Template

APQR template

An annual product quality review skeleton for drug products: every review area names its data source and gets an explicit trend call.

The annual product quality review answers one question a year, per drug product: is the process still consistent, and are the current specifications still appropriate? This template keeps the review honest about how that answer is reached — every review area names the data source actually pulled, states what it showed for the period, and makes the adverse-trend call explicitly, so the conclusion is drawn from named records rather than assembled from memory in the approval month. Actions get an owner in a tracker, not a mention in the closing paragraph.

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APQR template — the Excel workbook you download, showing the worked annual review example

What the template covers

Thirteen review areas, each with a named source and a Y/N trend call. The How to use tab covers the drug-GMP scope and the APR/APQR/PQR naming, and the failure modes: reviews assembled from memory in the approval month, batch counts that do not reconcile with the deviation rows, 'no adverse trend' calls with no data source named, actions raised in the conclusion text but never tracked. The APQR tab is the review skeleton. The Worked Example tab completes it for a fictional tablet product's unremarkable year — results stated as within specification, not as invented values.

  • Review header — product, review period, sites covered, review owner, approvers
  • Batch summary — batches manufactured, released, and rejected, as counts from a named register
  • Thirteen review-of-records rows — starting and packaging materials, in-process and finished product results, batches failing specification, deviations, complaints, returns and recalls, change control, stability, validation status, variations, post-marketing commitments, equipment qualification, technical agreements
  • Each review row carries a data source, a summary of findings, an explicit adverse trend? (Y/N) call, an action, and an owner
  • Trend conclusion, a CAPA/action tracker, and conclusion and approval rows

One review, three names — APR, APQR, PQR

This is a drug GMP document — it applies to drug products, not to medical devices. US practice calls it the Annual Product Review (APR) or Annual Product Quality Review (APQR), after the annual records-review expectation in 21 CFR 211.180(e). EU practice calls it the Product Quality Review (PQR), after the Product Quality Review expectation in EU GMP Part I Chapter 1. ICH Q7 carries a comparable product quality review concept for APIs. The names differ; the discipline is the same yearly look-back — see the APQR glossary entry for the concept and periodic review for the wider family it belongs to.

The review draws on a year of records that live elsewhere in the QMS — the deviation log this template's review rows point at is the kind of record a deviation investigation produces, and the change-control row reviews what a change control form leaves behind. The APQR does not replace any of them: it reads them once a year and makes the trend call.

Where 21 CFR 211.180(e) and the EU PQR expectation fit

21 CFR 211.180(e) expects records to be reviewed at least annually to evaluate the quality standards of each drug product and determine whether specifications or manufacturing and control procedures need changing. EU GMP Part I Chapter 1 carries the Product Quality Review expectation. Neither mandates this format — the template is a widely used convention, not a rule — so adapt the sections to your own APQR procedure and have QA approve the adapted version.

Two disciplines matter more than the format. First, every review area gets an explicit adverse-trend call with a named data source — a review with neither is a summary, and 'nothing happened' areas still get a row stating what was checked. Second, the numbers stay home: results are stated as within or outside specification with the record referenced, because numbers retyped into the review drift out of step with their source records over time. Actions the review raises go to the tracker with an owner — and their effectiveness is next year's evidence, which is where a CAPA effectiveness check earns its keep. An inspector picks one 'N' trend call and asks to see the named source, then adds the batch counts. A review assembled from memory is visible the moment a source cannot be produced.

Frequently asked questions

Are APR, APQR, and PQR the same document?

In practice, yes — three names for the same yearly look-back at one drug product. US practice says Annual Product Review or Annual Product Quality Review, following the annual records-review expectation in 21 CFR 211.180(e); EU practice says Product Quality Review, following EU GMP Part I Chapter 1's Product Quality Review expectation; ICH Q7 carries a comparable concept for APIs. The expectations are not word-for-word identical, so companies serving both markets typically write one procedure that satisfies both and use one document — which is what this template supports.

Does this template apply to medical devices?

No — it is a drug GMP artifact, and the template says so. Its review sections follow the records-review expectations for drug products; medical device quality systems reach periodic review through different mechanisms, management review chief among them. If your product is a device, start from your own standard's review requirements rather than adapting a drug-product review structure.

Why does every review area need an explicit adverse-trend call?

Because the trend call is the review. A section that summarizes the year's deviations without saying whether the pattern is adverse has reported data, not reviewed it — and a reviewer or inspector reading it cannot tell whether the question was ever asked. One explicit Y/N per review area, each backed by a named data source, is what separates a review from a summary; a 'Y' then has somewhere to go, because the action tracker sits two sections below.

Take one trend call back to its source

Pick a product and a 'no adverse trend' row. In a demo we ask for the named source and reconcile the batch counts with the rejection and deviation rows.