| name | scientific-toxicology-env |
| description | 毒性学・環境衛生スキル。CTD (Comparative Toxicogenomics Database)
化学-遺伝子-疾患関連・ToxCast/Tox21 高スループット毒性スクリーニング・
IRIS ヒトリスク評価・T3DB 食品/環境毒性物質・PubChem BioAssay 毒性データ。
|
| tu_tools | [{"key":"ctd","name":"CTD","description":"化学物質-疾患-遺伝子関連データ検索"}] |
Scientific Toxicology & Environmental Health
CTD / Tox21 / ToxCast / T3DB / IRIS を活用した毒性学・環境衛生
パイプラインを提供する。化学物質-遺伝子-疾患関連、高スループット
毒性スクリーニング、ヒト健康リスク評価。
When to Use
- 化学物質が関連する遺伝子・疾患を調べるとき (CTD)
- Tox21/ToxCast アッセイデータで毒性メカニズムを解析するとき
- IRIS リスク評価データ (RfD/RfC/UR) を参照するとき
- 食品・環境毒性物質の詳細データベース (T3DB) を検索するとき
- PubChem BioAssay から毒性関連ハイスループットデータを取得するとき
- ADMET 毒性予測と実験毒性データを併用するとき
Quick Start
1. CTD 化学-遺伝子-疾患関連検索
import requests
import pandas as pd
import json
CTD_BASE = "https://ctdbase.org/tools"
def ctd_chemical_gene(chemical_name, limit=100):
"""
CTD — 化学物質-遺伝子相互作用を検索。
Parameters:
chemical_name: str — 化学物質名 (例: "Bisphenol A")
limit: int — 最大取得数
"""
url = f"{CTD_BASE}/batchQuery.go"
params = {
"inputType": "chem",
"inputTerms": chemical_name,
"report": "genes_curated",
"format": "json",
}
resp = requests.get(url, params=params, timeout=60)
resp.raise_for_status()
data = resp.json()[:limit]
results = []
for entry in data:
results.append({
"chemical": entry.get("ChemicalName", ""),
"gene": entry.get("GeneSymbol", ""),
"organism": entry.get("Organism", ""),
"interaction": entry.get("Interaction", ""),
"pubmed_ids": entry.get("PubMedIDs", ""),
})
df = pd.DataFrame(results)
print(f"CTD: {chemical_name} → {len(df)} gene interactions")
return df
2. CTD 化学-疾患関連検索
def ctd_chemical_disease(chemical_name, limit=100):
"""
CTD — 化学物質-疾患関連を検索。
Parameters:
chemical_name: str — 化学物質名
limit: int — 最大取得数
"""
url = f"{CTD_BASE}/batchQuery.go"
params = {
"inputType": "chem",
"inputTerms": chemical_name,
"report": "diseases_curated",
"format": "json",
}
resp = requests.get(url, params=params, timeout=60)
resp.raise_for_status()
data = resp.json()[:limit]
results = []
for entry in data:
results.append({
"chemical": entry.get("ChemicalName", ""),
"disease": entry.get("DiseaseName", ""),
"disease_id": entry.get("DiseaseID", ""),
"direct_evidence": entry.get("DirectEvidence", ""),
"inference_score": float(entry.get("InferenceScore", 0)),
})
df = pd.DataFrame(results)
df = df.sort_values("inference_score", ascending=False)
print(f"CTD: {chemical_name} → {len(df)} disease associations")
return df
3. Tox21/ToxCast アッセイデータ取得
COMPTOX_BASE = "https://comptox.epa.gov/dashboard/api"
def tox21_assay_search(chemical_identifier, assay_source="Tox21"):
"""
CompTox Dashboard — Tox21/ToxCast アッセイ結果を取得。
Parameters:
chemical_identifier: str — DTXSID or CAS
assay_source: str — "Tox21" or "ToxCast"
"""
url = f"{COMPTOX_BASE}/chemical/search"
params = {"query": chemical_identifier}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
chem_data = resp.json()
dtxsid = chem_data.get("dtxsid", chemical_identifier)
url_assay = f"{COMPTOX_BASE}/chemical/{dtxsid}/assays"
resp_assay = requests.get(url_assay, timeout=30)
resp_assay.raise_for_status()
assays = resp_assay.json()
results = []
for assay in assays:
if assay_source.lower() in assay.get("assaySource", "").lower():
results.append({
"assay_name": assay.get("assayName", ""),
"assay_source": assay.get("assaySource", ""),
"endpoint": assay.get("assayEndpoint", ""),
"activity": assay.get("activity", ""),
"ac50_um": assay.get("ac50", None),
"hit_call": assay.get("hitCall", ),
})
df = pd.DataFrame(results)
n_active = (df[] == ).() (df) >
()
df
4. T3DB 毒性物質検索
T3DB_BASE = "https://t3db.ca/api"
def t3db_search(query, search_type="name"):
"""
T3DB — 食品/環境毒性物質データベース検索。
Parameters:
query: str — 検索語
search_type: str — "name", "cas", "category"
"""
url = f"{T3DB_BASE}/toxins/search"
params = {"query": query, "search_type": search_type}
resp = requests.get(url, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
results = []
for toxin in data.get("toxins", []):
results.append({
"name": toxin.get("name", ""),
"t3db_id": toxin.get("t3db_id", ""),
"cas_number": toxin.get("cas_number", ""),
"category": toxin.get("category", ""),
"toxicity_class": toxin.get("toxicity_class", ""),
"ld50_oral": toxin.get("ld50_oral", ""),
"target_organs": toxin.get("target_organs", []),
})
df = pd.DataFrame(results)
print(f"T3DB: '{query}' → {len(df)} toxins")
return df
5. EPA IRIS リスク評価
def iris_risk_assessment(chemical_name):
"""
EPA IRIS — ヒト健康リスク評価データ取得。
Parameters:
chemical_name: str — 化学物質名
"""
url = "https://iris.epa.gov/AtoZ"
resp = requests.get(url, timeout=30)
url_comptox = f"{COMPTOX_BASE}/chemical/search"
params = {"query": chemical_name}
resp = requests.get(url_comptox, params=params, timeout=30)
resp.raise_for_status()
data = resp.json()
risk_data = {
"chemical": chemical_name,
"dtxsid": data.get("dtxsid", ""),
"rfd_oral_mg_kg_day": data.get("rfdOral", None),
"rfc_inhalation_mg_m3": data.get("rfcInhalation", None),
"cancer_classification": data.get("cancerClassification", ""),
"oral_slope_factor": data.get("oralSlopeFactor", None),
"inhalation_unit_risk": data.get("inhalationUnitRisk", None),
}
print(f"IRIS: {chemical_name}")
for k, v in risk_data.items():
if v and k != "chemical":
print(f" {k}: {v}")
return risk_data
6. 毒性パスウェイ解析
def toxicity_pathway_analysis(chemical_name, species="Homo sapiens"):
"""
CTD + パスウェイ統合毒性解析。
Parameters:
chemical_name: str — 化学物質名
species: str — 生物種
"""
gene_df = ctd_chemical_gene(chemical_name, limit=500)
if species:
gene_df = gene_df[gene_df["organism"] == species]
gene_list = gene_df["gene"].unique().tolist()
disease_df = ctd_chemical_disease(chemical_name, limit=100)
enrichr_url = "https://maayanlab.cloud/Enrichr"
add_resp = requests.post(
f"{enrichr_url}/addList",
files={"list": (None, "\n".join(gene_list))}
)
user_list_id = add_resp.json()["userListId"]
enrich_resp = requests.get(
f"{enrichr_url}/enrich",
params={"userListId": user_list_id, "backgroundType": "KEGG_2021_Human"}
)
pathways = enrich_resp.json().get("KEGG_2021_Human", [])
pathway_results = []
for pw in pathways[:20]:
pathway_results.append({
"pathway": pw[1],
"p_value": pw[2],
"adj_p_value": pw[6],
"genes": pw[5],
})
print(f"Toxicity pathway: {chemical_name}")
print()
()
()
{
: gene_df,
: disease_df,
: pd.DataFrame(pathway_results),
}
パイプライン統合
admet-pharmacokinetics → toxicology-env → pharmacovigilance
(ADMET 毒性予測) (CTD/Tox21/IRIS) (市販後安全性)
│ │ ↓
cheminformatics ───────────────┘ disease-research
(RDKit 構造アラート) │ (疾患-遺伝子関連)
↓
public-health-data
(CDC/WHO 公衆衛生)
パイプライン出力
| ファイル | 説明 | 次スキル |
|---|
results/ctd_gene_interactions.csv | CTD 化学-遺伝子関連 | → pathway-enrichment |
results/ctd_disease_associations.csv | CTD 化学-疾患関連 | → disease-research |
results/tox21_assays.csv | Tox21/ToxCast アッセイ結果 | → admet-pharmacokinetics |
results/toxicity_pathways.json | 毒性パスウェイ解析結果 | → pharmacovigilance |
ToolUniverse 連携
| TU Key | ツール名 | 連携内容 |
|---|
ctd | CTD | 化学物質-疾患-遺伝子関連データ検索 |
