tooluniverse-drug-research

name: tooluniverse-drug-research description: Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work. disable-model-invocation: true

Drug Research Strategy

Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.

KEY PRINCIPLES:

1. Report-first approach - Create report file FIRST, then populate progressively
2. Compound disambiguation FIRST - Resolve identifiers before research
3. Citation requirements - Every fact must have inline source attribution
4. Evidence grading - Grade claims by evidence strength (T1-T4)
5. Mandatory completeness - All sections must exist, even if "data unavailable"
6. English-first queries - Always use English drug/compound names in tool calls, even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language

LOOK UP, DON'T GUESS

When asked about a drug, query ChEMBL/PubChem/DailyMed FIRST. Don't guess at mechanism, targets, or side effects — look them up. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.

Drug Mechanism Reasoning

When investigating a drug's mechanism of action, trace the full causal chain:

1. Target engagement - Which protein(s) does the drug bind, and with what affinity/selectivity?
2. Molecular effect - Does binding inhibit, activate, or modulate the target's function?
3. Pathway consequence - Which signaling or metabolic pathway is altered downstream?
4. Cellular phenotype - What changes occur at the cell level (proliferation, apoptosis, secretion)?
5. Physiological outcome - How does the cellular effect translate to the therapeutic benefit in the patient?

Workflow Overview

1. Report-First Approach (MANDATORY)

DO NOT show the search process or tool outputs to the user. Instead:

1. Create the report file FIRST - [DRUG]_drug_report.md with all 11 section headers and [Researching...] placeholders. See REPORT_TEMPLATE.md for the full template.
2. Progressively update the report - Replace placeholders with findings as you query each tool.
3. Use ALL relevant tools - Query multiple databases for each data type; cross-reference across sources.

2. Citation Requirements (MANDATORY)

Every piece of information MUST include its source. Use inline citations:

*Source: PubChem via `PubChem_get_compound_properties_by_CID` (CID: 4091)*

3. Progressive Writing Workflow

Step 1:  Create report file with all section headers
Step 2:  Resolve compound identifiers -> Update Section 1
Step 3:  Query PubChem/ADMET-AI/DailyMed SPL -> Update Section 2 (Chemistry)
Step 4:  Query FDA Label MOA + ChEMBL + DGIdb -> Update Section 3 (Mechanism)
Step 5:  Query ADMET-AI tools -> Update Section 4 (ADMET)
Step 6:  Query ClinicalTrials.gov -> Update Section 5 (Clinical)
Step 7:  Query FAERS/DailyMed -> Update Section 6 (Safety)
Step 8:  Query PharmGKB -> Update Section 7 (Pharmacogenomics)
Step 9:  Query DailyMed/Orange Book -> Update Section 8 (Regulatory)
Step 10: Query PubMed/literature -> Update Section 9 (Literature)
Step 11: Synthesize findings -> Update Executive Summary & Section 10
Step 12: Document all sources -> Update Section 11 (Data Sources)

Compound Disambiguation (Phase 1)

CRITICAL: Establish compound identity before any research.

Identifier Resolution Chain

1. PubChem_get_CID_by_compound_name(compound_name)
   -> Extract: CID, canonical SMILES, formula

2. ChEMBL_search_molecules(query=drug_name)
   -> Extract: ChEMBL ID, pref_name

3. DailyMed_search_spls(drug_name)
   -> Extract: Set ID, NDC codes (if approved)

4. PharmGKB_search_drugs(query=drug_name)
   -> Extract: PharmGKB ID (PA...)

Handle Naming Ambiguity

Research Paths Summary

Each path has detailed tool chains and output examples in REPORT_GUIDELINES.md.

PATH 1: Chemical Properties & CMC

Tools: PubChem properties -> ADMET-AI physicochemical -> ADMET-AI solubility -> DailyMed chemistry/description Output: Physicochemical table, Lipinski assessment, QED score, salt forms, formulation comparison

PATH 2: Mechanism & Targets

Tools: DailyMed MOA -> ChEMBL activities (NOT ChEMBL_get_molecule_targets) -> ChEMBL target details -> DGIdb -> PubChem bioactivity Critical: Derive targets from activities filtered to pChEMBL >= 6.0. Avoid ChEMBL_get_molecule_targets. Output: FDA MOA text, target table with UniProt/potency, selectivity profile

PATH 3: ADMET Properties

Tools: ADMET-AI (bioavailability, BBB, CYP, clearance, toxicity) Fallback: DailyMed clinical_pharmacology + pharmacokinetics + drug_interactions Critical: If ADMET-AI fails, automatically use fallback. Never leave Section 4 empty.

PATH 4: Clinical Trials

Tools: search_clinical_trials -> compute phase counts -> extract outcomes/AEs -> fda_pharmacogenomic_biomarkers Critical: Section 5.2 must show actual counts by phase/status in table format.

PATH 5: Post-Marketing Safety

Tools: FAERS (reactions, seriousness, outcomes, deaths, age) + DailyMed (DDI, dosing, warnings) Critical: Include FAERS date window, seriousness breakdown, and limitations paragraph.

PATH 6: Pharmacogenomics

Tools: PharmGKB (search -> details -> annotations -> guidelines) Fallback: DailyMed pharmacogenomics section + PubMed literature

PATH 7: Regulatory & Patents

Tools: FDA Orange Book (search, approval history, exclusivity, patents, generics) + DailyMed (special populations via LOINC codes) Note: US-only data; document EMA/PMDA limitation.

PATH 8: Real-World Evidence

Tools: ClinicalTrials.gov (OBSERVATIONAL studies) + PubMed (real-world, registry, surveillance)

PATH 9: Comparative Analysis

Tools: Abbreviated tool chains for each comparator + head-to-head trial search + PubMed meta-analyses

FDA Label Core Fields

For approved drugs, retrieve these DailyMed sections early (after getting set_id):

Fallback Chains

Quick Reference: Tools by Use Case

See TOOLS_REFERENCE.md for the complete tool listing with parameters and input format requirements.

Type Normalization

Many tools require string inputs. Always convert IDs before API calls:

• ChEMBL IDs, PubMed IDs, NCT IDs: convert int -> str
• SMILES for ADMET-AI: pass as list ["SMILES_STRING"]
• FAERS drug names: use UPPERCASE (e.g., "METFORMIN")
• ChEMBL IDs: full format "CHEMBL1431" not "1431"
• PharmGKB IDs: PA prefix "PA450657" not "450657"

Common Use Cases

Always maintain all section headers but adjust depth based on query focus and data availability.

When NOT to Use This Skill

• Target research -> Use target-intelligence-gatherer skill
• Disease research -> Use disease-research skill
• Literature-only -> Use literature-deep-research skill
• Single property lookup -> Call tool directly
• Structure similarity search -> Use PubChem_search_compounds_by_similarity directly

Cross-Skill References

For drug interaction checking, run: python3 skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py --type interaction --drug1 X --drug2 Y

Additional Resources

• Report template: REPORT_TEMPLATE.md - Initial file template, citation format, evidence grading, scorecard, audit template
• Report guidelines: REPORT_GUIDELINES.md - Detailed section-by-section instructions with output examples
• Tool reference: TOOLS_REFERENCE.md - Complete tool listing with parameters and input formats
• Verification checklist: CHECKLIST.md - Section-by-section pre-delivery verification
• Examples: EXAMPLES.md - Detailed workflow examples for different use cases