NextGenAMR turns bacterial whole-genome sequencing into interpretable, traceable antimicrobial resistance intelligence — a controlled infrastructure layer for the laboratories, hospitals and surveillance networks of the genomic era.
| Antibiotic | Status | Evidence |
|---|---|---|
| Ampicillin | Probable resistance | blaTEM-1B |
| Ciprofloxacin | Probable resistance | gyrA S83L · parC S80I |
| Ceftriaxone | Probable susceptibility | no ESBL markers |
| Gentamicin | Probable susceptibility | — |
| Meropenem | Probable susceptibility | no carbapenemase |
| Trimeth/Sulfa | Probable resistance | sul1 · dfrA17 |
| Nitrofurantoin | Probable susceptibility | — |
| Tetracycline | Indeterminate | tet(A) partial |
| Colistin | Non-emission | low coverage |
- AMR genes7
- Point mutations3
- Plasmid replicons2
- Mean coverage82×
- pipeline · ngamr-core@0.4.2
- db · card-2025.09 · resfinder-2025.10
- operator · lab.ops/41
- started · 2026-06-19 09:14 UTC
- Colistin call abstained — coverage below threshold.
- tet(A) partial alignment — flagged for review.
Interface preview — synthetic demo data. Decision support only, not a clinical diagnosis.
Genomic AMR interpretation today is spread across disconnected scripts, databases, spreadsheets and manual judgement. Evidence is hard to trace, results are hard to reproduce, and nothing is built for controlled institutional deployment. NextGenAMR replaces that scatter with one auditable layer.
Fragmented today
One intelligence layer
- 1Ingest isolate WGS
- 2Interpret with evidence
- 3Trace every step
- 4Emit auditable report
One workflow instead of a toolchain nobody fully owns.
Every result carries its evidence, versions and provenance.
Designed for controlled access in institutional environments.
Watch a demo isolate move through the system. Each stage checks, transforms and adds evidence — from raw reads to a structured, per-antibiotic result. This is a visual simulation of the workflow, not a live analysis.
- 01Quality control✓ donereads filtered · adapters trimmedreads passing QC 98.7%
- 02Taxonomic confirmation✓ donespecies confirmed within scopeE. coli assignment 99.2%
- 03Host depletion✓ donehuman reads removedhost reads removed 0.4%
- 04Assembly & assembly QC✓ donedraft genome assembledmean coverage 82×
- 05Gene annotation✓ donecoding sequences predictedCDS predicted 4,721
- 06AMR detection✓ doneresistance markers matchedAMR genes · mutations 7 · 3
- 07Aggregation & reportrunningper-antibiotic interpretation assembledantibiotic panel 9
Select a layer, or hover a node, to follow how evidence connects — from the genome, through the markers found in it, to the antibiotics they affect, to a per-drug interpretation, up to surveillance intelligence.
The full evidence graph.
graph online- E. coli genome→blaTEM-1B
- E. coli genome→gyrA S83L
- E. coli genome→parC S80I
- E. coli genome→sul1
- E. coli genome→dfrA17
- E. coli genome→tet(A)
- blaTEM-1B→Ampicillin
- gyrA S83L→Ciprofloxacin
- parC S80I→Ciprofloxacin
- sul1→TMP–SMX
- dfrA17→TMP–SMX
- tet(A)→Tetracycline
- Ampicillin→Interpretation
- Ciprofloxacin→Interpretation
- TMP–SMX→Interpretation
- Tetracycline→Interpretation
- Interpretation→Surveillance
Illustrative graph based on the ECOLI_DEMO_001 synthetic evidence. Not a clinical result.
Relationship model for the demo isolate ECOLI_DEMO_001:
- E. coli genome → blaTEM-1B (genome)
- E. coli genome → gyrA S83L (genome)
- E. coli genome → parC S80I (genome)
- E. coli genome → sul1 (genome)
- E. coli genome → dfrA17 (genome)
- E. coli genome → tet(A) (genome)
- blaTEM-1B → Ampicillin (resistance)
- gyrA S83L → Ciprofloxacin (resistance)
- parC S80I → Ciprofloxacin (resistance)
- sul1 → TMP–SMX (resistance)
- dfrA17 → TMP–SMX (resistance)
- tet(A) → Tetracycline (resistance)
- Ampicillin → Interpretation (clinical)
- Ciprofloxacin → Interpretation (clinical)
- TMP–SMX → Interpretation (clinical)
- Tetracycline → Interpretation (clinical)
- Interpretation → Surveillance (surveillance)
Not screenshots for show — the actual surfaces the system produces: interpretation, provenance, access, database integrity, evidence and the clinical boundary. Values are synthetic; the structure is real.
- pipelinengamr-core@0.4.2
- dbcard-2025.09
- resfinder-2025.10
- operatorlab.ops/41
- started2026-06-19 09:14 UTC
- readsfastq.gz · paired-end
- CARD 2025.09✓ verified
- ResFinder 2025.10✓ verified
- PointFinder 2025.10✓ verified
Supports qualified laboratory professionals under human supervision. It does not replace phenotypic AST and is not an autonomous diagnosis.
NextGenAMR turns bacterial whole-genome sequencing data into structured antimicrobial resistance intelligence, starting with Escherichia coli.
AMR is not an abstract problem. It is measurable.
Estimated global deaths directly attributable to bacterial antimicrobial resistance in 2019.
Estimated global deaths associated with bacterial antimicrobial resistance in 2019.
Estimated annual deaths in the EU/EEA directly caused by antimicrobial-resistant infections.
Approximate annual cost of AMR across OECD / EU / EEA countries, including health-system and broader economic impact.
Bacterial pathogens included in the 2024 WHO Bacterial Priority Pathogens List.
Estimated EU incidence of bloodstream infections caused by third-generation cephalosporin-resistant Escherichia coli in 2024.
Global AMR burden, 2019
Deaths directly attributable to bacterial AMR versus deaths associated with bacterial AMR.
Why Escherichia coli first?
Escherichia coli is one of the most relevant bacterial species for antimicrobial resistance surveillance and clinical microbiology workflows. NextGenAMR starts with a focused E. coli model to prioritize reliability, interpretability and controlled validation before expanding to additional organisms.
- High clinical relevance
- Strong AMR surveillance value
- Focused validation before expansion
Increase in EU incidence of third-generation cephalosporin-resistant E. coli bloodstream infections in 2024 compared with 2019.
Increase in EU incidence of carbapenem-resistant Klebsiella pneumoniae bloodstream infections in 2024 compared with 2019.
Klebsiella pneumoniae is roadmap / future expansion — not current product scope.
NextGenAMR platform scope
NextGenAMR starts narrow by design: one organism, one controlled panel, one traceable workflow.
Current focused workflow for Escherichia coli.
Structured interpretation across a defined antibiotic panel.
From raw sequencing reads to structured AMR report.
Machine-readable JSON and human-readable PDF output.
Designed around bacterial whole-genome sequencing data.
Built around provenance, controlled workflow execution and auditability.
Current E. coli antibiotic panel
| Code | Antibiotic / category | Status |
|---|---|---|
| AMP | Ampicillin | Current panel |
| AMC | Amoxicillin / clavulanate | Current panel |
| CIP | Ciprofloxacin | Current panel |
| CTZ | Ceftazidime / third-generation cephalosporin category* | Current panel |
| GEN | Gentamicin | Current panel |
| MER | Meropenem | Current panel |
| NIT | Nitrofurantoin | Current panel |
| PIT | Piperacillin / tazobactam | Current panel |
| TRS | Trimethoprim / sulfamethoxazole | Current panel |
Faster time to result
Illustrative comparisonGenomic AMR analysis is designed to deliver structured results far faster than culture-based phenotypic testing — shortening the path to informed treatment decisions for bacterial infections.
Illustrative comparison. Phenotypic turnaround is a typical range and NextGenAMR runtime is a target being measured in pilot validation; actual times depend on local workflows. Decision support only — does not replace clinical AST.
Validation metrics to track
Before expansion, the priority is validation, reproducibility and operational reliability.
| Metric | Current status | Why it matters |
|---|---|---|
| Number of isolates tested | Pilot validation in progress | Defines the size of the validation dataset |
| Species coverage | E. coli focused | Keeps validation controlled and interpretable |
| Antibiotics evaluated | 9-antibiotic E. coli panel | Locks interpretation to a defined scope |
| Agreement with reference AST | To be reported after validation | Measures genotype–phenotype concordance |
| Major errors | To be tracked | Critical for safety analysis |
| Very major errors | To be tracked | Critical for resistant/susceptible misclassification risk |
| Failed runs | To be tracked | Measures operational reliability |
| Median runtime | To be measured across pilot samples | Shows real-world execution performance |
| Provenance completeness | Designed into workflow | Supports auditability and reproducibility |
Estimated operational impact
Illustrative / conservative estimateNextGenAMR is designed to reduce manual interpretation burden, standardize genomic AMR reporting and improve auditability. Operational impact depends on local sequencing workflows, sample volume, validation status and microbiology team integration.
| Variable | Conservative estimate |
|---|---|
| Samples processed | 100 |
| Manual review time potentially saved per sample | 10–20 minutes |
| Estimated specialist time saved | 16–33 hours |
| Output standardization | JSON + PDF |
| Clinical decision replacement | No — decision support only |
| Dependency | Local validation and workflow integration required |
Public-health figures are based on WHO, ECDC and OECD estimates. Product metrics describe current NextGenAMR platform scope and should not be interpreted as clinical validation claims.
Species-aware analysis
Focused interpretation based on organism context, starting with Escherichia coli.
AMR-focused reporting
Structured outputs designed around antimicrobial resistance evidence and readable reports.
Pipeline traceability
Designed to preserve provenance, execution metadata and reproducibility signals.
Quality control
Quality, contamination and workflow checks help identify weak or invalid inputs.
Secure architecture
Built with a security-first mindset for controlled access, auditability and responsible deployment.
Clinical workflow awareness
Designed with laboratories, microbiology teams and hospital environments in mind.
Fast review experience
A clean interface that helps users move quickly from results to understanding.
Expandable foundation
Focused today, designed to evolve across organisms, panels and institutional needs.
Who's reading?
Tune the system to your context. Your choice reshapes what the page foregrounds — nothing is hidden, only re-ordered.
Select a context to tune the view. Or keep the neutral, full read.
Hospital laboratories
Support genomic AMR review and structured reporting workflows.
Discuss this scenarioMicrobiology teams
Organize resistance-related evidence in a clearer, more usable format.
Discuss this scenarioPublic health surveillance
Help transform genomic data into comparable, auditable AMR intelligence.
Discuss this scenarioResearch groups
Accelerate exploratory AMR analysis with a focused, reproducible workflow.
Discuss this scenarioPilot programs
Deploy a controlled platform for institutional evaluation and AMR innovation projects.
Discuss this scenarioEvery result should be explainable, every action traceable, every deployment controlled. Trust is not a badge on this page — it is how the system is built. States below are honest: what is designed in, what is an enforced boundary, and what is still on the roadmap.
Scientific and clinical validation is ongoing. NextGenAMR carries no certification (CE-IVD, FDA, IVDR, ISO) and does not replace phenotypic AST or a clinician's judgement.
Vanguard Biotech Systems (VBS) was founded on a conviction: antimicrobial resistance interpretation should not stay trapped between fragmented tools, slow workflows and processes no one can audit. We build focused biotech software where bioinformatics, security-aware design and an obsession with precision come together into one controlled layer — NextGenAMR.
We are not building another dashboard. We are building the operating layer for AMR intelligence.
Minimal by design. Rigorous by default. Systems that look simple because the complexity has been controlled.
Juan Manuel Gómez Vargas founded Vanguard Biotech Systems to build NextGenAMR — infrastructure that turns bacterial whole-genome sequencing into traceable antimicrobial resistance intelligence. His focus: rigorous bioinformatics, security-aware design and honest, auditable interpretation over hype.
What is NextGenAMR?
NextGenAMR is a controlled software layer by Vanguard Biotech Systems that turns bacterial whole-genome sequencing (WGS) into interpretable, traceable antimicrobial resistance (AMR) intelligence — from quality control and taxonomy to AMR marker detection, per-antibiotic interpretation and auditable reporting.
Who is NextGenAMR built for?
Clinical microbiology laboratories, hospitals, research institutions and public-health / AMR surveillance networks that work with bacterial genomic data and need structured, traceable resistance interpretation.
Is NextGenAMR clinically validated?
No. NextGenAMR is decision-support software, not a clinical diagnosis and not a regulatory-cleared medical device. It is in a controlled preview; current demonstrations use synthetic Escherichia coli data. Clinical judgement remains with qualified professionals.
Does NextGenAMR replace clinical judgement?
No. It structures and traces genomic evidence to support experts; interpretation and clinical decisions stay with the responsible clinicians and microbiologists.
What organisms are currently in scope?
The public demonstration focuses on Escherichia coli (ECOLI_DEMO_001) as a representative case. Additional organisms are addressed with institutions during controlled pilots.
How can an institution request access?
Access is controlled and reviewed case by case. Institutions can request access through the form on this site; each request is reviewed for institutional fit and deployment readiness.
NextGenAMR is preparing controlled pilots with selected clinical, research and surveillance institutions. Tell us your context — we review each request for institutional fit and deployment readiness.
- contact@vbs.bio
- Headquarters
- Europe
- Focus
- AMR · WGS · Clinical bioinformatics
This project would not exist without the people behind it. To the team — Ángel, Alejandro, Juan Carlos and Irene — for the work, the care and the long hours. To José Luis, for believing in it when it was only an idea. To my family, and above all to my parents, for everything. And to everyone who, directly or indirectly, made NextGenAMR possible — thank you, always.