tooluniverse-toxicology

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Original

English

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Translation

Chinese

Toxicology Assessment via Adverse Outcome Pathways & Signal Detection

基于不良结局通路与信号检测的毒理学评估

Systematic toxicology analysis that links molecular initiating events (MIEs) through adverse outcome pathways (AOPs) to apical adverse outcomes, then triangulates with real-world FAERS signals, FDA label data, and toxicogenomic associations.

系统性毒理学分析，将分子起始事件（MIEs）通过不良结局通路（AOPs）与顶端不良结局关联，再结合真实世界FAERS信号、FDA标签数据和毒理基因组学关联进行交叉验证。

Domain Reasoning

领域推理

Toxicity has many mechanisms, and the first interpretive question is temporal: is this acute toxicity (immediate effect from a high dose) or chronic toxicity (cumulative damage from long-term low-dose exposure)? Acute and chronic toxicity operate through different mechanisms — acute hepatotoxicity may reflect direct mitochondrial damage, while chronic hepatotoxicity may involve fibrosis from repeated low-level inflammation. They also have different regulatory frameworks: acute toxicity is captured by LD50 and emergency protocols, while chronic toxicity requires long-term carcinogenicity and repeat-dose studies.

毒性存在多种作用机制，首要的解读问题是时间维度：属于急性毒性（高剂量下的即时效应）还是慢性毒性（长期低剂量暴露的累积损伤）？急性和慢性毒性的作用机制不同——急性肝毒性可能反映线粒体直接损伤，而慢性肝毒性可能涉及反复低水平炎症引发的纤维化。二者的监管框架也不同：急性毒性通过LD50和应急方案评估，慢性毒性则需要长期致癌性和重复剂量研究。

LOOK UP DON'T GUESS

检索验证，勿主观臆断

Adverse outcome pathways for a chemical: query
```
AOPWiki_list_aops
```
and
```
AOPWiki_get_aop
```
; do not describe mechanisms from memory.
FAERS adverse event signals: retrieve from
```
FAERS_count_reactions_by_drug_event
```
and
```
FAERS_calculate_disproportionality
```
; never estimate PRR values.
FDA label warnings: call
```
DailyMed_parse_adverse_reactions
```
and related tools; do not state boxed warnings from memory.
CTD chemical-gene and chemical-disease associations: query
```
CTD_get_chemical_gene_interactions
```
and
```
CTD_get_chemical_diseases
```
; do not infer gene targets without database evidence.

化学品的不良结局通路：调用
```
AOPWiki_list_aops
```
和
```
AOPWiki_get_aop
```
查询；切勿凭记忆描述机制。

FAERS不良事件信号：通过

FAERS_count_reactions_by_drug_event

和

FAERS_calculate_disproportionality

获取；绝不估算PRR值。

FDA标签警告：调用
```
DailyMed_parse_adverse_reactions
```
及相关工具；切勿凭记忆陈述黑框警告内容。
CTD化学品-基因和化学品-疾病关联：查询
```
CTD_get_chemical_gene_interactions
```
和
```
CTD_get_chemical_diseases
```
；无数据库证据时切勿推断基因靶点。

COMPUTE, DON'T DESCRIBE

执行计算，勿仅描述

When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.

当分析需要计算（统计、数据处理、评分、富集分析）时，通过Bash编写并运行Python代码。不要描述你会做什么——直接执行并报告实际结果。使用ToolUniverse工具获取数据，再通过Python（pandas、scipy、statsmodels、matplotlib）进行分析。

When to Use This Skill

技能适用场景

Triggers:

"What are the toxicity mechanisms for [drug/chemical]?"
"Find adverse outcome pathways for [chemical]"
"What AOPs are relevant to [target/organ/effect]?"
"FAERS signal analysis for [drug]"
"Toxicogenomic profile for [chemical]"
"What is the mechanism of hepatotoxicity / cardiotoxicity / neurotoxicity for [drug]?"

Use Cases:

AOP Tracing: Map chemical MIE through key events to apical outcome using AOPWiki
Real-World Signal Detection: Quantify FAERS adverse event signals with PRR/ROR
Label Safety Mining: Extract FDA boxed warnings, contraindications, nonclinical toxicology
Toxicogenomics: Chemical-gene-disease associations from CTD
Integrated Mechanism Report: Combine AOP pathway + real-world signals into unified narrative

触发条件:

"[药物/化学品]的毒性机制是什么？"
"查找[化学品]的不良结局通路"
"哪些AOP与[靶点/器官/效应]相关？"
"[药物]的FAERS信号分析"
"[化学品]的毒理基因组学图谱"
"[药物]的肝毒性/心脏毒性/神经毒性机制是什么？"

应用场景:

AOP追踪：使用AOPWiki绘制化学品从MIE到关键事件再到顶端结局的通路
真实世界信号检测：用PRR/ROR量化FAERS不良事件信号
标签安全挖掘：提取FDA黑框警告、禁忌症、非临床毒理学信息
毒理基因组学：从CTD获取化学品-基因-疾病关联
整合机制报告：将AOP通路与真实世界信号整合为统一分析报告

KEY PRINCIPLES

核心原则

AOP-first thinking - Frame all toxicity in terms of MIE → Key Events → Adverse Outcome
Report-first approach - Create report file FIRST, update progressively
Evidence grading mandatory - T1 (regulatory/clinical) through T4 (computational/AOP annotation)
Distinguish mechanism from signal - AOPWiki = mechanism; FAERS = real-world signal
Disambiguation first - Resolve drug/chemical identity before any queries
English-first queries - Always use English names in tool calls

AOP优先思维 - 所有毒性分析均围绕MIE → 关键事件 → 不良结局展开
报告优先方法 - 先创建报告文件，再逐步更新内容
强制证据分级 - 采用T1（监管/临床）至T4（计算/AOP注释）的分级体系
区分机制与信号 - AOPWiki对应机制；FAERS对应真实世界信号
先消歧 - 任何查询前先明确药物/化学品的身份
英文优先查询 - 工具调用中始终使用英文名称

Evidence Grading

证据分级

Tier	Symbol	Criteria
T1	[T1]	FDA boxed warning, clinical trial toxicity finding, regulatory label
T2	[T2]	FAERS signal PRR > 2, AOP with high biological plausibility, CTD curated
T3	[T3]	CTD inferred association, AOP annotation with moderate plausibility
T4	[T4]	Text-mined CTD entry, early-stage AOP annotation

层级	标识	标准
T1	[T1]	FDA黑框警告、临床试验毒性发现、监管标签内容
T2	[T2]	FAERS信号PRR > 2、高生物学可信度AOP、CTD curated（已整理）数据
T3	[T3]	CTD inferred（推断）关联、中等可信度AOP注释
T4	[T4]	文本挖掘得到的CTD条目、早期阶段AOP注释

Workflow Overview

工作流程概览

Chemical/Drug Query
|
+-- PHASE 0: Disambiguation
|   Resolve name -> identifiers (ChEMBL, PubChem CID, SMILES)
|
+-- PHASE 1: Adverse Outcome Pathway Mapping (AOPWiki)
|   List AOPs by keyword; retrieve key events, MIEs, and biological plausibility scores
|
+-- PHASE 2: Real-World Adverse Event Signals (FAERS)
|   Top reactions by drug; disproportionality (PRR); serious event filter
|
+-- PHASE 3: FDA Label Safety Mining
|   Boxed warnings, contraindications, nonclinical toxicology, adverse reactions
|
+-- PHASE 4: Toxicogenomics (CTD)
|   Chemical-gene interactions; chemical-disease associations
|
+-- SYNTHESIS: Integrated Toxicology Report
    AOP-linked mechanism + FAERS signal + CTD gene targets + Risk classification

Chemical/Drug Query
|
+-- PHASE 0: Disambiguation
|   解析名称 -> 标识符（ChEMBL、PubChem CID、SMILES）
|
+-- PHASE 1: Adverse Outcome Pathway Mapping (AOPWiki)
|   按关键词列出AOP；获取关键事件、MIEs和生物学可信度评分
|
+-- PHASE 2: Real-World Adverse Event Signals (FAERS)
|   药物对应的主要不良反应；不成比例性分析（PRR）；严重事件筛选
|
+-- PHASE 3: FDA Label Safety Mining
|   黑框警告、禁忌症、非临床毒理学、不良反应
|
+-- PHASE 4: Toxicogenomics (CTD)
|   化学品-基因相互作用；化学品-疾病关联
|
+-- SYNTHESIS: Integrated Toxicology Report
    AOP关联机制 + FAERS信号 + CTD基因靶点 + 风险分类

Phase 0: Disambiguation

阶段0：身份消歧

Objective: Establish compound identity before any database queries.

Tools:

```
PubChem_get_CID_by_compound_name
```
(
```
name
```
: str) — get CID + SMILES
```
ChEMBL_search_drugs
```
(
```
query
```
: str) — get ChEMBL ID and max phase

Capture: generic name, SMILES, PubChem CID, ChEMBL ID, drug class.

目标：在任何数据库查询前明确化合物身份。

工具:

```
PubChem_get_CID_by_compound_name
```
（参数
```
name
```
: str）——获取CID + SMILES
```
ChEMBL_search_drugs
```
（参数
```
query
```
: str）——获取ChEMBL ID和研发最高阶段

记录信息：通用名、SMILES、PubChem CID、ChEMBL ID、药物类别。

Phase 1: Adverse Outcome Pathway Mapping

阶段1：不良结局通路映射

Objective: Find AOPs relevant to the chemical's known or suspected toxicity mechanisms.

目标：找到与化学品已知或疑似毒性机制相关的AOP。

Tools

工具

AOPWiki_list_aops:

Input:
```
keyword
```
(str) — e.g., organ ("liver", "kidney"), effect ("apoptosis", "inflammation"), or target ("AhR", "PPARalpha")
Output: List of AOP IDs, titles, and short descriptions
Use: Discovery scan to identify candidate AOPs

AOPWiki_get_aop:

Input:
```
aop_id
```
(int) — ID from list_aops result
Output: Full AOP details including MIE, key events (KEs), key event relationships (KERs), biological plausibility, and weight-of-evidence
Use: Retrieve mechanistic pathway details for selected AOPs

AOPWiki_list_aops:

输入：
```
keyword
```
(str) —— 例如器官（"liver"、"kidney"）、效应（"apoptosis"、"inflammation"）或靶点（"AhR"、"PPARalpha"）
输出：AOP ID、标题和简短描述列表
用途：发现性扫描以确定候选AOP

AOPWiki_get_aop:

输入：
```
aop_id
```
(int) —— 来自list_aops结果的ID
输出：完整AOP详情，包括MIE、关键事件（KEs）、关键事件关系（KERs）、生物学可信度和证据权重
用途：获取选定AOP的机制通路详情

Workflow

工作流程

Query
```
AOPWiki_list_aops
```
with organ-level keyword (e.g., "hepatotoxicity", "nephrotoxicity")
Query again with mechanism-level keyword (e.g., "oxidative stress", "mitochondria")
Select top 3-5 most relevant AOPs by title relevance
Call
```
AOPWiki_get_aop
```
for each selected AOP
Extract: MIE (molecular initiating event), key events in order, apical adverse outcome, biological plausibility score

用器官级关键词（如"hepatotoxicity"、"nephrotoxicity"）查询
```
AOPWiki_list_aops
```
再用机制级关键词（如"oxidative stress"、"mitochondria"）查询
按标题相关性选择前3-5个最相关的AOP
为每个选定的AOP调用
```
AOPWiki_get_aop
```
提取：MIE（分子起始事件）、关键事件顺序、顶端不良结局、生物学可信度评分

Decision Logic

决策逻辑

AOP found: Extract full pathway; note plausibility level (high/moderate/low)
No direct AOP match: Try broader organ or mechanism terms; document as "no AOP directly mapped"
Multiple AOPs: Report all; highlight shared key events as high-confidence mechanisms

找到AOP：提取完整通路；记录可信度等级（高/中/低）
无直接匹配AOP：尝试更宽泛的器官或机制术语；记录为"无直接映射的AOP"
多个AOP：全部报告；突出共享关键事件作为高可信度机制

AOP Table Format

AOP表格格式

AOP ID	Title	MIE	Apical Outcome	Plausibility
123	...	...	...	High

AOP ID	标题	MIE	顶端不良结局	可信度
123	...	...	...	High

Phase 2: Real-World Adverse Event Signals (FAERS)

阶段2：真实世界不良事件信号（FAERS）

Objective: Quantify observed adverse events with statistical signal measures.

目标：用统计信号指标量化观察到的不良事件。

Tools

工具

FAERS_count_reactions_by_drug_event:

Input:
```
drug_name
```
(str),
```
limit
```
(int, default 50)
Output: Top adverse reactions with counts
Note: param is
```
drug_name
```
not
```
drug
```

FAERS_calculate_disproportionality:

Input:
```
drug_name
```
(str),
```
reaction_meddra_pt
```
(str)
Output: PRR, ROR, IC with confidence intervals

FAERS_filter_serious_events:

Input:
```
drug_name
```
(str),
```
serious_type
```
(str: "death", "hospitalization", "life-threatening")
Output: Serious event count and case details

FAERS_stratify_by_demographics:

Input:
```
drug_name
```
(str),
```
reaction_meddra_pt
```
(str)
Output: Age/sex breakdown for specific reaction

FAERS_count_reactions_by_drug_event:

输入：
```
drug_name
```
(str)、
```
limit
```
(int，默认50)
输出：主要不良反应及对应计数
注意：参数为
```
drug_name
```
而非
```
drug
```

FAERS_calculate_disproportionality:

输入：
```
drug_name
```
(str)、
```
reaction_meddra_pt
```
(str)
输出：PRR、ROR、IC及置信区间

FAERS_filter_serious_events:

输入：
```
drug_name
```
(str)、
```
serious_type
```
(str: "death"、"hospitalization"、"life-threatening")
输出：严重事件计数和病例详情

FAERS_stratify_by_demographics:

输入：
```
drug_name
```
(str)、
```
reaction_meddra_pt
```
(str)
输出：特定不良反应的年龄/性别分布

Workflow

工作流程

Get top 25 reactions via
```
FAERS_count_reactions_by_drug_event
```
Filter to organ-system clusters matching the AOP outcomes from Phase 1
Calculate PRR for top 10 reactions via
```
FAERS_calculate_disproportionality
```
Check serious events (deaths, hospitalizations) for highest-PRR reactions

通过
```
FAERS_count_reactions_by_drug_event
```
获取前25种不良反应
筛选与阶段1中AOP结局匹配的器官系统集群
通过
```
FAERS_calculate_disproportionality
```
计算前10种反应的PRR
检查PRR最高的反应对应的严重事件（死亡、住院）

Signal Thresholds

信号阈值

Signal Strength	PRR	Case Count
Strong	> 3.0	>= 5
Moderate	2.0-3.0	>= 3
Weak	1.5-2.0	>= 3
None	< 1.5	any

信号强度	PRR	病例数
强	> 3.0	>= 5
中	2.0-3.0	>= 3
弱	1.5-2.0	>= 3
无	< 1.5	任意

Phase 3: FDA Label Safety Mining

阶段3：FDA标签安全挖掘

Objective: Extract regulatory safety findings from approved drug labels.

目标：从获批药物标签中提取监管安全发现。

Tools

工具

DailyMed_parse_adverse_reactions

(

drug_name

: str)

DailyMed_parse_contraindications

(

drug_name

: str)

DailyMed_parse_clinical_pharmacology

(

drug_name

: str)

DailyMed_parse_drug_interactions

(

drug_name

: str)

Note: These tools apply to FDA-approved drugs only. Environmental chemicals will have no label data — document explicitly.

DailyMed_parse_adverse_reactions

（参数

drug_name

: str）

DailyMed_parse_contraindications

（参数

drug_name

: str）

DailyMed_parse_clinical_pharmacology

（参数

drug_name

: str）

DailyMed_parse_drug_interactions

（参数

drug_name

: str）

注意：这些工具仅适用于FDA获批药物。环境化学品无标签数据——需明确记录。

Workflow

工作流程

Extract adverse reactions and note which match FAERS signals
Extract contraindications (highest evidence tier [T1])
Note pharmacological mechanism from clinical pharmacology section

提取不良反应，记录与FAERS信号匹配的条目
提取禁忌症（最高证据层级[T1]）
记录临床药理学部分的药理机制

Phase 4: Toxicogenomics (CTD)

阶段4：毒理基因组学（CTD）

Objective: Map chemical-gene interactions and chemical-disease associations.

目标：绘制化学品-基因相互作用和化学品-疾病关联。

Tools

工具

CTD_get_chemical_gene_interactions:

Input:
```
input_terms
```
(str) — chemical name or MeSH ID
Output: Gene targets with interaction type (increases/decreases expression)
Use: Find molecular targets mediating toxicity

CTD_get_chemical_diseases:

Input:
```
input_terms
```
(str) — chemical name or MeSH ID
Output: Disease associations with evidence type (curated/inferred)
Use: Find downstream disease endpoints

CTD_get_chemical_gene_interactions:

输入：
```
input_terms
```
(str) —— 化学品名称或MeSH ID
输出：基因靶点及相互作用类型（上调/下调表达）
用途：找到介导毒性的分子靶点

CTD_get_chemical_diseases:

输入：
```
input_terms
```
(str) —— 化学品名称或MeSH ID
输出：疾病关联及证据类型（curated/inferred）
用途：找到下游疾病终点

Workflow

工作流程

Query CTD with compound name; note curated (higher confidence) vs inferred entries
Cross-reference gene targets with Phase 1 AOP key events
Note which CTD disease endpoints match AOP apical outcomes

用化合物名称查询CTD；记录curated（高可信度）与inferred（推断）条目
将基因靶点与阶段1的AOP关键事件交叉验证
记录与AOP顶端结局匹配的CTD疾病终点

Synthesis: Integrated Toxicology Report

整合：毒理学报告

Structure:

undefined

结构:

undefined

Toxicology Report: [Compound Name]

毒理学报告：[化合物名称]

Generated: YYYY-MM-DD

生成时间: YYYY-MM-DD

Executive Summary

执行摘要

Risk tier: CRITICAL / HIGH / MEDIUM / LOW / INSUFFICIENT DATA Key finding summary (2-3 sentences)

风险等级: CRITICAL / HIGH / MEDIUM / LOW / INSUFFICIENT DATA 关键发现摘要（2-3句话）

1. Compound Identity

1. 化合物身份

(disambiguation table)

（消歧表格）

2. Adverse Outcome Pathways [T3-T4]

2. 不良结局通路 [T3-T4]

(AOP table; pathway diagrams in text form)

（AOP表格；文本形式的通路图）

3. Real-World Adverse Event Signals [T1-T2]

3. 真实世界不良事件信号 [T1-T2]

(FAERS top reactions + PRR table + serious events)

（FAERS主要反应 + PRR表格 + 严重事件）

4. FDA Label Safety [T1]

4. FDA标签安全 [T1]

(boxed warnings, contraindications, adverse reactions)

（黑框警告、禁忌症、不良反应）

5. Toxicogenomics [T2-T4]

5. 毒理基因组学 [T2-T4]

(CTD gene targets + disease associations)

（CTD基因靶点 + 疾病关联）

6. Mechanistic Integration

6. 机制整合

(How AOP key events map to observed FAERS signals and CTD gene targets)

（AOP关键事件如何与观察到的FAERS信号和CTD基因靶点关联）

7. Risk Classification

7. 风险分类

(Final tier with rationale)

（最终等级及理由）

Data Gaps & Limitations

数据缺口与局限性

(Missing data, confidence caveats)

undefined

（缺失数据、可信度说明）

undefined

Risk Classification

风险分类

Tier	Criteria
CRITICAL	FDA boxed warning OR FAERS PRR > 5 with deaths OR multiple T1 findings
HIGH	FAERS PRR 3-5 serious events OR FDA warning (non-boxed) OR high-plausibility AOP
MEDIUM	FAERS PRR 2-3 OR CTD curated associations OR moderate-plausibility AOP
LOW	All signals < PRR 2; no regulatory warnings; low-plausibility AOP only
INSUFFICIENT DATA	Fewer than 3 phases returned usable data

等级	标准
CRITICAL	FDA黑框警告或 FAERS PRR > 5且存在死亡病例或多个T1发现
HIGH	FAERS PRR 3-5且存在严重事件或 FDA警告（非黑框）或高可信度AOP
MEDIUM	FAERS PRR 2-3 或 CTD curated关联或中等可信度AOP
LOW	所有信号PRR < 2；无监管警告；仅低可信度AOP
INSUFFICIENT DATA	少于3个阶段返回可用数据

Fallback Chains

备选工具链

Primary Tool	Fallback 1	Fallback 2
`AOPWiki_list_aops`	Broaden keyword	Search by organ system
`FAERS_count_reactions_by_drug_event`	`OpenFDA_search_drug_events`	Literature search
`DailyMed_parse_adverse_reactions`	`OpenFDA_search_drug_events`	FAERS serious events
`CTD_get_chemical_diseases`	`CTD_get_chemical_gene_interactions`	PubMed search

主工具	备选工具1	备选工具2
`AOPWiki_list_aops`	扩大关键词范围	按器官系统搜索
`FAERS_count_reactions_by_drug_event`	`OpenFDA_search_drug_events`	文献检索
`DailyMed_parse_adverse_reactions`	`OpenFDA_search_drug_events`	FAERS严重事件
`CTD_get_chemical_diseases`	`CTD_get_chemical_gene_interactions`	PubMed检索

Tool Parameter Reference (Critical)

工具参数参考（关键）

Tool	WRONG	CORRECT
`FAERS_count_reactions_by_drug_event`	`drug`	`drug_name`
`AOPWiki_list_aops`	`query`	`keyword`
`CTD_get_chemical_gene_interactions`	`chemical`	`input_terms`
`CTD_get_chemical_diseases`	`chemical`	`input_terms`

工具	错误参数	正确参数
`FAERS_count_reactions_by_drug_event`	`drug`	`drug_name`
`AOPWiki_list_aops`	`query`	`keyword`
`CTD_get_chemical_gene_interactions`	`chemical`	`input_terms`
`CTD_get_chemical_diseases`	`chemical`	`input_terms`

Limitations

局限性

AOPWiki: AOPs are in development; many lack high plausibility scores
FAERS: Observational data; confounding by indication; underreporting bias
CTD: Inferred associations have high false-positive rate
DailyMed: FDA-approved drugs only; no environmental chemical coverage
Environmental chemicals: Primarily Phase 1 (AOP) + Phase 4 (CTD) data available

AOPWiki：AOP仍在开发中，许多缺乏高可信度评分
FAERS：观察性数据；存在适应症混杂、报告不足偏差
CTD：推断关联的假阳性率高
DailyMed：仅适用于FDA获批药物；无环境化学品覆盖
环境化学品：主要仅有阶段1（AOP）+ 阶段4（CTD）数据可用

References

参考文献

AOPWiki: https://aopwiki.org
FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
CTD: http://ctdbase.org
DailyMed: https://dailymed.nlm.nih.gov
OpenFDA: https://open.fda.gov

AOPWiki: https://aopwiki.org
FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers
CTD: http://ctdbase.org
DailyMed: https://dailymed.nlm.nih.gov
OpenFDA: https://open.fda.gov