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 (drug names, disease names, target names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language. Core Strategies 1. Target-Based Repurposing Start with disease targets → Find drugs that modulate those targets 2. Compound-Based Repurposing Start with approved drugs → Find new disease indications 3. Disease-Driven Repurposing Start with disease → Find targets → Match to existing drugs Quick Start 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... Complete Workflow Phase 1: Disease & Target Analysis # 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) Phase 2: Drug Discovery # 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 ) Phase 3: Safety & Feasibility Assessment # 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 ) Phase 4: Literature Evidence # 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' ] ) Phase 5: Scoring & Ranking Create a scoring function to rank candidates: 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 ) Alternative Strategies Strategy A: Mechanism-Based Repurposing # 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 ) Strategy B: Network-Based Repurposing # 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' ] ) Strategy C: Phenotype-Based Repurposing # 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 ) Key ToolUniverse Tools Disease & Target Tools : 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 Tools : 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 - Drug search ChEMBL_get_drug_mechanisms - Mechanism of action Drug Information Tools : drugbank_get_drug_basic_info_by_drug_name_or_id - Basic drug 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 Assessment Tools : 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 Tools : ADMETAI_predict_admet - ADMET properties ADMETAI_predict_toxicity - Toxicity prediction Literature Tools : PubMed_search_articles - PubMed search EuropePMC_search_articles - Europe PMC search ClinicalTrials_search - Clinical trials Output Format Present results as ranked candidates: ## 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] Scoring Criteria Target Association (0-40 points) : Strong genetic evidence: 40 Moderate association: 25 Pathway-level evidence: 15 Weak/predicted: 5 Safety Profile (0-30 points) : FDA approved: 20 Phase III: 15 Phase II: 10 Phase I: 5 No black box warning: +10 Known serious AE: -10 Literature Evidence (0-20 points) : Clinical trials: 5 points each (max 15) Preclinical studies: 1 point each (max 10) Case reports: 0.5 points each (max 5) Drug Properties (0-10 points) : High bioavailability: 5 Good BBB penetration (if CNS): 5 Low toxicity predictions: 5 Best Practices Start Broad : Query multiple databases (DrugBank, ChEMBL, DGIdb) Validate Targets : Confirm target-disease associations in OpenTargets Check Safety First : Prioritize approved drugs with known safety profiles Literature Mining : Always search for existing clinical/preclinical evidence Use Caching : Enable use_cache=True for expensive predictions Batch Operations : Use tu.run_batch() for parallel queries Consider Mechanism : Evaluate biological plausibility Patent Landscape : Check if indication is already protected Market Analysis : Consider unmet medical need and commercial viability Regulatory Path : FDA approved drugs have faster repurposing path Common Patterns Pattern 1: Rapid Screening # Quick screening of 100+ drugs against disease targets targets = get_disease_targets(disease_id)[: 10 ] all_drugs = [] for target in targets: drugs = tu.tools.DGIdb_get_drug_gene_interactions( gene_name=target[ 'gene_symbol' ] ) all_drugs.extend(drugs) # Filter to FDA approved only approved_drugs = [d for d in all_drugs if d.get( 'approved' )] Pattern 2: Deep Dive Single Drug # Comprehensive analysis of one drug candidate drug_name = "metformin" # Get everything info = tu.tools.drugbank_get_drug_basic_info_by_drug_name_or_id(drug_name_or_drugbank_id=drug_name) targets = tu.tools.drugbank_get_targets_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name) indications = tu.tools.drugbank_get_indications_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name) pharmacology = tu.tools.drugbank_get_pharmacology_by_drug_name_or_drugbank_id(drug_name_or_drugbank_id=drug_name)