Antimicrobial Resistance Evolutionary Trap Cluster
Two convergent computational hypotheses that, if confirmed experimentally, would provide the first mathematically grounded framework for indefinitely suppressing antibiotic resistance emergence. The QS fitness hypothesis creates an ecological disadvantage for resistant mutants; the collateral sensitivity graph hypothesis identifies closed resistance cycles exploitable by adaptive cycling. Together they target distinct but complementary evolutionary mechanisms in WHO priority pathogens.
0/6 confirmed
Hypotheses
1,520
GPU hours
$272k–$960k
Cost range
252 days
Critical path
Combined Impact if Confirmed
If both hypothesis clusters are confirmed experimentally, the combined result would establish: (1) a rational basis for evolutionary-trap antibiotic regimens exploiting obligate cooperative dependencies in ESKAPE pathogens, and (2) the first mathematically closed cycling protocol capable of indefinitely suppressing resistance in at least one WHO critical-priority pathogen. Estimated combined clinical impact: 50,000–200,000 deaths averted per year globally.
Aggregated Resource Requirements
| Paper | Timeline | GPU hrs | CPU hrs | Mem (GB) | Cost min | Cost max |
|---|---|---|---|---|---|---|
| QS Fitness Cost under Combined Therapy 0/3 hypotheses confirmed | 160d | 320 | 2,400 | 512 | $87k | $340k |
| Evolutionary Traps in Collateral Sensitivity Networks 0/3 hypotheses confirmed | 252d | 1,200 | 2,800 | 512 | $185k | $620k |
| Combined total | 160–252d | 1,520 | 5,200 | 512 | $272k | $960k |
Jun 14, 2026
160 days
Timeline
320
GPU hours
2,400
CPU hours
512 GB
Memory
$87k
Budget (min)
$340k
Budget (full)
Required Datasets
- Isogenic mutants: P. aeruginosa PA14 ΔlasR, ΔrhlR (mCherry/GFP labelled); S. aureus Newman Δagr
- Media: Artificial Sputum Medium (ASM) for CF-relevant P. aeruginosa; Todd-Hewitt broth for S. aureus
- Flow cytometer (mCherry:GFP ratio quantification)
- RNA-seq at passages 0, 10, 30
- LC-MS/MS proteomics: elastase, pyocyanin, rhamnolipids
- Allele-specific qPCR for compensatory mutation tracking
Experimental Protocol
Phase 1 (Days 1–50): Four-arm competition assays — Arm A (antibiotic monotherapy), Arm B (QS-inhibitor only), Arm C (combined), Arm D (no treatment). 30 serial passages at 24-hour intervals × 5 replicates per arm. Flow cytometry at each passage; MIC determination at passages 0, 5, 10, 20, 30.
Phase 2 (Days 51–110): Replicate in second QS system (Agr in S. aureus). Additional ≥5 independent passage cycles. RNA-seq at passages 0, 10, 30 to confirm QS regulon suppression.
Phase 3 (Days 111–160): LC-MS/MS proteomics for public-goods quantification. Allele-specific qPCR to confirm absence of compensatory mutations. Statistical analysis: logistic growth fit for selection coefficient s per passage; Mann-Whitney U for between-arm comparisons.
Success Criteria
Primary:
- s ≤ −0.05 per passage under Arm C in ≥5/6 replicates; Arm A shows s ≥ 0
- QS-deficient frequency ≤30% at passage 10 under Arm C vs. ≥70% under Arm A
- Result replicates in ≥2/3 QS systems (LasR/RhlR, Agr)
Secondary:
- Allele-specific qPCR: no compensatory mutations in >80% of tracked lineages
- RNA-seq: QS regulon suppression ≥2-fold reduction (FDR < 0.05)
- Public-goods proteins reduced ≥50% under Arms B and C
Failure Criteria
- s > −0.02 with 95% CI overlapping zero across all replicates in Arm C
- QS-deficient fixation > 50% in Arm C at passage 10
- Compensatory mutations conferring QS-independent resistance in > 20% of lineages
- Public-goods production equivalence between QS-proficient and QS-deficient strains
Abort Checkpoints
- Day 15: Abort if no measurable fitness difference between Arm C and control after 10 passages
- Day 30: Abort if QS-deficient frequency in Arm C ≥ Arm A (no selection pressure evident)
- Day 50: Abort Phase 2 if Arm C replicates show s > −0.01 across all 5 replicates
Commercial ROI
Rational combination therapy design pairing QS-inhibitors with QS-dependent antibiotics as an evolutionary trap. Applicable to P. aeruginosa (cystic fibrosis, ventilator-associated pneumonia) and S. aureus (wound infection, bacteremia) — combined market >$2B annually. Biomarker development: QS-deficient allele frequency as real-time resistance evolution tracker.
Research ROI
Provides the first formal experimental test of the dual evolutionary trap mechanism. If confirmed, establishes a rational basis for treatment sequencing protocols that exploit the ecological disadvantage window before compensatory mutations accumulate. Generalizable to any QS-dependent pathogen with extracellular public goods.
Hypotheses
In polymicrobial competition assays, QS loss-of-function mutants of Gram-negative ESKAPE pathogens that demonstrate reduced QS-dependent antibiotic susceptibility will exhibit mean selection coefficients s ≤ −0.05 per serial passage cycle under combined QS-inhibitor plus QS-dependent antibiotic therapy, compared to selection coefficients s > 0 under antibiotic monotherapy alone.
The magnitude of fitness cost (|s|) will be proportional to the fraction of competitive fitness attributable to QS-regulated public goods in the test environment, as measured by comparative growth of ΔQS strains in conditioned vs. unconditioned media.
The combined therapy effect will be demonstrable in ≥2 of 3 canonical QS systems tested (LasR/RhlR in P. aeruginosa; Agr in S. aureus; LuxR/LuxI in V. fischeri), confirming generalizability beyond a single pathogen.
Jun 14, 2026
252 days
Timeline
1,200
GPU hours
2,800
CPU hours
512 GB
Memory
$185k
Budget (min)
$620k
Budget (full)
Required Datasets
- GLASS WGS database (≥50,000 isolates, 6 WHO critical-priority pathogens)
- 47 published collateral sensitivity studies (standardized to EUCAST 2024 breakpoints)
- Deep mutational scanning (DMS) datasets for key resistance genes (GyrA, OmpF, PBP2, RpoB, OXA family)
- Sequential clinical isolate pairs from GLASS for Phase 5 retrospective analysis
- Isogenic reference strains: PA14 and ATCC clinical references for Phase 4 CRISPR validation
Experimental Protocol
Phase 1 (Weeks 1–8): Mine GLASS WGS database; logistic regression edge estimation with BH correction; phylogenetic confounding via ≥10 PCs; Tarjan's SCC detection; 1,000-permutation null model significance testing. Go/No-Go: ≥1 SCC size ≥3 with p < 0.05.
Phase 2 (Weeks 6–12): Single-step mutation enumeration within 2 steps of each SCC node; QSAR-model frequency estimation. Go/No-Go: no escape variant at frequency > 10⁻⁷.
Phase 3 (Weeks 10–24): Four arms (SCC cycling, monotherapy, random cycling, no treatment), 12 replicates each, 30 serial passages at 10⁶ CFU/mL CAMHB. MIC every 5 passages; WGS at passages 0, 5, 10, 20, 30 (1,440 libraries at 50× coverage). Log-rank test: time-to-resistance Arm A vs. Arm B.
Phase 4 (Weeks 20–30): CRISPR-Cas9 introduction of resistance mutations in top 3 SCC edges; isogenic PA14 or ATCC backgrounds; MIC confirmation.
Phase 5 (Weeks 24–36): Retrospective Cox proportional hazards on GLASS sequential isolate pairs; HR ≤ 0.7 (95% CI excludes 1.0) for SCC-approximating treatment sequences.
Success Criteria
- Phase 1: ≥1 SCC size ≥3, all edges OR > 2.0, empirical p < 0.05
- Phase 2: No simultaneous resistance variant at frequency > 10⁻⁷
- Phase 3: ≥75% replicates susceptible at passage 30 vs. ≤50% monotherapy reaching resistance by passage 15 (log-rank p < 0.01)
- Phase 4: ≥2/3 CRISPR-validated edges produce predicted collateral sensitivity (p < 0.05)
- Phase 5: HR ≤ 0.7, 95% CI excludes 1.0
Failure Criteria
- Phase 1 Go/No-Go fails: no SCC ≥3 identified in GLASS data at OR > 2.0 threshold
- SCC cycling fails to outperform random cycling (log-rank p > 0.10)
- WGS reveals escape mutations in > 30% of lineages by passage 20
- CRISPR validation fails all 3 edges
Abort Checkpoints
- Phase 1 Week 8: Abort if no SCC ≥3 found (empirical p > 0.10)
- Phase 2 Week 12: Abort if escape variant identified at frequency > 10⁻⁵
- Phase 3 Week 16: Abort if no difference in resistance emergence between Arm A and Arm B at passage 15
- Phase 4 Week 26: Scope down to retrospective analysis only if CRISPR efficiency < 30%
Commercial ROI
First mathematically closed antibiotic cycling framework — QIDP designation applicable, enabling faster FDA review pathway. Software-assisted clinical decision support for institution-specific collateral sensitivity graphs (recurring SaaS model). Generalizable to antifungal and phage-antibiotic combination strategies using same graph-theoretic framework.
Research ROI
Estimated 50,000–200,000 deaths averted per year globally if cycling protocols widely adopted. Establishes graph theory as a formal language for AMR evolutionary dynamics — creates entirely new research area. Provides regulatory-quality basis for adaptive cycling clinical trials in WHO priority pathogens.
Hypotheses
For at least one WHO critical-priority pathogen (K. pneumoniae, A. baumannii, P. aeruginosa, or E. coli), the directed collateral sensitivity graph G constructed from GLASS WGS data contains at least one SCC of size ≥3 where every directed edge (A → B) represents OR > 2.0 (p < 0.05, BH-corrected) for susceptibility to drug B conditioned on resistance to drug A.
No single-step resistance mutation accessible from any node in the SCC (frequency > 10⁻⁷) confers simultaneous clinical resistance (MIC > EUCAST breakpoint) to all drugs in the SCC.
A sequential cycling protocol following the SCC edge order maintains pathogen MIC below clinical resistance breakpoints in ≥75% of experimental replicates through passage 30, compared to ≤50% of monotherapy controls reaching resistance by passage 15.
Source discoveries on solver.press
All hypotheses in this cluster were sourced from AegisMind discoveries. Each discovery carries its own EVP, adversarial debate score, and formal verification status — click any hypothesis above to view it.
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