tooluniverse-drug-research

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

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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.

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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?

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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)

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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...)

5. DrugCentral_search(query=drug_name, size=...)
   -> Extract: InChIKey, CAS number, canonical SMILES, cross-referenced FDA/EMA/PMDA approval status in one call

DrugCentral (live-verified: DrugCentral_search, DrugCentral_get_drug, DrugCentral_get_targets) is a useful fifth disambiguation source and a fast Path 2 (Mechanism & Targets) shortcut — DrugCentral_get_targets(chem_id=<InChIKey or ChEMBL ID>) returns target names/classes directly, which can cross-check (not replace) the ChEMBL-activities-derived target table this skill's Path 2 already builds. Real example: DrugCentral_search(query="metformin") returns InChIKey XZWYZXLIPXDOLR-UHFFFAOYSA-N; DrugCentral_get_targets on that InChIKey returns 5'-AMP-activated protein kinase among its targets.

Dietary supplements are not in PubChem/ChEMBL/DailyMed/DrugCentral as prescription drugs — check the NIH DSLD instead. If the query resolves to zero hits in the chain above (or the user is clearly asking about an OTC vitamin/herbal/supplement product, e.g. "vitamin D gummies" or a specific brand), use NIHDSLD_search_products(query=<name or brand>) -> NIHDSLD_get_label(product_id=...) for the full per-serving ingredient list with amounts and %daily value. Live-verified: NIHDSLD_search_products(query="vitamin d") returns real product 20581 ("Vitamin D Gummy Vitamins", Nutrition Now); NIHDSLD_get_label(20581) returns its full label (2000 IU Vitamin D at 500% daily value per 2-gummy serving, plus calories/carbs/sugar) and an off_market flag. This complements rather than replaces the prescription-drug paths above — supplements have no FDA approval/mechanism-of-action data, so most other report sections legitimately stay "not applicable" for a supplement query.

Handle Naming Ambiguity

Issue	Example	Resolution
Salt forms	metformin vs metformin HCl	Note all CIDs; use parent compound
Isomers	omeprazole vs esomeprazole	Verify SMILES; separate entries if distinct
Prodrugs	enalapril vs enalaprilat	Document both; note conversion
Brand confusion	Different products same name	Clarify with user

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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

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FDA Label Core Fields

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

Batch	Sections	Maps to Report
Phase 1	mechanism_of_action, pharmacodynamics, chemistry	Sections 2-3
Phase 2	clinical_pharmacology, pharmacokinetics, drug_interactions	Sections 4, 6.5
Phase 3	warnings_and_cautions, adverse_reactions, dosage_and_administration	Sections 6, 8.2
Phase 4	pharmacogenomics, clinical_studies, description, inactive_ingredients	Sections 5, 7

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Fallback Chains

Primary Tool	Fallback	Use When
PubChem_get_CID_by_compound_name	ChEMBL_search_drugs	Name not in PubChem
ChEMBL_get_molecule_targets	Use ChEMBL_search_activities instead	Always avoid this tool
ChEMBL_get_activity	PubChemBioAssay_get_assay_summary	No ChEMBL ID
DailyMed_search_spls	PubChemTox_get_acute_effects	DailyMed timeout
PharmGKB_search_drugs	DailyMed PGx sections + PubMed	PharmGKB unavailable
PharmGKB_get_dosing_guidelines	DailyMed pharmacogenomics section	PharmGKB API error
FAERS_count_reactions_by_drug_event	Document "FAERS unavailable" + use label AEs	API error
ADMETAI_* (all tools)	DailyMed clinical_pharmacology + pharmacokinetics	Invalid SMILES or API error

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Quick Reference: Tools by Use Case

Use Case	Primary Tool	Fallback	Evidence
Name -> CID	PubChem_get_CID_by_compound_name	ChEMBL_search_drugs	T1
Properties	PubChem_get_compound_properties_by_CID	ADMET-AI physicochemical	T1/T2
FDA MOA	DailyMed_parse_clinical_pharmacology (mechanism_of_action)	-	T1
Targets	ChEMBL_search_activities -> ChEMBL_get_target	DGIdb_get_drug_info	T1
ADMET	ADMETAI_predict_* (5 tools)	DailyMed PK sections	T2/T1
Trials	search_clinical_trials	-	T1
Trial outcomes	extract_clinical_trial_outcomes	-	T1
FAERS	FAERS_count_reactions_by_drug_event	Label adverse_reactions	T1
Dose mods	DailyMed_parse_clinical_pharmacology (dosage, warnings)	-	T1
PGx	PharmGKB_search_drugs	DailyMed PGx + PubMed	T2/T1
Label	DailyMed_search_spls	PubChemTox_get_acute_effects	T1
Literature	PubMed_search_articles	EuropePMC_search_articles	Varies
Regulatory	FDA_OrangeBook_* tools	DailyMed label data	T1

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

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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"

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Common Use Cases

Use Case	Primary Sections	Light Sections
Approved Drug Profile	All 11 sections	None
Investigational Compound	1, 2, 3, 4, 9	5, 6, 7, 8
Safety Review	1, 5, 6, 7, 9	2, 3, 4, 8
ADMET Assessment	1, 2, 4	3, 5, 6, 7, 8, 9
Clinical Development Landscape	1, 5, 9	2, 3, 4, 6, 7, 8

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

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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

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Cross-Skill References

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

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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