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Antibodies (resized)

Realise Quality Processes for Biomanufacturing

To align with EDB’s drive to expand the footprint of Singapore as a biopharma hub
Cells (resized)

Research Excellence

A centre of excellence for groundbreaking research in Proteins, Cells, and Nucleic Acids bioprocessing technologies
Partnership (resized)

Industry Collaboration

Cultivating partnerships with industry to advance innovations and translate research into industrial applications
Bioreactor (resized)

Talent Development

Providing development and training programmes to support biomanufacturing and research activities in Singapore

A*STAR Bioprocessing Technology Institute (BTI)

Who We Are

A*STAR Bioprocessing Technology Institute (BTI) focuses on advancing bioprocessing technologies and research over its 35-year history. Our current vision is to Realise Quality Processes for Biomanufacturing, with a strong emphasis on Proteins, Cells, and Nucleic Acids.

 

BTI’s Key Focus Areas

To achieve BTI’s vision of ensuring Quality Processes for Biomanufacturing, the institute develops innovative research capabilities and advanced biomanufacturing technologies. Modality-specific strategies are key to BTI achieving its mission of developing Singapore's biomanufacturing sector where asset classes are in various stages of maturity and development.

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BTI Focus Area (PCN)

 

BTI's Core Capabilities

BTI’s core capabilities are systematically organised around its four principal divisions, which oversee 19 Enabling Research Groups, and Technology Platforms and Consortia. The matrix structure of BTI’s divisions enables the swift and flexible assembly of research teams with synergistic capabilities, allowing them to effectively address research and industry problem statements.

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BTI Capabilities

 

Smarter Cell Culture: AI Helps Improve Drug Yield and Quality

27 Feb 2026

Medicines like cancer-fighting antibodies are made using living cells, which require just the right mix of nutrients and conditions to grow effectively and produce high-quality drugs. Identifying the best “recipe” is normally slow and expensive, as it requires many experiments. In our study, we trained machine learning models to learn how different ingredients and conditions affect cell growth and product quality. We then used these models to suggest better combinations for subsequent testing - speeding up the search for optimal conditions using fewer experiments.

Read more: https://www.a-star.edu.sg/bti/bti-research-highlights/smarter-cell-culture-ai-helps-improve-drug-yield-and-quality

Posted in: BTI Research Highlights
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