
Imagene AI, an Israeli AI drug discovery company, has begun a joint project with Daiichi Sankyo to discover biomarkers and predict treatment effectiveness in cancer drug development. The partnership leverages Imagene AI's multimodal AI platform to integrate pathology images, molecular profiles, omics data, and clinical data, aiming to identify biomarkers linked to treatment response and advance patient stratification. Imagene AI's real-world data foundation of over 3.5 million tissue samples and omics data will support Daiichi Sankyo's antibody-drug conjugate programs.
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イスラエルのAI創薬企業Imagene AIが、第一三共と共同プロジェクトを開始します。第一三共がImagene AIのマルチモーダルAIプラットフォーム「OI Suite」を使い、病理画像、分子プロファイル、オミクスデータ、臨床データを統合解析して、がん治療薬開発におけるバイオマーカー探索と治療効果予測を進めます。
なぜ重要か
がん患者の個別治療が実現しやすくなる可能性があります。Imagene AIの技術により、患者ごとの治療反応を事前に予測し、患者層別化を高度化できるため、抗体薬物複合体(ADC)など複雑な治療薬の開発精度が向上することが期待できます。
注目点
Imagene AIは350万件以上の組織サンプルとオミクスデータ、臨床アウトカムを統合したリアルワールドデータ基盤を保有しており、第一三共はこれを活用してバイオマーカー探索とAI予測モデル構築を進めます。
The partnership between Imagene AI and Daiichi Sankyo represents a convergence of Israeli AI innovation in drug discovery with Japan's major pharmaceutical development capabilities. Imagene AI's platform consolidates multiple data types—pathology images, molecular profiles, and clinical outcomes—into a unified analytical framework, enabling the identification of biomarkers that predict which patients will respond to specific treatments. For Daiichi Sankyo, this addresses a critical challenge in antibody-drug conjugate (ADC) development: these complex therapeutic modalities require precise patient selection to maximize efficacy and minimize adverse effects. By leveraging Imagene AI's dataset of 3.5 million tissue samples and omics records, Daiichi Sankyo can build more robust predictive models during clinical development, potentially accelerating approval timelines and reducing development costs.
The use of Composite Continuous Scoring for quantitative protein expression evaluation from immunohistochemistry images signals a move toward objective, AI-driven pathology assessment—a shift that may reduce variability in companion diagnostic development. This technical capability is particularly valuable for ADC programs, where target antigen expression levels directly correlate with therapeutic benefit. The stated goal of "personalized treatment for each cancer patient" reflects current industry direction toward precision oncology, and the partnership equips both organizations to pursue data-driven drug discovery at scale.
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