Competitive Intelligence for Biotech

Build a competitive pipeline matrix for the [therapeutic modality or target class] space:

Modality/target: [e.g., targeted protein degradation, KRAS inhibitors, ADCs]
Scope: [global, specific geographies]
Company scope: [public, private, academic spin-outs]

For each company in the space, map:
1. Company name, ticker (if public), headquarters, founding year
2. Platform technology and differentiation
3. Each program with: target, compound name, modality, phase, indication, partner (if any)
4. Key clinical data readouts (past and upcoming)
5. Cash position and runway
6. Partnership and deal history with values

Present as:
- A company × target matrix showing phase for each cell
- Company profiles with pipeline summaries
- Deal flow timeline with values
- White space analysis — targets with disease validation but no clinical programs
- Competitive dynamics commentary — who is leading, who is differentiated, who is at risk

Highlight the top 3 strategic implications for a company entering this space.

Analyze partnership and M&A activity in [therapeutic area or modality]:

Scope: [e.g., targeted protein degradation deals, oncology partnerships, gene therapy M&A]
Time period: [last 2 years, last 5 years, all time]

For each deal, capture:
1. Date, acquirer/partner, target company, deal type (license, co-develop, acquisition)
2. Total potential value, upfront payment, milestones, royalties
3. Assets or technology involved
4. Strategic rationale — why did this deal happen?
5. Outcome so far — has the program advanced since the deal?

Synthesize:
- Deal value trends over time (are deals getting bigger or smaller?)
- Which acquirers are most active and what does their deal pattern reveal about strategy?
- Average deal terms by phase (preclinical, Phase 1, Phase 2, Phase 3)
- Which deal structures are most common (option/license vs. outright acquisition)
- Predictions — who is likely to be acquired next and by whom, based on the patterns

Present as a timeline visualization description and deal terms comparison table.

Identify white-space therapeutic targets in [disease area or modality]:

Disease area: [e.g., autoimmune disease, solid tumors, neurodegeneration]
Modality lens: [e.g., small molecule, degrader, biologic, gene therapy]
Competitive data available: [pipeline databases, ClinicalTrials.gov, published literature]

Analysis framework:
1. Map all targets with published disease validation (genetic, functional, clinical)
2. Cross-reference against clinical pipeline databases — which targets have active programs?
3. Identify validated targets with NO clinical-stage programs (true white space)
4. Identify targets with only early-stage programs (emerging opportunity)
5. Assess why each white-space target lacks clinical programs (druggability, safety, IP, commercial)
6. For each, evaluate whether the proposed modality could overcome historical barriers
7. Rank targets by: strength of validation × feasibility × commercial potential

Deliver:
- White space target list ranked by opportunity score
- For the top 5, a one-page target dossier with disease rationale, key evidence, and development hypothesis
- Recommended next steps (tool compound, knockout studies, etc.) to de-risk each

Create a detailed pipeline analysis for [biotech company]:

Company: [name and ticker if public]
Focus areas: [therapeutic areas and modality]

For each pipeline program, detail:
1. Compound name, target, mechanism of action, modality
2. Indication and patient population
3. Current development phase and trial status
4. Key clinical data — efficacy and safety results published or presented
5. Regulatory status — designations, planned filings
6. Partnership status — co-development, licensed, wholly owned
7. Competitive positioning — how does this program compare to others targeting the same biology?

Company-level analysis:
- Platform technology and its competitive moat
- Pipeline breadth vs. depth assessment
- Cash position, burn rate, and runway
- Key upcoming catalysts and their impact on valuation
- Biggest risks to the portfolio (binary events, competitive, execution)
- Strategic options — likely next partnership, acquisition target, or therapeutic expansion

Present as an investment-quality company profile with pipeline table and catalyst timeline.

Monitor for emerging competitors in [therapeutic area or target]:

Area to monitor: [e.g., STAT6 degraders, ADCs in breast cancer, LRRK2 for Parkinson's]
Known players: [list companies already tracked]

Design a monitoring framework across signal sources:
1. ClinicalTrials.gov — new trial registrations mentioning the target or modality
2. International registries (WHO ICTRP, EU CTR, ChiCTR) — programs not yet on CT.gov
3. Patent filings — composition-of-matter and method-of-use patents from new assignees
4. Publications — papers from academic groups demonstrating novel compounds or approaches
5. Preprints (bioRxiv, medRxiv) — early-stage disclosures before peer review
6. NIH grants (RePORTER) — SBIR/STTR funding for companies in the space
7. Conference abstracts — AACR, ASCO, ASH, ACR disclosures from unknown entities
8. SEC filings — S-1, 10-K disclosures from newly public companies

For each source, define:
- Specific search queries to run
- Frequency of monitoring (daily, weekly, monthly)
- Alert triggers (what constitutes a signal worth escalating)
- How to score and prioritize signals (composite confidence score)

Provide a ready-to-execute monitoring playbook.