# Scientific Pharmgkb Pgx > PharmGKB 薬理ゲノミクススキル。PharmGKB REST API による 臨床アノテーション・薬物遺伝子関連・投与量ガイドライン・ スターアレル解析。ToolUniverse 連携: pharmgkb。 - Skill: `nahisaho/scientific-pharmgkb-pgx-2` (Agent Skill) - Install (CLI): `npx skillmds@latest add nahisaho/scientific-pharmgkb-pgx-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/nahisaho/scientific-pharmgkb-pgx-2/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Integrations & APIs - Author: nahisaho (https://skillmd.com/u/nahisaho) - Updated: 2026-09-21 - Page: https://skillmd.com/skills/nahisaho/scientific-pharmgkb-pgx-2 --- # Scientific PharmGKB PGx PharmGKB (Pharmacogenomics Knowledgebase) REST API を活用した 薬理ゲノミクス臨床アノテーション・薬物遺伝子相互作用・投与量 ガイドライン検索パイプラインを提供する。 ## When to Use - 薬物と遺伝子変異の関連を調べるとき - 臨床アノテーション (エビデンスレベル付き) を検索するとき - 投与量調整ガイドライン (CPIC/DPWG) を取得するとき - スターアレルと表現型の対応を確認するとき - 特定薬物の薬理ゲノミクス情報を包括的に取得するとき - 精密医療の薬物選択を支援するとき --- ## Quick Start ## 1. 薬物・遺伝子検索 ```python import requests import pandas as pd PGKB_BASE = "https://api.pharmgkb.org/v1/data" def pharmgkb_search_drugs(query, limit=50): """ PharmGKB — 薬物検索。 Parameters: query: str — 薬物名 (例: "warfarin", "clopidogrel") limit: int — 最大結果数 """ url = f"{PGKB_BASE}/chemical" params = {"name": query, "view": "max"} resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): results.append({ "pharmgkb_id": item.get("id", ""), "name": item.get("name", ""), "generic_names": "; ".join( item.get("genericNames", [])), "trade_names": "; ".join( item.get("tradeNames", [])[:5]), "type": item.get("type", ""), "cross_references": len( item.get("crossReferences", [])), }) df = pd.DataFrame(results) print(f"PharmGKB drugs: {len(df)} results " f"(query='{query}')") return df def pharmgkb_search_genes(query, limit=50): """ PharmGKB — 遺伝子検索。 Parameters: query: str — 遺伝子シンボル (例: "CYP2D6") limit: int — 最大結果数 """ url = f"{PGKB_BASE}/gene" params = {"symbol": query, "view": "max"} resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): results.append({ "pharmgkb_id": item.get("id", ""), "symbol": item.get("symbol", ""), "name": item.get("name", ""), "chromosome": item.get("chromosomeFormatted", ""), "cpic_gene": item.get("cpicGene", False), "has_prescribing_info": item.get( "hasPrescribingInfo", False), }) df = pd.DataFrame(results) print(f"PharmGKB genes: {len(df)} results " f"(query='{query}')") return df ``` ## 2. 臨床アノテーション取得 ```python def pharmgkb_clinical_annotations(gene_or_drug, search_type="gene"): """ PharmGKB — 臨床アノテーション検索。 Parameters: gene_or_drug: str — 遺伝子シンボル or 薬物名 search_type: str — "gene" or "drug" """ url = f"{PGKB_BASE}/clinicalAnnotation" params = {"view": "max"} if search_type == "gene": # 遺伝子で検索 gene_url = f"{PGKB_BASE}/gene" g_resp = requests.get(gene_url, params={"symbol": gene_or_drug}, timeout=30) g_resp.raise_for_status() genes = g_resp.json().get("data", []) if genes: params["relatedGenes.id"] = genes[0].get("id", "") else: # 薬物で検索 drug_url = f"{PGKB_BASE}/chemical" d_resp = requests.get(drug_url, params={"name": gene_or_drug}, timeout=30) d_resp.raise_for_status() drugs = d_resp.json().get("data", []) if drugs: params["relatedChemicals.id"] = drugs[0].get("id", "") resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): genes = [g.get("symbol", "") for g in item.get("relatedGenes", [])] drugs = [c.get("name", "") for c in item.get("relatedChemicals", [])] results.append({ "annotation_id": item.get("id", ""), "level": item.get("level", ""), "score": item.get("score", ""), "genes": "; ".join(genes), "drugs": "; ".join(drugs), "phenotype_category": item.get( "phenotypeCategory", ""), "sentences": (item.get("textHtml") or "")[:300], }) df = pd.DataFrame(results) if not df.empty: df = df.sort_values("level") print(f"PharmGKB annotations: {len(df)} " f"({search_type}={gene_or_drug})") return df ``` ## 3. 投与量ガイドライン取得 ```python def pharmgkb_dosing_guidelines(drug_name=None, gene=None): """ PharmGKB — 投与量ガイドライン (CPIC/DPWG) 検索。 Parameters: drug_name: str — 薬物名 gene: str — 遺伝子シンボル """ url = f"{PGKB_BASE}/guideline" params = {"view": "max"} if drug_name: params["relatedChemicals.name"] = drug_name if gene: params["relatedGenes.symbol"] = gene resp = requests.get(url, params=params, timeout=30) resp.raise_for_status() data = resp.json() results = [] for item in data.get("data", []): genes = [g.get("symbol", "") for g in item.get("relatedGenes", [])] drugs = [c.get("name", "") for c in item.get("relatedChemicals", [])] results.append({ "guideline_id": item.get("id", ""), "name": item.get("name", ""), "source": item.get("source", ""), "genes": "; ".join(genes), "drugs": "; ".join(drugs), "recommendation": (item.get("textHtml") or "")[:500], }) df = pd.DataFrame(results) print(f"PharmGKB guidelines: {len(df)} " f"(drug={drug_name}, gene={gene})") return df ``` ## 4. PharmGKB 統合パイプライン ```python def pharmgkb_pipeline(drug_name, genes=None, output_dir="results"): """ PharmGKB 統合パイプライン。 Parameters: drug_name: str — 薬物名 genes: list[str] — 関連遺伝子リスト output_dir: str — 出力ディレクトリ """ from pathlib import Path output_dir = Path(output_dir) output_dir.mkdir(parents=True, exist_ok=True) # 1) 薬物検索 drugs = pharmgkb_search_drugs(drug_name) drugs.to_csv(output_dir / "drugs.csv", index=False) # 2) 薬物の臨床アノテーション annotations = pharmgkb_clinical_annotations( drug_name, search_type="drug") annotations.to_csv(output_dir / "annotations.csv", index=False) # 3) 投与量ガイドライン guidelines = pharmgkb_dosing_guidelines( drug_name=drug_name) guidelines.to_csv(output_dir / "guidelines.csv", index=False) # 4) 関連遺伝子解析 if genes: gene_results = [] for g in genes: try: g_ann = pharmgkb_clinical_annotations( g, search_type="gene") g_ann["query_gene"] = g gene_results.append(g_ann) except Exception: continue if gene_results: gene_df = pd.concat(gene_results, ignore_index=True) gene_df.to_csv( output_dir / "gene_annotations.csv", index=False) print(f"PharmGKB pipeline: {output_dir}") return { "drugs": drugs, "annotations": annotations, "guidelines": guidelines, } ``` --- ## ToolUniverse 連携 | TU Key | ツール名 | 連携内容 | |--------|---------|---------| | `pharmgkb` | PharmGKB | 臨床アノテーション・薬物遺伝子・PGx ガイドライン | ## パイプライン統合 ``` pharmacogenomics → pharmgkb-pgx → clinical-decision-support (PGx 解析全般) (PharmGKB API) (臨床意思決定) │ │ ↓ drug-discovery ──────┘ precision-oncology (薬物開発) │ (精密腫瘍学) ↓ variant-interpretation (変異臨床解釈) ``` ## パイプライン出力 | ファイル | 説明 | 次スキル | |---------|------|---------| | `results/drugs.csv` | 薬物情報 | → drug-discovery | | `results/annotations.csv` | 臨床アノテーション | → variant-interpretation | | `results/guidelines.csv` | 投与量ガイドライン | → clinical-decision-support | | `results/gene_annotations.csv` | 遺伝子別アノテーション | → pharmacogenomics |