多薬理学プロファイルへのリガンドの自動設計
Jérémy Besnard1, Gian Filippo Ruda, Vincent Setola
1Division of Biological Chemistry and Drug Discovery, College of Life Sciences, University of Dundee, Dundee DD1 5EH, UK.
Nature
|December 14, 2012
まとめ
特定のマルチターゲットのプロファイルを持つ薬剤を設計することは困難です. この研究は,複数の薬物標的に対するリガンドの設計のための自動化された方法を提示し,予測の75%の正確性を達成し,インビボ標的の関与を実証しています.
科学分野:
- 計算化学と薬物の発見
- 薬理学および医薬品化学について
背景:
- 薬剤の有効性と安全性は,タンパク質全体の活性プロファイルに依存しています.
- 複数のタンパク質を標的とする薬物の合理的な設計は複雑で困難です.
- マルチターゲットのプロファイルに対して,薬剤を設計するための方法の必要性.
研究 の 目的:
- 複数の薬物標的に対するリガンドの自動設計のための新しいアプローチを導入する.
- 既存の薬剤を望ましいポリファーマコロジーまたは選択性プロファイルを持つ新しいリガンドに進化させる方法の有用性を実証する.
主な方法:
- マルチターゲットのプロファイルに対してリガンドを設計するための自動計算方法を開発した.
- 承認されたアセチルコリンエステラゼ阻害剤にこの方法を適用した.
- 薬剤は,特定の多薬学またはGタンパク質結合受容体に対する選択性を持つ脳に浸透するリガンドに進化した.
主要な成果:
- 800個の前向きに設計されたリガンドを実験的にテストし,75%の予測精度を達成しました.
- 設計されたリガンドの in vivo ターゲットエンゲージメントが成功していることが実証されています.
- 多薬理学と選択性プロファイルに合わせたリガンドを成功裏に進化させた.
結論:
- 自動リガンド設計アプローチは,望ましいマルチターゲットのプロファイルを持つ薬剤を作成するのに有効です.
- この方法は,特にポリファーマコロジーや高い選択性を要求する条件において,薬剤発見を加速させることができます.
- このアプローチは,複雑な治療的ニーズのための貴重な薬剤誘導源を提供します.
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