PhageLab Financial Model
Biotech/Pharma Startup Financials (Free Excel Download)
AI-powered bacteriophage platform that replaces failing antibiotics in animal (and eventually human) health by developing custom phage cocktails in 45 days.
professionals from Deloitte
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About this model
PhageLab develops INSPEKTOR, a bacteriophage cocktail for Salmonella in commercial poultry. Its patented-pending LEIDEN algorithm matches bacterial isolates with phage combinations from the company repository, aiming to create custom solutions in 45 days and eventually extend the platform beyond poultry.
The product is expected to generate B2B treatment or supply revenue from commercial farms, potentially through distributors, but the deck gives no pricing, ASP, contract format, or customer count. The company cites industrial-scale approval and frames the offering as a replacement for failing antibiotics in animal health.
Field trials claim elimination of up to 100% of Salmonella presence, and INSPEKTOR has been published in Poultry Science. PhageLab has nearly 100 staff across three countries and more than ten years of experimentation data. The model should forecast farms, treatments, regulatory coverage, efficacy, manufacturing, repeat contracts, and expansion.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About PhageLab
phage-lab.com
How to build a detailed financial model for PhageLab
A complete walkthrough of the business, drivers, and assumptions behind the downloadable PhageLab model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Core product: INSPEKTOR® - a phage cocktail targeting Salmonella in commercial poultry, developed using the LEIDEN® epidemiological analysis algorithm.
- LEIDEN® is a proprietary AI/ML matching system (US patent pending US18/006,453) that matches bacterial isolates to optimal phage combinations from the company's phage repository.
- Platform claims to produce a "safe, effective, custom-made solution in 45 days" - stated as 0.1% of the average time for competing technologies.
- Scientific validation: INSPEKTOR® published in Poultry Science® (official journal of the Poultry Science Association); field trials claim up to 100% elimination of Salmonella presence.
- Moat layers: 10+ years of experimentation data; world's largest industrial bacteria repository (claimed); genomics/bioinformatics capability; LEIDEN® algorithm.
- Pipeline beyond poultry: deck names a "multi-billion dollar scenario: phages for poultry" as one segment, implying swine, cattle, and human health are on the roadmap.
Market
- TAM: Anti-infective drugs market = $220B
- Addressable sub-market: Anti-microbial drugs market = $104B at 4.5% CAGR
- Animal health sub-segment: $18.9B
- Human health sub-segment: $85.3B
- Sources cited: Fortune Business Insights 2021, CDDEP 2021, Grand View Research 2021
- Demand driver: Since 2019, 50% of all antibiotics used in intensive animal farming are ineffective for chickens; swine ~45%; cows ~38% (read from chart).
- Macro growth: Global meat production grew from 5 kg/capita (1960) to ~38 kg/capita (2020); antibiotic dose size in food animals grew in parallel.
Revenue model
- Not explicitly stated. No pricing slide, no revenue model slide, no ASP / contract structure disclosed.
- Implied model: B2B product sales of INSPEKTOR® phage cocktails to commercial broiler/poultry farms (and eventually swine/cattle). Custom "cocktail" suggests either per-treatment pricing or contract supply arrangements.
- Channel: direct to large commercial farms or via distributors (not stated). Deck mentions "industrial scale" approval suggesting regulatory clearance for commercial sale in at least one market.
- Human health commercialization path not specified.
Traction & metrics
- Field trials: "PhageLab eliminates up to 100% of Salmonella presence" - field trials in commercial broiler settings.
- Scientific publication: INSPEKTOR® published in Poultry Science® journal.
- Team size: "nearly 100 people in 3 countries."
- Regulatory: "first phage-based solution approved on an industrial scale" (executive summary claim).
- No revenue figures, customer counts, ARR, or growth rates disclosed anywhere in the deck.
Competition / moat
- Competitive framing: Big Pharma is exiting antibiotic R&D (FDA approvals declined from 70 in 1980s → 15 in 2010-2019); economics of new antibiotic development are poor (15 years, $1B investment, ~$200M expected revenue over avg. 5-year efficacy window).
- Barriers PhageLab claims to have solved:
- Diagnosis - bacterial sequencing capability and proprietary bacterial repository.
- Matching - LEIDEN® AI/ML algorithm + massive phage repository.
- Production - pharmaceutical-grade scale manufacturing.
- Regulatory - process for navigating complex approvals (avg. Pharma time >4 years).
- No named direct competitors mentioned in deck.
- Competitive advantage described as "huge growing MOATs."
Team & funding ask / use of funds
- Team: ~100 people across Chile, Spain, and Brazil.
- Key executives: Hans Pieringer (CEO, MSc Biotech, co-founder); Pablo Cifuentes (CTO, MSc Innovation, co-founder); Nicolás Ferreira (Director of Partnerships, co-founder); Alexander Jahn (CRO, former VP Global Commercial Operations at Bayer Animal Health); Sabrina Torgan (CFO, former CFO at Kley Hertz Pharmaceuticals).
- Key advisors/board: Nik Berman (Partner at Kaszek VC, former VP Mercado Libre); Rinse Boersman (former Global Marketing Director Dairy Cattle at Elanco and MSD); Sergio Schuler (VP Animal Nutrition Health at DSM); Luis Leon (Head Computational Biology, former Dir. Bioinformatics Psomagen, PhD CS/ML); Sebastian Bernales (Partner Humboldt Fund, CEO Praxis Biotech, former VP Drug Discovery Medivation).
- Prior investors: Kaszek VC implied via Nik Berman's board seat (but not confirmed as investor). Not stated explicitly.
Recommended financial model
- Archetype + why: B2B biotech product revenue model (agri-biologics) - revenue driven by volume of phage product units sold to commercial farms × ASP per treatment cycle. Near-term focus on animal health (poultry first); human health is a long-dated optionality. A 3-statement model with granular product revenue build is appropriate given the company is commercializing but has no disclosed revenue figures. If a funding round context is provided later, add a waterfall / cap table.
- Forecast horizon & granularity: 5 years (Year 1–5); quarterly for Year 1–2, annual for Year 3–5. Animal health products have seasonal farm cycles so quarterly granularity matters.
- Key drivers & assumptions:
- Number of farm customers / contracted flocks - ramp from 1–2 pilot farms in Year 1 to 30–50 large commercial operations by Year 3, based on typical agri-biotech commercialization curves.
- Flock size (birds per customer per cycle) - avg. 50,000–200,000 birds per farm; poultry cycles ~6–7 weeks; 6–7 cycles/year per farm.
- ASP per 1,000 birds treated - $0.05–$0.20/bird per cycle (placeholder; no deck data); benchmarked against antibiotic treatment costs in poultry ($0.10–$0.30/bird). Needs validation.
- Gross margin on product - 55–70%; biologics manufacturing has high fixed cost / relatively low variable COGS at scale, but early-stage margins likely lower (40–55%).
- Geographies active - Chile, Brazil, Peru, Mexico, Spain; model should apply a ramp schedule by country.
- R&D spend - substantial; company has ~100 employees and multiple R&D programs. Estimate 30–40% of total opex in early years.
- Headcount / opex growth - scale from ~100 to 150–200 by Year 3 as commercial team grows; blend of scientific + sales roles.
- Regulatory approval timing - first industrial-scale approval achieved; new country/species approvals 12–24 months each.
- Human health revenue - zero in Years 1–3; optionality scenario only.
- Anti-microbial market CAGR - 4.5%.
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: steady farm customer ramp, LATAM-first, poultry only, 60% gross margin by Year 3.
- Bull: faster penetration (EU traction, swine/cattle expansion), higher ASP (premium positioning), 70%+ gross margin.
- Bear: regulatory delays, slower farm adoption, lower ASP due to competitor pressure, gross margin stuck at 45%.
- Required sheets / outputs:
- Assumptions - all drivers with toggles for scenario.
- Revenue build - by geography × species × customer count × ASP.
- COGS & gross margin - manufacturing cost per unit, fixed vs. variable split.
- Opex - R&D, S&M, G&A (headcount-driven).
- Income Statement (P&L).
- Cash flow statement (burn / runway focus given pre-profitability stage).
- Balance sheet (simplified).
- KPI dashboard - birds treated, active farm customers, revenue per customer, gross margin %, cash runway.
- Scenario toggle (Base / Bull / Bear).
Frequently asked
Is the PhageLab financial model free?+
Yes. The PhageLab model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from PhageLab's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
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