy has become a widely used technique in biopharmaceutical analysis. According to Jerome Workman, Jr., who is the associate editorial director of Spectroscopy, “advances in AI-driven spectral interpretation, inline deployment strategies, and high-throughput experimental platforms” have led to Raman spectroscopy being widely adopted in this space to help support real-time release testing and digital biomanufacturing.²

What are the current ongoing challenges in biopharmaceutical analysis?

The review identifies three primary pressure points in current biopharmaceutical analysis. First, the structural complexity and heterogeneity of biologics require a broad and overlapping array of analytical methods, no single platform being sufficient to characterize a product fully.¹ Second, the instrumentation required for advanced characterization, including mass spectrometry (MS)-based multi-attribute monitoring and real-time biosensors, carries high capital and operational costs that place smaller manufacturers and testing laboratories at a disadvantage.¹ And finally, deploying these technologies effectively requires specialized scientific expertise that remains in short supply across the sector.¹

What can we expect in biopharmaceutical analysis in the future?

Moving forward, the biopharmaceutical industry is poised to benefit from the integration of AI and machine learning (ML) platforms.^1,2 Deep learning architectures (CNNs, transformers, hybrid models) are going to be used increasingly as well.² These tools could streamline the interpretation of complex data sets generated by high-throughput screening, improve predictive modeling for product stability, and accelerate quality control decision-making.¹

References

1. Cunha, D. R.; Quinaz, M. B.; Segundo, M. A. Biopharmaceutical Analysis — Current Analytical Challenges, Limitations, and Perspectives. Anal. Bioanal. Chem. 2026, 418, 373–394. DOI: 10.1007/s00216-025-06036-2
2. Workman, Jr., J. Top 10 Most Influential Articles on Raman Spectroscopy in Biopharmaceutical Applications during 2023–2025. Spectroscopy. Available at: https://www.spectroscopyonline.com/view/top-10-most-influential-articles-on-raman-spectroscopy-in-biopharmaceutical-applications-during-2023-2025 (accessed 2026-04-21).