tooluniverse-drug-repurposing

name

tooluniverse-drug-repurposing

description

Identify drug repurposing candidates using ToolUniverse for target-based, compound-based, and disease-driven strategies. Searches existing drugs for new therapeutic indications by analyzing targets, bioactivity, safety profiles, and literature evidence. Use when exploring drug repurposing opportunities, finding new indications for approved drugs, or when users mention drug repositioning, off-label uses, or therapeutic alternatives.

Drug Repurposing with ToolUniverse

Systematically identify and evaluate drug repurposing candidates using multiple computational strategies.

IMPORTANT: Always use English terms in tool calls. Respond in the user's language.

---

Core Strategies

1. Target-Based: Disease targets -> Find drugs that modulate those targets
2. Compound-Based: Approved drugs -> Find new disease indications
3. Disease-Driven: Disease -> Targets -> Match to existing drugs

---

Workflow Overview

Phase 1: Disease & Target Analysis
  Get disease info (OpenTargets), find associated targets, get target details

Phase 2: Drug Discovery
  Search DrugBank, DGIdb, ChEMBL for drugs targeting disease-associated genes
  Get drug details, indications, pharmacology

Phase 3: Safety & Feasibility Assessment
  FDA warnings, FAERS adverse events, drug interactions, ADMET predictions

Phase 4: Literature Evidence
  PubMed, Europe PMC, clinical trials for existing evidence

Phase 5: Scoring & Ranking
  Composite score: target association + safety + literature + drug properties

See: PROCEDURES.md for detailed step-by-step procedures and code patterns.

---

Quick Start

from tooluniverse import ToolUniverse
tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Step 1: Get disease targets
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName="rheumatoid arthritis")
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=disease_info['data']['id'], limit=10)

# Step 2: Find drugs for each target
for target in targets['data'][:5]:
    drugs = tu.tools.DGIdb_get_drug_gene_interactions(gene_name=target['gene_symbol'])

---

Key ToolUniverse Tools

Disease & Target:

• OpenTargets_get_disease_id_description_by_name - Disease lookup
• OpenTargets_get_associated_targets_by_disease_efoId - Disease targets
• UniProt_get_entry_by_accession - Protein details

Drug Discovery:

• drugbank_get_drug_name_and_description_by_target_name - Drugs by target
• drugbank_get_drug_name_and_description_by_indication - Drugs by indication
• DGIdb_get_drug_gene_interactions - Drug-gene interactions
• ChEMBL_search_drugs / ChEMBL_get_drug_mechanisms - Drug search and MOA

Drug Information:

• drugbank_get_drug_basic_info_by_drug_name_or_id - Basic info
• drugbank_get_indications_by_drug_name_or_drugbank_id - Approved indications
• drugbank_get_pharmacology_by_drug_name_or_drugbank_id - Pharmacology
• drugbank_get_targets_by_drug_name_or_drugbank_id - Drug targets

Safety:

• FDA_get_warnings_and_cautions_by_drug_name - FDA warnings
• FAERS_search_reports_by_drug_and_reaction - Adverse events
• FAERS_count_death_related_by_drug - Serious outcomes
• drugbank_get_drug_interactions_by_drug_name_or_id - Interactions

Property Prediction:

• ADMETAI_predict_admet / ADMETAI_predict_toxicity - ADMET and toxicity

Literature:

• PubMed_search_articles / EuropePMC_search_articles / ClinicalTrials_search

---

Scoring Criteria

Category	Points	Breakdown
Target Association	0-40	Strong genetic: 40, Moderate: 25, Pathway-level: 15, Weak: 5
Safety Profile	0-30	FDA approved: 20, Phase III: 15, Phase II: 10, No black box: +10
Literature Evidence	0-20	Clinical trials: 5 each (max 15), Preclinical: 1 each (max 10)
Drug Properties	0-10	High bioavailability: 5, BBB penetration (CNS): 5, Low toxicity: 5

---

Best Practices

1. Start broad: Query multiple databases (DrugBank, ChEMBL, DGIdb)
2. Validate targets: Confirm target-disease associations in OpenTargets
3. Safety first: Prioritize approved drugs with known safety profiles
4. Literature mining: Search for existing clinical/preclinical evidence
5. Consider mechanism: Evaluate biological plausibility
6. Batch operations: Use tu.run_batch() for parallel queries

---

Troubleshooting

Problem	Solution
Disease not found	Try synonyms or EFO ID lookup
No drugs for target	Check HUGO nomenclature, expand to pathway-level, try similar targets
Insufficient literature	Search drug class instead, check preclinical/animal studies
Safety data unavailable	Drug may not be US-approved, check EMA or clinical trial safety

---

Reference Files

• REFERENCE.md - Detailed reference documentation
• EXAMPLES.md - Sample repurposing analyses
• PROCEDURES.md - Step-by-step procedures with code
• REPORT_TEMPLATE.md - Output report template
• Related skills: disease-intelligence-gatherer, chemical-compound-retrieval, tooluniverse-sdk