What Is Pharmacogenomics (PGx) Testing? A Lab's Guide

Pharmacogenomics — PGx for short — studies how a person's genes affect the way they respond to medications. For a lab, a PGx test turns a DNA sample into a clinical report that helps a provider choose or dose a drug more safely. This guide explains the core concepts, the evidence behind them, and what actually goes into a PGx report.

What is pharmacogenomics testing?

Everyone metabolizes drugs a little differently, and a meaningful share of that variation is genetic. Pharmacogenomic testing looks at specific genes — many of them drug-metabolizing enzymes such as CYP2D6, CYP2C19, CYP2C9, TPMT, and DPYD — to predict how a patient is likely to process certain medications. The result can inform whether a drug is a good fit, whether a dose should be adjusted, or whether the risk of an adverse reaction is elevated.

From genotype to phenotype: the core concepts

PGx has its own vocabulary. Four terms do most of the work:

• Star allele. A standardized name for a specific gene variant (for example, CYP2C19*2). Each star allele carries an expected functional effect — normal, reduced, or no function.
• Diplotype. The pair of star alleles a person carries (one from each parent), e.g. *1/*2.
• Phenotype / metabolizer status. The functional interpretation of that diplotype — commonly poor, intermediate, normal, rapid, or ultrarapid metabolizer.
• Activity score. For some genes, a numeric score is used to translate a diplotype into a metabolizer phenotype consistently.

The chain is: detect variants → assemble the diplotype → translate to a metabolizer phenotype → apply guidance for the relevant drug(s). Each step needs to be consistent and traceable, which is where software and clear rules matter.

The evidence behind a PGx result

PGx interpretation doesn't come from nowhere — it draws on established, curated knowledge that a lab applies under its own validated rules and review:

• CPIC (Clinical Pharmacogenetics Implementation Consortium) publishes peer-reviewed gene-drug guidelines that translate genotype into prescribing recommendations.
• FDA includes pharmacogenomic information in many drug labels (the table of pharmacogenomic biomarkers in drug labeling).
• PharmGKB curates gene-drug and clinical annotations and supporting evidence.
• DPWG (the Dutch Pharmacogenetics Working Group) publishes additional guidance used internationally.

Importantly, these are inputs. The laboratory remains responsible for validating its assays, defining its interpretation rules, reviewing results, and approving every report. Good software supports that — it does not replace clinical judgment.

What goes into a PGx report

A clear pharmacogenomics report usually includes:

• The genes tested and the detected diplotypes
• The predicted phenotype / metabolizer status per gene
• Medication-level implications, organized by drug or drug class
• References to the guidance applied (e.g. CPIC, FDA labeling)
• Clear, plain-language summaries for the provider — and sometimes the patient
• Lab identification, methodology, limitations, and sign-off

The reporting bar is high because the audience is clinical. Reports need to be accurate, consistent across patients, branded to the lab, and easy for a busy provider to act on.

The PGx lab workflow, end to end

Behind every report is an operational pipeline: order intake → accessioning → sample tracking → genotyping (array, PCR, or NGS) → variant and diplotype calling → phenotype translation → report drafting → lab review and approval → delivery to the provider and patient → billing. A delay or a manual hand-off at any step slows the whole thing down.

Where software helps (and where it shouldn't)

The bottleneck for most PGx labs isn't the lab chemistry — it's producing clear, consistent, evidence-informed reports at volume. Software helps by standardizing the genotype-to-phenotype logic, pulling structured references, drafting report language, and keeping the operational workflow connected from order to delivery and billing. AI can accelerate drafting and summarization. But review, validation, approval, and clinical responsibility stay with the lab — the platform should make qualified review faster, not bypass it.

That's the model behind Labrynix PGx Reporting: branded, evidence-informed reporting with AI-assisted drafting, on a connected LIS/LIMS platform that runs the full workflow under lab control.