tooluniverse-drug-repurposing

What this skill does

# 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

---

## 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

```python
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])
```

---

## 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

**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 similari