Biotech Catalyst Monitor: Clinical Trial Readouts and Phase Transitions

Turn a company watchlist into a scored biotech catalyst feed. Give one or more trial sponsors and get their studies from ClinicalTrials.gov, classified and ranked by potential market impact: upcoming Phase 3 readouts, results just posted, and phase transitions. This is not a raw trial dump. It is a curated biotech catalyst dataset that surfaces the clinical trial readouts most likely to matter, per company, ready for research and monitoring.

A single Phase 2 or Phase 3 readout can move a biotech stock sharply in a day. The hard part is knowing which of a company's many studies is the next real catalyst. This Actor does that filtering and scoring for you, every run, from official public data.

Why this is different

Most clinical trial scrapers hand you raw study records and leave the analysis to you. This Actor computes the signal instead of just the records:

• Catalyst classification. Every trial is labeled as an upcoming readout, results posted, completed and awaiting results, or a plain status note. You see the catalyst type, not just a status code.
• Impact score (0 to 100). Later phases and near-term readouts rank highest. A Phase 3 readout outranks a Phase 1 status note, so the studies most likely to move a stock float to the top.
• Per-company ranking. Catalysts come back sorted by impact, highest first, with a high-impact count so you can scan a watchlist quickly.
• Verifiable. Every catalyst carries the NCT id and a direct link to the study on ClinicalTrials.gov, so any data point can be checked at the source.

The result is a drug pipeline view organized around phase transitions and readout timing, not an undifferentiated list of trials.

Who it's for

• Biotech investors and traders who want a ready-made trial-catalyst dataset for research, screening, and watchlist monitoring rather than profit promises.
• Fintech and research apps that need to drop clinical trial catalysts into their own product, dashboard, or alerting layer.
• Pharma competitive-intelligence teams tracking a competitor's drug pipeline, phase transitions, and readout timing.

It pairs naturally with SEC insider, 13F, and 8-K data sources for a broader market-catalyst workflow.

Use cases

• Watchlist catalyst scan. Pass a list of biotech tickers' parent company names and get each company's next likely catalyst, ranked, in one run.
• High-impact-only screening. Set a minimum impact score so only Phase 3 readouts and posted results come back, filtering out routine status updates.
• Competitive pipeline tracking. Monitor a rival sponsor's trials to see which programs are nearing primary completion or have just posted results.
• Catalyst calendar building. Use the primary completion dates on upcoming-readout catalysts to assemble a forward calendar of clinical trial readouts.
• Research dataset feed. Schedule recurring runs and push the JSON into a database for ongoing analysis of phase transitions across a sector.

Input

Field	Type	Required	Description
sponsors	array	yes	One or more company / trial sponsor names, for example Moderna, Vertex Pharmaceuticals, BioNTech. Each name is scanned for catalysts.
maxStudies	integer	no	How many of the company's trials to scan and rank. Default 50, range 1 to 100.
minImpact	integer	no	Only return catalysts at or above this impact score. Default 0 (all). For example, 70 returns high-impact only.

Output

One dataset item per company: the company query, trials found, a high-impact count, and a ranked list of catalysts. Each catalyst includes the NCT id, title, sponsor, phase, status, primary completion date, last update date, results flag, conditions, catalyst type, impact score, and a direct study URL.

{
  "type": "sponsor_catalysts",
  "sponsor_query": "Moderna",
  "trials_found": 40,
  "high_impact_count": 6,
  "catalysts": [
    {
      "nct_id": "NCT05678430",
      "title": "A Study of mRNA Vaccine in Adults",
      "sponsor": "ModernaTX, Inc.",
      "phase": "PHASE3",
      "status": "ACTIVE_NOT_RECRUITING",
      "primary_completion": "2026-06-30",
      "last_update": "2026-05-01",
      "has_results": false,
      "conditions": ["Respiratory Syncytial Virus Infection"],
      "impact": 100,
      "catalyst": "Upcoming readout (primary completion 2026-06-30)",
      "url": "https://clinicaltrials.gov/study/NCT05678430"
    }
  ]
}

The NCT id and title above are illustrative of the structure; live values come straight from ClinicalTrials.gov at run time.

How the impact score works

The score is fully transparent and derived in code, not from a black box. It starts from the trial phase and is adjusted by catalyst type:

• Phase base score: Phase 3 = 80, Phase 2 = 60, Phase 1 = 35, Early Phase 1 = 25, not applicable = 15. When a trial lists multiple phases, the highest applies.
• Catalyst adjustment: results posted adds 10; an upcoming readout (active status with a primary completion date inside the next 270 days) adds 20; completed and awaiting results adds 5; a plain status note adds nothing.
• Cap: the final score is capped at 100.

So a Phase 3 trial that is active with a near-term primary completion date scores 80 + 20 = 100 and is flagged as an upcoming readout, while a Phase 1 status note scores 35. Catalysts are returned sorted by this score, highest first.

Use with AI agents and automation

This Actor is built to be called by code and by AI agents, not just clicked in the console:

• AI agent frameworks. Invoke it from LangChain or LlamaIndex as a tool, so an agent can pull biotech catalysts on demand inside a research chain.
• Apify MCP server. Expose the Actor through the Apify MCP server so assistants such as Claude, ChatGPT, and Cursor can call it directly and read the scored catalysts back.
• No-code automation. Wire it into Zapier or Make to trigger downstream steps when new catalysts appear.
• Webhooks and schedules. Run it on a schedule or trigger it via the Apify API, with webhooks firing when a run finishes so the JSON lands where you need it, on demand or recurring.

Pricing

Pay-per-event: one charge per company analyzed, billed through the sponsor-analyzed event. There is no subscription and no monthly minimum. Scanning a 10-company watchlist is 10 charges. You pay only for the companies you actually scan.

Data source and compliance

All data comes from the official ClinicalTrials.gov v2 API, the public U.S. registry of clinical studies. It is free, key-free, JSON, and stable, which keeps results clean and avoids scraping fragility. The registry is updated continuously, so each run reads the latest available data. Only public study information is processed; no personal data is collected.

FAQ

Which companies can I track? Any sponsor in ClinicalTrials.gov. Use the company name; the Actor matches the sponsor field and falls back to full-text search when the sponsor match is too narrow.

How current is the data? ClinicalTrials.gov is the official registry and is updated continuously. Each run reads the latest data at the time it executes.

What does the impact score mean? It is a 0 to 100 ranking of potential market relevance based on trial phase and catalyst type, as described above. It ranks catalysts; it does not predict outcomes.

How do I get only the big catalysts? Set minImpact to 70 to return high-impact items such as Phase 3 readouts and posted results, and skip routine status notes.

Can an AI agent call this automatically? Yes. Expose the Actor through the Apify MCP server and assistants like Claude, ChatGPT, and Cursor can call it directly, or invoke it as a tool from LangChain or LlamaIndex.

How am I charged? One pay-per-event charge per company analyzed, with no subscription.

---

Keywords: biotech catalyst, clinical trial readouts, ClinicalTrials.gov, drug pipeline, phase transitions, Phase 3 readout, results posted, catalyst calendar, biotech investing, pharma competitive intelligence, biotech catalyst data.

---

Note: Provided as data for research, screening, and monitoring. This is not investment advice. Historical patterns do not guarantee future results.