Construction of dynamic prediction model of EGFR tyrosine kinase inhibitors efficacy for mutated EGFR based on protein three-dimensional structure analysis
Project/Area Number |
15K08652
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Laboratory medicine
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Research Institution | Kyorin University |
Principal Investigator |
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Co-Investigator(Kenkyū-buntansha) |
渡邊 卓 杏林大学, 医学部, 教授 (00191768)
広川 貴次 国立研究開発法人産業技術総合研究所, 生命工学領域, 研究チーム長 (20357867)
中村 浩之 東京工業大学, 科学技術創成研究院, 教授 (30274434)
大塚 弘毅 杏林大学, 医学部, 学内講師 (70439165)
|
Project Period (FY) |
2015-04-01 – 2019-03-31
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Project Status |
Completed (Fiscal Year 2018)
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Budget Amount *help |
¥4,940,000 (Direct Cost: ¥3,800,000、Indirect Cost: ¥1,140,000)
Fiscal Year 2017: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2016: ¥1,690,000 (Direct Cost: ¥1,300,000、Indirect Cost: ¥390,000)
Fiscal Year 2015: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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Keywords | EGFR / チロシンキナーゼ阻害剤 / 肺癌 / 動的分子シミュレーション / 動的立体構造解析 / チロシンキナーゼ阻害薬 / 分子動力学モデル / 分子動態モデリング |
Outline of Final Research Achievements |
Non-small cell lung cancer(NSCLC) patients harboring the epidermal growth factor receptor (EGFR) mutations benefit from therapies by the EGFR tyrosine kinase inhibitors (TKIs). We developed a new dynamic prediction model of TKIs efficacy using computed simulation of binding of mutated EGFR protein and TKIs. We were able to correctly predict the responsiveness of various TKIs to NSCLC with EGFR mutation. Especially, this computer dynamic simulation calculates that third generation EGFR TKIs will be more effective than 1st and 2nd generation EGFR TKIs. It was also predictable that most TKIs are not effective against the rare V843I+L858R mutant. Our new model is very promising for appropriate selection of various existing TKIs for various EGFR mutants as well as discovery of new TKIs by application of dynamic molecular simulation technology.
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Academic Significance and Societal Importance of the Research Achievements |
従来の分子結合の静的モデルでは、種々のEGFRチロシンキナーゼ阻害剤(TKI)と変異EGFR蛋白との結合性を正確に予測することは困難であった。今回開発した動的分子シミュレーションモデルを用いることにより、EGFRの変異ごとのTKI感受性や、第1-3世代のTKIのT790M変異に対する効果の違いをほぼ正確に予測することができた。今後この手法を用いることで、新たな肺癌の分子標的薬(EGFR-TKI)の開発において、実際の臨床効果を正確に予測しうる効率的な創薬が可能になることが期待される。
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Report
(4 results)
Research Products
(2 results)