PB

Pepper Bio Financial Model

• Proprietary "transomics" platform integrating genomics, transcriptomics, proteomics, and phosphoproteomics to identify causal disease targets (not just correlative ones).
• Claims 12x better than leading NLP over 30M+ scientific sources and 4x better than traditional multiomic analyses at target ID.
• Platform applicable across full drug development workflow: target discovery → lead discovery → lead optimization → toxicity prediction → IND filing → clinical trial patient stratification.
• Two core capabilities: (1) Understand disease (what drives/sustains it), (2) Understand drug (full impact on patient).

• Oncology drug market: $201B (2021), 9.7% CAGR.
• Neurodegenerative drug market: $34B (2020), 4.9% CAGR.
• Inflammatory diseases drug market: $98B (2020), 9.3% CAGR.
• Short-term TAM (pre-clinical omics R&D tech in oncology, neurology, inflammatory - i.e. partnership addressable market): $3.3B.
• Internal pipeline oncology SOM (US & EU5, 3 cancer indications): $5.3B total across Lymphoma ($1.5B), Liver cancer / HCC ($1.5B), NSCLC EGFRm ($2.3B).
• Jumbo pharma platform deals benchmark: $1B+ (e.g. Roche, Bayer, BMS, Gilead comparable deals).

Two-track model:

Track 1 - Pharma partnerships (near-term revenue)

• Service contracts with pharma/biopharma for target ID, mechanism studies, lead optimization, toxicity prediction, and clinical patient stratification.
• Current deal structure: upfront fees per drug program.
• Deal progression roadmap:
• Current: $100K+ upfront, total deal value $100K+
• 2023 Q3+: $1M+ upfront + $10M+ milestones per drug program, total deal value $10M–$100M+
• Late 2024 (drug in clinic): $10M+ upfront + $100M+ milestones + tiered royalties (<10%), total deal value $100M–$1B+

Track 2 - Internal pipeline

• Develop proprietary drugs in oncology (starting with HCC/liver cancer, then lymphoma and NSCLC).
• Pre-clinical efficacy data shown for HCC target; expect IND/clinic entry in HCC "next year" (relative to deck date).
• Revenue eventually from drug sales / licensing / royalties (long horizon).

• Total revenue to date: <$1M from 3 completed/active partnerships.
• Partnership 1: Top 5 pharma - mechanism for novel modality - $100K–$400K - 7 months duration.
• Partnership 2: Public commercial-stage biopharma - target ID for AML - $100K–$400K - 3 months duration.
• Partnership 3: Public clinical-stage biopharma - mechanism for clinical asset - $100K–$400K - 8 months duration.
• No customer count, retention rate, or ARR figure disclosed beyond the above.

• Contract value per deal (current): $100K–$400K per engagement.
• Contract duration: 3–8 months per engagement.
• Implied ACV range: ~$150K–$600K annualised per engagement (rough extrapolation from duration; not stated).
• No CAC, LTV, gross margin, or payback period data disclosed in deck.
• Cost structure not disclosed (R&D-intensive; lab / compute / headcount driven).

• Moat framed as technology superiority over:
• NLP/literature mining (12x better).
• Traditional multiomic analyses (4x better).
• Differentiation: phosphoproteomics layer provides functional/causal signal vs. static/correlative genomic/transcriptomic data.
• No named competitors cited in deck.
• IP/patents: not mentioned.
• Published validation data: pre-clinical HCC mouse model shown.

Team:

• Samantha Dale Strasser, PhD - CSO & Co-founder; PhD MIT (EECS, multi-omics); NSF Graduate Research Fellow; Churchill Scholar; Goldwater Scholar; developed Pepper's core technology.
• Jon Hu - CEO & Co-founder; MBA Harvard Business School; former CEO of Atidiv (200→300 people), COO Techweek.
• Simon Fricker, PhD - CDO; PhD University of Warwick; 25+ years drug industry; founding member AnorMED (acq. by Genzyme); worked on 3 FDA-approved drugs.

• Archetype + why: Dual-track biotech platform model - (A) near-term B2B services revenue model (contract research / pharma partnerships) with milestone-escalation schedule, plus (B) long-term internal pipeline P&L with R&D burn, IND/clinical timelines, and probability-weighted terminal value. The partnership track resembles a services/SaaS ramp (recurring contract revenue growing in deal size); the pipeline track requires a standard biotech NPV / probability-of-success model. A 3-statement integrated model with a pipeline NPV module is appropriate. Do NOT model as a pure SaaS ARR model - deal sizes and structures are milestone-gated, not subscription.

• Forecast horizon & granularity:
• Partnership revenue: quarterly, 2023–2027 (5 years), building from existing 3 contracts to a larger partner base.
• Internal pipeline: annual, 2023–2030+, with milestone gates (IND, Phase I, Phase II, approval/licensing event).
• Burn / cash: monthly for 18-month runway model; quarterly thereafter.

• Key drivers & assumptions:

*Partnership track:*

• Number of active partnerships per year
• Average deal size (upfront + milestones) - model each deal tier separately
• Deal duration / contract length
• Royalty rate on partnered drugs
• Revenue recognition: recognize upfront at contract start, milestones on achievement

*Internal pipeline track:*

• Programs in development
• R&D cost per program per year
• Time to IND/clinic for HCC
• Probability of success (POS) by stage
• Peak sales / royalty income on approval

*Cost / OpEx:*

• Headcount
• R&D spend as % of revenue
• G&A
• Partnership R&D costs are low-effort/scalable per deck messaging

*Capital:*

• Burn rate

• Scenarios (Base / Bull / Bear):
• Flex variables: number of partnership deals closed per year, average deal size, time to IND/clinic, probability of success on internal programs, royalty rates.
• Bear: partnership ramp slow (3→5 deals, deals stay at $100K range, HCC IND delayed 12 months).
• Base: partnership ramp per deck trajectory (2023 Q3 uplift materialises, 1 jumbo deal by 2025), HCC IND 2024.
• Bull: 1 jumbo platform deal ($100M+) closes in 2024, second drug enters clinic 2025, licensing event on lead program.

• Required sheets / outputs:

1. Assumptions & inputs (centralized toggles: deal ramp, deal sizes by tier, pipeline stage timelines, POS, royalty rates, headcount, burn)
2. Partnership revenue schedule (by contract, by tier, upfront + milestone + royalty)
3. Internal pipeline timeline & milestone tracker
4. P&L (IS): revenue, R&D expense, G&A, operating loss, EBITDA
5. Cash & runway (burn bridge from current raise / existing cash)
6. Pipeline NPV module (probability-weighted DCF per program, 10–15% discount rate)
7. Scenario toggle (Base / Bull / Bear outputs)
8. KPI dashboard: active partnerships, total deal value pipeline, burn rate, runway, programs in clinic