Genotype into prescribing action
7% of prescriptions involve a gene-drug pair where CPIC guidelines require dose adjustment or an alternative. 90% of patients with pharmacogenomic test results never have them applied at the point of prescribing. The data exists. The decision support doesn't — until now.
Select alternative antiplatelet. CYP2C19 *2/*2 poor metabolizers cannot convert clopidogrel to its active metabolite — inadequate platelet inhibition increases MACE risk.
What a response looks like
Select a gene-drug scenario to see a representative API response — recommendation tier, phenotype, dosing guidance, and sourced evidence counts.
Select an alternative antiplatelet agent. CYP2C19 *2/*2 poor metabolizers cannot convert clopidogrel to its active metabolite — platelet inhibition is inadequate, increasing MACE risk at standard dosing.
Simulated output representative of real API responses. Production results sourced live from CPIC guidelines and PharmGKB annotations.
Six capabilities. One API.
Drug + Gene Check
Returns recommendation tier (ACTIONABLE / INFORMATIVE / PRELIMINARY), calibrated confidence score, and sourced CPIC/PharmGKB evidence for any drug-gene pair. Accepts an optional genotype for diplotype-aware results in the same call.
Diplotype Dosing
Translates a patient's diplotype (e.g. CYP2C19 *2/*2) into a specific CPIC dosing action — contraindicated, use alternative, dose reduction, dose increase, or normal. Deterministic CPIC table lookup, not a model inference.
Multi-Gene Panel Checker
Cross-references a complete gene panel against a full medication list in one call. Returns per-drug, per-gene dosing guidance across the entire panel — surfacing every actionable interaction, not just the highest-priority one.
Gene Profile
Returns all drugs affected by a specific gene, ranked by evidence strength and recommendation tier. CYP2D6 alone influences 25% of all marketed drugs — Gene Profile maps the full prescribing landscape for any gene in the supported panel.
Gene–Drug Crosswalk
Bidirectional lookup: given a gene, return all affected drugs; given a drug, return all relevant genes with evidence strength and phenotype implications. Useful for population-level screening and formulary analysis.
PGx Drug Index
Full index of drugs with CPIC or PharmGKB evidence in the evidence base. Returns gene associations, highest-evidence tier, and CPIC level for each drug — useful for formulary gap analysis and prior-authorization workflows.
Clinically actionable gene–drug pairs
The pairs where wrong prescribing causes serious harm — and where a genotype result changes the decision.
Antiplatelet — *2/*2 poor metabolizers cannot activate the drug; MACE risk without genotype-guided alternatives
Opioid — ultrarapid metabolizers convert to toxic morphine doses; contraindicated under CPIC guidelines
Anticoagulant — genotype-guided dosing required; combined variants cause 5× dose variance
Oncology — poor metabolizers produce minimal active endoxifen, significantly reducing efficacy
Oncology — DPYD*2A carriers face life-threatening toxicity at standard doses; dose reduction mandatory
Immunosuppressant — poor metabolizer accumulates toxic thiopurine metabolites; severe myelotoxicity
HIV antiretroviral — HLA-B*57:01 carriers face potentially fatal hypersensitivity reaction; CONTRAINDICATED. FDA black box warning. Pre-screening mandatory.
Antiepileptic — HLA-B*15:02 carriers face severe cutaneous reactions (SJS/TEN); CONTRAINDICATED. FDA black box warning for Asian ancestry patients.
Statin — decreased-function variants impair hepatic uptake, raising plasma exposure and risk of myopathy; limit to ≤20 mg/day or switch to rosuvastatin.
A patient with 3 genes tested and 4 active medications has 12 gene-drug combinations to evaluate. Panel Checker returns all of them — with per-drug, per-gene dosing guidance — in one call.
In a health system with 500,000 members, approximately 35,000 patients are currently on at least one medication with a CPIC Level A gene-drug interaction. Without systematic PGx integration at the point of prescribing, most of those interactions go unchecked every time the prescription is renewed.
Three tiers, calibrated by evidence level
Every result is classified by the strength of underlying CPIC and PharmGKB evidence — so clinicians know whether to act, consider, or monitor.
Genotype-guided prescribing is required or strongly recommended. Wrong prescribing at this tier causes measurable patient harm — the gene result must change the drug or dose before the prescription is written.
Credible evidence of gene-drug effect. Consider genotyping for high-risk patients, high-cost therapies, or therapeutic areas where response variability is clinically significant.
Early-stage evidence. No prescribing action required. Monitor response and flag for review as evidence matures — useful for research, registry programs, and clinical trial stratification.
CYP2D6 ultra-rapid metabolizers converting codeine to toxic morphine doses. Warfarin over-anticoagulation in CYP2C9 poor metabolizers. Clopidogrel treatment failure in CYP2C19 poor metabolizers. Abacavir hypersensitivity in HLA-B*57:01 carriers — potentially fatal on re-challenge.
These are known, preventable events that continue to occur because pharmacogenomics is not systematically integrated at point of care. Skippy Pharmacogenomics closes that gap — surfacing the right action at the moment of prescribing.
“Over 90% of patients who have had pharmacogenomic testing do not have their results systematically applied at the point of prescribing.”
CPIC / PharmGKB Implementation Consortium · Clin Pharmacol Ther, 2023
The data exists — in genotyping reports, EHR problem lists, lab systems. It is almost never surfaced when a prescriber is writing the order. Skippy Pharmacogenomics closes that gap at themedication-prescribehook — before the prescription is written, not after.
Health systems, PBMs, and labs
CPIC guidelines, citable in prescribing records
All recommendations sourced from CPIC peer-reviewed guidelines (Level A = strongest evidence) and PharmGKB clinical annotations (Level 1 = highest clinical impact). Confidence scores are weighted by evidence level with documented methodology.
Output is citable in prescribing documentation as a pharmacogenomics-aware safety check. Every check generates an audit record documenting that a PGx-guided review was performed — relevant for malpractice documentation and prescribing liability. HIPAA-ready with BAA available.
Not a drug database. Not an LLM.
Recommendations come from the CPIC dosing table — not a language model inference. Diplotype *2/*2 in → "Use Alternative" out. Same answer every time, citable in prescribing records.
A patient on 4 medications with 3 genes tested has 12 gene-drug combinations to evaluate. One call to /api/panel returns all of them — with per-drug, per-gene action guidance and a priority-ranked summary.
Every check generates an immutable record: drug queried, gene tested, diplotype, recommendation returned, and timestamp. Citable in prescribing documentation. Relevant to malpractice liability.
CPIC Level A returns confidence 1.00. Level D returns 0.55. The tier (ACTIONABLE / INFORMATIVE / PRELIMINARY) tells prescribers whether to act now, consider, or monitor — not just whether an interaction exists.
Embedded at the moment of prescribing
PGx guidance delivered via CDS Hooks — the HL7 standard for real-time clinical decision support inside Epic, Cerner, and other EHR workflows. Clinicians see the alert without leaving the prescribing screen.
Genotype-guided dosing alert fires when a prescriber writes the order — before it's signed. The highest-impact integration point.
Surface unresolved PGx flags when a clinician opens the patient record — useful for medication reconciliation and rounds.
Pharmacist-facing alerts at order verification — catch gene-drug conflicts before dispense, not after.
Often deployed together
CYP enzyme interaction checking that complements PGx — when a co-medication inhibits or induces the gene your patient is already sensitive to, DDI surfaces it.
ACMG/AMP variant interpretation for genomic reports. When a novel variant needs clinical interpretation before PGx diplotype assignment, Variants provides the upstream evidence.
Real-time order verification via CDS Hooks. PGx alerts deploy through the same CDS infrastructure — one integration, multiple safety layers.
Skippy Pharmacogenomics is a decision-support tool for clinical professionals. It does not replace laboratory genotyping, clinical pharmacist review, or physician judgment. Recommendations should be validated against current institutional protocols and patient-specific factors before prescribing. HIPAA-ready; BAA available.
See Pharmacogenomics in your prescribing workflow
We work with health systems, PBMs, specialty pharmacies, and clinical labs. Let's talk about integrating genotype-guided prescribing.