Pathways to Sustainable Biomanufacturing: Scalable Production of Biopharmaceutical Raw Materials and Biologics for Health Security
持続可能なバイオマニュファクチャリングへの道筋:健康安全保障のためのバイオ医薬品原料とバイオロジクスのスケーラブル生産 (AI 翻訳)
Jamie M. Reedy, Richard M. Mariita, Hyrine G. Munga, Britt Hafner
🤖 gxceed AI 要約
日本語
本レビューは、持続可能なバイオマニュファクチャリング経路の採用がグローバルヘルスセキュリティと主権の強化に不可欠であると評価。従来の哺乳類細胞から植物ベース、無細胞合成までの生産プラットフォームをトリプルボトムラインで分析し、AIやデジタルツインの統合による最適化とネットゼロ排出達成の可能性を示す。
English
This review evaluates the imperative for sustainable biomanufacturing pathways to enhance global health security, analyzing platforms from mammalian cells to plant-based and cell-free systems against the Triple Bottom Line. It highlights AI and digital twins as catalysts for optimizing yield and achieving net-zero emissions, emphasizing decentralized production for the Global South.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本ではバイオ医薬品産業が成長しており、本レビューの提唱する持続可能な製造方法は、日本のGX戦略における製造業の脱炭素化にも示唆を与える。特に、植物ベースや無細胞合成などの低炭素技術は、日本のバイオテクノロジー企業にとって競争力強化につながる可能性がある。
In the global GX context
Globally, this review addresses the need for sustainable biomanufacturing in the context of health security and supply chain resilience, aligning with UN SDGs and net-zero targets. The integration of AI and single-use technologies is relevant for reducing carbon footprints in the pharmaceutical industry, a sector under increasing scrutiny from investors and regulators.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of sustainable biomanufacturing platforms and the role of AI, useful for researchers in biotechnology and green chemistry.
🏢実務担当者:Offers insights on adopting scalable and eco-friendly production methods, relevant for biopharma companies seeking to reduce environmental impact and costs.
🏛政策担当者:Highlights the importance of decentralized production for health security and equitable access, informing policies on pandemic preparedness and sustainable industrial development.
📄 Abstract(原文)
The increasing global demand for biopharmaceutical biologics, including vaccines, monoclonal antibodies, enzymes, cytokines, hormones and growth factors, is driven by aging populations, rising cancer incidences, the persistent threat of pandemics such as COVID-19 and endemic infectious diseases such as Tuberculosis and Malaria. While the global biologics market is projected to surpass $1 trillion by 2034, current centralized manufacturing models rely heavily on resource-intensive mammalian systems that pose significant economic and logistical barriers, particularly for the Global South. This review evaluates the imperative for adopting sustainable biomanufacturing pathways to enhance global health security and sovereignty. We critically assess diverse production platforms, ranging from traditional microbial and mammalian cell lines to emerging green systems such as transgenic plants, algae, and cell-free synthesis, against the Triple Bottom Line framework: environmental stewardship, economic viability, and social responsibility. The analysis highlights that while mammalian cells remain the industry standard for complex post-translational modifications (PTMs), plant-based and cell-free platforms offer scalability, reduced carbon footprints, and the potential for decentralized production. Furthermore, the integration of artificial intelligence (AI), digital twins, and single-use technologies is identified as a catalyst for optimizing yield and facilitating net-zero emissions targets. For the Global South, these approaches offer opportunities to overcome resource limitations through localized, low-input platforms. We conclude that transitioning toward resilient, localized, and eco-friendly biomanufacturing is essential to mitigate supply chain vulnerabilities, ensure equitable access to life-saving therapeutics, and safeguard populations against future biological threats.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.53941/gssd.2026.100019first seen 2026-06-19 05:21:46
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。