| name | tooluniverse-pharmacogenomics |
| description | Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation. |
COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
Pharmacogenomics (PGx) Research Skill
Systematic PGx analysis: resolve gene-drug pairs, retrieve CPIC dosing guidelines, annotate alleles and variants with PharmGKB, check FDA PGx biomarker labeling, and generate evidence-graded clinical recommendations.
When to Use
- "What CPIC guidelines exist for CYP2D6?"
- "Get dosing recommendations for codeine based on CYP2D6 poor metabolizer status"
- "Which drugs have FDA pharmacogenomic biomarkers for CYP2C19?"
- "Find PharmGKB clinical annotations for rs1799853"
- "Is this patient's CYP2D6 genotype relevant to tamoxifen dosing?"
- "What is the functional status of CYP2D6*4?"
- "List all CPIC level A gene-drug pairs for CYP2D6"
Workflow Overview
Input (gene/drug/variant/phenotype)
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Phase 0: Disambiguation (resolve gene symbols, drug names, rsIDs)
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Phase 1: Gene-Drug Pair Identification (CPIC pairs + evidence levels)
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Phase 2: Guideline & Dosing Retrieval (CPIC recommendations + PharmGKB)
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Phase 3: Allele & Variant Annotation (star alleles, function, activity scores)
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Phase 4: FDA Biomarker Labeling (regulatory PGx status)
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Phase 5: Cross-Database Enrichment (EpiGraphDB, DGIdb, OpenTargets PGx)
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Phase 6: Report (evidence-graded clinical summary)
Phase 0: Disambiguation
Resolve user input to canonical identifiers before querying PGx databases.
PharmGKB_search_genes: query (string REQUIRED, e.g., "CYP2D6"). Returns {status, data: [{id, symbol, name}]}.
- Use to get PharmGKB gene accession ID (e.g., "PA128" for CYP2D6).
PharmGKB_search_drugs: query (string REQUIRED, e.g., "codeine"). Returns {status, data: [{id, name, types}]}.
- Use to get PharmGKB chemical ID (e.g., "PA449088" for codeine).
PharmGKB_search_variants: query (string REQUIRED, rsID e.g., "rs1799853"). Returns {status, data: [{id, symbol, changeClassification, clinicalSignificance}]}.
- Use to resolve rsIDs and find PharmGKB annotation IDs.
CPIC_get_drug_info: name (string REQUIRED, lowercase, e.g., "codeine"). Returns drug identifiers including drugid, rxnormid, drugbankid, atcid, guidelineid, and flowchart URL.
- Also resolves drug names: can be used to find the
guidelineid directly from a drug name.
CPIC_get_gene_info: symbol (string REQUIRED, e.g., "CYP2D6"). Returns gene coordinates, PharmGKB/HGNC/Ensembl IDs, lookupmethod (ACTIVITY_SCORE or PHENOTYPE), and allele frequency methodology.
Phase 1: Identify Gene-Drug Pairs
CPIC_search_gene_drug_pairs: gene_symbol (string), cpiclevel ("A"/"B"/"C"/"D"), limit (int, default 50). Returns {status, data: [{genesymbol, drugid, cpiclevel, guidelineid, pgxtesting, clinpgxlevel, usedforrecommendation}]}.
- Primary tool for filtering by evidence level. CPIC levels: A = strongest/actionable, B = moderate, C = informational, D = insufficient.
- PostgREST auto-normalization: Accepts plain gene symbols (e.g., "CYP2D6") -- the tool auto-prepends
eq. prefix.
- Also accepts aliases:
gene or gene_symbol both resolve to genesymbol.
CPIC_get_gene_drug_pairs: genesymbol (string REQUIRED). Returns ALL pairs for one gene including drug: {name}, citations, guidelineid.
- Returns drug names in response (unlike search which returns RxNorm IDs only).
CPIC_list_drugs: No params. Returns all drugs with guideline IDs. Use for browsing.
CPIC_list_pgx_genes: No params. Returns all PGx genes curated by CPIC with symbol, lookupmethod, ensemblid.
EpiGraphDB_get_gene_drug_associations: gene_name (string REQUIRED, e.g., "CYP2D6"). Returns {status, data: {gene_drug_associations: [{gene, drug, source, pharmgkb_evidence, cpic_level, pgx_on_fda_label, guideline}]}}.
- Aggregates CPIC + PharmGKB evidence with FDA label status in one call. Good for quick overview.
Finding Guideline IDs
Don't memorize guideline IDs. Use CPIC_list_guidelines(gene="CYP2D6") or CPIC_list_guidelines(drug="codeine") to discover them. Each result includes both the numeric id (for CPIC_get_recommendations) and the clinpgxid string (for PharmGKB_get_dosing_guidelines).
Phase 2: Retrieve Dosing Guidelines
CPIC_get_recommendations (CPICGetRecommendationsTool): guideline_id (integer, OR drug/drug_name string for auto-resolution), limit (int, default 50), offset (int). Returns {status, data: {guideline_id, recommendations: [{drugrecommendation, classification, phenotypes, implications, activityscore, lookupkey, population, drug: {name}}], count}}.
- Preferred usage:
CPIC_get_recommendations(drug="codeine", limit=50) — auto-resolves drug name to guideline_id via CPIC API, and filters within multi-drug guidelines (e.g., CYP2D6 opioid guideline covers codeine + tramadol) using RxNorm ID matching.
classification: "Strong", "Moderate", or "Optional". phenotypes: maps gene → metabolizer phenotype. activityscore: maps gene → activity score.- Fallback:
CPIC_get_drug_info(name="codeine") to extract guidelineid, then CPIC_get_recommendations(guideline_id=100416, limit=50).
CPIC_get_drug_info: name (string REQUIRED, lowercase, e.g., "codeine"). Returns {status, data: [{drugid, guidelineid, flowchart, rxnormid, drugbankid}]}.
- Key shortcut: returns
guidelineid directly. Still useful for extracting DrugBank/ATC IDs.
PharmGKB_get_dosing_guidelines: guideline_id (string REQUIRED -- use clinpgxid from CPIC_list_guidelines, e.g., "PA166251445"). Returns {status, data: {id, name, level, literature: [{title, crossReferences}], link}}.
- Provides CPIC guideline metadata, literature citations, and link to full guideline.
CPIC_list_guidelines: gene (string, optional), drug (string, optional). Returns {status, data: [{id, name, url, genes, clinpgxid}]}. Returns all ~29 guidelines; supports built-in filtering by gene/drug.
- Use this to discover
clinpgxid values for PharmGKB_get_dosing_guidelines.
Note: PharmGKB_get_clinical_annotations requires an annotation_id (e.g., "1447954390"). To discover annotation IDs, use PharmGKB_search_variants(query=rsID) first, then extract annotation IDs from the results.
Gotchas
- Warfarin (guideline 100425): Algorithm-based dosing;
CPIC_get_recommendations returns 0 rows. Direct users to CPIC website or PharmGKB.
- PharmGKB guideline linking: Use
clinpgxid (e.g., "PA166251445"), NOT pharmgkbid (old format returns 404).
- Multi-gene guidelines: TCA guideline (100414) covers both CYP2D6 and CYP2C19; recommendations have phenotype combinations.
- Drug name case:
CPIC_get_drug_info requires lowercase. CPIC_get_recommendations with drug= uses ilike matching (case-insensitive).
- CPIC_get_recommendations returns wrapped data: Response is
{status, data: {guideline_id, recommendations: [...], count}} -- recommendations are nested under data.recommendations.
Phase 3: Allele & Variant Annotation
CPIC_get_alleles: genesymbol (string REQUIRED), limit (int, default 50). Returns {status, data: [{name, clinicalfunctionalstatus, activityvalue, functionalstatus}]}.
- Use
clinicalfunctionalstatus (not functionalstatus which may be null). Values: "Normal function", "Decreased function", "No function", "Increased function", "Uncertain function", "Unknown function".
activityvalue: numeric string (e.g., "1.0", "0.5", "0.0") or "n/a".
PharmGKB_search_variants: query (string REQUIRED, rsID). Returns variant classification and clinical significance.
PharmGKB_get_clinical_annotations: annotation_id (string REQUIRED, e.g., "1447954390"). Returns {status, data: {accessionId, allelePhenotypes: [{allele, phenotype, limitedEvidence}], levelOfEvidence: {term}}}.
- REQUIRES annotation_id -- cannot query by gene/drug directly. Discover IDs via
PharmGKB_search_variants(query=rsID) or from the PharmGKB website.
levelOfEvidence.term: "1A", "1B", "2A", "2B", "3", "4" (PharmGKB evidence levels).
PharmGKB_get_gene_details: gene_id (string REQUIRED, PharmGKB accession e.g., "PA128"). Returns detailed gene info including allele definition files, VIP citations.
PharmGKB_get_drug_details: drug_id (string REQUIRED, PharmGKB chemical ID e.g., "PA449088"). Returns drug metadata including SMILES, InChI, type (Drug/Prodrug).
OpenTargets_drug_pharmacogenomics_data: chemblId (string REQUIRED, e.g., "CHEMBL1201560"), size (int). Returns PGx variant data from OpenTargets including variant consequences and drug associations.
- Queries by drug (ChEMBL ID), not by gene. Use when you have a ChEMBL ID and want PGx variant annotations from OpenTargets.
Metabolizer Status Reasoning
A poor metabolizer has reduced or absent enzyme activity. What that means clinically depends entirely on whether the drug is active or a prodrug:
- Active drug + poor metabolizer: drug accumulates → toxicity risk (e.g., codeine is a prodrug — this case doesn't apply; but nortriptyline is active — PM → high plasma levels → side effects).
- Prodrug + poor metabolizer: less conversion to active form → reduced efficacy (e.g., codeine → morphine; clopidogrel → active thienopyridine).
- Prodrug + ultrarapid metabolizer: excess activation → toxicity (classic case: codeine in CYP2D6 UM → morphine accumulation → respiratory depression).
This active-vs-prodrug distinction determines the direction of clinical concern. Get it right before interpreting any metabolizer phenotype.
Star allele reasoning: Don't memorize allele tables. The logic is always: allele function status (normal / decreased / no function) → diplotype → predicted enzyme activity → phenotype (UM/NM/IM/PM) → clinical recommendation. Use CPIC_get_alleles(genesymbol=...) to look up function status for any specific allele.
Phase 4: FDA Biomarker Labeling
fda_pharmacogenomic_biomarkers: drug_name (string, optional), biomarker (string, optional, e.g., "CYP2D6"), limit (integer, default 10). Returns {status, count, shown, results: [{Drug, TherapeuticArea, Biomarker, LabelingSection}]}.
- ALWAYS pass
limit=1000 for complete results (default is 10).
LabelingSection values: "Dosage and Administration", "Clinical Pharmacology", "Precautions", "Use in Specific Populations", "Boxed Warning", "Contraindications".- Can query by drug, biomarker, or both.
- Not all drugs have entries (e.g., simvastatin absent for SLCO1B1; use rosuvastatin for SLCO1B1 PGx testing).
FDA_get_pharmacogenomics_info_by_drug_name: drug_name (string REQUIRED). Returns FDA label PGx sections with brand/generic names. Good for finding PGx labeling text in actual FDA labels.
FDA PGx Label Reasoning
The LabelingSection field tells you how actionable the PGx information is. "Boxed Warning" or "Contraindications" means testing may be required or the drug contraindicated in certain genotypes — highest urgency. "Dosage and Administration" means genotype directly drives dose selection. "Clinical Pharmacology" is usually informational (PK/PD data), not a prescribing directive. When in doubt, retrieve the full label text with FDA_get_pharmacogenomics_info_by_drug_name.
Phase 5: Cross-Database Enrichment
DGIdb_get_drug_gene_interactions: genes (array of strings REQUIRED, e.g., ["CYP2D6"]), interaction_types (array, optional), interaction_sources (array, optional). Returns drug-gene interactions with sources.
- Broader coverage than CPIC; includes non-PGx interactions.
- Client-side filtering applied for
interaction_types and sources parameters.
DGIdb_get_gene_druggability: genes (array of strings REQUIRED). Returns {status, data: {data: {genes: {nodes: [{name, geneCategories}]}}}}.
- Returns gene categories (e.g., "CLINICALLY ACTIONABLE", "DRUGGABLE GENOME").
PharmGKB_get_dosing_guidelines: (also in Phase 2) Provides DPWG (Dutch Pharmacogenetics Working Group) guidelines alongside CPIC.
OpenTargets_drug_pharmacogenomics_data: chemblId (string REQUIRED), size (int). Returns PGx variant annotations from the OpenTargets platform.
- Complements CPIC data with additional variant-level PGx evidence.
Adverse Event Signal Detection for PGx-Relevant Drugs
FAERS_filter_serious_events: drug_name (string REQUIRED), seriousness_type ("all"/"death"/"hospitalization"/"disability"/"life_threatening"), adverse_event (string, optional). Use to detect serious adverse event signals for PGx-relevant drugs — e.g., respiratory depression reports for codeine in the context of CYP2D6 UM status. The adverse_event parameter filters to reports containing that specific reaction term.
Optional: DisGeNET_get_vda for variant-disease associations (requires DISGENET_API_KEY).
When PGx Testing Changes Clinical Decisions
PGx testing changes clinical decisions ONLY for drugs with narrow therapeutic indices metabolized by polymorphic enzymes where genotype reliably predicts outcome. If the drug has a wide therapeutic index or is cleared by multiple redundant pathways, PGx status rarely alters the prescribing decision even when a variant is present.
Evidence grading — reasoning approach: CPIC levels A/B represent actionable evidence; C/D are informational. PharmGKB level 1A means the annotation is already embedded in a CPIC or DPWG guideline — the highest confidence tier. Levels 3/4 are hypothesis-generating, not prescribing-grade. CPIC recommendation strength (Strong/Moderate/Optional) within a guideline reflects how certain the genotype-to-outcome link is for that specific phenotype.
The key question is not "what level is this?" but "does this level justify changing the prescription?" For Level A CPIC with Strong classification, yes. For PharmGKB level 3, no — report it but don't act on it alone.
Key Parameter Notes
Critical parameter behaviors to remember — these are the ones that actually cause failures:
CPIC_get_recommendations: accepts guideline_id (integer) OR drug/drug_name (string). Never pass guideline_id as a string.PharmGKB_get_dosing_guidelines: requires clinpgxid (e.g., "PA166251445"), not the numeric pharmgkbid. Get clinpgxid from CPIC_list_guidelines.PharmGKB_get_clinical_annotations: requires annotation_id. Cannot query by gene or drug. Discover IDs via PharmGKB_search_variants(query=rsID).fda_pharmacogenomic_biomarkers: default limit=10 is almost always too small. Pass limit=1000.CPIC_get_drug_info: drug name must be lowercase.DGIdb_get_drug_gene_interactions and DGIdb_get_gene_druggability: genes is an array, not a string (e.g., ["CYP2D6"]).CPIC_search_gene_drug_pairs: returns RxNorm IDs, not drug names. Use CPIC_get_gene_drug_pairs when you need drug names.
CPIC Guideline Application Reasoning
CPIC guidelines give genotype → phenotype → recommendation mappings. The skill is knowing when to apply them, not memorizing the mappings themselves. Use tools to retrieve the specific recommendation for the specific phenotype. The reasoning chain is:
- Does a CPIC guideline exist for this gene-drug pair? (Level A or B = actionable)
- What is the patient's phenotype? (from diplotype + allele function statuses)
- What does the guideline recommend for that phenotype? (retrieve with
CPIC_get_recommendations)
- Is there FDA label reinforcement? (check
fda_pharmacogenomic_biomarkers)
If step 1 is no, fall back to PharmGKB variant annotations for evidence-graded but non-guideline information.
Fallback Strategies
- No CPIC guideline -> Use
PharmGKB_search_variants(query=rsID) for variant-level annotations; check EpiGraphDB for gene-drug evidence
- CPIC_get_recommendations returns 0 rows -> Check if algorithm-based (warfarin); use PharmGKB_get_dosing_guidelines
- CPIC_get_recommendations drug auto-resolve fails -> Fall back to CPIC_get_drug_info(name=drug) + manual guideline_id extraction
- No FDA biomarker entry -> Check DGIdb for known interactions; check EpiGraphDB
pgx_on_fda_label field
- Unknown variant -> PharmGKB_search_variants by rsID; note as uncharacterized if absent
- Need drug name from RxNorm ID -> Use CPIC_get_gene_drug_pairs (returns
drug: {name}) instead of CPIC_search_gene_drug_pairs (returns only RxNorm IDs)
- PharmGKB annotation_id unknown -> Get annotation IDs from PharmGKB website or via
PharmGKB_search_variants(query=rsID)
- Need additional PGx variant data -> Use
OpenTargets_drug_pharmacogenomics_data(chemblId=...) with ChEMBL ID
Example Workflows
Drug-first (codeine + CYP2D6 PM): CPIC_get_recommendations(drug="codeine", limit=50) → filter phenotypes.CYP2D6="Poor Metabolizer" → Strong recommendation: avoid codeine. Then CPIC_get_alleles(genesymbol="CYP2D6", limit=100) to confirm star allele function (e.g., *4/*4 → no function, AS 0.0). Then fda_pharmacogenomic_biomarkers(drug_name="codeine", limit=1000) to confirm FDA label status. Gene-first alternative: CPIC_get_gene_drug_pairs(genesymbol="CYP2D6") to list all associated drugs.
Gene-first (all CYP2C19 drugs): CPIC_search_gene_drug_pairs(gene_symbol="CYP2C19", cpiclevel="A") for Level A pairs. EpiGraphDB_get_gene_drug_associations(gene_name="CYP2C19") for CPIC + PharmGKB + FDA label overview in one call. fda_pharmacogenomic_biomarkers(biomarker="CYP2C19", limit=1000) for complete FDA coverage.
Variant-first (rs1799853): PharmGKB_search_variants(query="rs1799853") → CYP2C9 variant, drug-response significance. CPIC_get_alleles(genesymbol="CYP2C9", limit=100) → maps to *2, "Decreased function". CPIC_get_gene_drug_pairs(genesymbol="CYP2C9") → warfarin, phenytoin, NSAIDs. For each drug with a guideline: CPIC_get_recommendations(drug="phenytoin", limit=50).
Drug Class Context (RxClass)
When PGx analysis involves understanding which drug class a substrate belongs to, or finding all drugs in a class that share the same metabolizing enzyme: use RxClass_get_drug_classes(drug_name=...) to get all class memberships for a drug, RxClass_find_classes(query=..., class_type=...) to find class IDs from a keyword, and RxClass_get_class_members(class_id=..., rela_source=..., ttys="IN") to list all drugs in a class. Example: find all SSRIs to advise which require CYP2D6 testing as a class note.
FDA Substance Identification (FDAGSRS)
For canonical FDA substance identification (UNII codes, cross-references to ATC/DrugBank/CAS): use FDAGSRS_search_substances(query=...) to find the UNII code, FDAGSRS_get_substance(unii=...) for the full record with all names and cross-references, and FDAGSRS_get_structure(unii=...) for SMILES/InChIKey. Useful to confirm that two drug name variants (e.g., "warfarin sodium" and "warfarin") share the same UNII before cross-referencing in CPIC or PharmGKB.
Limitations
- CPIC covers ~29 guidelines (~130 genes); many drug-gene pairs lack formal guidelines.
- PharmGKB clinical annotation IDs must be discovered (not derivable from gene/drug names alone -- use PharmGKB website or PharmGKB_search_variants for rsID-based lookup).
- Warfarin dosing requires algorithmic calculation (CPIC website), not simple table lookup.
- FDA biomarker table may lag behind current labeling changes.
- DisGeNET requires API key (DISGENET_API_KEY).
- CPIC_search_gene_drug_pairs returns RxNorm drug IDs, not drug names; use CPIC_get_gene_drug_pairs for names.
- Activity score interpretation varies by gene (CYP2D6 uses numeric scores; others may use phenotype-based lookup).
- CPIC_get_recommendations drug auto-resolution uses ilike matching -- ambiguous drug names may match multiple entries.
