数据与复现临床医学与医药FreedomIntelligence/OpenClaw-Medical-Skills数据与复现

organoid-drug-response-agent

维护者 FreedomIntelligence · 最近更新 2026年4月1日

Analyze drug response in patient-derived organoids for personalized therapy prediction.

OpenClawNanoClaw分析处理复现实验organoid-drug-response-agent🧠 bioos extended suiteoncology & precision medicine agentsanalyze

原始来源

FreedomIntelligence/OpenClaw-Medical-Skills

https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/organoid-drug-response-agent

技能摘要

2 min

Organoid Drug Response Agent provides AI-driven analysis of patient-derived organoid (PDO) drug screening data ，用于 personalized treatment selection. It correlates organoid drug responses ，支持 patient outcomes 、 molecular profiles to guide precision oncology decisions。
When interpreting organoid drug screening results ，用于 treatment selection。
To correlate PDO drug sensitivity ，支持 molecular features。
用于 identifying combination therapies ，使用 organoid co-culture systems。
When predicting patient response ，面向 organoid-derived data。

原始文档

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.

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

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.