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, fi

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 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 4. Signature Based : Disease gene expression signature Find drugs whose perturbation signature reverses it (connectivity map reasoning). Use when the disease has a well characterized differential expression signature (e.g. from GEO/TCGA) but no single validated target — this strategy needs no target hypothesis at all, only up/down gene lists. Workflow Overview See: PROCEDURES.md for detailed step by step procedures and code patterns. Quick Start 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. 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].interactions ChEMBL search drugs / ChEMBL get drug mechanisms Drug search and MOA Patent/IP (USPTO Patent File Wrapper — see "Patent Landscape Check" below): get patent overview by text query Keyword/title patent search get patent application metadata , get patent term adjustment data , get patent continuity data , get patent foreign priority data , get associated documents metadata Per application detail lookups by application number Drug 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 warnings FAERS 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 Interactions Property Prediction : ADMETAI predict physicochemical properties / ADMETAI predict toxicity ADMET and toxicity Pathway & 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=9606 CTD 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 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 59 : Speculative — needs significant additional evidence <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: 1. 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. 2. 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. 3. 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. 4. 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. 5. 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 1. Check clinical trials FIRST : search clinical trials(condition="[disease]", intervention="[drug]") — if a trial already exists, start there 2. Validate targets with genetics : Genetic evidence (GWAS, rare variants) is the strongest predictor of successful drug development 3. Safety first : Prioritize approved drugs with known safety profiles 4. Dose matters : A drug that hits a disease target at 100x its approved dose is not a repurposing candidate 5. Mechanism over correlation : Network proximity alone is insufficient — explain WHY the drug should work 6. Consider IP and formulation : Generic drugs are easier to repurpose but harder to fund trials for. See "Patent Landscape Check" below to verify IP status rather than assuming a drug is off patent because it's old or generic in one market — patent term adjustments and continuation filings can extend protection well past the nominal 20 year term. Patent Landscape Check (USPTO) Before prioritizing a repurposing candidate, check whether the new use (or the compound itself) is already claimed by a live patent. Six tools wrap the USPTO Patent File Wrapper API for this: Tool Purpose get patent overview by text query Keyword/title search, e.g. applicationMetaData.inventionTitle:"your drug or use" get patent application metadata Full metadata for one application (filing/grant dates, CPC classification) by application number get patent term adjustment data Patent term adjustment (PTA) — how much extra protection time was granted beyond the nominal 20 year term get patent continuity data Parent/child continuation application relationships — a compound's protection can extend well past its original filing via continuations get patent foreign priority data Foreign priority claims (useful for global IP landscape, not just US) get associated documents metadata Associated publications/grants for the application Requires USPTO API KEY (free, register at https://data.uspto.gov/myodp). Without a valid key every call returns HTTP 403 — verified live: get patent overview by text query and get patent application metadata both return HTTP Error: 403 with USPTO's own guidance that a 403 means key rejection (invalid, or a newly issued key still activating), not a malformed query. A 404 on a specific patent number means that patent is outside the Patent File Wrapper dataset's coverage, not that the tool is broken. Workflow : search by drug/compound name or title keyword with get patent overview by text query (wrap multi word phrases in escaped double quotes for exact matching) → take an applicationNumberText from a hit → pull term adjustment and continuity data to see the actual remaining protection window, since continuations and PTA both push the real expiration later than the nominal grant date + 20 years math suggests. A drug whose base composition patent looks expired may still be covered by a use patent or formulation continuation — always check continuity data before concluding a compound is open for repurposing without licensing. Signature Based Repurposing (LINCS / L1000FWD) For Strategy 4, reverse a disease's own gene expression signature rather than reasoning through a single target: Tool Purpose LINCS search signatures(drug name=..., cell line=..., limit=...) Find existing L1000/other assay perturbation signatures for a known drug — useful for confirming what a candidate compound's own transcriptional footprint looks like before comparing it to a disease signature LINCS l

tooluniverse-drug-repurposing