治療による抗生物質耐性の発生を最小限に抑える
Mathew Stracy1,2, Olga Snitser1, Idan Yelin1
1Faculty of Biology, Technion-Israel Institute of Technology, Haifa, Israel.
Science (New York, N.Y.)
|February 24, 2022
まとめ
抗生物質耐性は 再感染によって生じます 進化によって生じません 機械学習は 抗生物質の選択を患者に個別化し 耐性菌の拡散を減らすことで このリスクを予測し 最小限に抑えることができます
科学分野:
- 微生物学
- 感染症
- コンピュータ生物学
背景:
- 現在の抗生物質戦略では 病原体の感受性を優先していますが 治療によって引き起こされる耐性を無視しています
- 抗生物質耐性の出現は,効果的な細菌感染治療に重大な脅威をもたらします.
研究 の 目的:
- 治療中に抗生物質耐性を引き起こすメカニズムの調査
- 治療による耐性の予測モデルを開発する.
- 抗生物質への抵抗を最小限に抑えるために パーソナライズされた抗生物質の推奨を検討する.
主な方法:
- バクテリア単離体1113の全ゲノムシーケンシング (処理前および後).
- 尿路感染症 (140,349) と傷口感染症 (7365) の大規模なデータセットの機械学習分析.
- 患者の感染歴を分析して再発を予測する.
主要な成果:
- 治療による耐性症は一般的であり,新しい進化ではなく,耐性菌株による再感染によるものです.
- 機械学習モデルは 患者のレベルでの抵抗の出現を予測できます
- 患者の病歴に基づいた個別化された抗生物質の推奨は,耐性の発生を最小限に抑えることができます.
結論:
- 治療中に抗生物質耐性の出現は予測可能であり,予防可能である.
- 機械学習とゲノムデータを活用した パーソナライズド・メディカルアプローチは 抗生物質耐性との闘いに不可欠です
- 抵抗性の再発を減らすことで,耐性病原体の拡散を緩和できます.
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