Antimicrobial resistance is the broader term: bacteria, fungi, viruses and parasites all evolve resistance to the agents used against them. In antibacterial discovery, AMR mostly means one specific and uncomfortable fact, that the priority pathogen list keeps outrunning the pipeline.
AMR and antibiotic resistance are not the same word
- Antimicrobial resistance covers every class of pathogen and every class of agent, including antifungals such as the azoles and antivirals.
- Antibiotic resistance is the bacterial subset, and it is what a compound versus strain screen is about. The mechanisms are set out on the antibiotic resistance page.
The distinction matters when reading surveillance documents, because the headline framing is usually AMR while the actionable content for a discovery team is almost always bacterial.
The priority pathogen list
The World Health Organization maintains a bacterial priority pathogen list to steer research and development toward the organisms where new agents are most needed. Carbapenem resistant Gram-negatives sit at the top: Acinetobacter baumannii, Pseudomonas aeruginosa and the carbapenem resistant Enterobacterales, together with third generation cephalosporin resistant Enterobacterales. Below them sit organisms including methicillin resistant Staphylococcus aureus, vancomycin resistant Enterococcus faecium, drug resistant Salmonella, Shigella, Neisseria gonorrhoeae and Streptococcus pneumoniae.
The list is revised as the picture changes, so the useful takeaway is the shape rather than the exact ordering: the hardest problems are Gram-negative, and they are hard for the transport reasons described under Gram-negative bacteria.
What surveillance networks actually track
| Network | Scope | What it reports |
|---|---|---|
| GLASS | Global, coordinated by the WHO | Resistance rates for selected pathogen and agent combinations |
| EARS-Net | Europe, coordinated by the ECDC | Invasive isolate resistance by country and year |
| National programmes | Country level | Isolate collections, often with MIC distributions |
| Published surveys | Sponsor or consortium led | MIC50 and MIC90 across large isolate sets |
For a discovery team the most useful output of all of these is the MIC distribution, because it tells you what a realistic band looks like for an agent class against a species today rather than at the time of registration. How those numbers are produced is explained under minimum inhibitory concentration.
The discovery gap
Several structural problems keep the antibacterial pipeline thin, and none of them is a mystery:
- Scientific difficulty. Very few novel chemical classes reach the clinic against Gram-negatives, largely because of the accumulation problem.
- Commercial mismatch. A successful new antibacterial is deliberately reserved rather than widely used, so the reward structure works against the developer.
- Small programs. A great deal of antibacterial discovery now happens in small biotechs and academic groups with limited plate budgets, which makes triage before the plate disproportionately valuable.
- Fragmented data. What is already known about a scaffold is spread across primary literature, surveillance reports and databases such as ChEMBL.
Alternatives and where they fit
Interest in non traditional approaches follows directly from the gap. Phage therapy is real, narrow and mostly compassionate use or trial stage today. Antimicrobial peptides kill quickly and select resistance slowly, with development liabilities of their own. Monoclonal antibodies, anti virulence agents and microbiome approaches each address part of the problem. None of them removes the need for small molecules with predictable exposure, which is why small molecule triage is still where most programs spend their time.
What a small AMR program can do about it
Practically: screen before you plate, scope the panel to organisms with a realistic chance, and record why each compound was excluded. That last point is what turns a shortlist into something a grant reviewer or a program lead can assess. The screen below runs against the ESKAPE panel and names the mechanism it expects for each organism.