--- name: jaechang-hits/clinpgx-database source: https://app.decimal.ai/s/jaechang-hits-clinpgx-database@1/SKILL.md source_sha256: 5b4b3d7840c2 --- # ClinPGx (PharmGKB) Pharmacogenomics Database ## Overview PharmGKB rebranded as **ClinPGx** in 2024 and the API moved from `api.pharmgkb.org` to `api.clinpgx.org`. The old host now returns 404/405; every example here uses the new endpoints. Two complementary APIs are used together: - **ClinPGx Data API** (`api.clinpgx.org/v1`) — record-style access to genes, drugs, variants, clinical annotations, guideline annotations, drug labels, and pathways. Responses wrap data as `{"data": [...], "status": "success"}`. Filters use dotted property paths (e.g. `relatedChemicals.name=clopidogrel`, `levelOfEvidence.term=1A`). - **CPIC PostgREST API** (`api.cpicpgx.org/v1`) — relational lookup of genotype → drug recommendation rows. PostgREST filter syntax (`column=eq.value`, JSON `cs.{...}` for jsonb containment). Returns flat JSON arrays. Use ClinPGx for *what is known* about a gene/drug/variant; use CPIC for *how to prescribe* given a phenotype. The pattern is `ClinPGx for annotations, CPIC for recommendations`. ## When to Use - Retrieving CPIC genotype-specific dosing recommendations for a gene-drug pair (e.g., CYP2C19 + clopidogrel) — use CPIC - Looking up all pharmacogenomic clinical annotations for a drug or evidence level — use ClinPGx `data/clinicalAnnotation` - Finding all CPIC/DPWG guideline annotations for a pharmacogene — use ClinPGx `data/guidelineAnnotation` - Resolving a gene symbol, drug name, or rsID to ClinPGx PA identifiers — use `data/{gene,drug,variant}` - Free-text search across all ClinPGx record types (genes, drugs, variants, annotations) — use `POST /site/search` - Retrieving FDA/EMA pharmacogenomic drug label annotations — use ClinPGx `data/label` - Building precision-medicine prescribing workflows that combine annotation evidence with phenotype-specific recommendations - For germline disease pathogenicity (not PGx) use `clinvar-database` - For somatic cancer pharmacogenomics use `cosmic-database` or `opentargets-database` ## Prerequisites - **Python packages**: `requests`, `pandas` — both already in standard environments - **Data requirements**: HGNC gene symbols, drug names (lowercase generic), dbSNP rsIDs, or PA identifiers - **Environment**: internet connection; no authentication required for either host - **Rate limits**: the ClinPGx host occasionally returns HTTP 429; insert `time.sleep(0.3–0.5)` between sequential calls. CPIC is more permissive. If you are inside a pixi/conda environment that already provides `requests` and `pandas`, skip the install — invoke scripts with `pixi run python ...`. ```bash pip install requests pandas ``` ## Quick Start ```python import requests CLINPGX = "https://api.clinpgx.org/v1" CPIC = "https://api.cpicpgx.org/v1" # CPIC genotype → recommendation: clopidogrel + CYP2C19 Poor Metabolizer drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{drug['drugid']}", "phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'}).json() print(f"clopidogrel CYP2C19=PM: {len(recs)} recommendation(s)") for rec in recs[:2]: print(f" [{rec['classification']}] {rec['drugrecommendation'][:80]}…") # ClinPGx side: how many CPIC guideline annotations cover CYP2C19? glines = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedGenes.symbol": "CYP2C19", "source": "CPIC", "view": "base"}).json()["data"] print(f"CYP2C19 CPIC guidelines: {len(glines)}") ``` ## Core API ### Module 1: Free-text site search `POST /site/search` with a JSON body `{"query": ""}` is the canonical entry point when you don't know the PA ID. It searches across drugs, genes, variants, clinical annotations, guideline annotations, and labels in one shot. ```python import requests CLINPGX = "https://api.clinpgx.org/v1" r = requests.post(f"{CLINPGX}/site/search", json={"query": "rs4149056"}, timeout=15) r.raise_for_status() hits = r.json()["data"]["hits"] print(f"Total hits: {r.json()['data']['total']}") for h in hits[:5]: print(f" id={h.get('id')} name={h.get('name')[:80]}") ``` ```python # Broader concept search r = requests.post(f"{CLINPGX}/site/search", json={"query": "TPMT azathioprine"}, timeout=15) hits = r.json()["data"]["hits"] print(f"TPMT+azathioprine hits: {len(hits)}") for h in hits[:5]: print(f" {h.get('id'):>15} {h.get('name','')[:80]}") ``` ### Module 2: Gene, drug, and variant record lookup The `/data/{type}` endpoints accept simple property filters. All return `{"data": [...], "status": "success"}` — use `view=base` for summary, `view=max` for full nested objects. ```python import requests CLINPGX = "https://api.clinpgx.org/v1" # Gene by HGNC symbol gene = requests.get(f"{CLINPGX}/data/gene", params={"symbol": "CYP2D6", "view": "base"}).json()["data"][0] print(f"{gene['symbol']} id={gene['id']} {gene['name']}") # Drug by name (lowercase generic preferred) drug = requests.get(f"{CLINPGX}/data/drug", params={"name": "warfarin", "view": "base"}).json()["data"][0] print(f"{drug['name']} id={drug['id']}") # Variant by rsID var = requests.get(f"{CLINPGX}/data/variant", params={"name": "rs4149056", "view": "base"}).json()["data"][0] print(f"{var['name']} id={var['id']} significance={var.get('clinicalSignificance')}") ``` ```python # Direct record fetch when you already have a PA ID r = requests.get(f"{CLINPGX}/data/drug/PA449088", params={"view": "max"}).json() d = r["data"] print(f"PA449088 → {d['name']} (objCls={d['objCls']})") ``` ### Module 3: Clinical annotations `data/clinicalAnnotation` records associate a variant (`location`) with one or more drugs (`relatedChemicals`) and an evidence level (`levelOfEvidence.term`). The two supported filters are `relatedChemicals.name=` and `levelOfEvidence.term=`. There is **no working `gene=` filter on this endpoint** — see Module 4 for gene-driven access. ```python import requests, pandas as pd CLINPGX = "https://api.clinpgx.org/v1" # All clinical annotations for clopidogrel data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", params={"relatedChemicals.name": "clopidogrel", "view": "base"}).json()["data"] print(f"clopidogrel annotations: {len(data)}") rows = [] for ann in data[:10]: loc = ann.get("location") or {} drugs = ", ".join(c.get("name", "") for c in ann.get("relatedChemicals", [])) rows.append({ "id": ann["id"], "variant": loc.get("displayName"), "gene": (loc.get("genes") or [{}])[0].get("symbol"), "drug": drugs, "level": (ann.get("levelOfEvidence") or {}).get("term"), "score": ann.get("score"), }) print(pd.DataFrame(rows).to_string(index=False)) ``` ```python # All Level 1A clinical annotations (highest evidence) data = requests.get(f"{CLINPGX}/data/clinicalAnnotation", params={"levelOfEvidence.term": "1A", "view": "base"}).json()["data"] print(f"Level 1A annotations: {len(data)}") drug_to_count = {} for ann in data: for c in ann.get("relatedChemicals") or []: drug_to_count[c["name"]] = drug_to_count.get(c["name"], 0) + 1 top = sorted(drug_to_count.items(), key=lambda x: -x[1])[:10] for d, n in top: print(f" {n:3} {d}") ``` ### Module 4: Guideline annotations (gene-driven access) `data/guidelineAnnotation` supports both `relatedGenes.symbol=` and `relatedChemicals.name=`, plus `source=` (`CPIC`, `DPWG`, `CPNDS`, `RNPGx`). This is the canonical way to get gene→guideline coverage. ```python import requests CLINPGX = "https://api.clinpgx.org/v1" # All CPIC guidelines mentioning CYP2C19 data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedGenes.symbol": "CYP2C19", "source": "CPIC", "view": "base"}).json()["data"] print(f"CYP2C19 CPIC guidelines: {len(data)}") for g in data[:5]: print(f" PA{g['id']}: {g['name'][:80]}") ``` ```python # Guidelines for a specific drug across all bodies (CPIC, DPWG, …) data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedChemicals.name": "clopidogrel", "view": "base"}).json()["data"] by_source = {} for g in data: for s in (g.get("crossReferences") or []): by_source.setdefault(s.get("resource", "?"), 0) by_source[s["resource"]] = by_source.get(s["resource"], 0) + 1 print(f"clopidogrel guidelines: {len(data)} ({list({g.get('source') for g in data})})") ``` ### Module 5: Regulatory drug labels (FDA / EMA) `data/label` records are PharmGKB-curated annotations of FDA/EMA pharmacogenomic labeling. Filter by `relatedChemicals.name=` and `source=` (`FDA`, `EMA`, `HCSC`, `PMDA`, `Swissmedic`). ```python import requests, pandas as pd CLINPGX = "https://api.clinpgx.org/v1" data = requests.get(f"{CLINPGX}/data/label", params={"relatedChemicals.name": "warfarin", "source": "FDA", "view": "base"}).json()["data"] print(f"warfarin FDA labels: {len(data)}") rows = [{ "name": d["name"][:60], "biomarker_status": d.get("biomarkerStatus"), "testing_required": d.get("testingRequired"), "alternate_drug": d.get("alternateDrugAvailable"), } for d in data] print(pd.DataFrame(rows).to_string(index=False)) ``` ### Module 6: CPIC genotype → recommendation chain CPIC's PostgREST API uses `column=eq.value` for equality and `column=cs.{...}` for JSONB containment. The standard lookup chain is `drug → drugid → recommendation`, optionally filtered by phenotype. ```python import requests CPIC = "https://api.cpicpgx.org/v1" # Resolve drug name to drugid (RxNorm-prefixed) drug = requests.get(f"{CPIC}/drug", params={"name": "eq.clopidogrel"}).json()[0] print(f"clopidogrel drugid: {drug['drugid']}") # All phenotype-specific recommendations for clopidogrel recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{drug['drugid']}"}).json() print(f"Total recommendations: {len(recs)}") for rec in recs[:3]: print(f" {rec['phenotypes']} [{rec['classification']}]") print(f" {rec['drugrecommendation'][:90]}…") ``` ```python # Phenotype filter via jsonb containment (cs.{...}) # The phenotypes column is a jsonb dict; cs. checks that the query is a subset. recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{drug['drugid']}", "phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'} ).json() for rec in recs: print(f" [{rec['classification']}] {rec['drugrecommendation'][:90]}…") # Gene-driven: list every drug with a CPIC pair for CYP2C19 pairs = requests.get(f"{CPIC}/pair", params={"genesymbol": "eq.CYP2C19"}).json() print(f"\nCYP2C19 CPIC pairs: {len(pairs)}") drug_ids = sorted({p["drugid"] for p in pairs}) print(f"Sample drug IDs: {drug_ids[:5]}") ``` ## Key Concepts ### Two-host architecture | Question | Use | Why | | ----------------------------------------------- | ----------------------------------- | -------------------------------------------------------------------------------- | | What clinical annotations exist for this drug? | ClinPGx `data/clinicalAnnotation` | Annotation-level evidence with curated levelOfEvidence.term | | What CPIC guidelines cover this gene? | ClinPGx `data/guidelineAnnotation` | Filter by `relatedGenes.symbol`; no working `gene=` filter on clinicalAnnotation | | Given phenotype X, what should I prescribe? | CPIC `recommendation` + `phenotypes`| Structured genotype→action rows; CPIC is the prescribing-rule oracle | | What FDA labels mention this drug + gene? | ClinPGx `data/label?source=FDA` | Curated regulatory PGx labeling | | Free-text "anything about X" | ClinPGx `POST /site/search` | Cross-record-type fan-out | ### PharmGKB / ClinPGx evidence levels Levels 1A → 4 in decreasing evidence quality: - **1A** — Annotation of a variant–drug pair in a clinical guideline or FDA label (strongest) - **1B** — Significant association replicated in multiple studies - **2A** — Variant in a known PGx gene, significant association - **2B** — Moderate evidence, often single study - **3** — Limited evidence (single study or unreplicated) - **4** — Case reports / biological plausibility only Filter via `levelOfEvidence.term` on `data/clinicalAnnotation`. The term is a *string*, not an enum (`"1A"` not `1A`). ### ClinPGx response envelope and view modes Every ClinPGx `/data/...` response is `{"data": [...] | {...}, "status": "success" | "fail"}`. On failure the body is `{"status": "fail", "data": {"errors": [{"message": "..."}]}}` — always read both keys. - `view=base` (default) — flat summary record; recommended for bulk filters - `view=max` — full nested objects (relatedDiseases, allelePhenotypes, scoreDetails, …). Larger payload, slower; use only for single-record details. ## Common Workflows ### Workflow 1: Pharmacogene panel CPIC coverage **Goal:** Given a patient's pharmacogene panel, count how many CPIC guideline annotations cover each gene. ```python import requests, pandas as pd, time CLINPGX = "https://api.clinpgx.org/v1" pharmacogenes = ["CYP2D6", "CYP2C19", "CYP2C9", "DPYD", "TPMT", "SLCO1B1"] rows = [] for g in pharmacogenes: data = requests.get(f"{CLINPGX}/data/guidelineAnnotation", params={"relatedGenes.symbol": g, "source": "CPIC", "view": "base"}, timeout=20).json()["data"] drugs = sorted({c["name"] for guideline in data for c in (guideline.get("relatedChemicals") or [])}) rows.append({"gene": g, "cpic_guidelines": len(data), "n_drugs": len(drugs), "sample": ", ".join(drugs[:3])}) time.sleep(0.3) df = pd.DataFrame(rows).sort_values("cpic_guidelines", ascending=False) print(df.to_string(index=False)) df.to_csv("pharmacogene_cpic_coverage.csv", index=False) ``` ### Workflow 2: Drug panel — CPIC prescribing rule lookup **Goal:** Given a prescribed drug list, identify which have CPIC genotype-specific recommendations and surface the rule rows. ```python import requests, pandas as pd, time CPIC = "https://api.cpicpgx.org/v1" drugs = ["warfarin", "clopidogrel", "codeine", "simvastatin", "metoprolol", "omeprazole", "azathioprine", "tacrolimus"] rows = [] for name in drugs: drug = requests.get(f"{CPIC}/drug", params={"name": f"eq.{name}"}, timeout=15).json() if not drug: rows.append({"drug": name, "in_cpic": False, "n_recs": 0, "phenotypes": ""}); continue did = drug[0]["drugid"] recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}"}, timeout=15).json() phens = sorted({f"{k}={v}" for rec in recs for k, v in (rec.get("phenotypes") or {}).items()}) rows.append({"drug": name, "in_cpic": True, "n_recs": len(recs), "phenotypes": "; ".join(phens[:3])}) time.sleep(0.3) df = pd.DataFrame(rows).sort_values(["in_cpic", "n_recs"], ascending=[False, False]) print(df.to_string(index=False)) ``` ### Workflow 3: Variant → drug interactions (rsID-driven) **Goal:** Starting from a single rsID (e.g., SLCO1B1 *5 = rs4149056), find every clinical annotation that involves it. The Data API does **not** accept rsID as a filter property. Use `POST /site/search` to discover related annotation IDs, then fetch each by ID. ```python import requests CLINPGX = "https://api.clinpgx.org/v1" rsid = "rs4149056" hits = requests.post(f"{CLINPGX}/site/search", json={"query": rsid}, timeout=15).json()["data"]["hits"] print(f"{rsid}: {len(hits)} hits") # Filter hits that look like clinical annotations ann_hits = [h for h in hits if h.get("name", "").lower().startswith("clinical annotation")] print(f"Clinical-annotation hits: {len(ann_hits)}") for h in ann_hits[:5]: print(f" id={h['id']} {h['name'][:90]}") # Dereference one annotation by ID for full detail if ann_hits: ann = requests.get(f"{CLINPGX}/data/clinicalAnnotation/{ann_hits[0]['id']}", params={"view": "max"}, timeout=15).json()["data"] drugs = ", ".join(c["name"] for c in (ann.get("relatedChemicals") or [])) print(f"\nFirst annotation:") print(f" drugs: {drugs}") print(f" level: {(ann.get('levelOfEvidence') or {}).get('term')}") ``` ## Key Parameters | Parameter | Module / Endpoint | Default | Range / Options | Effect | | --------------------------- | ---------------------------------------------- | ------- | -------------------------------------------------------- | ----------------------------------------------------------------------- | | `view` | all `/data/...` | `base` | `base`, `min`, `max` | Field detail level; `max` includes all nested arrays (slow but complete) | | `relatedChemicals.name` | `clinicalAnnotation`, `variantAnnotation`, `guidelineAnnotation`, `label`, `pathway` | — | lowercase generic drug name | Filter records related to a drug | | `relatedGenes.symbol` | `guidelineAnnotation`, `pathway` | — | HGNC gene symbol | Filter records related to a gene (not available on `clinicalAnnotation`) | | `levelOfEvidence.term` | `clinicalAnnotation` | — | `"1A"`, `"1B"`, `"2A"`, `"2B"`, `"3"`, `"4"` | Minimum evidence level | | `source` | `guidelineAnnotation`, `label` | — | `CPIC`, `DPWG`, `FDA`, `EMA`, `HCSC`, `PMDA`, `Swissmedic` | Issuing body | | `symbol` | `data/gene` | — | HGNC gene symbol | Gene record lookup | | `name` | `data/drug`, `data/variant` | — | drug name or rsID | Record lookup by canonical name | | CPIC `column=eq.value` | all `api.cpicpgx.org/v1/...` | — | PostgREST equality | Filter by exact match | | CPIC `phenotypes=cs.{json}` | `recommendation` | — | JSON-encoded jsonb subset | Filter by phenotype containment (must URL-encode if special chars) | ## Best Practices 1. **Resolve PA identifiers once.** Never hand-construct ClinPGx PA IDs. Call `data/{type}?{symbol|name}=...` (or `site/search`) once and cache the returned `id` for reuse — `gene/PA128` for CYP2D6, `drug/PA449088` for clopidogrel, `variant/PA166154579` for rs4149056. 2. **Pick the right host for the question.** Use ClinPGx for `what is annotated` and CPIC for `what to prescribe`. Trying to derive genotype-specific recommendations from ClinPGx alone misses the structured `recommendation.phenotypes` rows. 3. **Filter by evidence level upfront** when building clinical workflows. `levelOfEvidence.term=1A` returns 312 actionable annotations across all of ClinPGx; Level 3/4 records are exploratory and shouldn't drive prescribing. 4. **Don't filter `clinicalAnnotation` by gene — filter by `guidelineAnnotation`** with `relatedGenes.symbol`. The `clinicalAnnotation` endpoint has no working gene property and returns HTTP 400 for any attempt. 5. **Use `view=base` for bulk filters, `view=max` for single-record drill-downs.** A list query with `view=max` can time out or hit 429; the difference is roughly 5–10× payload size. 6. **Throttle the ClinPGx host.** Insert `time.sleep(0.3)` between sequential queries in loops; the API returns occasional HTTP 429s on tight loops. CPIC tolerates faster iteration. 7. **URL-encode `cs.{...}` jsonb filters** when phenotype values contain spaces or special characters. `requests.get(..., params={"phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'})` works because `requests` does the encoding; a manual URL string needs `urllib.parse.quote`. ## Common Recipes ### Recipe 1 — Free-text discovery via site/search When to use: you have an arbitrary string (rsID, drug name, gene, allele) and want to find related ClinPGx records without knowing which endpoint to hit. ```python import requests r = requests.post("https://api.clinpgx.org/v1/site/search", json={"query": "VKORC1 warfarin"}, timeout=15) hits = r.json()["data"]["hits"] for h in hits[:10]: print(f" {h.get('id'):>15} {h.get('name','')[:80]}") ``` ### Recipe 2 — Top drugs by Level 1A annotation count When to use: build a leaderboard of the most actionable PGx drugs. ```python import requests, pandas as pd data = requests.get("https://api.clinpgx.org/v1/data/clinicalAnnotation", params={"levelOfEvidence.term": "1A", "view": "base"}, timeout=30).json()["data"] counts = {} for ann in data: for c in ann.get("relatedChemicals") or []: counts[c["name"]] = counts.get(c["name"], 0) + 1 df = pd.DataFrame(sorted(counts.items(), key=lambda x: -x[1]), columns=["drug", "n_1A_annotations"]).head(15) print(df.to_string(index=False)) ``` ### Recipe 3 — Patient genotype → drug recommendations When to use: given a phenotype call from a PGx test, surface every CPIC recommendation row. ```python import requests CPIC = "https://api.cpicpgx.org/v1" genotype = {"CYP2C19": "Poor Metabolizer"} drug = "clopidogrel" did = requests.get(f"{CPIC}/drug", params={"name": f"eq.{drug}"}).json()[0]["drugid"] import json recs = requests.get(f"{CPIC}/recommendation", params={"drugid": f"eq.{did}", "phenotypes": f"cs.{json.dumps(genotype)}"}).json() for rec in recs: print(f"[{rec['classification']}] {rec['drugrecommendation']}") print(f" implications: {rec['implications']}") ``` ### Recipe 4 — Robust session with retry When to use: long-running loops over many genes / drugs / variants. ```python import requests from requests.adapters import HTTPAdapter from urllib3.util.retry import Retry s = requests.Session() s.headers.update({"Accept": "application/json"}) s.mount("https://", HTTPAdapter(max_retries=Retry( total=4, backoff_factor=1.0, status_forcelist=[429, 500, 502, 503, 504], allowed_methods=["GET", "POST"]))) r = s.get("https://api.clinpgx.org/v1/data/gene", params={"symbol": "CYP2D6", "view": "base"}, timeout=20) r.raise_for_status() print(r.json()["data"][0]["name"]) ``` ## Troubleshooting | Problem | Cause | Solution | | ----------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- | | HTTP 404/405 on `https://api.pharmgkb.org/v1/...` | Old PharmGKB host is dead; the service rebranded to ClinPGx in 2024 | Migrate to `https://api.clinpgx.org/v1/...`. Old `/clinicalAnnotation?gene=X` is now `data/clinicalAnnotation` with different filters. | | `{"status":"fail","data":{"errors":[{"message":"No such property: 'gene'"}]}}` | `data/clinicalAnnotation` does **not** accept `gene=` or `relatedGenes.symbol=` | Use `data/guidelineAnnotation?relatedGenes.symbol=X` for gene-driven access, or `?relatedChemicals.name=Y` for drug-driven. | | `{"status":"fail","data":{"errors":[{"message":"Missing criteria."}]}}` | A `data/{type}` list query has no filter and no ID | Add at least one filter (`name=`, `symbol=`, `relatedChemicals.name=`, …) or fetch by ID via `data/{type}/{paId}`. | | HTTP 405 on `GET /site/search?query=...` | `site/search` only accepts POST with a JSON body | Use `requests.post(url, json={"query": "..."})`. | | HTTP 429 mid-loop | Hit ClinPGx rate limit | Insert `time.sleep(0.3–0.5)` between calls; use the Retry session in Recipe 4. | | HTTP 400 on `https://api.cpicpgx.org/v1/recommendation?phenotypes=cs.{...}` | The `cs.` JSON wasn't URL-encoded | Pass via `requests` `params={"phenotypes": 'cs.{"CYP2C19":"Poor Metabolizer"}'}` (auto-encoded) or `urllib.parse.quote` manually. | | Empty `data` list for an obviously-real drug | Drug name mismatch (brand vs. generic; capitalization) | Try lowercase generic name; fall back to `POST /site/search` to fan out and find the canonical PA ID. | | `data/variant?name=rs...` returns 1 record but `data/clinicalAnnotation?location.name=rs...` returns 404 | rsID is stored under `location.displayName`/`location.rsid`, not exposed as a filterable property | Use `site/search` to discover annotation IDs by rsID, then dereference each with `data/clinicalAnnotation/{id}`. (Workflow 3.) | ## Related Skills - `clinvar-database` — germline pathogenicity / clinical significance for variants found in PharmGKB (complementary; ClinVar is disease-focused, ClinPGx is drug-response-focused) - `opentargets-database` — drug-target associations and safety signals overlapping ClinPGx pharmacogene targets - `chembl-database-bioactivity` — bioactivity and binding data for the drugs annotated in ClinPGx - `cosmic-database` — somatic cancer mutations and tumor-specific PGx (orthogonal to germline PGx covered here) ## References - [ClinPGx (formerly PharmGKB) website](https://www.clinpgx.org/) — Database front end; web links match the `data/{type}/{paId}` URL shape - [ClinPGx REST API documentation](https://www.clinpgx.org/page/webResources) — Official endpoint reference, `data/...` and `site/search` schemas - [CPIC API documentation](https://api.cpicpgx.org/) — Swagger UI for the PostgREST companion API - [CPIC guidelines](https://cpicpgx.org/guidelines/) — Source of `recommendation` rows; canonical genotype-prescribing oracle - [Relling & Klein (2011) Nature Reviews Drug Discovery — PharmGKB foundational paper](https://doi.org/10.1038/nrd3499) - [Whirl-Carrillo et al. (2021) Clin Pharmacol Ther — PharmGKB data architecture update](https://doi.org/10.1002/cpt.2350)