| Project/Area Number |
18K05353
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 37030:Chemical biology-related
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| Research Institution | Kyoto University |
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Principal Investigator |
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| Co-Investigator(Kenkyū-buntansha) |
山田 久嗣 徳島大学, 大学院社会産業理工学研究部(生物資源産業学域), 講師 (80512764)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | 光音響イメージング / 高分子プローブ / 近赤外蛍光色素 / ナノ粒子 / ベタイン / 活性酸素種 / ホスホリルコリン |
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| Outline of Final Research Achievements |
Three-dimensional representation of a tumor with respect to its size, shape, location, and boundaries is still a challenge in photoacoustic (PA) imaging using artificial contrast agents as probes. We carried out PA imaging of tumors in mice using 800RS-PMPC, a highly selective tumor-targeting methacrylate polymer having phosphorylcholine side chains, as a probe. The conjugate 800RS-PMPC forms compact nanoparticles (dDLS = 14.3 nm), retains the biocompatibility of the parent polymer (PMPC) and exhibits unprecedented PA performance. When applied to mice bearing a 6 × 3 × 3 mm3 tumor buried 6 mm beneath the skin, the probe 800RS-PMPC selectively accumulates in the tumor and emits PA signals that are strong enough to be unambiguously distinguished from noise signals of endogenous blood/hemoglobin. The PA image thus obtained under high-threshold conditions allows 3D characterization of the tumor in terms of its size, shape, location, and boundaries.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発した NIR-PMPC プローブを用いる次世代の「光音響イメージング（PAI）」の実現は、腫瘍の高度画像診断や手術中の画像診断（術中ナビゲーション）への応用に加え、患者の負担軽減、薬物療法の最適化、さらには「がん（悪性腫瘍）」による死亡率の低減と医療費抑制に大きく貢献する。
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