Synthetic Glycans to Improve Current Glycoconjugate Vaccines and Fight Antimicrobial Resistance.
Linda Del BinoKitt Emilie ØsterlidDung-Yeh WuFrancesca NonneMaria Rosaria RomanoJeroen D C CodéeRoberto AdamoPublished in: Chemical reviews (2022)
Antimicrobial resistance (AMR) is emerging as the next potential pandemic. Different microorganisms, including the bacteria <i>Acinetobacter baumannii</i>, <i>Clostridioides difficile</i>, <i>Escherichia coli</i>, <i>Enterococcus faecium</i>, <i>Klebsiella pneumoniae</i>, <i>Neisseria gonorrhoeae</i>, <i>Pseudomonas aeruginosa</i>, non-typhoidal <i>Salmonella</i>, and <i>Staphylococcus aureus</i>, and the fungus <i>Candida auris</i>, have been identified by the WHO and CDC as urgent or serious AMR threats. Others, such as group A and B <i>Streptococci</i>, are classified as concerning threats. Glycoconjugate vaccines have been demonstrated to be an efficacious and cost-effective measure to combat infections against <i>Haemophilus influenzae</i>, <i>Neisseria meningitis</i>, <i>Streptococcus pneumoniae</i>, and, more recently, <i>Salmonella typhi</i>. Recent times have seen enormous progress in methodologies for the assembly of complex glycans and glycoconjugates, with developments in synthetic, chemoenzymatic, and glycoengineering methodologies. This review analyzes the advancement of glycoconjugate vaccines based on synthetic carbohydrates to improve existing vaccines and identify novel candidates to combat AMR. Through this literature survey we built an overview of structure-immunogenicity relationships from available data and identify gaps and areas for further research to better exploit the peculiar role of carbohydrates as vaccine targets and create the next generation of synthetic carbohydrate-based vaccines.
Keyphrases
- antimicrobial resistance
- escherichia coli
- acinetobacter baumannii
- klebsiella pneumoniae
- pseudomonas aeruginosa
- biofilm formation
- multidrug resistant
- staphylococcus aureus
- drug resistant
- cystic fibrosis
- systematic review
- sars cov
- coronavirus disease
- candida albicans
- cross sectional
- listeria monocytogenes
- deep learning
- cerebrospinal fluid
- data analysis