Steal This Grant 🧬📑

An Open, Forkable NIH-Style Proposal — A Precision Bacteriophage Cocktail to Prevent Necrotizing Enterocolitis in Very-Low-Birth-Weight Infants

License: CC0 / public domain. Fork it, gut it, rename it, submit it. Attribution appreciated, never required. What this is: (A) a complete, specific, genuinely fundable mock proposal you can adapt, and (B) a reusable, funder-agnostic template + AI prompt library so you can write your own phage grant for a different indication. What this is not: a substitute for your IRB, your IND pre-submission meeting, your biostatistician, or your program officer. Illustrative figures are explicitly marked [ILLUSTRATIVE]. Budget numbers are sketches, not quotes.

How to use this document

• Reading top to bottom gives you a model R01-scale proposal.
• Part A is the filled grant.
• Part B is the empty skeleton + the prompt library (methodology adapted from eseckel/ai-for-grant-writing).
• Search-and-replace tokens like [INDICATION], [PATHOGEN], [POPULATION] are pre-wired in Part B.

PART A — THE FILLED GRANT

Title

Pre-emptive precision phage therapy against the pre-symptomatic Klebsiella bloom to prevent necrotizing enterocolitis in very-low-birth-weight infants (the PHAGE-NEC program)

Alternate titles considered (kept here so you can see the title-iteration move):

• Intercepting the bloom: a strain-resolved bacteriophage cocktail for NEC prevention
• From metagenome to medicine: replication-rate–guided phage prophylaxis in the preterm gut
• PREEMPT-NEC: Precision Removal of Enterobacteriaceae via Engineered Microbial Phage Therapy

Project Summary / Abstract

Necrotizing enterocolitis (NEC) is the most lethal acquired gastrointestinal disease of prematurity, striking 5–10% of very-low-birth-weight (VLBW, <1500 g) infants, killing 20–30% of those affected, and leaving survivors with short-bowel syndrome and neurodevelopmental injury. Despite four decades of trials, no targeted therapy exists; care remains supportive, and broad-spectrum antibiotics — the current reflex — paradoxically deplete protective commensals and select for the very pathobionts implicated in disease.

A precise, mechanistic target has now emerged. Strain-resolved metagenomics shows that a gut bloom of Klebsiella (and related Enterobacteriaceae), accompanied by elevated bacterial replication rates, precedes the clinical onset of NEC by days (Olm et al., Sci Adv 2019). In gnotobiotic and preterm-piglet models, transfer of the fecal viral (bacteriophage) fraction prevents NEC, and UV-inactivation of that virome abolishes protection — establishing that intact, replication-competent phages, not residual metabolites, carry the protective effect (Brunse et al., ISME J 2021; Spiegelhauer et al., Gut Microbes 2025). In parallel, a rationally composed anti-Klebsiella phage cocktail has been shown to suppress disease-driving Klebsiella strains in the mammalian gut and attenuate inflammation (Federici et al., Cell 2022).

We propose to convert these convergent findings into a deployable preventive: a defined, GMP-grade, host-range-matched bacteriophage cocktail administered enterally to VLBW infants during the pre-symptomatic Klebsiella bloom window, identified by rapid strain- and replication-rate–aware surveillance. Aim 1 builds the phage–host matching engine and a clinical-isolate biobank, and assembles a cocktail with quantified host range, resistance-suppression (via cocktail design and phage steering), and safety/manufacturability criteria. Aim 2 establishes efficacy and mechanism in the preterm-piglet NEC model, with the UV-inactivated cocktail as the decisive mechanistic control, and defines pharmacokinetics/pharmacodynamics (phage replication on target in vivo, off-target sparing of commensals). Aim 3 runs a phase 1b randomized, placebo-controlled safety/biomarker trial in VLBW infants, with bloom-triggered dosing, pre-specified safety stopping rules, and longitudinal strain-resolved metagenomics as the primary mechanistic readout.

Impact: Success delivers the first precision, non-antibiotic, commensal-sparing preventive for NEC, a validated bloom-surveillance pipeline reusable across neonatal pathobionts, and an open phage-matching framework. Because every reagent, protocol, and analysis pipeline is released openly, the program is designed to catalyze a field, not just a product.

Specific Aims (one page)

The problem. NEC remains a top cause of death in VLBW infants. Management is supportive and reactive; broad-spectrum antibiotics worsen the dysbiosis they are meant to treat. We lack any therapy that selectively removes the implicated pathobiont while sparing the protective community.

The opportunity. Four independent lines of evidence now point to a single, actionable intervention:

1. A pre-symptomatic Klebsiella/Enterobacteriaceae bloom with elevated in-situ replication rates precedes NEC and is detectable by strain-resolved metagenomics (Olm 2019).
2. Fecal-virome transfer prevents NEC in the preterm-piglet model (Brunse 2021).
3. UV-inactivating that virome abolishes protection, proving the active principle is replication-competent phage, not filtrate chemistry (Spiegelhauer 2025).
4. A defined anti-Klebsiella phage cocktail suppresses the target strain in vivo and dampens inflammation (Federici 2022).

Central hypothesis. Enteral delivery of a defined, host-range–matched bacteriophage cocktail, timed to the pre-symptomatic Klebsiella bloom, will selectively collapse the bloom, preserve commensal diversity, and reduce NEC incidence and severity.

Aim 1 — Build the phage–host matching engine and assemble a defined, manufacturable anti-bloom cocktail. Establish a biobank of NEC-associated Klebsiella/Enterobacteriaceae clinical isolates from VLBW cohorts; characterize each by whole-genome sequencing, capsule (K-) type, and resistance/virulence content. Screen a curated phage library for host range, build a quantitative phage–host interaction matrix, and assemble a ≤6-phage cocktail meeting pre-specified criteria: ≥90% coverage of circulating bloom strains, suppression of resistant-mutant outgrowth in vitro, strictly lytic genomes free of toxin/AMR/integrase genes, and stable titer. Outcome: a locked, characterized cocktail + an open matching pipeline.

Aim 2 — Establish efficacy, mechanism, and PK/PD in the preterm-piglet NEC model. Test the cocktail vs. vehicle and vs. UV-inactivated cocktail (the mechanistic control that pins causality to phage replication). Primary endpoint: NEC incidence/severity. Mechanistic endpoints: target-strain reduction, commensal sparing, mucosal inflammation, and in vivo phage amplification on target (replication-rate readouts). Outcome: causal, dose-anchored efficacy with a go/no-go threshold for clinical translation.

Aim 3 — Phase 1b randomized, placebo-controlled safety and biomarker trial in VLBW infants with bloom-triggered dosing. Deploy rapid bloom surveillance; randomize bloom-positive infants to cocktail vs. placebo. Primary: safety/tolerability. Secondary/mechanistic: bloom collapse, commensal preservation, inflammatory biomarkers, and exploratory NEC incidence. Outcome: a safety/PK package and effect-size estimate to power a pivotal efficacy trial.

Payoff. The first precision, commensal-sparing NEC preventive; a reusable bloom-surveillance + phage-matching platform; and a fully open toolkit to accelerate phage therapy across neonatal and beyond.

Significance

Burden and unmet need. NEC affects roughly 1 in 10 VLBW infants and is among the leading causes of death in the neonatal intensive care unit (NICU) beyond the first week of life. Mortality is 20–30% overall and exceeds 40% for surgical NEC. Survivors face intestinal failure, prolonged parenteral nutrition, cholestasis, repeated surgeries, and elevated risk of cerebral palsy and cognitive impairment. The lifetime cost per surgical-NEC survivor runs into the hundreds of thousands of dollars; the aggregate U.S. burden is in the billions annually.

Why current approaches fail.

• Supportive care is reactive. By the time pneumatosis intestinalis is visible on radiograph, the cascade — barrier failure, translocation, ischemic necrosis — is often irreversible.
• Antibiotics are a blunt instrument that backfires. Prolonged empiric broad-spectrum antibiotic exposure is itself an independent risk factor for NEC: it collapses commensal diversity, removes colonization resistance, and enriches Enterobacteriaceae — precisely the organisms implicated in the bloom.
• Probiotics are non-specific and carry their own risks. General probiotic supplementation has shown inconsistent benefit, lot-to-lot variability, and rare but real bacteremia/fungemia in this fragile population. They add organisms; they do not remove the offender.

The mechanistic pivot. The field now has a pre-symptomatic, strain-resolved target. Olm et al. (2019) showed, using genome-resolved metagenomics and replication-rate inference (iRep-type measures), that NEC is preceded by a bloom of specific Klebsiella/Enterobacteriaceae strains replicating faster in situ — a signal that appears before clinical disease. This reframes NEC from "inflammation to be suppressed" to "a specific bacterial expansion to be intercepted."

Why phage, why now. Bacteriophages are the only known antibacterial modality that is simultaneously (i) strain-specific — capable of removing the bloom strain while sparing Bifidobacterium, Lactobacillus, and other protective commensals; (ii) self-amplifying on target, so dose tracks pathogen load; and (iii) non-antibiotic, avoiding cross-resistance with the antibiotics this population already over-receives. The Brunse (2021) → Spiegelhauer (2025) arc is the linchpin: virome transfer prevents NEC, and UV-killing the virome abolishes the protection — a clean loss-of-function experiment that excludes "it's just the filtrate" and implicates replication-competent phages as the active agent. Federici (2022) supplies the translational template: a rationally designed anti-Klebsiella cocktail that suppresses a disease-driving strain in the mammalian gut and reduces inflammation, with a path through manufacturing and resistance management.

What changes if we succeed. A positive program shifts NEC prevention from broad microbial suppression toward targeted ecological correction, gives neonatologists a tool that replaces an antibiotic reflex rather than adding to it, and validates a generalizable "surveil the bloom → match the phage → intercept" paradigm extensible to late-onset sepsis and other pathobiont-driven neonatal diseases.

Innovation

This program is innovative in concept, in timing, and in tooling.

• Conceptual: prevention by ecological interception, not suppression. We do not treat established NEC; we intercept its upstream cause — a defined bacterial bloom — during a pre-symptomatic window. This inverts the standard reactive paradigm.

• Timing as the active ingredient. Most antibacterial strategies are dosed by clinical event or schedule. We dose by biology: a strain- and replication-rate–aware surveillance trigger derived directly from the Olm 2019 signal. The when is as engineered as the what.

• A causal mechanistic control built into the design. Few microbiome interventions can point to a loss-of-function experiment that isolates the active agent. We can: the UV-inactivated cocktail arm (after Spiegelhauer 2025) makes Aim 2 a true mechanism test, not just an efficacy screen.

• Precision phage matching with resistance pre-emption. Rather than a fixed cocktail, we build a quantitative phage–host interaction matrix and select combinations explicitly for (i) coverage of circulating bloom strains and (ii) suppression of resistant-mutant escape — including evolutionary "phage steering," where resistance to one phage forces a fitness/virulence trade-off exploited by another, consistent with the Federici design logic.

• Commensal-sparing by construction. Strictly lytic, narrow-host-range phages are selected to leave protective taxa untouched — the opposite of the antibiotic externality.

• Open by design. Isolate genomes, the phage–host matrix, matching code, animal protocols, and trial analysis pipelines are released openly. The deliverable is not only a candidate therapeutic but a reusable platform the whole field can fork — which is also why this proposal itself is published as a steal-this-grant template.

Approach

Overview & experimental logic

The program advances along a de-risking staircase: (Aim 1) make and characterize the right cocktail against the right strains; (Aim 2) prove it works and why in the most NEC-faithful animal model, with the UV control nailing causality and PK/PD anchoring dose; (Aim 3) show it is safe and biologically active in the target infants with bloom-triggered dosing, generating the effect size to power a pivotal trial. Surveillance (strain- and replication-rate–resolved metagenomics) is the connective tissue across all three aims.

Aim 1 — Build the phage–host matching engine and assemble a defined, manufacturable anti-bloom cocktail

Rationale. A cocktail is only as good as its match to the strains actually blooming in contemporary NICUs, and only as durable as its resistance-suppression. We therefore couple a clinical-isolate biobank to a curated phage library through a quantitative interaction matrix.

Design.

1. Isolate biobank. Recover ≥300 Klebsiella/Enterobacteriaceae isolates from banked and prospectively collected VLBW stool across ≥3 NICUs, prioritizing isolates from bloom timepoints. Whole-genome sequence each; assign species, MLST, capsule (K-) type, and screen for AMR/virulence/toxin loci. Capture phylogenetic and capsular diversity representative of circulating bloom strains.
2. Phage library. Assemble ≥150 candidate lytic phages from environmental sampling (wastewater, NICU-adjacent sources), public collections, and collaborator stocks. Sequence all; exclude any phage carrying integrase, known toxin, or AMR genes; retain strictly lytic genomes.
3. Interaction matrix. Quantify host range by high-throughput efficiency-of-plating (EOP) and liquid-killing kinetics for every phage × isolate pair. Build a coverage model that maps cocktails to fraction-of-strains-covered.
4. Resistance pre-emption. For lead phages, isolate resistant mutants in vitro, sequence resistance loci, and measure fitness/virulence trade-offs. Select complementary phages whose combined use either covers escape mutants or steers resistance toward attenuated phenotypes (capsule loss, reduced colonization).
5. Cocktail assembly & lock. Select ≤6 phages meeting pre-specified release criteria, then lock composition before Aim 2.

Pre-specified cocktail release criteria (go/no-go):

Expected outcomes. A locked, fully characterized cocktail; an open phage–host interaction matrix and matching pipeline; a versioned isolate/phage biobank.

Potential pitfalls & alternatives.

• Insufficient coverage from natural phages. → Expand environmental sampling; add host-range-engineered or receptor-binding-protein–swapped phages; permit a 6-phage rather than 3-phage formulation.
• Rapid resistance. → Lean on steering pairs and capsule-targeting phages whose escape mutants lose the protective capsule; pre-register an adaptive cocktail-refresh SOP.
• Strain drift between sites/years. → Build the matching engine to be re-run, not one-shot; define a re-qualification cadence.

Aim 2 — Establish efficacy, mechanism, and PK/PD in the preterm-piglet NEC model

Rationale. The preterm-piglet model is the most translationally faithful NEC system (preterm delivery, enteral-feeding–induced NEC, human-relevant pathophysiology) and is the model in which virome transfer was shown to prevent NEC (Brunse 2021) and UV-inactivation to abolish it (Spiegelhauer 2025). It is therefore the right arena to test our defined cocktail and to run the decisive mechanistic control.

Design (preterm-piglet NEC model).

• Arms: (1) vehicle/placebo; (2) active cocktail; (3) UV-inactivated cocktail (matched particle dose, replication-incompetent); optional (4) antibiotic comparator for context. Randomized, blinded scoring.
• Dosing: enteral, anchored to PK/PD from a dose-ranging sub-study; timed to colonization/bloom onset.
• Primary endpoint: NEC incidence and severity (blinded macroscopic + histologic scoring).
• Mechanistic endpoints:
  • Target-strain reduction (strain-resolved qPCR/metagenomics).
  • Commensal sparing (community diversity vs. antibiotic comparator).
  • In vivo phage amplification on target — phage titer rising with pathogen load — and bacterial replication-rate readouts (iRep-type) to show the bloom's growth signature is blunted.
  • Mucosal inflammation (histology, cytokines, barrier markers).
• Power: size to detect a pre-specified absolute NEC reduction at 80% power, two-sided α 0.05; biostatistician-set group sizes; ARRIVE-compliant reporting.

The logic of the UV arm. If the active cocktail prevents NEC but the UV-inactivated cocktail does not — despite identical particle dose — protection requires replication-competent phage. This is the in-program replication of the Spiegelhauer 2025 loss-of-function result and the cleanest possible causal claim for a microbiome-targeted agent.

Expected outcomes. Causal, dose-anchored efficacy; demonstration that benefit tracks phage replication and target collapse, not filtrate chemistry; commensal sparing relative to antibiotics; a defined effective enteral dose and a clinical go/no-go.

Potential pitfalls & alternatives.

• Model variability / low baseline NEC rate. → Standardize induction; pre-register scoring; adequate n; consider a sensitized sub-cohort.
• Phage fails to amplify in vivo (gut transit, pH, bile). → Encapsulation/buffering; acid protection; redosing schedule; confirm gut viability ex vivo.
• Active and UV arms both protect (filtrate effect). → Would overturn the hypothesis; pre-specified to trigger mechanistic re-evaluation rather than silent reinterpretation — an honest off-ramp.
• Immune/endotoxin effects of particle load. → Endotoxin-purified prep; the UV arm also controls for non-specific particle immunomodulation.

Aim 3 — Phase 1b randomized, placebo-controlled safety & biomarker trial in VLBW infants with bloom-triggered dosing

Rationale. With a locked, animal-validated cocktail and PK/PD in hand, the first-in-infant study must (i) establish safety/tolerability in VLBW infants and (ii) confirm the intended biology — bloom collapse with commensal preservation — while generating an effect-size estimate for a pivotal trial. Dosing is triggered by the same surveillance signal that defines the therapeutic window.

Design.

• Population: VLBW (<1500 g) infants; pre-specified inclusion/exclusion; informed parental consent.
• Surveillance trigger: rapid strain- and replication-rate–aware screening of serial stool/rectal samples to identify the pre-symptomatic Klebsiella bloom; only bloom-positive infants are randomized (enrichment by mechanism).
• Randomization: bloom-positive infants 1:1 to cocktail vs. placebo, stratified by site and gestational age; double-blind.
• Primary endpoint: safety/tolerability (adverse events, feeding tolerance, vitals, labs; pre-specified stopping rules).
• Secondary/mechanistic endpoints: bloom collapse (target-strain load), commensal preservation (diversity), inflammatory biomarkers (e.g., fecal calprotectin, circulating cytokines), phage pharmacokinetics (enteral persistence, systemic translocation screen).
• Exploratory: NEC incidence/severity (hypothesis-generating; trial is safety-powered).
• Oversight: independent DSMB; pre-registered; IND under FDA; pre-specified interim safety looks.

Expected outcomes. A clean safety/tolerability and PK package in the target population; demonstration of on-target bloom collapse with commensal sparing; an effect-size estimate to power a definitive efficacy trial; a validated, deployable bloom-surveillance workflow.

Potential pitfalls & alternatives.

• Bloom-trigger too slow for the window. → Invest in turnaround-time reduction (targeted qPCR panel as a fast proxy for the metagenomic signal); pre-bank dosing so therapy can start within hours of a positive trigger.
• Low bloom-positive yield / slow accrual. → Multi-site design; broaden surveillance frequency; adaptive enrollment.
• Safety signal (e.g., translocation, immune activation). → Conservative dose-escalation; DSMB stopping rules; systemic phage and endotoxin monitoring built in.
• Regulatory novelty of a live phage biologic in neonates. → Early and iterative FDA engagement (pre-IND), leveraging existing phage IND precedents; CMC package front-loaded in Aim 1.

Timeline (illustrative, 5-year R01-scale) [ILLUSTRATIVE]

Year:                 1        2        3        4        5
AIM 1  Biobank      ███████
       Phage lib    ███████
       Matrix/lock    █████████
AIM 2  PK/PD                  ██████
       Efficacy+UV            ████████████
AIM 3  IND/CMC                  ████████
       Surveillance                  ██████
       Phase 1b trial                  ██████████████████
Cross  Open data/pipeline releases  ░░░░░░░░░░░░░░░░░░░░░░░  (continuous)

• Y1: Biobank + phage library + interaction matrix; cocktail lock by end of Y1/early Y2.
• Y2: PK/PD dose-ranging; begin efficacy + UV-control study; start CMC/IND-enabling work.
• Y3: Complete Aim 2; IND submission; stand up clinical surveillance.
• Y4–Y5: Phase 1b enrollment, analysis, effect-size estimate; continuous open releases.

Budget Justification Sketch (illustrative, not a quote) [ILLUSTRATIVE]

Replace with your institution's actual rates and your sponsored-programs office's numbers. Shown as proportional emphasis, not dollars.

• Personnel (largest share): PD/PIs (microbial ecology/phage biology + neonatology); phage microbiologist; bioinformatician (strain-resolved metagenomics, iRep); large-animal study coordinator; clinical research coordinator(s); regulatory/CMC specialist; biostatistician. Justification: the program is method- and people-intensive across wet lab, animal, computation, and clinic.
• GMP/CMC manufacturing & QC: phage amplification, purification, endotoxin removal, stability, fill-finish for Aim 2 (research grade → GMP) and Aim 3 (clinical grade). Justification: a defined live biologic for neonates demands rigorous CMC; front-loaded to de-risk the IND.
• Sequencing & computation: WGS of ~300 isolates + ~150 phages; longitudinal metagenomics across animal and clinical samples; compute for matrix and replication-rate analyses.
• Preterm-piglet studies: per-diem, surgery/feeding, histology, blinded scoring; powered group sizes across ≥3 arms + dose-ranging.
• Clinical trial costs: IND fees, DSMB, monitoring, pharmacy, surveillance assays, biomarker panels, parental-consent infrastructure across sites.
• Open-science / dissemination: data deposition, pipeline packaging, open protocols.
• Indirects: per negotiated F&A rate.

Vertebrate Animals (Aim 2)

• Species/justification. Preterm piglets — the most translationally faithful NEC model (preterm delivery, enteral-feeding–induced disease, human-like intestinal pathophysiology) and the model underpinning Brunse 2021 / Spiegelhauer 2025; no non-animal system reproduces the systemic NEC phenotype.
• Numbers. Biostatistician-determined to detect the pre-specified NEC reduction at 80% power; minimized via robust scoring and within-litter design where feasible.
• Procedures / welfare. Standardized rearing/feeding, humane endpoints, blinded scoring, analgesia per veterinary guidance; ARRIVE-compliant reporting; IACUC-approved.
• 3Rs. Replace — in vitro killing/coverage assays do maximal upstream filtering so only locked candidates enter animals. Reduce — pre-specified power, shared controls across sub-studies. Refine — validated humane endpoints, early removal criteria.

Human Subjects (Aim 3)

• Population & protections. VLBW infants — a vulnerable population (Subpart D); minimized risk via animal-validated dose, conservative escalation, independent DSMB, pre-specified stopping rules, and IND oversight. Informed parental consent; assent not applicable.
• Scientific/clinical rationale for risk. NEC's high mortality and the absence of any targeted preventive justify a carefully bounded first-in-infant safety study; mechanism-based enrollment (bloom-positive only) concentrates potential benefit and limits exposure.
• Design rigor. Randomized, double-blind, placebo-controlled; pre-registered; sex/gestational-age as analysis variables; data-sharing plan honoring infant privacy.
• Inclusion across the lifespan / sex. Both sexes enrolled and analyzed; the lifespan focus (neonates) is intrinsic to the indication.

Team & Environment (template — fill with real names)

• Multi-PI structure: a phage biology / microbial ecology PI (cocktail design, host-range matrix, resistance steering) + a neonatology PI (NICU surveillance, trial conduct, regulatory). Why: the program lives or dies at the lab–clinic interface.
• Key personnel: strain-resolved metagenomics bioinformatician (Olm/iRep-style analysis); large-animal NEC model expert; GMP/CMC lead; regulatory affairs (phage IND experience); biostatistician.
• Environment: a NICU with VLBW volume and biobanking; BSL-2 phage facility; preterm-piglet facility (or qualified collaborator); GMP manufacturing access; high-performance computing; pediatric IRB and IND infrastructure.
• Collaborations & open-science commitment: data/pipeline release plan; collaborator letters for piglet model and GMP manufacturing.

References

1. Olm MR, Bhattacharya N, Crits-Christoph A, et al. Necrotizing enterocolitis is preceded by increased gut bacterial replication, Klebsiella, and fimbriae-encoding bacteria. Science Advances 5:eaax5727 (2019).
2. Brunse A, Deng L, Pan X, et al. Fecal filtrate transplantation protects against necrotizing enterocolitis. The ISME Journal 16:686–694 (2022; advance 2021).
3. Spiegelhauer MR, Brunse A, Deng L, et al. UV-inactivation of the transferred virome abolishes protection against necrotizing enterocolitis (fecal virome transfer / NEC). Gut Microbes (2025).
4. Federici S, Kredo-Russo S, Valdés-Mas R, et al. Targeted suppression of human IBD-associated gut microbiota commensals by phage combination therapy. Cell 185:2879–2898 (2022).
5. (Add) Neu J, Walker WA. Necrotizing enterocolitis. N Engl J Med (review) — for burden/epidemiology framing.
6. (Add) Your institution's NEC epidemiology / cost-of-illness citation.
7. (Add) Relevant phage-therapy IND / safety precedent citations for the regulatory section.

⚠️ Verify every citation against the primary source before submission — details (volume/pages/year) are reconstructed here and must be confirmed. Add the bracketed references for a complete bibliography.

PART B — THE REUSABLE TEMPLATE

Fork this to write your own phage grant for a different indication. Replace tokens, then run the prompt library on each section. Pre-wired tokens: [INDICATION] · [POPULATION] · [PATHOGEN] · [BLOOM/TRIGGER SIGNAL] · [ANIMAL MODEL] · [KEY PAPER 1–4] · [FUNDER] · [REVIEW CRITERIA] · [MECHANISTIC CONTROL]

B1. Funder-agnostic proposal skeleton

Title

[One precise, benefit-forward title naming PATHOGEN, INDICATION, POPULATION, and the "precision/phage" hook.]

Project Summary / Abstract (~30 lines)

• Problem & burden in POPULATION (incidence, mortality, cost, why current care fails)
• The mechanistic opening: PATHOGEN bloom / TRIGGER SIGNAL precedes INDICATION [KEY PAPER 1]
• Proof that phages are the active protective agent [KEY PAPER 2 + MECHANISTIC CONTROL, KEY PAPER 3]
• Translational template: a defined cocktail suppresses PATHOGEN in vivo [KEY PAPER 4]
• Central hypothesis (one sentence)
• Aims 1–3 in one line each
• Impact + open-science commitment

Specific Aims (ONE page)

• Problem paragraph → Opportunity (numbered evidence) → Central hypothesis
• Aim 1: build/characterize the cocktail + matching engine → Outcome
• Aim 2: efficacy + MECHANISTIC CONTROL + PK/PD in ANIMAL MODEL → Outcome
• Aim 3: first-in-POPULATION safety/biomarker trial, TRIGGER-based dosing → Outcome
• Payoff paragraph

Significance

• Burden & unmet need; why supportive care, antibiotics, and non-specific approaches fail
• The mechanistic pivot: from "suppress the disease" to "intercept the cause"
• Why phage, why now (strain-specific, self-amplifying, non-antibiotic, commensal-sparing)
• What changes in the field if you succeed

Innovation

• Conceptual inversion (prevention by interception)
• Timing/trigger as an engineered active ingredient
• A built-in causal control (MECHANISTIC CONTROL)
• Resistance pre-emption / phage steering
• Commensal-sparing by construction
• Open platform

Approach

Overview & experimental logic (the de-risking staircase)

Aim 1 — Rationale · Design · Release criteria table · Expected outcomes · Pitfalls & alternatives

Aim 2 — Rationale · Arms (incl. MECHANISTIC CONTROL) · Endpoints · Power · Expected outcomes · Pitfalls

Aim 3 — Rationale · Population/trigger · Randomization · Endpoints · Oversight · Outcomes · Pitfalls

Timeline [ILLUSTRATIVE]

Budget Justification Sketch [ILLUSTRATIVE]

Vertebrate Animals (species justification, numbers, welfare, 3Rs)

Human Subjects (population protections, risk rationale, design rigor, inclusion)

Team & Environment (multi-PI lab↔clinic, key personnel, facilities, open-science plan)

References (verify every one against primary source)

**Reusable design moves that made Part A strong (steal these):**
1. **Anchor on a pre-symptomatic, measurable trigger** so the intervention is *preventive and timed*, not reactive.
2. **Find your loss-of-function control** (the UV-inactivation move) so one aim proves *mechanism*, not just effect.
3. **Make the cocktail a process, not a product** — ship the matching engine and re-qualification SOP.
4. **Turn externalities into selling points** — commensal sparing vs. the antibiotic it replaces.
5. **Pre-register go/no-go criteria** in tables; reviewers reward decision rules.
6. **Be open** — releasing data/pipelines is both a scientific multiplier and a fundability signal.

---

## B2. The prompt library (adapted from *eseckel/ai-for-grant-writing*)

> **Core methodology from the repo:** use AI primarily as a **reviewer and refiner against explicit criteria**, not a ghostwriter. Give it **role + context + the target review criterion**, ask for **specific, actionable feedback**, and **iterate**. Always fact-check AI output — it does not know your data and will invent citations.
>
> **Prompt-engineering baseline (apply to every prompt below):** (1) assign a role ("act as an NIH study-section reviewer in [INDICATION]"); (2) give context (paste the section + the funder's review criteria); (3) be specific about the output you want; (4) ask for critique + a revised version + the reasoning; (5) iterate.

### 1 — Clarity & lay accessibility
- "Act as a NICU clinician with no phage-biology background. Identify every sentence in this [SECTION] that a non-specialist would stumble on, and suggest plainer phrasing without losing precision."
- "As a non-native-English reviewer, revise the following for clarity and flow; flag any ambiguous antecedents or run-ons."

### 2 — Compelling narrative / the hook
- "Suggest five stronger opening sentences for my Significance that convey the lethality and unmet need of [INDICATION] in [POPULATION] in one breath."
- "Review my Abstract's first three sentences. Are they a *hook* or a *throat-clear*? Rewrite for urgency while staying accurate."

### 3 — Structure & flow
- "I want to improve the structure of my Specific Aims for [INDICATION]. Propose a reordering that builds a logical de-risking staircase from cocktail → animal mechanism → first-in-human, and explain the logic."
- "Critique the flow of my Approach. Does each Aim clearly hand off to the next? Where does the argument jump?"

### 4 — Funder & mission alignment
- "Here is [FUNDER]'s mission and this funding opportunity's goals: [PASTE]. Show me, line by line, where my Significance does and does *not* speak to them, and suggest edits to close the gaps."
- "Rewrite my closing paragraph to align explicitly with [FUNDER]'s stated priority of [PRIORITY]."

### 5 — Review-criteria alignment (the repo's highest-value move)
- "Score my Approach against each NIH criterion — Significance, Innovation, Approach, Investigators, Environment — as a study-section reviewer would (1–9), justify each score, and list the top three fixes that would most raise the overall impact score."
- "Here is the specific review criterion: [PASTE]. Give blunt feedback on how well I address it and exactly what to add."

### 6 — Title generation
- "Suggest five titles for a grant on a phage cocktail targeting [PATHOGEN] to prevent [INDICATION] in [POPULATION] — each must signal *precision*, *prevention*, and *mechanism*, and stay under 150 characters."

### 7 — Challenge / pitfall identification (pre-empt the reviewer)
- "Act as a skeptical study-section reviewer. List the ten most likely objections to this phage-prevention proposal for [INDICATION] — scientific, regulatory, manufacturing, and ethical — and for each, draft a one-paragraph rebuttal or a 'Pitfalls & Alternatives' entry."
- "What resistance, PK, or safety concerns will reviewers raise about a live phage biologic in [POPULATION], and how should I address each in the Approach?"

### 8 — Timeline & feasibility
- "Given these activities [PASTE], build a feasible 5-year Gantt-style timeline with milestones and go/no-go decision points, front-loading CMC/IND-enabling work."

### 9 — Aim-specific design critique
- "Critique my [ANIMAL MODEL] efficacy design. Is my mechanistic control ([MECHANISTIC CONTROL]) sufficient to isolate the active agent? What additional arm or readout would a tough reviewer demand?"
- "Review my phase 1b safety design in [POPULATION]: are my stopping rules, DSMB plan, and trigger-based enrollment adequate? What would the FDA flag at pre-IND?"

### 10 — Budget & justification framing
- "Given these aims, list the personnel and core cost categories a reviewer expects to see justified, and draft one-sentence justifications tying each to a specific aim."

### 11 — Reverse red-team (run last, before submission)
- "You are the assigned reviewer who wants to *triage* this application. Make the strongest case for why it should not be discussed, citing specific weaknesses. Then tell me the minimum set of changes that would move it to 'discuss.'"

### ⚠️ AI guardrails (non-negotiable)
- **Never** let AI generate citations — it fabricates them. Verify every reference against the primary literature.
- **Never** paste unpublished data, patient information, or confidential collaborator material into a third-party model without authorization.
- Treat AI output as a **first-draft critique**, not truth. You own every claim, number, and citation that goes to the funder.
- Disclose AI assistance per your funder's and institution's current policy.

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## B3. One-page "fill-in" worksheet

INDICATION: ____________________________________ POPULATION: ____________________________________ PATHOGEN / target: ____________________________________ PRE-SYMPTOMATIC TRIGGER / biomarker: ______________________ KEY PAPER 1 (trigger precedes disease): __________________ KEY PAPER 2 (phage/virome protects): _________________ KEY PAPER 3 (loss-of-function control): _________________ KEY PAPER 4 (defined cocktail in vivo): _________________ ANIMAL MODEL: ____________________________________ MECHANISTIC CONTROL (your "UV-inactivation"): ____________ FUNDER + FOA #: ____________________________________ REVIEW CRITERIA to target: _______________________________ CENTRAL HYPOTHESIS (one sentence): _______________________ GO/NO-GO at end of Aim 1: _________________________________ GO/NO-GO at end of Aim 2: _________________________________ PRIMARY ENDPOINT, Aim 3: __________________________________ OPEN-SCIENCE DELIVERABLES: ________________________________

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## License & contribution

**CC0 — public domain.** No rights reserved. Fork, adapt, submit, profit, save lives. If this helped you land funding, consider opening *your* funded proposal too — that's how the field compounds.

*Built as a public, forkable resource. Methodology adapted from [eseckel/ai-for-grant-writing](https://github.com/eseckel/ai-for-grant-writing). Science anchored on Olm 2019 (Sci Adv), Brunse 2021/22 (ISME J), Spiegelhauer 2025 (Gut Microbes), and Federici 2022 (Cell) — verify all before use.*