Drug Repurposing

Drug Repurposing Pipeline

Systematically evaluate an existing drug for new therapeutic indications by mining evidence across six dimensions: pharmacology, target networks, clinical trials, literature, patents, and safety.

When to Use

• "帮我找 metformin 的新适应症"
• "Sorafenib 除了肝癌还能治什么"
• "Drug repurposing opportunities for thalidomide"
• "X 的老药新用潜力"
• Any query about finding new uses for existing drugs

Pipeline

Step 1: Drug Profile

Gather comprehensive drug information:

bash: echo "=== DrugBank ===" && \
curl -s "https://go.drugbank.com/unearth/q?searcher=drugs&query=DRUGNAME&button=" 2>/dev/null && \
echo -e "\n=== ChEMBL ===" && \
curl -s "https://www.ebi.ac.uk/chembl/api/data/molecule/search.json?q=DRUGNAME&limit=5" && \
echo -e "\n=== PubChem ===" && \
curl -s "https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/name/DRUGNAME/JSON"

Extract:

• Approved indications and year of first approval
• Primary mechanism of action
• Known molecular targets (with confidence)
• Chemical class and properties (MW, logP, PSA)
• Half-life, bioavailability, metabolism pathway (CYP enzymes)

Step 2: Target Network Analysis

Map drug targets to disease associations:

bash: echo "=== STRING PPI Network ===" && \
curl -s "https://string-db.org/api/json/network?identifiers=TARGET_GENE&species=9606&required_score=700" && \
echo -e "\n=== OpenTargets Disease Associations ===" && \
curl -s -X POST "https://api.platform.opentargets.org/api/v4/graphql" \
  -H "Content-Type: application/json" \
  -d '{"query":"{ target(ensemblId:\"ENSG_ID\") { id approvedSymbol associatedDiseases(page:{size:20}) { rows { disease { id name } score datatypeScores { componentId score } } } } }"}'

Key analysis:

• Primary targets → known diseases (already approved)
• Secondary/off-targets → new disease candidates
• PPI network neighbors → diseases associated with interacting proteins
• Pathway enrichment → which disease pathways are modulated

Step 3: Clinical Evidence Mining

Search for ALL clinical activity of this drug, including off-label and failed trials:

bash: echo "=== ClinicalTrials.gov (all indications) ===" && \
curl -s "https://clinicaltrials.gov/api/v2/studies?query.term=DRUGNAME&pageSize=50&sort=LastUpdatePostDate:desc" && \
echo -e "\n=== PubMed (off-label + repurposing) ===" && \
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=30&sort=relevance&term=DRUGNAME+AND+(repurpos*+OR+off-label+OR+repositioning+OR+novel+indication+OR+unexpected+effect)"

What to look for:

• Trials in non-approved indications (especially Phase 2+ with positive signals)
• Failed trials that revealed unexpected beneficial effects
• Case reports of off-label use with documented outcomes
• Systematic reviews of repurposing evidence

Step 4: Patent Landscape

bash: web_search "DRUGNAME patent expiry date generic availability"

Assess:

• Patent status: Active / Expired / Expiring soon
• Generic availability: Already available = lower barrier to repurposing
• New formulation patents: May protect specific delivery methods
• Method-of-use patents: Filed for new indications?

Patent scoring:

• Expired + generic available: 20/20 (immediate repurposing potential)
• Expiring within 3 years: 15/20
• Active but no method-of-use patent for new indication: 10/20
• Active with broad claims: 5/20

Step 5: Safety Profile

bash: echo "=== OpenFDA Adverse Events ===" && \
curl -s "https://api.fda.gov/drug/event.json?search=patient.drug.openfda.generic_name:%22DRUGNAME%22&count=patient.reaction.reactionmeddrapt.exact&limit=20" && \
echo -e "\n=== PubMed Safety ===" && \
curl -s "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi?db=pubmed&retmode=json&retmax=10&term=DRUGNAME+AND+(safety+OR+adverse+OR+toxicity)+AND+review"

Dual-use signal detection: Some adverse effects hint at therapeutic potential:

Flag these "adverse effects as therapeutic signals" prominently.

Step 6: Repurposing Candidate Ranking

Score each candidate indication across four dimensions:

Total score interpretation:

• ≥70: Strong candidate — proceed to clinical validation planning
• 50–69: Moderate candidate — needs more preclinical evidence
• 30–49: Weak candidate — interesting signal but insufficient evidence
• <30: Not recommended — speculative at best

Output Report Structure

# Drug Repurposing Analysis: [DRUGNAME]

## Executive Summary
- [Drug] is approved for [indication] since [year]
- Analysis identified [N] potential repurposing candidates
- Top candidate: [Disease] (score: XX/100, evidence: ...)

## 1. Drug Profile
[Mechanism, targets, pharmacology]

## 2. Target Network & New Disease Associations
[Network figure + disease mapping table]

## 3. Clinical Evidence
[Trial summaries, off-label reports, case studies]

## 4. Patent Landscape
[Patent status, generic availability, IP barriers]

## 5. Safety Profile
[AE summary + dual-use signal analysis]

## 6. Ranked Repurposing Candidates

| Rank | Indication | Target Evidence | Clinical Evidence | Safety | Patent | Total | Verdict |
|------|-----------|----------------|-------------------|--------|--------|-------|---------|
| 1 | [Disease A] | 25/30 | 20/30 | 18/20 | 20/20 | 83/100 | Strong |
| 2 | [Disease B] | 20/30 | 15/30 | 15/20 | 15/20 | 65/100 | Moderate |
| ... | ... | ... | ... | ... | ... | ... | ... |

## 7. Recommended Next Steps
[For each strong candidate: specific preclinical/clinical validation steps]

## References
[GB/T 7714 format]

Follow-up Suggestions

After completing the repurposing analysis, suggest:

1. For the top candidate: "可以用 gene-landscape recipe 深入分析 [target] 在 [new disease] 中的角色"
2. If clinical trials exist: "可以用 clinical-query recipe 详细了解 [new disease] 的现有治疗方案，评估竞争格局"
3. If target validation needed: "可以用 target-validation recipe 全面评估 [target] 的成药性"