File contents name: 'organoid-drug-response-agent'
description: 'AI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.'
measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.
allowed-tools:
- read_file
- run_shell_command
Organoid Drug Response Agent
The Organoid Drug Response Agent provides AI-driven analysis of patient-derived organoid (PDO) drug screening data for personalized treatment selection. It correlates organoid drug responses with patient outcomes and molecular profiles to guide precision oncology decisions.
When to Use This Skill
When interpreting organoid drug screening results for treatment selection.
To correlate PDO drug sensitivity with molecular features.
For identifying combination therapies using organoid co-culture systems.
When predicting patient response from organoid-derived data.
To discover biomarkers from large-scale organoid screens.
Core Capabilities
Drug Response Analysis : Process organoid viability data to calculate IC50, AUC, and response metrics.
Patient-Organoid Concordance : Assess molecular fidelity between PDO and donor tumor.
Biomarker Discovery : Identify molecular features predicting drug sensitivity.
Combination Screening : Analyze drug synergy from combination matrices.
Clinical Translation : Project organoid findings to patient treatment recommendations.
Microenvironment Modeling : Analyze immune co-culture and CAF interactions.
Organoid Advantages
Feature
Organoids
Cell Lines
PDX
Patient fidelity
High
Low
High
Establishment rate
60-90%
Variable
30-50%
Turnaround
4-8 weeks
Fast
3-6 months
Throughput
Medium-high
Very high
Low
Microenvironment
Partial
None
Mouse
Cost
Medium
Low
High
Workflow
Input : Organoid drug screening data, organoid molecular profiles, patient tumor data.
QC : Assess organoid viability and growth metrics.
Response Calculation : Compute drug sensitivity metrics.
Concordance : Compare organoid to donor tumor molecular profiles.
Biomarker Analysis : Correlate sensitivity with molecular features.
Translation : Generate patient treatment recommendations.
Output : Drug rankings, biomarkers, recommended treatments.
Example Usage
User : "Analyze organoid drug screening results for this colorectal cancer patient and recommend treatments."
Agent Action :
python3 Skills/Oncology/Organoid_Drug_Response_Agent/organoid_analyzer.py \
--screening_data drug_screen_384well.csv \
--organoid_rnaseq organoid_expression.tsv \
--organoid_mutations organoid_variants.maf \
--patient_tumor patient_expression.tsv \
--tumor_type colorectal \
--combination_matrix combo_screen.csv \
--output organoid_report/
Drug Response Metrics
Metric
Calculation
Interpretation
IC50
50% inhibition concentration
Potency
AUC
Area under dose-response
Overall sensitivity
GR50
Growth rate-adjusted IC50
Normalized potency
DSS
Drug sensitivity score
Selective activity
Emax
Maximum effect
Efficacy plateau
Organoid-Patient Concordance Studies
Study
Tumor Type
Accuracy
Reference
Vlachogiannis 2018
GI cancers
88%
Science
Ooft 2019
Colorectal
80%
Science Transl Med
Tiriac 2018
Pancreatic
83%
Cancer Discovery
Ganesh 2019
Rectal
84%
Nature Medicine
Combination Synergy Analysis
Methods :
Bliss independence
Loewe additivity
ZIP (Zero Interaction Potency)
HSA (Highest Single Agent)
Output :
Synergy scores
Combination indices
Dose-effect surfaces
Optimal ratio identification
AI/ML Models
Response Prediction :
Multi-omic features (expression, mutation, CNV)
Drug structural features
Graph neural networks for drug-response
Biomarker Discovery :
LASSO regression for feature selection
Random forest for interaction detection
SHAP values for interpretability
Translation Modeling :
Transfer learning (organoid → patient)
Concordance-weighted predictions
Uncertainty quantification
Organoid Co-Culture Systems
Immune Co-Culture :
T-cell killing assays
Checkpoint inhibitor testing
CAR-T efficacy evaluation
Stromal Co-Culture :
CAF interactions
Drug resistance mechanisms
ECM-mediated effects
Prerequisites
Python 3.10+
Drug response analysis packages
Machine learning frameworks
Organoid molecular databases
Related Skills
PDX_Model_Analysis_Agent - For complementary models
Drug_Repurposing - For additional drug candidates
Multi_Omics_Integration - For molecular characterization
Quality Control Metrics
Metric
Threshold
Purpose
Z' factor
>0.5
Assay quality
CV
<20%
Reproducibility
Passage number
<10
Genetic stability
Growth rate
>1.5x/week
Viability
Clinical Implementation
Turnaround Time : 4-8 weeks from biopsyPanel Size : 50-100+ drugs typically testedDecision Support : Ranked drug recommendationsMonitoring : Re-screen on progression
Author
AI Group - Biomedical AI Platform
1 --- 2 name: organoid-drug-response-agent 3 description: <!-- 4 --- 5 <!-- 6 # COPYRIGHT NOTICE 7 # This file is part of the "Universal Biomedical Skills" project. 8 # Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu> 9 # All Rights Reserved. 10 # 11 # This code is proprietary and confidential. 12 # Unauthorized copying of this file, via any medium is strictly prohibited. 13 # 14 # Provenance: Authenticated by MD BABU MIA 15 16 --> 17 18 --- 19 name: 'organoid-drug-response-agent' 20 description: 'AI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.' 21 measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. 22 allowed-tools: 23 - read_file 24 - run_shell_command 25 --- 26 27 28 # Organoid Drug Response Agent 29 30 The **Organoid Drug Response Agent** provides AI-driven analysis of patient-derived organoid (PDO) drug screening data for personalized treatment selection. It correlates organoid drug responses with patient outcomes and molecular profiles to guide precision oncology decisions. 31 32 ## When to Use This Skill 33 34 * When interpreting organoid drug screening results for treatment selection. 35 * To correlate PDO drug sensitivity with molecular features. 36 * For identifying combination therapies using organoid co-culture systems. 37 * When predicting patient response from organoid-derived data. 38 * To discover biomarkers from large-scale organoid screens. 39 40 ## Core Capabilities 41 42 1. **Drug Response Analysis**: Process organoid viability data to calculate IC50, AUC, and response metrics. 43 44 2. **Patient-Organoid Concordance**: Assess molecular fidelity between PDO and donor tumor. 45 46 3. **Biomarker Discovery**: Identify molecular features predicting drug sensitivity. 47 48 4. **Combination Screening**: Analyze drug synergy from combination matrices. 49 50 5. **Clinical Translation**: Project organoid findings to patient treatment recommendations. 51 52 6. **Microenvironment Modeling**: Analyze immune co-culture and CAF interactions. 53 54 ## Organoid Advantages 55 56 | Feature | Organoids | Cell Lines | PDX | 57 |---------|-----------|------------|-----| 58 | Patient fidelity | High | Low | High | 59 | Establishment rate | 60-90% | Variable | 30-50% | 60 | Turnaround | 4-8 weeks | Fast | 3-6 months | 61 | Throughput | Medium-high | Very high | Low | 62 | Microenvironment | Partial | None | Mouse | 63 | Cost | Medium | Low | High | 64 65 ## Workflow 66 67 1. **Input**: Organoid drug screening data, organoid molecular profiles, patient tumor data. 68 69 2. **QC**: Assess organoid viability and growth metrics. 70 71 3. **Response Calculation**: Compute drug sensitivity metrics. 72 73 4. **Concordance**: Compare organoid to donor tumor molecular profiles. 74 75 5. **Biomarker Analysis**: Correlate sensitivity with molecular features. 76 77 6. **Translation**: Generate patient treatment recommendations. 78 79 7. **Output**: Drug rankings, biomarkers, recommended treatments. 80 81 ## Example Usage 82 83 **User**: "Analyze organoid drug screening results for this colorectal cancer patient and recommend treatments." 84 85 **Agent Action**: 86 ```bash 87 python3 Skills/Oncology/Organoid_Drug_Response_Agent/organoid_analyzer.py \ 88 --screening_data drug_screen_384well.csv \ 89 --organoid_rnaseq organoid_expression.tsv \ 90 --organoid_mutations organoid_variants.maf \ 91 --patient_tumor patient_expression.tsv \ 92 --tumor_type colorectal \ 93 --combination_matrix combo_screen.csv \ 94 --output organoid_report/ 95 ``` 96 97 ## Drug Response Metrics 98 99 | Metric | Calculation | Interpretation | 100 |--------|-------------|----------------| 101 | IC50 | 50% inhibition concentration | Potency | 102 | AUC | Area under dose-response | Overall sensitivity | 103 | GR50 | Growth rate-adjusted IC50 | Normalized potency | 104 | DSS | Drug sensitivity score | Selective activity | 105 | Emax | Maximum effect | Efficacy plateau | 106 107 ## Organoid-Patient Concordance Studies 108 109 | Study | Tumor Type | Accuracy | Reference | 110 |-------|------------|----------|-----------| 111 | Vlachogiannis 2018 | GI cancers | 88% | Science | 112 | Ooft 2019 | Colorectal | 80% | Science Transl Med | 113 | Tiriac 2018 | Pancreatic | 83% | Cancer Discovery | 114 | Ganesh 2019 | Rectal | 84% | Nature Medicine | 115 116 ## Combination Synergy Analysis 117 118 **Methods**: 119 - Bliss independence 120 - Loewe additivity 121 - ZIP (Zero Interaction Potency) 122 - HSA (Highest Single Agent) 123 124 **Output**: 125 - Synergy scores 126 - Combination indices 127 - Dose-effect surfaces 128 - Optimal ratio identification 129 130 ## AI/ML Models 131 132 **Response Prediction**: 133 - Multi-omic features (expression, mutation, CNV) 134 - Drug structural features 135 - Graph neural networks for drug-response 136 137 **Biomarker Discovery**: 138 - LASSO regression for feature selection 139 - Random forest for interaction detection 140 - SHAP values for interpretability 141 142 **Translation Modeling**: 143 - Transfer learning (organoid → patient) 144 - Concordance-weighted predictions 145 - Uncertainty quantification 146 147 ## Organoid Co-Culture Systems 148 149 **Immune Co-Culture**: 150 - T-cell killing assays 151 - Checkpoint inhibitor testing 152 - CAR-T efficacy evaluation 153 154 **Stromal Co-Culture**: 155 - CAF interactions 156 - Drug resistance mechanisms 157 - ECM-mediated effects 158 159 ## Prerequisites 160 161 * Python 3.10+ 162 * Drug response analysis packages 163 * Machine learning frameworks 164 * Organoid molecular databases 165 166 ## Related Skills 167 168 * PDX_Model_Analysis_Agent - For complementary models 169 * Drug_Repurposing - For additional drug candidates 170 * Multi_Omics_Integration - For molecular characterization 171 172 ## Quality Control Metrics 173 174 | Metric | Threshold | Purpose | 175 |--------|-----------|---------| 176 | Z' factor | >0.5 | Assay quality | 177 | CV | <20% | Reproducibility | 178 | Passage number | <10 | Genetic stability | 179 | Growth rate | >1.5x/week | Viability | 180 181 ## Clinical Implementation 182 183 1. **Turnaround Time**: 4-8 weeks from biopsy 184 2. **Panel Size**: 50-100+ drugs typically tested 185 3. **Decision Support**: Ranked drug recommendations 186 4. **Monitoring**: Re-screen on progression 187 188 ## Author 189 190 AI Group - Biomedical AI Platform 191 192 193 <!-- AUTHOR_SIGNATURE: 9a7f3c2e-MD-BABU-MIA-2026-MSSM-SECURE -->
commit 75af94b847
Frequently asked questions How do I install the Organoid Drug Response Agent skill? Run npx skillmds@latest add biotender-max/organoid-drug-response-agent in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.
What does the Organoid Drug Response Agent skill do? <!-- It is listed under AI & ML on SkillMD.
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Who published Organoid Drug Response Agent? BioTender-max (@biotender-max) published this skill. Their other Agent Skills are listed on their SkillMD profile.
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