Target Profile Skill
Generate comprehensive target dossiers for drug discovery decision-making.
Quick Start
code
/target EGFR /target-profile KRAS G12C Create a target dossier for HER2 including clinical trials Analyze druggability of BRAF V600E
What's Included
| Section | Description | Data Source |
|---|---|---|
| Executive Summary | Key insights in one page | Aggregated |
| Target Overview | Gene/protein name, class, location | UniProt |
| Druggability | Tractability scores, target class | Open Targets |
| Disease Associations | Associated diseases, evidence scores | Open Targets |
| Pathways | Signaling pathways, interactions | KEGG, Reactome |
| Competition | Existing drugs, pipeline | ChEMBL, DrugBank |
| Safety | Known safety concerns | Pharos, SIDER |
Data Sources
| Source | API | Coverage |
|---|---|---|
| Open Targets | api.opentargets.org | 20k+ targets, 1.2M associations |
| UniProt | rest.uniprot.org | 200M+ proteins |
| ChEMBL | www.ebi.ac.uk/chembl/api | 2.5M+ compounds |
| KEGG | rest.kegg.jp | 500+ pathways |
| Reactome | reactome.org | 2600+ pathways |
Output Structure
markdown
# EGFR Target Profile ## Executive Summary EGFR is a high-tractability receptor tyrosine kinase with strong validation in NSCLC. 9 drugs approved, 34 in development. Key opportunity: resistance mechanisms and combination therapies. ## Quick Stats | Metric | Value | |--------|-------| | Tractability | 8.2/10 (Small molecule) | | Disease Associations | 142 diseases | | Approved Drugs | 9 | | Pipeline Candidates | 34 | | Safety Tier | 2 (Moderate risk) | ## 1. Target Overview - **Gene**: EGFR (ERBB1) - **Protein**: Epidermal growth factor receptor - **Class**: Receptor tyrosine kinase - **Location**: Cell membrane (Plasma membrane) - **Length**: 1210 amino acids - **MW**: 134 kDa ## 2. Druggability Assessment ### Tractability Scores | Modality | Score | Evidence | |----------|-------|----------| | Small molecule | 8.2/10 | 9 approved drugs | | Antibody | 7.8/10 | 4 approved antibodies | | PROTAC | 6.5/10 | Emerging approach | ### Target Development Level **Tclin (Highest)** - Target with drugs approved for clinical use ## 3. Disease Associations | Disease | Association Score | Evidence Type | |---------|------------------|---------------| | Lung adenocarcinoma | 0.95 | Genetic association | | Glioblastoma | 0.87 | Somatic mutation | | Head and neck cancer | 0.82 | Genetic association | ## 4. Pathway Context - **Primary Pathway**: ErbB signaling pathway (KEGG: hsa04012) - **Upstream**: EGF, TGF-alpha, Amphiregulin - **Downstream**: MAPK, PI3K-Akt, JAK-STAT - **Cross-talk**: MET, HER2, HER3 ## 5. Competitive Landscape ### Approved Drugs | Drug | Company | Year | Type | Indication | |------|---------|------|------|------------| | Erlotinib | Astellas | 2004 | TKI | NSCLC | | Gefitinib | AstraZeneca | 2003 | TKI | NSCLC | | Osimertinib | AstraZeneca | 2015 | 3rd-gen TKI | NSCLC | ### Pipeline (Selected) | Drug | Company | Phase | Differentiation | |------|---------|-------|----------------| | Lazertinib | Yuhan | III | 3rd-gen, wild-type sparing | | Nazartinib | Novartis | III | 3rd-gen, CNS active | ## 6. Safety Considerations - **On-target toxicity**: Skin rash, diarrhea (class effect) - **Off-target concerns**: Cardiac toxicity (rare) - **Safety Tier**: 2 (Manageable risk) ## 7. Key Opportunities 1. Resistance mechanisms (C797S, MET amplification) 2. Combination therapies (EGFR + MET) 3. CNS-penetrant candidates 4. Biomarker-driven patient selection ## 8. Key Risks 1. Crowded competitive space 2. Generic competition (1st gen) 3. Resistance development
Examples
Basic Profile
code
/target EGFR
With Specific Focus
code
/target KRAS --focus safety Analyze safety profile of BTK /target HER2 --focus competition
Compare Multiple Targets
code
Compare targets EGFR, HER2, HER3 for NSCLC treatment Rank KRAS, NRAS, HRAS by druggability
Specific Analysis
code
/target BRAF V600C Assess druggability of mutant BRAF What is the tractability of KRAS G12C?
Running Scripts
The scripts/ directory contains data fetching utilities:
bash
# Fetch basic target data python scripts/fetch_target_data.py EGFR --output data.json # Include all sources python scripts/fetch_target_data.py EGFR --uniprot --chembl --pathways -o full.json # Specific data only python scripts/fetch_target_data.py KRAS --diseases-only
Requirements
None for basic use (uses public APIs).
For advanced features and scripts:
bash
pip install requests pandas
Additional Resources
Best Practices
- •Use official gene symbols (HGNC nomenclature) for best results
- •Include mutation if relevant (e.g., "KRAS G12C")
- •Specify focus when you need deeper analysis on one area
- •Compare targets to support decision-making
Common Pitfalls
| Pitfall | Solution |
|---|---|
| Ambiguous gene names | Use official HGNC symbols |
| Multiple isoforms | Specify isoform number if needed |
| Species confusion | Assume human unless specified |
| Outdated info | Data is current as of last API fetch |