| Literature DB >> 31921119 |
Antonio J Martín-Galiano1, Michael J McConnell1.
Abstract
Over the past few decades, antimicrobial resistance has emerged as an important threat to public health due to the global dissemination of multidrug-resistant strains from several bacterial species. This worrisome trend, in addition to the paucity of new antibiotics with novel mechanisms of action in the development pipeline, warrants the development of non-antimicrobial approaches to combating infection caused by these isolates. Monoclonal antibodies (mAbs) have emerged as highly effective molecules for the treatment of multiple diseases. However, in spite of the fact that antibodies play an important role in protective immunity against bacteria, only three mAb therapies have been approved for clinical use in the treatment of bacterial infections. In the present review, we briefly outline the therapeutic potential of mAbs in the treatment of bacterial diseases and discuss how their development can be facilitated when assisted by "omics" technologies and interpreted under a systems biology paradigm. Specifically, methods employing large genomic, transcriptomic, structural, and proteomic datasets allow for the rational identification of epitopes. Ideally, these include those that are present in the majority of circulating isolates, highly conserved at the amino acid level, surface-exposed, located on antigens essential for virulence, and expressed during critical stages of infection. Therefore, these knowledge-based approaches can contribute to the identification of high-value epitopes for the development of effective mAbs against challenging bacterial clones.Entities:
Keywords: antibiotic resistance; big data; bound rationality; immunoinformatics; monoclonal antibodies; multidrug resistance; systems biology
Year: 2019 PMID: 31921119 PMCID: PMC6914692 DOI: 10.3389/fimmu.2019.02841
Source DB: PubMed Journal: Front Immunol ISSN: 1664-3224 Impact factor: 7.561
Use of omics technologies and systems biology in antibacterial mAb development.
| Comparative genomics | - Identification of epitopes with highly conserved sequences |
| Transcriptomics | - Identification of antigens preferentially expressed during infection |
| Proteomics | - Identification of antigens highly expressed during infection |
| Molecular modeling and dynamics | - Identification of surface-exposed epitopes |
| Interactomics/systems biology | - Identification of optimal synergistic mixtures of epitopes (for use in developing mAb cocktails) |
Figure 1Coverage of ESKAPE organisms by omics databases utilized in rational mAb development. (A) Number of available complete and draft/scaffold genomes; (B) number of expression experiments, either transcriptomic (GEO database) or proteomic (PRIDE database); (C) number of identified molecular interactions between pathogen and host (total and those involving only mammal hosts).
Pros and cons of systems biology/big data/reverse vaccinology approaches vs. empirical screening vs. expert selection for mAb development.
| Use for mAb cocktail development | ++ | – | – |
| Reduced cost | + | – | ++ |
| Time required | + | – | ++ |
| Focus on clinical clones | ++ | + | + |
| Requires bioinformatics expertise | – | ++ | ++ |
| Requires computational infrastructure | – | ++ | ++ |
| Requires experimental infrastructure | ++ | – | + |
| Intrinsic experimental validation | – | ++ | ++ |
| Rational selection | ++ | – | ++ |
| Resistance to “bound rationality” | – | ++ | + |
| Room for improvement | ++ | + | + |
| Scalability to many targets | ++ | + | – |
| Species completeness | ++ | + | + |
| Systemic view | ++ | – | – |
| Transferability to other species | ++ | – | – |
++ Highly efficient.
+ Moderately efficient.
– Low efficiency.