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Drug Mechanism of Action Investigation

药物作用机制研究

Investigation Philosophy

研究理念

Drug mechanism research follows one core question chain:
Target -> Downstream Effect -> Pathway -> Organ Effect -> Clinical Outcome
Start with the drug's primary target. What receptor, enzyme, or transporter does it bind? Then trace forward: what does inhibiting/activating that target do immediately? What pathway is disrupted? What organ-level change results? What does the patient experience?
The LLM already knows drug pharmacology. This skill teaches HOW TO INVESTIGATE using available tools, not what mechanisms exist.
药物机制研究遵循一条核心问题链：
靶点 -> 下游效应 -> 通路 -> 器官效应 -> 临床结果
从药物的主要靶点入手。它结合的是哪种受体、酶或转运蛋白？然后向前追踪：抑制/激活该靶点会立即产生什么效果？哪些通路会被干扰？会导致哪些器官层面的变化？患者会有什么体验？
大语言模型已掌握药物药理学知识。本技能教授的是如何利用现有工具开展研究，而非讲解已有的机制内容。

When to Use

适用场景

  • "What is the mechanism of action of [drug]?"
  • "What are the molecular targets of [drug]?"
  • "Which pathways are affected by [drug]?"
  • "What pharmacogenomic interactions exist for [drug]?"
  • "What are the off-targets of [drug]?"
  • "Compare mechanisms of [drug A] vs [drug B]"
  • "[药物]的作用机制是什么？"
  • "[药物]的分子靶点有哪些？"
  • "[药物]会影响哪些通路？"
  • "[药物]存在哪些药物基因组学相互作用？"
  • "[药物]的脱靶靶点有哪些？"
  • "对比[药物A]与[药物B]的作用机制"

NOT for (use other skills)

不适用场景（请使用其他技能）

  • Drug safety/adverse events profiling -> 
    tooluniverse-adverse-event-detection
  • Drug repurposing/new indications -> 
    tooluniverse-drug-repurposing
  • Target druggability assessment -> 
    tooluniverse-drug-target-validation
  • Network pharmacology/polypharmacology -> 
    tooluniverse-network-pharmacology
  • CPIC dosing guidelines specifically -> 
    tooluniverse-pharmacogenomics
  • 药物安全性/不良事件分析 -> 
    tooluniverse-adverse-event-detection
  • 药物重定位/新适应症研究 -> 
    tooluniverse-drug-repurposing
  • 靶点成药性评估 -> 
    tooluniverse-drug-target-validation
  • 网络药理学/多药理学研究 -> 
    tooluniverse-network-pharmacology
  • 特定的CPIC给药指南 -> 
    tooluniverse-pharmacogenomics

Step 1: Resolve the Drug

步骤1：确定药物标识

Before investigating mechanism, resolve the drug name to a canonical identifier. You need a ChEMBL ID for most downstream queries.
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在研究作用机制之前，需将药物名称解析为标准标识符。大多数下游查询需要ChEMBL ID。
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Resolve drug name to ChEMBL ID

将药物名称解析为ChEMBL ID

result = tu.tools.OpenTargets_get_drug_id_description_by_name(drugName="metformin")
result = tu.tools.OpenTargets_get_drug_id_description_by_name(drugName="metformin")

Alternative: OpenTargets_get_drug_chembId_by_generic_name(drugName="metformin")

替代方案：OpenTargets_get_drug_chembId_by_generic_name(drugName="metformin")

Get PharmGKB ID (needed for PGx queries)

获取PharmGKB ID（用于药物基因组学查询）

result = tu.tools.PharmGKB_search_drugs(query="metformin")

**Fallback**: If OpenTargets returns no hits, try `PharmGKB_search_drugs` or `ChEMBL_get_drug` with a known ChEMBL ID.

---
result = tu.tools.PharmGKB_search_drugs(query="metformin")

**备用方案**：若OpenTargets未返回结果，可尝试使用`PharmGKB_search_drugs`或已知ChEMBL ID调用`ChEMBL_get_drug`。

---

Step 2: Identify the Primary Target

步骤2：识别主要靶点

The first question: what does this drug bind to, and what does it do to that target?
Two complementary sources give you this:
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第一个问题：该药物会结合什么靶点，以及对该靶点有什么作用？
以下两个互补来源可提供相关信息：
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OpenTargets: quick summary of MOA with target gene symbols

OpenTargets：快速获取作用机制摘要及靶点基因符号

moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId="CHEMBL1431") for row in moa["data"]["drug"]["mechanismsOfAction"]["rows"]: print(f"{row['mechanismOfAction']} ({row['actionType']}) -> {row['targetName']}") for t in row.get("targets", []): print(f" Target gene: {t['approvedSymbol']} ({t['id']})")
moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId="CHEMBL1431") for row in moa["data"]["drug"]["mechanismsOfAction"]["rows"]: print(f"{row['mechanismOfAction']} ({row['actionType']}) -> {row['targetName']}") for t in row.get("targets", []): print(f" 靶点基因: {t['approvedSymbol']} ({t['id']})")

ChEMBL: detailed MOA with literature references and direct_interaction flag

ChEMBL：包含文献引用和direct_interaction标记的详细作用机制

mechs = tu.tools.ChEMBL_get_drug_mechanisms(drug_chembl_id__exact="CHEMBL1431") for m in mechs["data"]["mechanisms"]: print(f"MOA: {m['mechanism_of_action']}, Direct: {m['direct_interaction']}") print(f" Refs: {[r['ref_id'] for r in m.get('mechanism_refs', [])]}")

**Key fields to extract**: action_type (INHIBITOR, AGONIST, ANTAGONIST, etc.), target gene symbol, direct_interaction (boolean), and literature references.

**Known issue**: `OpenTargets_get_associated_targets_by_drug_chemblId` may fail (GraphQL schema change). Extract targets from the MOA results instead.

---
mechs = tu.tools.ChEMBL_get_drug_mechanisms(drug_chembl_id__exact="CHEMBL1431") for m in mechs["data"]["mechanisms"]: print(f"作用机制: {m['mechanism_of_action']}, 直接作用: {m['direct_interaction']}") print(f" 参考文献: {[r['ref_id'] for r in m.get('mechanism_refs', [])]}")

**需提取的关键字段**：action_type（INHIBITOR、AGONIST、ANTAGONIST等）、靶点基因符号、direct_interaction（布尔值）以及文献引用。

**已知问题**：`OpenTargets_get_associated_targets_by_drug_chemblId`可能失效（GraphQL schema变更）。请从作用机制结果中提取靶点信息。

---

Step 3: Assess Off-Target Effects

步骤3：评估脱靶效应

Most drugs bind more than one target at clinical concentrations. After identifying the primary target, ask: what other proteins does this drug interact with? Off-target binding explains many side effects and drug interactions.
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大多数药物在临床浓度下会结合多个靶点。识别主要靶点后，需进一步探究：该药物还会与哪些蛋白质相互作用？脱靶结合是许多副作用和药物相互作用的原因。
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ChEMBL bioactivity data shows binding affinity across targets

ChEMBL生物活性数据展示靶点间的结合亲和力

activities = tu.tools.ChEMBL_get_target_activities(target_chembl_id__exact="CHEMBL2364")
activities = tu.tools.ChEMBL_get_target_activities(target_chembl_id__exact="CHEMBL2364")

STRING interaction partners reveal the target's protein network

STRING相互作用伙伴揭示靶点的蛋白质网络

partners = tu.tools.STRING_get_interaction_partners(identifiers="PRKAA1", species=9606)

**Reasoning strategy**: If ChEMBL MOA lists multiple targets, compare their action types. Same action type across related targets suggests on-pathway polypharmacology. Different action types suggest true off-target effects. The binding affinity (IC50/Ki from bioactivity data) tells you which targets matter at clinical doses -- nanomolar affinity is primary, micromolar is likely off-target.

---
partners = tu.tools.STRING_get_interaction_partners(identifiers="PRKAA1", species=9606)

**推理策略**：若ChEMBL作用机制列出多个靶点，对比它们的作用类型。相关靶点具有相同作用类型表明是通路内多药理学效应；不同作用类型则表明是真正的脱靶效应。结合亲和力（生物活性数据中的IC50/Ki）可告诉你哪些靶点在临床剂量下具有重要作用——纳摩尔级亲和力为主要靶点，微摩尔级则可能是脱靶靶点。

---

Step 4: Map to Pathway Context

步骤4：映射至通路背景

A drug target does not work in isolation. Map it to its pathway to understand the breadth of effect.
Key question: Is the target upstream (affects many downstream genes, broader effects, more side effects) or downstream (narrow, specific effect)?
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药物靶点并非孤立发挥作用。将其映射至所属通路，以理解效应的广度。
核心问题：靶点是处于上游（影响众多下游基因，效应更广，副作用更多）还是下游（效应狭窄、特异性强）？
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KEGG: find gene ID, then get pathways

KEGG：查找基因ID，然后获取通路信息

genes = tu.tools.kegg_find_genes(keyword="PRKAA1", organism="hsa") pathways = tu.tools.KEGG_get_gene_pathways(gene_id="hsa:5562")
genes = tu.tools.kegg_find_genes(keyword="PRKAA1", organism="hsa") pathways = tu.tools.KEGG_get_gene_pathways(gene_id="hsa:5562")

Reactome: map protein to pathways (needs UniProt ID)

Reactome：将蛋白质映射至通路（需要UniProt ID）

reactome = tu.tools.Reactome_map_uniprot_to_pathways(uniprot_id="Q13131")
reactome = tu.tools.Reactome_map_uniprot_to_pathways(uniprot_id="Q13131")

WikiPathways: search by gene symbol

WikiPathways：按基因符号搜索

wp = tu.tools.WikiPathways_find_pathways_by_gene(gene="PRKAA1")
wp = tu.tools.WikiPathways_find_pathways_by_gene(gene="PRKAA1")

STRING: functional annotations (GO terms, pathway memberships)

STRING：功能注释（GO术语、通路成员信息）

annot = tu.tools.STRING_get_functional_annotations(identifiers="PRKAA1", species=9606)

**For multi-target drugs**, run pathway enrichment to find convergent pathways:

```python
annot = tu.tools.STRING_get_functional_annotations(identifiers="PRKAA1", species=9606)

**对于多靶点药物**，运行通路富集分析以找到汇聚通路：

```python

Reactome enrichment (space-separated gene list, NOT array)

Reactome富集分析（空格分隔的基因列表，而非数组）

enrichment = tu.tools.ReactomeAnalysis_pathway_enrichment(identifiers="PRKAA1 PRKAA2 PRKAB1")
enrichment = tu.tools.ReactomeAnalysis_pathway_enrichment(identifiers="PRKAA1 PRKAA2 PRKAB1")

STRING enrichment

STRING富集分析

enrichment = tu.tools.STRING_functional_enrichment(identifiers="PRKAA1 PRKAA2", species=9606)

**Reasoning strategy**: If multiple drug targets converge on the same pathway, that pathway is the drug's true mechanism. If targets are in different pathways, the drug has genuinely multi-pathway effects -- report each separately.

---
enrichment = tu.tools.STRING_functional_enrichment(identifiers="PRKAA1 PRKAA2", species=9606)

**推理策略**：若多个药物靶点汇聚于同一通路，则该通路是药物的真实作用机制。若靶点位于不同通路，则药物具有真正的多通路效应——需分别报告每个通路。

---

Step 5: Get the Regulatory View (DailyMed)

步骤5：获取监管视角（DailyMed）

Drug labels describe WHAT the drug does. This is the FDA-approved mechanism narrative.
DailyMed requires a two-step process: search for the drug to get a 
setid
, then parse specific label sections.
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药物标签描述了药物的作用，这是FDA批准的作用机制说明。
DailyMed需要两步流程：搜索药物以获取
setid
，然后解析特定标签章节。
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Step 1: Get setid

步骤1：获取setid

spls = tu.tools.DailyMed_search_spls(drug_name="metformin") setid = spls["data"][0]["setid"]
spls = tu.tools.DailyMed_search_spls(drug_name="metformin") setid = spls["data"][0]["setid"]

Step 2: Parse the clinical pharmacology section (MOA, PK/PD, metabolism)

步骤2：解析临床药理学章节（作用机制、药代动力学/药效学、代谢）

pharmacology = tu.tools.DailyMed_parse_clinical_pharmacology( operation="parse_clinical_pharmacology", setid=setid)
pharmacology = tu.tools.DailyMed_parse_clinical_pharmacology( operation="parse_clinical_pharmacology", setid=setid)

Drug interactions from the label

标签中的药物相互作用信息

interactions = tu.tools.DailyMed_parse_drug_interactions( operation="parse_drug_interactions", setid=setid)
interactions = tu.tools.DailyMed_parse_drug_interactions( operation="parse_drug_interactions", setid=setid)

Contraindications

禁忌症

contra = tu.tools.DailyMed_parse_contraindications( operation="parse_contraindications", setid=setid)

**Other DailyMed parse tools**: `DailyMed_parse_adverse_reactions`, `DailyMed_parse_dosing`.

**Reasoning strategy**: The label's clinical pharmacology section often describes the mechanism differently from database entries. The label emphasizes clinically relevant effects; databases emphasize molecular detail. Both perspectives are needed.

---
contra = tu.tools.DailyMed_parse_contraindications( operation="parse_contraindications", setid=setid)

**其他DailyMed解析工具**：`DailyMed_parse_adverse_reactions`、`DailyMed_parse_dosing`。

**推理策略**：标签的临床药理学章节对机制的描述往往与数据库条目不同。标签强调临床相关效应；数据库则侧重分子细节。两种视角均不可或缺。

---

Step 6: Check Pharmacogenomics

步骤6：检查药物基因组学

Pharmacogenomic variants affect how a patient responds to the drug. This matters for mechanism because PGx genes are often the drug's metabolizing enzymes or targets.
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药物基因组学变异会影响患者对药物的反应。这对机制研究至关重要，因为药物基因组学基因通常是药物的代谢酶或靶点。
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CPIC gene-drug pairs (gold standard for PGx)

CPIC基因-药物对（药物基因组学的黄金标准）

pairs = tu.tools.CPIC_search_gene_drug_pairs(gene_symbol="CYP2C19", cpiclevel="A", limit=20)
pairs = tu.tools.CPIC_search_gene_drug_pairs(gene_symbol="CYP2C19", cpiclevel="A", limit=20)

Or search by drug

或按药物搜索

drug_info = tu.tools.CPIC_get_drug_info(name="clopidogrel")
drug_info = tu.tools.CPIC_get_drug_info(name="clopidogrel")

FDA PGx biomarkers (what's on the label)

FDA药物基因组学生物标志物（标签上的内容）

fda_pgx = tu.tools.fda_pharmacogenomic_biomarkers(drug_name="clopidogrel", limit=100)
fda_pgx = tu.tools.fda_pharmacogenomic_biomarkers(drug_name="clopidogrel", limit=100)

Or find all drugs affected by a gene

或查找受某基因影响的所有药物

fda_pgx = tu.tools.fda_pharmacogenomic_biomarkers(biomarker="CYP2D6", limit=100)
fda_pgx = tu.tools.fda_pharmacogenomic_biomarkers(biomarker="CYP2D6", limit=100)

PharmGKB gene details

PharmGKB基因详情

gene_info = tu.tools.PharmGKB_search_genes(query="CYP2C19")

**Reasoning strategy**: CPIC Level A/B pairs have strong evidence and actionable guidelines. If a drug has CPIC Level A interactions, those genes are critical to its mechanism (usually metabolizing enzymes or direct targets). FDA PGx biomarkers tell you what's on the approved label.

---
gene_info = tu.tools.PharmGKB_search_genes(query="CYP2C19")

**推理策略**：CPIC A/B级对具有强有力的证据和可操作的指南。若某药物存在CPIC A级相互作用，则这些基因对其机制至关重要（通常是代谢酶或直接靶点）。FDA药物基因组学生物标志物则告知你获批标签上的相关内容。

---

Step 7: Gather Literature Evidence

步骤7：收集文献证据

Literature describes WHY the mechanism works. Combine with labels (what) for a complete picture.
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文献解释了机制为何生效。结合标签内容（作用是什么）可构建完整图景。
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PubMed: returns a plain list of article dicts

PubMed：返回文章字典的纯列表

articles = tu.tools.PubMed_search_articles( query="metformin mechanism of action AMPK mitochondrial", limit=10)
articles = tu.tools.PubMed_search_articles( query="metformin mechanism of action AMPK mitochondrial", limit=10)

EuropePMC: returns {status, data, metadata}

EuropePMC：返回{status, data, metadata}

articles = tu.tools.EuropePMC_search_articles( query="metformin mechanism action mitochondrial", limit=10)
articles = tu.tools.EuropePMC_search_articles( query="metformin mechanism action mitochondrial", limit=10)

Follow citation chains for seminal papers

追踪经典论文的引用链

citations = tu.tools.EuropePMC_get_citations(source="MED", identifier="12345678")

**Search strategy**: Start with "[drug] mechanism of action [primary target]". If the mechanism is debated, add the competing hypotheses as separate queries. Recent reviews (add "review" to query) give the current consensus.

---
citations = tu.tools.EuropePMC_get_citations(source="MED", identifier="12345678")

**搜索策略**：从"[药物] mechanism of action [主要靶点]"开始。若机制存在争议，添加竞争性假设作为单独查询。近期综述（在查询中添加"review"）可提供当前共识。

---

Step 8: Integrate and Report

步骤8：整合与报告

Evidence Hierarchy

证据层级

  • Tier 1 (Regulatory): FDA label (DailyMed), CPIC Level A, FDA PGx biomarker
  • Tier 2 (Experimental): ChEMBL mechanisms with literature refs, binding data
  • Tier 3 (Database): OpenTargets MOA, pathway databases (KEGG/Reactome/WikiPathways)
  • Tier 4 (Literature): PubMed/EuropePMC articles
  • 一级（监管）：FDA标签（DailyMed）、CPIC A级、FDA药物基因组学生物标志物
  • 二级（实验）：带文献引用的ChEMBL机制、结合数据
  • 三级（数据库）：OpenTargets作用机制、通路数据库（KEGG/Reactome/WikiPathways）
  • 四级（文献）：PubMed/EuropePMC文章

Report Structure

报告结构

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Drug Mechanism Report: [Drug Name]

药物机制报告：[药物名称]

Drug Identity

药物标识

  • ChEMBL ID, PharmGKB ID, approval status
  • ChEMBL ID、PharmGKB ID、获批状态

Primary Mechanism

主要作用机制

  • Target: [gene symbol], Action: [INHIBITOR/AGONIST/etc.]
  • Mechanism narrative (from DailyMed + databases)
  • Direct interaction: yes/no
  • 靶点：[基因符号]，作用类型：[INHIBITOR/AGONIST等]
  • 机制说明（来自DailyMed + 数据库）
  • 直接作用：是/否

Off-Target Effects

脱靶效应

  • Additional targets with action types and binding affinities
  • Which off-targets explain known side effects
  • 其他靶点及其作用类型与结合亲和力
  • 哪些脱靶靶点可解释已知副作用

Pathway Context

通路背景

  • Key pathways (from KEGG/Reactome/WikiPathways)
  • Upstream vs downstream position of target
  • Convergent pathways for multi-target drugs
  • 关键通路（来自KEGG/Reactome/WikiPathways）
  • 靶点的上游/下游定位
  • 多靶点药物的汇聚通路

Pharmacogenomics

药物基因组学

  • CPIC gene-drug pairs with levels
  • FDA PGx biomarkers
  • 带等级的CPIC基因-药物对
  • FDA药物基因组学生物标志物

Drug Interactions

药物相互作用

  • Mechanism-based interactions (enzyme inhibition/induction)
  • Key interactions from DailyMed
  • 基于机制的相互作用（酶抑制/诱导）
  • DailyMed中的关键相互作用

Evidence Summary

证据汇总

FindingSourceTier
Primary MOAChEMBL + DailyMedT1/T2
Off-targetsChEMBL bioactivityT2
PathwaysKEGG/ReactomeT3
PGxCPIC/FDAT1

---
发现来源层级
主要作用机制ChEMBL + DailyMedT1/T2
脱靶靶点ChEMBL生物活性数据T2
通路KEGG/ReactomeT3
药物基因组学CPIC/FDAT1

---

Comparing Two Drugs

两种药物对比

When comparing mechanisms, run Steps 2-4 for both drugs, then align:
  1. Same target, different action? (e.g., agonist vs antagonist at the same receptor)
  2. Different targets, same pathway? (e.g., both affect insulin signaling but at different nodes)
  3. Different pathways entirely? (e.g., metformin on AMPK vs pioglitazone on PPAR-gamma)
python
for drug in [("metformin", "CHEMBL1431"), ("pioglitazone", "CHEMBL595")]:
    moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=drug[1])
    clin = tu.tools.DailyMed_parse_clinical_pharmacology(drug_name=drug[0])
对比作用机制时，为两种药物分别执行步骤2-4，然后进行对齐分析：
  1. 相同靶点，不同作用类型？（例如，同一受体的激动剂 vs 拮抗剂）
  2. 不同靶点，相同通路？（例如，均影响胰岛素信号通路但作用于不同节点）
  3. 完全不同的通路？（例如，二甲双胍作用于AMPK vs 吡格列酮作用于PPAR-gamma）
python
for drug in [("metformin", "CHEMBL1431"), ("pioglitazone", "CHEMBL595")]:
    moa = tu.tools.OpenTargets_get_drug_mechanisms_of_action_by_chemblId(chemblId=drug[1])
    clin = tu.tools.DailyMed_parse_clinical_pharmacology(drug_name=drug[0])

Fallback Strategies

备用策略

StepPrimary ToolFallback
Drug IDOpenTargets_get_drug_id_description_by_namePharmGKB_search_drugs
MOAOpenTargets_get_drug_mechanisms_of_action_by_chemblIdChEMBL_get_drug_mechanisms
PathwaysKEGG_get_gene_pathwaysWikiPathways_find_pathways_by_gene, Reactome_map_uniprot_to_pathways
PGxCPIC_search_gene_drug_pairsfda_pharmacogenomic_biomarkers
Clinical infoDailyMed_parse_clinical_pharmacologyOpenTargets_get_drug_description_by_chemblId
DDIDailyMed_parse_drug_interactionsPubMed_search_articles (DDI query)
LiteraturePubMed_search_articlesEuropePMC_search_articles
MetaCyc note: MetaCyc requires a paid account and is not available. Use KEGG, Reactome, or WikiPathways instead.
步骤主要工具备用工具
药物IDOpenTargets_get_drug_id_description_by_namePharmGKB_search_drugs
作用机制OpenTargets_get_drug_mechanisms_of_action_by_chemblIdChEMBL_get_drug_mechanisms
通路KEGG_get_gene_pathwaysWikiPathways_find_pathways_by_gene, Reactome_map_uniprot_to_pathways
药物基因组学CPIC_search_gene_drug_pairsfda_pharmacogenomic_biomarkers
临床信息DailyMed_parse_clinical_pharmacologyOpenTargets_get_drug_description_by_chemblId
药物相互作用DailyMed_parse_drug_interactionsPubMed_search_articles（DDI查询）
文献PubMed_search_articlesEuropePMC_search_articles
MetaCyc说明：MetaCyc需要付费账户，无法使用。请改用KEGG、Reactome或WikiPathways。