
Multi-drug resistance is increasing at alarming rates. The efficacy of phage therapy, treating bacterial infections with bacteriophages alone or in combination with traditional antibiotics, has been demonstrated in emergency cases in the United States and in other countries, however remains to be approved for wide-spread use in the US. One limiting factor is a lack of guidelines for assessing the genomic safety of phage candidates. We present the phage characterization workflow used by our team to generate data for submitting phages to the Federal Drug Administration (FDA) for authorized use. Essential analysis checkpoints and warnings are detailed for obtaining high-quality genomes, excluding undesirable candidates, rigorously assessing a phage genome for safety and evaluating sequencing contamination. This workflow has been developed in accordance with community standards for high-throughput sequencing of viral genomes as well as principles for ideal phages used for therapy. The feasibility and utility of the pipeline is demonstrated on two new phage genomes that meet all safety criteria. We propose these guidelines as a minimum standard for phages being submitted to the FDA for review as investigational new drug candidates.
phage therapy, high-throughput sequencing, Reproducibility of Results, Guidelines as Topic, Genome, Viral, Genomics, phage therapy; viral genomes; best practices; IND; high-throughput sequencing, Microbiology, QR1-502, Article, Workflow, viral genomes, IND, best practices, Humans, Bacteriophages, Phage Therapy, Phylogeny
phage therapy, high-throughput sequencing, Reproducibility of Results, Guidelines as Topic, Genome, Viral, Genomics, phage therapy; viral genomes; best practices; IND; high-throughput sequencing, Microbiology, QR1-502, Article, Workflow, viral genomes, IND, best practices, Humans, Bacteriophages, Phage Therapy, Phylogeny
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 142 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
