Synergistic D‐Amino Acids Based Antimicrobial Cocktails Formulated via High‐Throughput Screening and Machine Learning
Abstract: Antimicrobial resistance (AMR) from pathogenic bacterial biofilms has become a global health issue while developing novel antimicrobials is inefficient and costly. Combining existing multiple drugs with enhanced efficacy and/or reduced toxicity may be a promising approach to treat AMR. D‐amino acids mixtures coupled with antibiotics can provide new therapies for drug‐resistance infection with reduced toxicity by lower drug dosage requirements. However, iterative trial‐and‐error experiments are not tenable to prioritize credible drug formulations, owing to the extremely large number of possible combinations. Herein, a new avenue is provide to accelerate the exploration of desirable antimicrobial formulations via high‐throughput screening and machine learning optimization. Such an intelligent method can navigate the large search space and rapidly identify the D‐amino acid mixtures with the highest anti‐biofilm efficiency and also the synergisms between D‐amino acid mixtures and antibiotics. The optimized drug cocktails exhibit high antimicrobial efficacy while remaining non‐toxic, which is demonstrated not only from in vitro assessments but also the first in vivo study using a lung infection mouse model.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Synergistic D‐Amino Acids Based Antimicrobial Cocktails Formulated via High‐Throughput Screening and Machine Learning ; day:21 ; month:12 ; year:2023 ; extent:13
Advanced science ; (21.12.2023) (gesamt 13)
- Urheber
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Yang, Jingzhi
Ran, Yami
Liu, Shaopeng
Ren, Chenhao
Lou, Yuntian
Ju, Pengfei
Li, Guoliang
Li, Xiaogang
Zhang, Dawei
- DOI
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10.1002/advs.202307173
- URN
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urn:nbn:de:101:1-2023122114381491020169
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:29 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Yang, Jingzhi
- Ran, Yami
- Liu, Shaopeng
- Ren, Chenhao
- Lou, Yuntian
- Ju, Pengfei
- Li, Guoliang
- Li, Xiaogang
- Zhang, Dawei
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