From scattered sources to one reconciled view

In most PGx programs, evidence is not a system — it is a scavenger hunt. Someone opens the CPIC guideline for a gene, cross-references the FDA biomarker table, checks DailyMed for the current label, and hopes the DPWG position agrees. What starts as diligence quickly becomes slow, manual, and error-prone reconciliation.

SignalPGx moves that work into curated evidence sources. Instead of relying on analysts to chase each reference, the platform reconciles more than fifteen of them automatically. Genotype calls map to diplotypes, phenotypes are assigned, and every gene-drug pair resolves to one graded recommendation. The result is not just speed, but consistency.

Grading evidence, not just collecting it

A major limitation of manual PGx review is inconsistency. Two reviewers can read the same references and reach different conclusions, because each weighs CPIC, FDA labeling, and DPWG a little differently.

SignalPGx applies one consistent evidence hierarchy to every pair. Each recommendation becomes:

• graded

• reproducible

• auditable

This removes dependency on any single reviewer and makes reliability a property of the system, not the person.

From one-time reports to living evidence

A PGx report is usually treated as a one-time artifact. But guidelines are revised, new drugs gain pharmacogenomic labeling, and a patient’s medication list keeps changing.

Living Reports connect each result back to its sources. When a guideline moves or a patient’s medications change, affected reports are flagged automatically and returned to the lab’s medical director for a fresh review. Over time, a report stops being a snapshot and starts behaving like a monitored record.

Built for real clinical inputs

Real lab data is messy. Genotyping arrives as VCF, CSV, Agena MassARRAY, or array output, and no two sources describe alleles in exactly the same way.

SignalPGx normalizes those inputs before it reasons over them, mapping raw calls to standardized star alleles across 50 pharmacogenes and roughly 950 medications. Reconciliation adapts to the data it is given rather than demanding a perfect file. That makes the pipeline far more resilient than a rigid lookup table.

Scaling coverage without scaling review time

Traditionally, covering more genes and more drugs means more manual literature review and more hours spent per report.

With a reconciled evidence graph, scale shifts to the platform.

SignalAI assists interpretation and flags gene-drug interactions across the full panel, while the lab’s medical director reviews and signs out every report. Coverage grows without a matching rise in review burden.

decision = model.predict(input_data)
execute(decision)

Making the evidence trail visible

One of the most overlooked advantages of a single evidence source is transparency. When every recommendation is graded against named guidelines, each conclusion can be traced back to the exact source behind it.

This gives labs and clinicians clarity on:

1. which guideline was applied

2. how the phenotype was derived

3. why a drug was flagged

Instead of taking a recommendation on faith, prescribers can see exactly why it was made.

Conclusion

A unified evidence source doesn’t just save time — it changes what a PGx report can be trusted to say.

Labs that treat evidence as one reconciled source, not a loose pile of references, gain a real advantage: PGx reports that are consistent, defensible, and ready to deliver into the EHR. SignalPGx is not a diagnostic test and never replaces the physician; final prescribing decisions stay with the ordering clinician.