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
5. Mandatory completeness - All sections must exist, even if "data unavailable"

Critical Workflow Requirements

1. Report-First Approach (MANDATORY)

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

1. Create the report file FIRST - Before any data collection, create a markdown file:

   • File name: [DRUG]_drug_report.md (e.g., metformin_drug_report.md)
   • Initialize with all 11 section headers from the template
   • Add placeholder text: [Researching...] in each section

2. Progressively update the report - As you gather data:

   • Update each section with findings immediately after retrieving data
   • Replace [Researching...] with actual content
   • The user sees the report growing, not the search process

3. Use ALL relevant tools - For comprehensive coverage:

   • Query multiple databases for each data type
   • Cross-reference information across sources
   • Use fallback tools when primary tools return limited data

2. Citation Requirements (MANDATORY)

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

## 3. Mechanism & Targets

### 3.1 Primary Mechanism
Metformin activates AMP-activated protein kinase (AMPK), reducing hepatic glucose 
production and increasing insulin sensitivity in peripheral tissues.

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

### 3.2 Primary Target(s)
| Target | UniProt | Activity | Potency | Source |
|--------|---------|----------|---------|--------|
| AMPK (PRKAA1) | Q13131 | Activator | EC50 ~10 µM | ChEMBL |
| Mitochondrial Complex I | - | Inhibitor | IC50 ~1 mM | Literature |

*Source: ChEMBL via `ChEMBL_get_target_by_chemblid` (CHEMBL1431)*

Citation Format

For each data section, include at the end:

---
**Data Sources for this section:**
- PubChem: `PubChem_get_compound_properties_by_CID` (CID: 4091)
- ChEMBL: `ChEMBL_get_bioactivity_by_chemblid` (CHEMBL1431)
- DGIdb: `DGIdb_get_drug_info` (metformin)
---

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 → Update Section 2 (Chemistry)
        ↓
Step 4: Query ChEMBL/DGIdb → Update Section 3 (Mechanism & Targets)
        ↓
Step 5: Query ADMET-AI tools → Update Section 4 (ADMET)
        ↓
Step 6: Query ClinicalTrials.gov → Update Section 5 (Clinical Development)
        ↓
Step 7: Query FAERS/DailyMed → Update Section 6 (Safety)
        ↓
Step 8: Query PharmGKB → Update Section 7 (Pharmacogenomics)
        ↓
Step 9: Query DailyMed → 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)

4. Report Detail Requirements

Each section must be comprehensive and detailed:

• Tables: Use tables for structured data (targets, trials, adverse events)
• Lists: Use bullet points for features, findings, key points
• Paragraphs: Include narrative summaries that synthesize findings
• Numbers: Include specific values, counts, percentages (not vague terms)
• Context: Explain what the data means, not just what it is

BAD (too brief):

### Clinical Trials
Multiple trials completed. Approved for diabetes.

GOOD (detailed with sources):

### 5.2 Clinical Trial Landscape

| Phase | Total | Completed | Recruiting | Status |
|-------|-------|-----------|------------|--------|
| Phase 4 | 89 | 72 | 12 | Post-marketing |
| Phase 3 | 156 | 134 | 15 | Pivotal |
| Phase 2 | 203 | 178 | 18 | Dose-finding |
| Phase 1 | 67 | 61 | 4 | Safety |

*Source: ClinicalTrials.gov via `search_clinical_trials` (intervention="metformin")*

**Total Registered Trials**: 515 (as of 2026-02-04)
**Primary Indications Under Investigation**: Type 2 diabetes (312), PCOS (87), Cancer (45), Obesity (38), NAFLD (33)

### Trial Outcomes Summary
- **Glycemic Control**: Mean HbA1c reduction of 1.0-1.5% in monotherapy [★★★: NCT00123456]
- **Cardiovascular**: UKPDS showed 39% reduction in MI risk [★★★: PMID:9742976]
- **Cancer Prevention**: Mixed results; ongoing investigation [★★☆: NCT02019979]

*Source: `extract_clinical_trial_outcomes` for NCT IDs listed*

Initial Report Template (Create This First)

When starting research, immediately create this file before any tool calls:

File: [DRUG]_drug_report.md

# Drug Research Report: [DRUG NAME]

**Generated**: [Date] | **Query**: [Original query] | **Status**: In Progress

---

## Executive Summary
[Researching...]

---

## 1. Compound Identity
### 1.1 Database Identifiers
[Researching...]
### 1.2 Structural Information
[Researching...]
### 1.3 Names & Synonyms
[Researching...]

---

## 2. Chemical Properties
### 2.1 Physicochemical Profile
[Researching...]
### 2.2 Drug-Likeness Assessment
[Researching...]
### 2.3 Solubility & Permeability
[Researching...]

---

## 3. Mechanism & Targets
### 3.1 Primary Mechanism of Action
[Researching...]
### 3.2 Primary Target(s)
[Researching...]
### 3.3 Target Selectivity & Off-Targets
[Researching...]
### 3.4 Bioactivity Profile (ChEMBL)
[Researching...]

---

## 4. ADMET Properties
### 4.1 Absorption
[Researching...]
### 4.2 Distribution
[Researching...]
### 4.3 Metabolism
[Researching...]
### 4.4 Excretion
[Researching...]
### 4.5 Toxicity Predictions
[Researching...]

---

## 5. Clinical Development
### 5.1 Development Status
[Researching...]
### 5.2 Clinical Trial Landscape
[Researching...]
### 5.3 Approved Indications
[Researching...]
### 5.4 Investigational Indications
[Researching...]
### 5.5 Key Efficacy Data
[Researching...]

---

## 6. Safety Profile
### 6.1 Clinical Adverse Events
[Researching...]
### 6.2 Post-Marketing Safety (FAERS)
[Researching...]
### 6.3 Black Box Warnings
[Researching...]
### 6.4 Contraindications
[Researching...]
### 6.5 Drug-Drug Interactions
[Researching...]

### 6.6 Drug-Target Interactions (Off-Target)
[Researching...]

---

## 7. Pharmacogenomics
### 7.1 Relevant Pharmacogenes
[Researching...]
### 7.2 Clinical Annotations
[Researching...]
### 7.3 Dosing Guidelines (CPIC/DPWG)
[Researching...]
### 7.4 Actionable Variants
[Researching...]

---

## 8. Regulatory & Labeling
### 8.1 Approval Status
[Researching...]
### 8.2 Label Highlights
[Researching...]
### 8.3 Patents & Exclusivity
[Researching...]

---

## 9. Literature & Research Landscape
### 9.1 Publication Metrics
[Researching...]
### 9.2 Research Themes
[Researching...]
### 9.3 Recent Key Publications
[Researching...]

---

## 10. Conclusions & Assessment
### 10.1 Drug Profile Scorecard
[Researching...]
### 10.2 Key Strengths
[Researching...]
### 10.3 Key Concerns/Limitations
[Researching...]
### 10.4 Research Gaps
[Researching...]

---

## 11. Data Sources & Methodology
[Will be populated as research progresses...]

Then progressively replace [Researching...] with actual findings as you query each tool.

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_compounds(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

Tool Chains by Research Path

PATH 1: Chemical Properties

Objective: Full physicochemical profile and drug-likeness

Multi-Step Chain:

1. PubChem_get_compound_properties_by_CID(cid)
   └─ Extract: MW, formula, XLogP, TPSA, HBD, HBA, rotatable bonds
   
2. ADMETAI_predict_physicochemical_properties(smiles=[smiles])
   └─ Extract: MW, logP, HBD, HBA, Lipinski, QED, stereo_centers, TPSA
   
3. ADMETAI_predict_solubility_lipophilicity_hydration(smiles=[smiles])
   └─ Extract: Solubility_AqSolDB, Lipophilicity_AstraZeneca

Output for Report:

### 2.1 Physicochemical Profile

| Property | Value | Drug-Likeness | Source |
|----------|-------|---------------|--------|
| **Molecular Weight** | 129.16 g/mol | ✓ (< 500) | PubChem |
| **LogP** | -2.64 | ✓ (< 5) | ADMET-AI |
| **TPSA** | 91.5 Ų | ✓ (< 140) | PubChem |
| **H-Bond Donors** | 2 | ✓ (≤ 5) | PubChem |
| **H-Bond Acceptors** | 5 | ✓ (< 10) | PubChem |
| **Rotatable Bonds** | 2 | ✓ (< 10) | PubChem |

**Lipinski Rule of Five**: ✓ PASS (0 violations)
**QED Score**: 0.74 (Good drug-likeness)

*Sources: PubChem via `PubChem_get_compound_properties_by_CID`, ADMET-AI via `ADMETAI_predict_physicochemical_properties`*

PATH 2: Targets & Bioactivity

Objective: Primary targets, mechanism, selectivity, drug-protein interactions

Multi-Step Chain:

1. ChEMBL_get_bioactivity_by_chemblid(chembl_id)
   └─ Extract: Target names, activity types, potency values [★★★]
   
2. ChEMBL_get_target_by_chemblid(chembl_id)
   └─ Extract: Target ChEMBL IDs, UniProt accessions [★★★]
   
3. DGIdb_get_drug_info(drugs=[drug_name])
   └─ Extract: Target genes, interaction types, sources [★★☆]
   
4. PubChem_get_bioactivity_summary_by_CID(cid)
   └─ Extract: Assay summary, active/inactive counts [★★☆]

5. STITCH_get_chemical_protein_interactions(identifiers=[smiles], species=9606)
   └─ Extract: Predicted/known protein targets, confidence scores [★★☆-★☆☆]

6. STITCH_get_interaction_partners(identifiers=[drug_name], species=9606)
   └─ Extract: Full interaction network [★☆☆ for predictions]

Evidence Tier Guide for Targets:

• ChEMBL binding assay (IC50 <100nM) = ★★★
• ChEMBL functional assay = ★★☆
• Predicted interaction (STITCH) = ★☆☆

Output for Report:

### 3.2 Primary Target(s)

| Target | UniProt | Type | Potency | Assays | Source |
|--------|---------|------|---------|--------|--------|
| PRKAA1 (AMPK α1) | Q13131 | Activator | EC50 ~10 µM | 12 | ChEMBL |
| PRKAA2 (AMPK α2) | P54646 | Activator | EC50 ~15 µM | 8 | ChEMBL |
| SLC22A1 (OCT1) | O15245 | Substrate | Km ~1.5 mM | 5 | DGIdb |

*Source: ChEMBL via `ChEMBL_get_target_by_chemblid` (CHEMBL1431)*

### 3.4 Bioactivity Profile

**Total ChEMBL Activities**: 847 datapoints across 234 assays
- **Potency Range**: IC50/EC50 from 1 µM to 10 mM
- **Primary Activity**: AMPK activation (kinase assays)
- **Secondary Activities**: Mitochondrial complex I inhibition

*Source: `ChEMBL_get_bioactivity_by_chemblid`*

PATH 3: ADMET Properties

Objective: Full ADMET profile with predictions

Multi-Step Chain:

1. ADMETAI_predict_bioavailability(smiles=[smiles])
   └─ Extract: Bioavailability_Ma, HIA_Hou, PAMPA_NCATS, Caco2_Wang, Pgp_Broccatelli
   
2. ADMETAI_predict_BBB_penetrance(smiles=[smiles])
   └─ Extract: BBB_Martins (0-1 probability)
   
3. ADMETAI_predict_CYP_interactions(smiles=[smiles])
   └─ Extract: CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP3A4 (inhibitor/substrate)
   
4. ADMETAI_predict_clearance_distribution(smiles=[smiles])
   └─ Extract: Clearance, Half_Life_Obach, VDss_Lombardo, PPBR_AZ
   
5. ADMETAI_predict_toxicity(smiles=[smiles])
   └─ Extract: AMES, hERG, DILI, ClinTox, LD50_Zhu, Carcinogens

Output for Report:

### 4.1 Absorption

| Endpoint | Prediction | Interpretation |
|----------|------------|----------------|
| **Oral Bioavailability** | 0.72 | Good (>50%) |
| **Human Intestinal Absorption** | 0.89 | High |
| **Caco-2 Permeability** | -5.2 (log cm/s) | Moderate |
| **PAMPA** | 0.34 | Low-moderate |
| **P-gp Substrate** | 0.23 | Unlikely substrate |

*Source: ADMET-AI via `ADMETAI_predict_bioavailability`*

### 4.5 Toxicity Predictions

| Endpoint | Prediction | Risk Level |
|----------|------------|------------|
| **AMES Mutagenicity** | 0.08 | Low risk |
| **hERG Inhibition** | 0.12 | Low risk |
| **Hepatotoxicity (DILI)** | 0.15 | Low risk |
| **Clinical Toxicity** | 0.21 | Low risk |
| **LD50** | 2.8 (log mol/kg) | Moderate |

*Source: ADMET-AI via `ADMETAI_predict_toxicity`*

**Summary**: Low predicted toxicity across all endpoints. Favorable safety profile.

PATH 4: Clinical Trials

Objective: Complete clinical development picture

Multi-Step Chain:

1. search_clinical_trials(intervention=drug_name, pageSize=100)
   └─ Extract: Total count, NCT IDs, phases, statuses
   
2. get_clinical_trial_conditions_and_interventions(nct_ids=[top_ids])
   └─ Extract: Conditions, interventions, arm groups
   
3. extract_clinical_trial_outcomes(nct_ids=[completed_phase3])
   └─ Extract: Primary outcomes, efficacy measures, p-values
   
4. extract_clinical_trial_adverse_events(nct_ids=[completed_ids])
   └─ Extract: Serious AEs, common AEs by organ system

PATH 5: Post-Marketing Safety & Drug Interactions

Objective: Real-world safety signals from FAERS + drug-drug interactions

Multi-Step Chain (FAERS):

1. FAERS_count_reactions_by_drug_event(medicinalproduct=drug_name)
   └─ Extract: Top 20 adverse reactions by MedDRA term [★★★ real-world]
   
2. FAERS_count_seriousness_by_drug_event(medicinalproduct=drug_name)
   └─ Extract: Serious vs non-serious ratio [★★★]
   
3. FAERS_count_outcomes_by_drug_event(medicinalproduct=drug_name)
   └─ Extract: Recovered, recovering, fatal, unresolved counts [★★★]
   
4. FAERS_count_death_related_by_drug(medicinalproduct=drug_name)
   └─ Extract: Fatal outcome count [★★★]
   
5. FAERS_count_patient_age_distribution(medicinalproduct=drug_name)
   └─ Extract: Reports by age group [★★★]

Multi-Step Chain (Drug-Drug Interactions):

6. STITCH_get_chemical_protein_interactions(identifiers=[drug1, drug2], species=9606)
   └─ Extract: Shared targets (potential DDI mechanism) [★★☆]

7. DailyMed_search_spls(drug_name)
   └─ Extract: Drug interactions section from label [★★★ FDA-approved]

8. drugbank_get_targets_by_drug_name_or_drugbank_id(query=drug_name)
   └─ Extract: Targets, enzymes, transporters for DDI prediction [★★☆]

DDI Mechanism Analysis: For each major interaction found, note:

• CYP enzyme involved (CYP3A4, CYP2D6, etc.)
• Interaction type (inhibitor/inducer/substrate)
• Clinical severity (contraindicated, major, moderate, minor)

Output for Report:

### 6.2 Post-Marketing Safety (FAERS)

**Total FAERS Reports**: 45,234 (as of 2026-02-04)

#### Top Adverse Reactions
| Reaction (MedDRA PT) | Count | % of Reports |
|----------------------|-------|--------------|
| Diarrhoea | 8,234 | 18.2% |
| Nausea | 6,892 | 15.2% |
| Lactic acidosis | 3,456 | 7.6% |
| Vomiting | 2,987 | 6.6% |
| Abdominal pain | 2,543 | 5.6% |

*Source: FDA FAERS via `FAERS_count_reactions_by_drug_event`*

#### Outcome Distribution
| Outcome | Count | Percentage |
|---------|-------|------------|
| Recovered/Resolved | 18,234 | 40.3% |
| Not Recovered | 12,456 | 27.5% |
| Fatal | 2,134 | 4.7% |
| Unknown | 12,410 | 27.4% |

*Source: `FAERS_count_outcomes_by_drug_event`*

**Signal Assessment**: Lactic acidosis signal consistent with known labeling. GI events expected class effect.

PATH 6: Pharmacogenomics

Objective: PGx associations and dosing guidelines

Multi-Step Chain:

1. PharmGKB_search_drugs(query=drug_name)
   └─ Extract: PharmGKB drug ID
   
2. PharmGKB_get_drug_details(drug_id)
   └─ Extract: Cross-references, related genes
   
3. PharmGKB_get_clinical_annotations(gene_id)  # For each related gene
   └─ Extract: Variant-drug associations, evidence levels
   
4. PharmGKB_get_dosing_guidelines(gene=gene_symbol)
   └─ Extract: CPIC/DPWG guideline recommendations

Output for Report:

### 7.1 Relevant Pharmacogenes

| Gene | Role | Evidence Level | Source |
|------|------|----------------|--------|
| **SLC22A1** (OCT1) | Transporter (uptake) | 2A | PharmGKB |
| **SLC22A2** (OCT2) | Transporter (renal) | 2B | PharmGKB |
| **SLC47A1** (MATE1) | Transporter (efflux) | 3 | PharmGKB |

*Source: PharmGKB via `PharmGKB_get_drug_details`*

### 7.3 Dosing Guidelines

**CPIC Guideline**: No CPIC guideline currently available for metformin.

**Clinical Annotations**:
- rs628031 (SLC22A1): Reduced metformin response in *4/*4 carriers
- rs316019 (SLC22A2): May affect renal clearance

*Source: `PharmGKB_get_clinical_annotations`*

Evidence Grading System

Evidence Tiers

Application in Report

Metformin reduces hepatic glucose output via AMPK activation [★★★: FDA Label].
Phase 3 trials demonstrated HbA1c reduction of 1.0-1.5% [★★★: NCT00123456].
Preclinical studies suggest anti-cancer properties [★☆☆: PMID:23456789].
ADMET-AI predicts low hERG liability (0.12) [☆☆☆: computational].

Per-Section Summary

Include evidence quality summary for each major section:

### 5. Clinical Development
**Evidence Quality**: Strong (156 Phase 3 trials, 203 Phase 2, 67 Phase 1)
**Data Confidence**: High - mature clinical program with decades of data

Section Completeness Checklist

Before finalizing any report, verify each section meets minimum requirements:

Section 1 (Identity) - Minimum Requirements

• PubChem CID with link
• ChEMBL ID with link (or "Not in ChEMBL")
• Canonical SMILES
• Molecular formula and weight
• At least 3 brand names OR "Generic only"
• Salt forms identified (or "Parent compound only")

Section 2 (Chemistry) - Minimum Requirements

• 6+ physicochemical properties in table format
• Lipinski rule assessment with pass/fail
• QED score with interpretation
• Solubility prediction with interpretation

Section 3 (Mechanism) - Minimum Requirements

• Primary mechanism described in 2-3 sentences
• At least 1 primary target with UniProt ID
• Activity type and potency (IC50/EC50/Ki)
• Off-target activity addressed (or "Highly selective")

Section 4 (ADMET) - Minimum Requirements

• All 5 subsections present (A, D, M, E, T)
• Absorption: bioavailability + at least 2 other endpoints
• Distribution: BBB + VDss or PPB
• Metabolism: CYP substrate/inhibitor status for 3+ CYPs
• Excretion: clearance OR half-life
• Toxicity: AMES + hERG + at least 1 other

Section 5 (Clinical) - Minimum Requirements

• Development status clearly stated (Approved/Investigational/Preclinical)
• Trial counts by phase in table format
• Approved indications with year (or "Not approved")
• Key efficacy data with trial references (or "No outcome data")

Section 6 (Safety) - Minimum Requirements

• Top 5 adverse events with frequencies
• FAERS data OR explicit "Insufficient FAERS data"
• Black box warnings (or "None")
• At least 3 drug interactions OR "No significant interactions"

Section 7 (PGx) - Minimum Requirements

• Pharmacogenes listed (or "None identified in PharmGKB")
• CPIC/DPWG guideline status
• At least 1 clinical annotation OR "No annotations"

Section 10 (Conclusions) - Minimum Requirements

• 5-point scorecard covering: efficacy, safety, PK, druggability, competition
• 3+ key strengths
• 3+ key concerns/limitations
• At least 2 research gaps identified

Drug Profile Scorecard Template

Include in Section 10:

### 10.1 Drug Profile Scorecard

| Criterion | Score (1-5) | Rationale |
|-----------|-------------|-----------|
| **Efficacy Evidence** | 5 | Multiple Phase 3 trials, decades of use |
| **Safety Profile** | 4 | Well-tolerated; lactic acidosis rare but serious |
| **PK/ADMET** | 4 | Good bioavailability; renal elimination |
| **Target Validation** | 4 | AMPK mechanism well-established |
| **Competitive Position** | 3 | First-line but many alternatives |
| **Overall** | 4.0 | **Strong drug profile** |

**Interpretation**: 
- 5 = Excellent, 4 = Good, 3 = Moderate, 2 = Concerning, 1 = Poor

Fallback Chains

Quick Reference: Tools by Use Case

Common Use Cases

Approved Drug Profile

User: "Tell me about metformin" → Full 11-section report emphasizing clinical data, FAERS, PGx

Investigational Compound

User: "What do we know about compound X (ChEMBL123456)?" → Emphasize preclinical data, mechanism, early trials; safety sections may be sparse

Safety Review

User: "What are the safety concerns with drug Y?" → Deep dive on FAERS, black box warnings, interactions, PGx; lighter on chemistry

ADMET Assessment

User: "Evaluate this compound's drug-likeness [SMILES]" → Focus on Sections 2 and 4; other sections may be brief or N/A

Clinical Development Landscape

User: "What trials are ongoing for drug Z?" → Heavy emphasis on Section 5; trial tables with status, phases, indications

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

Use this skill for comprehensive, multi-dimensional drug profiling.

Additional Resources

• Tool reference: TOOLS_REFERENCE.md - Complete tool listing
• Verification checklist: CHECKLIST.md - Pre-delivery verification
• Examples: EXAMPLES.md - Detailed workflow examples