Unmet needs for management of drug-resistant infections: low- and middle-income countries’ viewpoint

Sharma, Monica; Walia, Kamini; Bansal, N.K.

doi:10.33393/dti.2022.2532

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Nevertheless, the eff ectiveness of these molecules depends on the drug resistance mechanism and there is no one standard regimen for all MDR-BGN strains. Besides, these new drugs are hardly available regarding high treatment costs in middle-and low-income countries where AMR rates were reported at their highest [5].…”

Section: Discussionmentioning

confidence: 99%

New Strategies Needed in the Fight against Antimicrobial Resistance

Azouzi*

2023

J Biomed Res Environ Sci

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Section: Discussionmentioning

confidence: 99%

New Strategies Needed in the Fight against Antimicrobial Resistance

Azouzi*

2023

J Biomed Res Environ Sci

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Genome analysis of triple phages that curtails MDR E. coli with ML based host receptor prediction and its evaluation

Unnikrishnan,

Sundaramoorthy,

Nair

et al. 2023

Sci Rep

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Infections by multidrug resistant bacteria (MDR) are becoming increasingly difficult to treat and alternative approaches like phage therapy, which is unhindered by drug resistance, are urgently needed to tackle MDR bacterial infections. During phage therapy phage cocktails targeting different receptors are likely to be more effective than monophages. In the present study, phages targeting carbapenem resistant clinical isolate of E. coli U1007 was isolated from Ganges River (U1G), Cooum River (CR) and Hospital waste water (M). Capsid architecture discerned using TEM identified the phage families as Podoviridae for U1G, Myoviridae for CR and Siphoviridae for M phage. Genome sequencing showed the phage genomes varied in size U1G (73,275 bp) CR (45,236 bp) and M (45,294 bp). All three genomes lacked genes encoding tRNA sequence, antibiotic resistant or virulent genes. A machine learning (ML) based multi-class classification model using Random Forest, Logistic Regression, and Decision Tree were employed to predict the host receptor targeted by receptor binding protein of all 3 phages and the best performing algorithm Random Forest predicted LPS O antigen, LamB or OmpC for U1G; FhuA, OmpC for CR phage; and FhuA, LamB, TonB or OmpF for the M phage. OmpC was validated as receptor for U1G by physiological experiments. In vivo intramuscular infection study in zebrafish showed that cocktail of dual phages (U1G + M) along with colsitin resulted in a significant 3.5 log decline in cell counts. Our study highlights the potential of ML tool to predict host receptor and proves the utility of phage cocktail to restrict E. coli U1007 in vivo.

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