67 · CPIC

Clinical Pharmacogenomics Implementation Consortium — gene-based prescribing guidelines Category: Drug-centric | Type: DB | Subcategory: Drug Knowledgebase API: https://api.cpicpgx.org/v1 (PostgREST, free, no key required)

Resource	URL
Homepage	https://cpicpgx.org/
API / Data	https://cpicpgx.org/cpic-data/
Paper	https://pubmed.ncbi.nlm.nih.gov/33479744/

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What it provides

• Drug metadata: drugid (RxNorm), DrugBank ID, ATC codes, flowchart links
• Guidelines: peer-reviewed pharmacogenomics prescribing guidelines (drug + gene → dosing advice)
• Gene-drug pairs: curated pairs with CPIC level, PharmGKB level, PGx testing status
• Dosing recommendations: phenotype-specific dosing adjustments per drug-gene combination

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API schema note

The pair and recommendation tables use drugid (e.g. RxNorm:32968), not drug name. This script resolves drug names automatically via the /v1/drug table before querying.

Guideline lookup uses two strategies: (1) name substring match, (2) guidelineid from the drug table. This is necessary because some guidelines use class names (e.g. simvastatin → "SLCO1B1, ABCG2, CYP2C9, and Statins", codeine → "CYP2D6, OPRM1, COMT, and Opioids").

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Quick start

from 67_CPIC import query

# Single drug
results = query("clopidogrel")

# Multiple drugs
results = query(["warfarin", "codeine"])

# Query by gene symbol
results = query("CYP2D6", fields="pairs")

# Specific fields only
results = query("codeine", fields="guidelines")
results = query("codeine", fields="recommendations")

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query() interface

query(entities, fields="all") -> list[dict]

Parameter	Type	Description
entities	str | list[str]	Drug name(s) or gene symbol(s)
fields	str	"all" — everything; "guidelines" / "pairs" / "recommendations"

Return structure (fields="all")

[
  {
    "query": "clopidogrel",
    "drug_info": [
      {"drugid": "RxNorm:32968", "name": "clopidogrel",
       "drugbankid": "DB00758", "atcid": ["B01AC04"], "flowchart": "..."}
    ],
    "guidelines": [
      {"name": "CYP2C19 and Clopidogrel", "url": "...", "version": 66}
    ],
    "gene_drug_pairs": [
      {"genesymbol": "CYP2C19", "drugid": "RxNorm:32968",
       "cpiclevel": "A", "clinpgxlevel": "1A",
       "pgxtesting": "Actionable PGx", "citations": ["21716271", ...]}
    ],
    "recommendations": [
      {"drugid": "RxNorm:32968",
       "phenotypes": {"CYP2C19": "Ultrarapid Metabolizer"},
       "implications": {"CYP2C19": "Increased active metabolite ..."},
       "recommendation": "Use at standard dose (75 mg/day)",
       "classification": "Strong",
       "population": "CVI ACS PCI"}
    ]
  }
]

On error: {"query": "xxx", "error": "..."}.

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Lower-level functions

Function	Input	Output	Description
get_drug_info(drug_name)	drug name	list[dict]	Drug table lookup (fuzzy)
get_guidelines(drug_name=None)	optional drug name	list[dict]	All or filtered guidelines
get_gene_drug_pairs(drug_name=None, gene=None)	optional filters	list[dict]	Gene-drug pairs (name auto-resolved to drugid)
get_recommendations(drug_name)	drug name	list[dict]	Dosing recommendations (name auto-resolved)

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Notes

• CPIC levels: A = guideline published, B = in progress, C/D = lower evidence.
• Gene symbols are auto-detected (uppercase, ≤12 chars) and routed to genesymbol filter.
• Drug names are fuzzy-matched via ilike on the /v1/drug table.
• No rate limit documented, but keep requests reasonable.