Drug Repurposing

Drug Repurposing

Overview

This skill teaches the agent how to reason about drug repurposing — identifying approved or clinical-stage compounds that may treat a new indication. Repurposing succeeds when three conditions align: (1) a credible mechanistic link between the drug and the new disease, (2) sufficient evidence strength from orthogonal sources, and (3) a realistic translatability path given existing safety, PK, formulation, and IP data.

Use this skill to evaluate proposals, rank candidates, spot weak evidence, and guide the user toward a defensible repurposing strategy before committing to wet-lab or clinical work.

Usage

Invoke this skill when the user:

• Has a disease target and wants to find approved drugs that modulate it.
• Has a disease transcriptomic signature and wants to find drugs with opposing profiles.
• Asks how to query DGIdb, OpenTargets, ChEMBL, or DrugBank for repurposing candidates.
• Claims a drug "should work" for a new indication based on a single data source.
• Wants to rank multiple repurposing candidates by evidence strength.
• Asks whether a repurposing candidate is translatable to clinical trials.
• Needs to design a repurposing study or write a repurposing rationale.

The agent should respond by:

1. Classifying the approach — target-based or phenotype-based (or hybrid).
2. Auditing the evidence — what databases were queried, what evidence types support the candidate.
3. Applying the evidence hierarchy — genetic > clinical observation > in vitro > computational.
4. Assessing translatability — safety profile, PK, dose feasibility, formulation, patent status.
5. Identifying gaps — what evidence is missing and what experiments would fill them.

Response Format

• Lead with the direct recommendation or classification (≤3 sentences)
• Structure as: recommendation → justification (citing specific criteria/thresholds) → caveats
• Use tables for comparisons; bullet points for criteria lists
• Omit background the user already knows — they asked the question
• Target: 200-400 words unless the user requests exhaustive detail

The decision trees and frameworks in this skill are for internal reasoning only. Apply them to reach your conclusion, but do not reproduce them in your response. Present only the final recommendation with supporting evidence.

Core Concepts

1. Two Fundamental Approaches

Every repurposing effort starts from one of two directions. The choice determines the databases, methods, and evidence types involved.

| Approach | Starting point | Method | Key databases | Strength | Limitation | |---|---|---|---|---|---| | Target-based | Known disease target (gene/protein) | Find approved drugs that bind or modulate the target | DGIdb, ChEMBL, DrugBank, OpenTargets | Mechanistically interpretable; leverages structural biology | Assumes the target is causal and druggable | | Phenotype-based | Disease molecular signature (transcriptomic, proteomic) | Find drugs whose signature opposes the disease signature | CMap (Connectivity Map), LINCS L1000 | Agnostic to mechanism; can discover unexpected connections | Signatures are noisy; opposing expression ≠ therapeutic effect |

Decision tree: which approach?

Start
│
├── Do you have a validated disease target (gene/protein)?
│     ├── Yes → Target-based approach.
│     │         Query DGIdb, ChEMBL, DrugBank for drugs hitting this target.
│     │         Cross-reference with OpenTargets for target-disease association strength.
│     └── No  → Continue.
│
├── Do you have a disease molecular signature (e.g., differentially expressed genes)?
│     ├── Yes → Phenotype-based approach.
│     │         Query CMap/L1000 for drugs with negatively correlated signatures.
│     │         Validate top hits against known biology.
│     └── No  → Continue.
│
├── Do you have both a target and a signature?
│     ├── Yes → Hybrid: run both approaches independently, then intersect.
│     │         Candidates appearing in both lists have stronger support.
│     └── No  → Insufficient starting data. Gather target evidence or generate
│               disease signatures before attempting repurposing.

2. Key Databases and What They Provide

Query multiple databases — no single source is sufficient.

| Database | Content | Use for | |---|---|---| | OpenTargets | Target-disease association scores (genetics, literature, pathways) | Validating target relevance; scoring association strength | | DGIdb | Drug-gene interactions from 30+ sources | Finding approved drugs that interact with a target gene | | ChEMBL | Bioactivity data (IC50, Ki, EC50) | Quantitative binding/activity evidence; SAR context | | DrugBank | Drug targets, PK, indications, interactions | Drug metadata, safety profiles, formulations | | CMap / LINCS L1000 | Expression signatures of ~20,000 compounds | Phenotype-based repurposing; signature reversal | | TTD | Targets, drugs, clinical trial status | Cross-referencing target validation and development stage | | STITCH | Chemical-protein interaction network | Network-level drug-target-disease relationships |

Rules for database queries

1. Always query at least two independent databases. A hit in DGIdb alone is insufficient; cross-reference with ChEMBL bioactivity data.
2. Check interaction type. DGIdb reports inhibitors, activators, binders, and more. An "inhibitor" hit is useless if the disease requires target activation.
3. Check activity values. A ChEMBL IC50 of 50 µM is not a drug-like interaction. Require IC50/Ki > Cmax (cannot achieve target engagement) |

| Selectivity acceptable | > Cmax | IC50 ~ 2–5× Cmax | IC50 ≤ Cmax at approved dose | IC50 10 per SNP | F 1, FDR 0.5). │ ├── DGIdb → list of drugs with known interactions for the target. │ └── ChEMBL → bioactivity data (IC50, Ki) for each drug-target pair. │ └── Phenotype-based: └── CMap/LINCS L1000 → query disease signature → rank drugs by negative connectivity score (require 10× Cmax (cannot achieve target engagement). ├── Prior Phase II/III failure for the same indication. ├── Unacceptable safety profile for indication severity. └── Blocked IP (active composition-of-matter patent, no generic).

Step 4: Rank surviving candidates │ └── Apply the scoring framework (Section 6) across all dimensions. Prioritize candidates scoring ≥ 12/15.

Step 5: Validate top candidates │ └── Follow the study design decision tree (Section 7).

### 10. Network Pharmacology Considerations

Network-based approaches complement direct target queries by considering the drug's effect in the context of protein-protein interaction (PPI) networks and disease modules.

**When network pharmacology adds value:**

- The disease is polygenic with no single dominant target.
- The drug has polypharmacology (multiple targets) and you want to assess aggregate effect.
- You want to identify synergistic repurposing combinations.

**When network pharmacology is insufficient:**

- As the sole evidence for a repurposing candidate (Tier 4 only).
- When the PPI network is incomplete for the tissue of interest.
- When the disease module is poorly defined (few known disease genes).

**Key metrics:**

| Metric | Interpretation | Threshold |
|---|---|---|
| Network proximity (z-score) | How close drug targets are to disease genes in PPI network | z  0 required; > 0.1 is notable |
| Separation score (SAB) | Topological separation between drug and disease modules | SAB  10), no horizontal pleiotropy (MR-Egger intercept), and replication across ancestries
  **Why:** A single underpowered MR study without sensitivity analyses does not meet the Tier 1 evidence standard

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [awslabs](https://github.com/awslabs)
- **Source:** [awslabs/hcls-agent-skills](https://github.com/awslabs/hcls-agent-skills)
- **License:** MIT-0

Install and usage instructions live in the source repository linked above.