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.
license: MIT
metadata:
author: AI Group
version: "1.0.0"
created: "2026-01-19"
compatibility:
system: Python 3.10+
allowed-tools:
run_shell_command
read_file
write_file
keywords:
organoid-drug-response-agent
automation
biomedical
measurable_outcome: execute task with >95% success rate.
---"
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: ---name: organoid-drug-response-agent 4 --- 5
6 ---name: organoid-drug-response-agent
7 description: AI-powered analysis of patient-derived organoid (PDO) drug screening for personalized oncology treatment selection and biomarker discovery.
8 license: MIT
9 metadata:
10 author: AI Group
11 version: "1.0.0"
12 created: "2026-01-19"
13 compatibility:
14 - system: Python 3.10+
15 allowed-tools:
16 - run_shell_command
17 - read_file
18 - write_file
19
20 keywords:
21 - organoid-drug-response-agent
22 - automation
23 - biomedical
24 measurable_outcome: execute task with >95% success rate.
25 ---"
26
27 # Organoid Drug Response Agent
28
29 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.
30
31 ## When to Use This Skill
32
33 * When interpreting organoid drug screening results for treatment selection.
34 * To correlate PDO drug sensitivity with molecular features.
35 * For identifying combination therapies using organoid co-culture systems.
36 * When predicting patient response from organoid-derived data.
37 * To discover biomarkers from large-scale organoid screens.
38
39 ## Core Capabilities
40
41 1. **Drug Response Analysis**: Process organoid viability data to calculate IC50, AUC, and response metrics.
42
43 2. **Patient-Organoid Concordance**: Assess molecular fidelity between PDO and donor tumor.
44
45 3. **Biomarker Discovery**: Identify molecular features predicting drug sensitivity.
46
47 4. **Combination Screening**: Analyze drug synergy from combination matrices.
48
49 5. **Clinical Translation**: Project organoid findings to patient treatment recommendations.
50
51 6. **Microenvironment Modeling**: Analyze immune co-culture and CAF interactions.
52
53 ## Organoid Advantages
54
55 | Feature | Organoids | Cell Lines | PDX |
56 |---------|-----------|------------|-----|
57 | Patient fidelity | High | Low | High |
58 | Establishment rate | 60-90% | Variable | 30-50% |
59 | Turnaround | 4-8 weeks | Fast | 3-6 months |
60 | Throughput | Medium-high | Very high | Low |
61 | Microenvironment | Partial | None | Mouse |
62 | Cost | Medium | Low | High |
63
64 ## Workflow
65
66 1. **Input**: Organoid drug screening data, organoid molecular profiles, patient tumor data.
67
68 2. **QC**: Assess organoid viability and growth metrics.
69
70 3. **Response Calculation**: Compute drug sensitivity metrics.
71
72 4. **Concordance**: Compare organoid to donor tumor molecular profiles.
73
74 5. **Biomarker Analysis**: Correlate sensitivity with molecular features.
75
76 6. **Translation**: Generate patient treatment recommendations.
77
78 7. **Output**: Drug rankings, biomarkers, recommended treatments.
79
80 ## Example Usage
81
82 **User**: "Analyze organoid drug screening results for this colorectal cancer patient and recommend treatments."
83
84 **Agent Action**:
85 ```bash
86 python3 Skills/Oncology/Organoid_Drug_Response_Agent/organoid_analyzer.py \
87 --screening_data drug_screen_384well.csv \
88 --organoid_rnaseq organoid_expression.tsv \
89 --organoid_mutations organoid_variants.maf \
90 --patient_tumor patient_expression.tsv \
91 --tumor_type colorectal \
92 --combination_matrix combo_screen.csv \
93 --output organoid_report/
94 ```
95
96 ## Drug Response Metrics
97
98 | Metric | Calculation | Interpretation |
99 |--------|-------------|----------------|
100 | IC50 | 50% inhibition concentration | Potency |
101 | AUC | Area under dose-response | Overall sensitivity |
102 | GR50 | Growth rate-adjusted IC50 | Normalized potency |
103 | DSS | Drug sensitivity score | Selective activity |
104 | Emax | Maximum effect | Efficacy plateau |
105
106 ## Organoid-Patient Concordance Studies
107
108 | Study | Tumor Type | Accuracy | Reference |
109 |-------|------------|----------|-----------|
110 | Vlachogiannis 2018 | GI cancers | 88% | Science |
111 | Ooft 2019 | Colorectal | 80% | Science Transl Med |
112 | Tiriac 2018 | Pancreatic | 83% | Cancer Discovery |
113 | Ganesh 2019 | Rectal | 84% | Nature Medicine |
114
115 ## Combination Synergy Analysis
116
117 **Methods**:
118 - Bliss independence
119 - Loewe additivity
120 - ZIP (Zero Interaction Potency)
121 - HSA (Highest Single Agent)
122
123 **Output**:
124 - Synergy scores
125 - Combination indices
126 - Dose-effect surfaces
127 - Optimal ratio identification
128
129 ## AI/ML Models
130
131 **Response Prediction**:
132 - Multi-omic features (expression, mutation, CNV)
133 - Drug structural features
134 - Graph neural networks for drug-response
135
136 **Biomarker Discovery**:
137 - LASSO regression for feature selection
138 - Random forest for interaction detection
139 - SHAP values for interpretability
140
141 **Translation Modeling**:
142 - Transfer learning (organoid → patient)
143 - Concordance-weighted predictions
144 - Uncertainty quantification
145
146 ## Organoid Co-Culture Systems
147
148 **Immune Co-Culture**:
149 - T-cell killing assays
150 - Checkpoint inhibitor testing
151 - CAR-T efficacy evaluation
152
153 **Stromal Co-Culture**:
154 - CAF interactions
155 - Drug resistance mechanisms
156 - ECM-mediated effects
157
158 ## Prerequisites
159
160 * Python 3.10+
161 * Drug response analysis packages
162 * Machine learning frameworks
163 * Organoid molecular databases
164
165 ## Related Skills
166
167 * PDX_Model_Analysis_Agent - For complementary models
168 * Drug_Repurposing - For additional drug candidates
169 * Multi_Omics_Integration - For molecular characterization
170
171 ## Quality Control Metrics
172
173 | Metric | Threshold | Purpose |
174 |--------|-----------|---------|
175 | Z' factor | >0.5 | Assay quality |
176 | CV | <20% | Reproducibility |
177 | Passage number | <10 | Genetic stability |
178 | Growth rate | >1.5x/week | Viability |
179
180 ## Clinical Implementation
181
182 1. **Turnaround Time**: 4-8 weeks from biopsy
183 2. **Panel Size**: 50-100+ drugs typically tested
184 3. **Decision Support**: Ranked drug recommendations
185 4. **Monitoring**: Re-screen on progression
186
187 ## Author
188
189 AI Group - Biomedical AI Platform
commit 6b74d18a62
Frequently asked questions How do I install the Organoid Drug Response Agent skill? Run npx skillmds add majiayu000/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? ---name: organoid-drug-response-agent It is listed under AI & ML on SkillMD.
Is Organoid Drug Response Agent safe to use? This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.
Which AI agents work with Organoid Drug Response Agent? This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.
Is Organoid Drug Response Agent free to use? Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.
Who published Organoid Drug Response Agent? majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.
majiayu000