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.
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.
Start by asking: WHY might this drug work for a new disease? Three strategies:
Each strategy uses different tools and has different evidentiary weight. Identify which strategy applies FIRST, then choose the corresponding workflow below. Do not run all three strategies blindly — reason about which is most plausible given the drug's mechanism.
LOOK UP DON'T GUESS: Never assume a drug hits a target, never assume a target is disease-relevant, never assume pathway overlap. Verify each link with tool calls.
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.
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
# Step 1: Get disease targets
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(diseaseName="rheumatoid arthritis")
# Response nests ID at data.search.hits[0].id
disease_id = disease_info['data']['search']['hits'][0]['id']
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(efoId=disease_id, limit=10)
# Step 2: Find drugs for each target
# Response nests targets at data.disease.associatedTargets.rows
rows = targets['data']['disease']['associatedTargets']['rows']
for target in rows[:5]:
gene = target['target']['approvedSymbol']
drugs = tu.tools.DGIdb_get_drug_gene_interactions(genes=[gene])
Disease & Target:
OpenTargets_get_disease_id_description_by_name - Disease lookupOpenTargets_get_associated_targets_by_disease_efoId - Disease targetsUniProt_get_entry_by_accession - Protein detailsDrug Discovery:
drugbank_get_drug_name_and_description_by_target_name - Drugs by target. Param: query= (NOT target_name=)drugbank_get_drug_name_and_description_by_indication - Drugs by indication. Param: query= (NOT indication=)DGIdb_get_drug_gene_interactions - Drug-gene interactions. Response path: data.data.genes.nodes[0].interactionsChEMBL_search_drugs / ChEMBL_get_drug_mechanisms - Drug search and MOADrug Information (ALL DrugBank tools use query= as the search parameter, plus case_sensitive=False, exact_match=False, limit=N):
drugbank_get_drug_basic_info_by_drug_name_or_id - Basic info. Param: query="drug_name"drugbank_get_indications_by_drug_name_or_drugbank_id - Approved indications. Param: query="drug_name"drugbank_get_pharmacology_by_drug_name_or_drugbank_id - Pharmacology. Param: query="drug_name"drugbank_get_targets_by_drug_name_or_drugbank_id - Drug targets. Param: query="drug_name"Safety:
FDA_get_warnings_and_cautions_by_drug_name - FDA warningsFAERS_search_reports_by_drug_and_reaction - Adverse events. Param: medicinalproduct= (NOT drug_name=)FAERS_count_death_related_by_drug - Serious outcomes. Param: medicinalproduct= (NOT drug_name=)drugbank_get_drug_interactions_by_drug_name_or_id - InteractionsProperty Prediction:
ADMETAI_predict_physicochemical_properties / ADMETAI_predict_toxicity - ADMET and toxicityPathway & Network Analysis:
ReactomeAnalysis_pathway_enrichment - Pathway enrichment. Param: identifiers="SOD1\nTARDBP\nFUS" (newline-separated string, NOT array)STRING_get_network - Protein interaction networks. Param: identifiers="SOD1\rTARDBP\rFUS" (CR-separated string), species=9606CTD_get_gene_diseases - Curated gene-disease associations. Param: input_terms="gene_symbol" (NOT gene_symbol=)Literature & Clinical Trials:
PubMed_search_articles / EuropePMC_search_articles - Literature searchsearch_clinical_trials - ClinicalTrials.gov search. Use condition for disease name. The intervention filter is strict and may miss trials — use query_term for broader drug-name matching as fallback.CNS diseases note: For neurological indications (ALS, Alzheimer's, Parkinson's), prioritize BBB-penetrant candidates. Use ChEMBL molecular properties (MW < 500, PSA < 90) as BBB proxy since
ADMETAI_predict_BBB_penetrancemay require thetooluniverse[ml]extra. Consider route of administration (oral preferred for patients with swallowing difficulty) and sex-specific effects from preclinical models.
| Category | Points | How to Score |
|---|---|---|
| Target Association | 0-40 | 40: Target has genetic evidence in disease (GWAS, rare variants); 25: Target is in a disease-associated pathway (Reactome, KEGG); 15: Target is differentially expressed in disease tissue; 5: Target shares a GO term with disease genes |
| Safety Profile | 0-30 | 30: FDA-approved drug, no black box warning, established safety record; 20: FDA-approved with manageable warnings; 10: Phase II+ data, acceptable safety; 0: Preclinical only or serious safety signals |
| Literature Evidence | 0-20 | 20: Phase II+ trial for the new indication exists; 15: Case reports or retrospective studies show efficacy; 10: Preclinical in-vivo evidence (animal models); 5: In-vitro evidence only; 0: No prior evidence |
| Drug Properties | 0-10 | 10: Oral, good bioavailability, IP available; 5: Injectable or narrow therapeutic window; 0: Poor PK or formulation challenges |
Classification:
| Grade | Definition | Action |
|---|---|---|
| E1 (Clinical) | Existing clinical trial for new indication (any phase) | High priority — check trial results |
| E2 (Epidemiological) | Retrospective/observational data showing benefit | Moderate priority — design prospective study |
| E3 (Preclinical) | Animal model evidence for new indication | Standard priority — validate mechanism |
| E4 (Computational) | Target overlap, network proximity, or molecular similarity only | Low priority — needs experimental validation |
After running Phases 1-4, synthesize by answering:
Is the target validated for this disease? Check OpenTargets association score (>0.5 = strong). Cross-reference with genetic evidence (GWAS hits, rare variant studies). If target association is only pathway-level, the repurposing hypothesis is speculative.
Does the drug actually hit the target at achievable doses? Check ChEMBL IC50/Ki values. If the drug's affinity for the new target is >10x weaker than for its original target, clinical efficacy is unlikely at safe doses.
What's the safety margin? Compare the dose needed for the new indication to the approved dose. If higher doses are needed, safety data from the original indication may not apply.
Is there prior clinical evidence? A Phase II trial for the new indication (even failed) is more informative than 100 computational predictions. Check search_clinical_trials first.
What's the competitive landscape? If better drugs already exist for the disease, repurposing offers little value. Check DrugBank indications for approved therapies.
search_clinical_trials(condition="[disease]", intervention="[drug]") — if a trial already exists, start thereA drug that hits a new target only at 100x its approved dose is NOT a viable repurposing candidate. Use this procedure after identifying drug-target pairs:
# Drug-target dose feasibility analysis
# Uses ChEMBL bioactivity data from ToolUniverse
from tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
def check_dose_feasibility(drug_name, original_target, new_target):
"""
Compare drug's potency at original vs new target.
If new_target IC50 > 10x original_target IC50, flag as unlikely feasible.
"""
# Get bioactivity for original target
orig = tu.run_one_function({
'name': 'ChEMBL_get_bioactivities',
'arguments': {
'molecule_chembl_id': drug_name, # or search first
'target_chembl_id': original_target,
'limit': 10
}
})
# Get bioactivity for new target
new = tu.run_one_function({
'name': 'ChEMBL_get_bioactivities',
'arguments': {
'molecule_chembl_id': drug_name,
'target_chembl_id': new_target,
'limit': 10
}
})
# Extract IC50/Ki values and compare
# If new target requires >10x concentration → NOT FEASIBLE at safe doses
# If new target is within 3x → PROMISING
# If new target is within 1x → STRONG candidate
pass # Parse actual values from results
# Alternative: Quick Cmax check
# If published Cmax at approved dose < IC50 for new target → NOT FEASIBLE
# Cmax data can be found in:
# - DrugBank pharmacology section
# - DailyMed clinical pharmacology section
# - PubMed PK studies
Key principle: The most common reason repurposing fails is insufficient drug exposure at the new target. Always check whether the drug's concentration at approved doses reaches the IC50 for the new target.
| 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 |