協力的な計算と実験的探査を通じて多形リスクを最小限に抑える
Christopher R Taylor1, Matthew T Mulvee2, Domonkos S Perenyi2
1Computational Systems Chemistry, School of Chemistry, University of Southampton, Southampton SO17 1NX, U.K.
Journal of the American Chemical Society
|September 8, 2020
まとめ
コンピューターによる結晶構造予測 (CSP) と実験を組み合わせて,アイソニアジドとイプロニアジドの難解なポリモルフを成功裏に特定し,医薬品の固体形態に関連するリスクを最小限に抑えました.
科学分野:
- 医薬品科学
- 材料科学
- コンピュータ化学
背景:
- 医薬品のポリモルフィズムは 医薬品開発と製造に課題を 抱えています
- 予期せぬ多形体は 発達後期に現れ リスクを高めます
- 計算式結晶構造予測 (CSP) は,固形の景観を探索するための潜在的な解決策を提供します.
研究 の 目的:
- 最先端のCSPを実験的な方法と統合し,製薬の結晶構造を調査する.
- 特定のポリモルフを得ることの困難を合理化し,遅れて現れる形態のリスクを最小限に抑える.
- イソニアジドとイプロニアジドの固体構造を調べる
主な方法:
- 先進的な計算式結晶構造予測 (CSP) 技術を活用した.
- 高圧実験を含む幅広い実験結晶化方法を採用した.
- 実験観察を合理化するために自由エネルギー計算を適用した.
主要な成果:
- CSPは数十年前に解明されていなかった イソニアジド形式IIIの構造を 予測することに成功しました
- CSPはイプロニアジドの多形性のリスクを正確に予測した.
- CSPによって予測されたイプロニアジドの最初の3つの非溶解結晶形態を実験的に取得し,特徴づけました.
結論:
- 合成計算-実験アプローチは 医薬品の固体形態の風景に リスクを効果的に軽減します
- CSPはポリモルフを予測し理解するための強力なツールであり,薬の開発に役立ちます.
- 高圧実験は,CSPの予測によって, 難解な結晶の形を得ることに成功しました.
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