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コンテンツは David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal
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116: Revolutionizing Biologics Development with Hyper Throughput Screening and AI with Jeremy Agresti - Part 2

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コンテンツは David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

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The intersection of artificial intelligence and biology presents immense opportunities for transforming bioprocess development. As the biotech industry continues to evolve, data-driven innovations are critical to optimizing biologics manufacturing. High-quality datasets stand at the forefront of this transformation, empowering researchers to make informed predictions and advance therapeutic discoveries. As AI tools become more commoditized, the focus shifts toward generating robust and extensive datasets to maximize the potential of machine learning in biological applications.

Miniaturization has emerged as a vital enabler in this data-driven approach. Miniaturized systems allow researchers to conduct thousands of tests in an area no larger than the palm of your hand. This drastic reduction in material and resource requirements makes high-throughput screening feasible, economical, and scalable.

Traditional liquid handling robots can manage thousands of tests per day, but each test requires considerable amounts of material, usually leading to high costs. Conventional systems can cost anywhere from $10 to $100 to get a single genotype sequence from discovery to sequencing. Miniaturization can bring these costs down to mere pennies per data point, making it possible to scale the dataset size exponentially.

Key takeaways from our discussion:

  • The future of AI in biology relies heavily on large, well-annotated datasets. Without them, the full potential of AI remains untapped. High-quality data enables more accurate predictions of protein structures and functions.
  • Success in bioprocess development often involves collaboration with partners across the value chain. By working together, companies can leverage their unique strengths and expertise to overcome barriers and innovate more efficiently.
  • Advancements in miniaturization technology allow for high throughput screening at reduced costs. This shift makes it viable to generate large datasets, speeding up the pace of discovery and making AI-driven predictions more accessible.

This episode is essential for anyone eager to explore the transformative fusion of AI and biotechnology. Jeremy Agresti highlights the future of bioprocessing at the intersection of high-throughput screening, miniaturization, and AI, revealing how these innovations are solving complex challenges, driving breakthroughs, and shaping the future of science and medicine.

Connect with Jeremy Agresti:

LinkedIn: www.linkedin.com/in/jeremy-agresti-88546850/

Triplebar: www.triplebar.com

Next Steps:

Overwhelmed by complex bioprocess development decisions? Get strategic guidance to avoid costly mistakes and stay ahead: https://bruehlmann-consulting.com

Are soaring manufacturing costs keeping your lifesaving therapies out of reach for patients? Book your free assessment at: https://bruehlmann-consulting.com/assessment

  continue reading

117 つのエピソード

Artwork
iconシェア
 
Manage episode 456280896 series 3525243
コンテンツは David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、David Brühlmann: Biotech Entrepreneur & Cell Culture Technology Innovation Aficionado, David Brühlmann: Biotech Entrepreneur, and Cell Culture Technology Innovation Aficionado またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作物をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

Send us a text

The intersection of artificial intelligence and biology presents immense opportunities for transforming bioprocess development. As the biotech industry continues to evolve, data-driven innovations are critical to optimizing biologics manufacturing. High-quality datasets stand at the forefront of this transformation, empowering researchers to make informed predictions and advance therapeutic discoveries. As AI tools become more commoditized, the focus shifts toward generating robust and extensive datasets to maximize the potential of machine learning in biological applications.

Miniaturization has emerged as a vital enabler in this data-driven approach. Miniaturized systems allow researchers to conduct thousands of tests in an area no larger than the palm of your hand. This drastic reduction in material and resource requirements makes high-throughput screening feasible, economical, and scalable.

Traditional liquid handling robots can manage thousands of tests per day, but each test requires considerable amounts of material, usually leading to high costs. Conventional systems can cost anywhere from $10 to $100 to get a single genotype sequence from discovery to sequencing. Miniaturization can bring these costs down to mere pennies per data point, making it possible to scale the dataset size exponentially.

Key takeaways from our discussion:

  • The future of AI in biology relies heavily on large, well-annotated datasets. Without them, the full potential of AI remains untapped. High-quality data enables more accurate predictions of protein structures and functions.
  • Success in bioprocess development often involves collaboration with partners across the value chain. By working together, companies can leverage their unique strengths and expertise to overcome barriers and innovate more efficiently.
  • Advancements in miniaturization technology allow for high throughput screening at reduced costs. This shift makes it viable to generate large datasets, speeding up the pace of discovery and making AI-driven predictions more accessible.

This episode is essential for anyone eager to explore the transformative fusion of AI and biotechnology. Jeremy Agresti highlights the future of bioprocessing at the intersection of high-throughput screening, miniaturization, and AI, revealing how these innovations are solving complex challenges, driving breakthroughs, and shaping the future of science and medicine.

Connect with Jeremy Agresti:

LinkedIn: www.linkedin.com/in/jeremy-agresti-88546850/

Triplebar: www.triplebar.com

Next Steps:

Overwhelmed by complex bioprocess development decisions? Get strategic guidance to avoid costly mistakes and stay ahead: https://bruehlmann-consulting.com

Are soaring manufacturing costs keeping your lifesaving therapies out of reach for patients? Book your free assessment at: https://bruehlmann-consulting.com/assessment

  continue reading

117 つのエピソード

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