Identify drug repurposing candidates via target-based, compound-based, and disease-based strategies. Combines drug-target-disease network reasoning with mechanism rationale, clinical-trial precedent, and patent/regulatory feasibility. Use for hypothesis-generating repurposing for orphan diseases, finding existing drugs for new indications, and prioritizing candidates by evidence and feasibility.
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Identify drug repurposing candidates via target-based, compound-based, and disease-based strategies. Combines drug-target-disease network reasoning with mechanism rationale, clinical-trial precedent, and patent/regulatory feasibility. Use for hypothesis-generating repurposing for orphan diseases, finding existing drugs for new indications, and prioritizing candidates by evidence and feasibility.
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.
Reasoning Before Searching
Start by asking: WHY might this drug work for a new disease? Three strategies:
(a) Same target: The drug's primary target is also involved in the new disease. This is the strongest hypothesis — use OpenTargets to check if the target has genetic evidence in both diseases before any other search.
(b) Off-target activity: The drug has secondary targets or off-target effects that are relevant to the new disease. Check ChEMBL bioactivity data for all known targets of the drug, not just its primary one.
(c) Shared pathways: The original indication and new disease share molecular pathways, even if the target itself is not genetically linked. Use Reactome and STRING to compare pathway overlap between diseases.
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.
Core Strategies
Target-Based: Disease targets -> Find drugs that modulate those targets
Compound-Based: Approved drugs -> Find new disease indications
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()
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])
PubMed_search_articles / EuropePMC_search_articles - Literature search
search_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_penetrance may require the tooluniverse[ml] extra. Consider route of administration (oral preferred for patients with swallowing difficulty) and sex-specific effects from preclinical models.
Scoring & Decision Framework
Repurposing Viability Score (0-100)
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:
80-100: Strong candidate — proceed to clinical evaluation
60-79: Promising — worth preclinical validation or retrospective study
<40: Weak — likely not worth pursuing without new mechanistic insight
Evidence Grading for Repurposing
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
How to Interpret and Combine Results
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.
Best Practices
Check clinical trials FIRST: search_clinical_trials(condition="[disease]", intervention="[drug]") — if a trial already exists, start there
Validate targets with genetics: Genetic evidence (GWAS, rare variants) is the strongest predictor of successful drug development
Safety first: Prioritize approved drugs with known safety profiles
Dose matters: A drug that hits a disease target at 100x its approved dose is not a repurposing candidate
Mechanism over correlation: Network proximity alone is insufficient — explain WHY the drug should work
Consider IP and formulation: Generic drugs are easier to repurpose but harder to fund trials for
A 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 ToolUniversefrom tooluniverse import ToolUniverse
tu = ToolUniverse()
tu.load_tools()
defcheck_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 candidatepass# 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.
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
