{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Yang J"],"funding":["National Natural Science Foundation of China","Joint Fund of Basic and Applied Basic Research Fund of Guangdong Province","The Beijing Nova Program"],"pagination":["e2307173"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10916672"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(9)"],"pubmed_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."],"journal":["Advanced science (Weinheim, Baden-Wurttemberg, Germany)"],"pubmed_title":["Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning."],"pmcid":["PMC10916672"],"funding_grant_id":["2021B1515130009","20220484224","52071015"],"pubmed_authors":["Li G","Li X","Yang J","Zhang D","Lou Y","Ju P","Ran Y","Liu S","Ren C"],"additional_accession":[]},"is_claimable":false,"name":"Synergistic D-Amino Acids Based Antimicrobial Cocktails Formulated via High-Throughput Screening and Machine Learning.","description":"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.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-06-09T07:09:56.924Z","creation":"2026-06-09T03:12:20.751Z"},"accession":"S-EPMC10916672","cross_references":{"pubmed":["38126652"],"doi":["10.1002/advs.202307173"]}}