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---
name: 'pdx-model-analysis-agent'
description: 'AI-powered analysis of patient-derived xenograft (PDX) models for drug response prediction, translational research, and personalized treatment selection.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
---
# PDX Model Analysis Agent
The **PDX Model Analysis Agent** provides AI-driven analysis of patient-derived xenograft models for preclinical drug testing, translational research, and personalized oncology. It correlates PDX drug responses with patient outcomes and molecular profiles for treatment selection.
## When to Use This Skill
* When selecting drug treatments based on PDX drug response data.
* To correlate PDX molecular profiles with patient tumor characteristics.
* For analyzing PDX-patient concordance in drug sensitivity.
* When designing preclinical drug combination studies.
* To identify biomarkers predicting PDX and patient drug response.
## Core Capabilities
1. **PDX-Patient Concordance**: Analyze molecular similarity between PDX and donor tumor.
2. **Drug Response Modeling**: ML models correlating PDX drug sensitivity to patient outcomes.
3. **Biomarker Discovery**: Identify molecular features predicting drug response in PDX panels.
4. **Combination Screening**: Analyze synergy in PDX drug combination studies.
5. **Translational Prediction**: Project PDX findings to patient treatment selection.
6. **Quality Assessment**: Evaluate PDX fidelity and stability across passages.
## PDX Quality Metrics
| Metric | Threshold | Interpretation |
|--------|-----------|----------------|
| Genetic concordance | >90% | Variants maintained |
| Expression correlation | >0.85 | Transcriptome preserved |
| CNV fidelity | >85% | Copy number stable |
| Tumor take rate | Variable | Engraftment success |
| Passage stability | <P5 recommended | Minimal drift |
## Workflow
1. **Input**: PDX molecular data, drug response curves, patient tumor data.
2. **Concordance Analysis**: Compare PDX to donor tumor at molecular level.
3. **Drug Response Processing**: Calculate IC50, AUC, TGI from growth curves.
4. **Biomarker Analysis**: Correlate molecular features with drug sensitivity.
5. **Patient Prediction**: Project findings to patient treatment recommendations.
6. **Quality Assessment**: Flag PDX models with significant drift.
7. **Output**: Drug rankings, biomarker associations, treatment recommendations.
## Example Usage
**User**: "Analyze PDX drug response data for this breast cancer patient and recommend treatments."
**Agent Action**:
```bash
python3 Skills/Oncology/PDX_Model_Analysis_Agent/pdx_analyzer.py \
--pdx_rnaseq pdx_expression.tsv \
--pdx_mutations pdx_variants.maf \
--patient_tumor patient_expression.tsv \
--drug_responses pdx_drug_panel.csv \
--tumor_type breast_cancer \
--concordance_check true \
--output pdx_recommendations/
```
## Drug Response Metrics
| Metric | Calculation | Interpretation |
|--------|-------------|----------------|
| IC50 | Concentration for 50% inhibition | Potency |
| AUC | Area under dose-response curve | Overall sensitivity |
| TGI | Tumor growth inhibition % | In vivo efficacy |
| T/C | Treated/Control volume ratio | Treatment effect |
| Best response | Maximum tumor regression | Depth of response |
## PDX Resource Integration
| Resource | Coverage | Data Types |
|----------|----------|------------|
| PDXFINDER | 4000+ models | Multi-omic, drug response |
| PDMR (NCI) | 500+ models | Genomic, drug response |
| Champions/Crown | 1500+ models | Drug response |
| EurOPDX | 1000+ models | European cohort |
## AI/ML Models
**Drug Response Prediction**:
- Gradient boosting on multi-omic features
- Gene expression signatures for drug classes
- Mutation-based response predictors
**PDX-Patient Translation**:
- Transfer learning from PDX to patient
- Domain adaptation for species differences
- Concordance-weighted predictions
**Combination Synergy**:
- Bliss independence model
- Loewe additivity analysis
- Machine learning synergy prediction
## Clinical Translation Considerations
**Factors Affecting Translation**:
1. **Tumor heterogeneity**: PDX from single biopsy
2. **Microenvironment**: Mouse vs human stroma
3. **Immune system**: Immunodeficient hosts
4. **Pharmacokinetics**: Species differences
5. **Passage number**: Drift over time
**Best Practices**:
- Use early passage PDX (P1-P5)
- Confirm molecular concordance
- Test drug at clinically-relevant doses
- Consider humanized PDX for immunotherapy
## Prerequisites
* Python 3.10+
* scikit-learn, pandas
* Drug response databases
* PDX molecular datasets
## Related Skills
* Drug_Repurposing - For alternative drug identification
* Multi_Omics_Integration - For PDX characterization
* Clinical_Trials - For trial matching
## Output Report
1. **Concordance Summary**: PDX-patient molecular similarity
2. **Drug Rankings**: Predicted efficacy from PDX data
3. **Biomarker Associations**: Features driving sensitivity
4. **Quality Flags**: PDX reliability assessment
5. **Treatment Recommendations**: Prioritized drug list
## Author
AI Group - Biomedical AI Platform
<!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
