Drug Repurposing

from tooluniverse import ToolUniverse

tu = ToolUniverse(use_cache=True)
tu.load_tools()

# Example: Find repurposing candidates for a disease
disease_name = "rheumatoid arthritis"

# Step 1: Get disease information
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(
    diseaseName=disease_name
)

# Step 2: Get associated targets
disease_id = disease_info['data']['id']
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(
    efoId=disease_id,
    limit=10
)

# Step 3: Find drugs for each target
for target in targets['data'][:5]:
    drugs = tu.tools.DGIdb_get_drug_gene_interactions(
        gene_name=target['gene_symbol']
    )
    # Evaluate each drug candidate...

# 1.1 Get disease information
disease_info = tu.tools.OpenTargets_get_disease_id_description_by_name(
    diseaseName="[disease_name]"
)

# 1.2 Find associated targets
targets = tu.tools.OpenTargets_get_associated_targets_by_disease_efoId(
    efoId=disease_info['data']['id'],
    limit=20
)

# 1.3 Get target details for top candidates
target_details = []
for target in targets['data'][:10]:
    details = tu.tools.UniProt_get_entry_by_accession(
        accession=target['uniprot_id']
    )
    target_details.append(details)

# 2.1 Find drugs targeting disease-associated targets
drug_candidates = []

for target in targets['data'][:10]:
    # Search DrugBank
    drugbank_results = tu.tools.drugbank_get_drug_name_and_description_by_target_name(
        target_name=target['gene_symbol']
    )
    
    # Search DGIdb
    dgidb_results = tu.tools.DGIdb_get_drug_gene_interactions(
        gene_name=target['gene_symbol']
    )
    
    # Search ChEMBL
    chembl_results = tu.tools.ChEMBL_search_drugs(
        query=target['gene_symbol'],
        limit=10
    )
    
    drug_candidates.extend([drugbank_results, dgidb_results, chembl_results])

# 2.2 Get drug details
for drug_name in unique_drugs:
    # Get DrugBank info
    drug_info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(
        drug_name_or_drugbank_id=drug_name
    )
    
    # Get current indications
    indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id(
        drug_name_or_drugbank_id=drug_name
    )
    
    # Get pharmacology
    pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(
        drug_name_or_drugbank_id=drug_name
    )

# 3.1 Check FDA safety data
for drug in top_candidates:
    # Get warnings and precautions
    warnings = tu.tools.FDA_get_warnings_and_cautions_by_drug_name(
        drug_name=drug['name']
    )
    
    # Get adverse event reports
    adverse_events = tu.tools.FAERS_search_reports_by_drug_and_reaction(
        drug_name=drug['name'],
        limit=100
    )
    
    # Get drug interactions
    interactions = tu.tools.drugbank_get_drug_interactions_by_drug_name_or_id(
        drug_name_or_id=drug['name']
    )

# 3.2 Assess ADMET properties (for novel formulations)
for drug in top_candidates:
    if 'smiles' in drug:
        admet = tu.tools.ADMETAI_predict_admet(
            smiles=drug['smiles'],
            use_cache=True
        )

# 4.1 Search for existing evidence
for drug in top_candidates:
    # PubMed search
    query = f"{drug['name']} AND {disease_name}"
    pubmed_results = tu.tools.PubMed_search_articles(
        query=query,
        max_results=50
    )
    
    # Europe PMC search
    pmc_results = tu.tools.EuropePMC_search_articles(
        query=query,
        limit=50
    )
    
    # Clinical trials
    trials = tu.tools.ClinicalTrials_search(
        condition=disease_name,
        intervention=drug['name']
    )

def score_repurposing_candidate(drug, target_score, safety_data, literature_count):
    """Score drug repurposing candidate (0-100)."""
    score = 0
    
    # Target association strength (0-40 points)
    score += min(target_score * 40, 40)
    
    # Safety profile (0-30 points)
    if drug['approval_status'] == 'approved':
        score += 20
    elif drug['approval_status'] == 'clinical':
        score += 10
    
    if not safety_data.get('black_box_warning'):
        score += 10
    
    # Literature evidence (0-20 points)
    score += min(literature_count / 5 * 20, 20)
    
    # Drug-likeness (0-10 points)
    if drug.get('bioavailability') == 'high':
        score += 10
    
    return score

# Score all candidates
scored_candidates = []
for drug in drug_candidates:
    score = score_repurposing_candidate(
        drug=drug,
        target_score=drug['target_association_score'],
        safety_data=drug['safety_profile'],
        literature_count=drug['supporting_papers']
    )
    drug['repurposing_score'] = score
    scored_candidates.append(drug)

# Sort by score
ranked_candidates = sorted(
    scored_candidates,
    key=lambda x: x['repurposing_score'],
    reverse=True
)

# Find drugs with similar mechanism of action
known_drug = "metformin"

# Get mechanism
moa = tu.tools.drugbank_get_drug_desc_pharmacology_by_moa(
    mechanism_of_action="[moa_term]"
)

# Get similar drugs
similar = tu.tools.ChEMBL_search_similar_molecules(
    query=known_drug,
    similarity_threshold=70
)

# Use pathway analysis
pathways = tu.tools.drugbank_get_pathways_reactions_by_drug_or_id(
    drug_name_or_drugbank_id="[drug_name]"
)

# Find drugs affecting same pathways
pathway_drugs = tu.tools.drugbank_get_drug_name_and_description_by_pathway_name(
    pathway_name=pathways['data'][0]['pathway_name']
)

# Search by indication/phenotype
indication_drugs = tu.tools.drugbank_get_drug_name_and_description_by_indication(
    indication="[related_indication]"
)

# Analyze adverse events as therapeutic effects
# Example: minoxidil (hypertension) → hair growth
adverse_as_therapeutic = tu.tools.FAERS_search_reports_by_drug_and_reaction(
    drug_name="[drug_name]",
    limit=1000
)

## Drug Repurposing Analysis: [Disease Name]

### Top 10 Repurposing Candidates

#### 1. [Drug Name] (Score: 87/100)

**Current Indications**: [list approved uses]
**Proposed Indication**: [new disease/condition]
**Repurposing Rationale**: Targets [gene/protein] with high association to disease

**Evidence Summary**:
- Target association score: 0.85
- Approval status: FDA approved (safer profile)
- Literature support: 23 papers, 4 clinical trials
- Safety profile: No black box warnings

**Mechanism**: [Brief mechanism description]

**Next Steps**: 
- Phase II trial feasibility assessment
- Patient population identification
- Dosing optimization study

**Key Papers**:
1. Smith et al. 2024 - Clinical efficacy in similar condition
2. Jones et al. 2023 - Mechanism validation

---

#### 2. [Drug Name] (Score: 79/100)
[Similar structure...]

### Supporting Analysis

**Target Network**: [visualization or description]
**Pathway Overlap**: [affected pathways]
**Safety Considerations**: [major concerns]
**Development Timeline**: [estimated phases]