Clinical Trial Search Skill

This skill converts natural language questions into structured API queries against a clinical trial database, then presents the results in a readable format.

Workflow

1. Parse user intent — Extract key entities from the user's question
2. Build query parameters — Map entities to the query schema below
3. Execute the query — Run scripts/search.py
4. Present results — Format and display trials to the user

Step 1: Extract Keywords

Identify the following entity types from the user's question:

Dict field format:

{"logic": "or", "data": ["value1", "value2"]}

• logic controls how multiple values are combined: "or" (any match) or "and" (all must match). Default to "or" unless the user explicitly wants all terms to apply simultaneously.
• data is the list of keyword strings to match.

Type rules:

• indication, acronym, company, nctid, phase → plain List[str]

• target, drug_name, drug_modality, drug_feature, location, route_of_administration → dict with logic and data

• Default to page_num: 0, page_size: 10 unless the user specifies otherwise

• Prefer English keywords (the database is indexed in English); translate non-English terms

• drug_modality must use exact strings from this set:

  [
    "Steroids", "Vaccine", "Antisense RNA", "Antibody-Drug Conjugates, ADCs", "Unknown", "Protein Degrader",
    "Monoclonal Antibodies", "mRNA", "Others", "Cell-based Therapies", "Imaging Agents", "Gene Therapy",
    "miRNA", "Polypeptide", "Recombinant Proteins", "Small Molecule", "siRNA/RNAi", "Trispecific Antibodies",
    "Polyclonal Antibodies", "Bi-specific Antibodies", "Glycoconjugates", "Radiopharmaceutical",
    "Nucleic Acid-based", "Carbohydrates"
  ]
  

• drug_feature must use exact strings from this set:

  [
    "505b2", "Bacterial Product", "Biologic", "Biosimilar", "Device", "Fixed-Dose Combination", "Immuno-Oncology",
    "New Molecular Entity (NME)", "Non-NME", "Precision Medicine", "Reformulation", "Specialty Drug", "Viral"
  ]
  

Step 2: Execute the Query

python scripts/search.py --params '\x3CJSON string>'

Or using a parameter file:

python scripts/search.py --params-file /tmp/query.json

Add --raw to receive the unformatted JSON response.

Step 3: Interpret Results

The response contains:

• total_count — total number of matching trials
• results — current page of results, each with NCT ID, title, phase, status, indication, drugs, sponsor, etc.

If results exceed 100, prompt the user to narrow the query. If no results are returned, apply the fallback strategies below before giving up.

Step 3: Review and Fallback Search Strategies

If no results are returned, apply the fallback strategies below before giving up. When an initial query returns zero or poor results, try these strategies in order:

Strategy 1 — Drug Name Variant Expansion

Trial registries may store drug names inconsistently (INN vs brand name, with/without hyphens, partial codes). Expand drug_name.data to include multiple variants in a single or query.

{
  "drug_name": {"logic": "or", "data": ["SHR-A1904", "SHR A1904", "A1904", "SHR1904"]},
  "page_num": 0,
  "page_size": 50
}

Also try substituting the trial acronym if known:

{
  "acronym": ["KEYNOTE-590", "KEYNOTE590", "KN590"],
  "page_num": 0,
  "page_size": 10
}

Common variant patterns:

• Remove or replace hyphens: SHR-A1904 → SHR A1904, SHRA1904
• Strip prefix: 9MW-2821 → MW-2821, 9MW2821
• Try both INN and internal code together in the same data array

Strategy 2 — Sponsor-First with Application-Layer Filtering

When drug name matching is unreliable, anchor on the sponsor company and pull a broad result set, then filter locally by indication, phase, or modality.

{
  "company": ["Roche", "Roche Inc"],
  "page_num": 0,
  "page_size": 200
}

After retrieval, apply local filters:

• phase in ["II", "III"]
• indication contains "breast cancer"
• drug_name matches known code pattern

Use this strategy when the drug code is ambiguous or when searching for a company's full trial portfolio.

Strategy 3 — Broad Target/Indication Search with Post-Filtering

When neither drug name nor company yields results, search by biological target and indication, then narrow client-side by sponsor or drug name pattern.

{
  "target": {"logic": "or", "data": ["CLDN18.2", "Nectin-4", "HER2"]},
  "indication": ["gastric cancer", "breast cancer"],
  "page_num": 0,
  "page_size": 200
}

After retrieval, filter by:

• Sponsor name substring (e.g. contains "Hengrui")
• Drug code prefix (e.g. starts with SHR, 9MW, A166)
• Trial status (Recruiting, Active, not recruiting)

Note: If the API supports regex, patterns like (SHR|9MW|A166) can be passed directly in drug_name.data to broaden matching in a single call.

Strategy 4 — Relax Filters Incrementally

If all strategies above still return no results, drop filters one at a time in this order:

1. Drop has_result_summary (many trials have no posted results)
2. Drop phase filter
3. Drop location filter
4. Broaden indication (e.g. "NSCLC" → "lung cancer" → "cancer")
5. Remove drug_modality or drug_feature constraints

Re-run after each relaxation and stop as soon as results appear.

Decision Tree

Initial query returns results?
├── Yes → present results
└── No  → Strategy 1: expand drug_name / acronym variants
          └── Still no → Strategy 2: sponsor anchor + local filter
                         └── Still no → Strategy 3: target/indication broad search
                                        └── Still no → Strategy 4: relax filters incrementally
Any step hits HTTP 429?
└── Pause entire chain 15s → resume from current strategy
    (sleep ≥5s between every request to avoid triggering 429)

Conversion Examples

User: "Find Phase 3 trials of PD-1 antibodies in lung cancer that have results"

{
  "target": {"logic": "or", "data": ["PD-1"]},
  "drug_modality": {"logic": "or", "data": ["Monoclonal Antibodies"]},
  "indication": ["lung cancer"],
  "phase": ["III"],
  "has_result_summary": true,
  "page_num": 0,
  "page_size": 10
}

User: "Look up NCT04280783"

{
  "nctid": ["NCT04280783"],
  "page_num": 0,
  "page_size": 1
}

User: "Roche bispecific antibody trials in China"

{
  "company": ["Roche"],
  "location": {"logic": "or", "data": ["China"]},
  "drug_modality": {"logic": "or", "data": ["Bi-specific Antibodies"]},
  "page_num": 0,
  "page_size": 10
}

User: "Oral small molecule KRAS G12C inhibitors in colorectal cancer"

{
  "target": {"logic": "or", "data": ["KRAS G12C"]},
  "drug_modality": {"logic": "or", "data": ["Small Molecule"]},
  "indication": ["colorectal cancer"],
  "page_num": 0,
  "page_size": 10
}

Dependencies

• Python 3.8+
• requests library (pip install requests)
• Environment variable NOAH_API_TOKEN — API authentication token (required)
  • Register for a free account at noah.bio to obtain your API key.

Security & Packaging Notes

• This skill only calls NoahAI official HTTPS endpoints under https://www.noah.bio/api/ and does not contact third-party services.
• It requires exactly one environment variable: NOAH_API_TOKEN. Store it in the environment or a local .env file, and never place it inline in commands, chats, or packaged files.
• The token is scoped to read medical public details only and cannot access private user records.
• The skill does not intentionally persist request parameters locally. Any server-side retention is determined by the NoahAI API service and its operational logging policies.
• It does not request persistent or system-level privileges and does not modify system configuration.
• The skill is source-file based (Python scripts only) and does not require runtime installs, package downloads, or external bootstrap steps.