Columns | Column: Questions of Quality

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Tired of life as your analytical results are always out of specification (OOS)? Fed up with yet another laboratory investigation? Get those rotten chromatograms to generate passing results by learning ways to manipulate peak integration from the experts... and now I have your undivided attention...and how reviewers, QA and inspectors can detect them!

The term orphan data is used frequently in the context of data integrity. What does it mean for chromatography data systems? How can we prevent or detect orphan data?

A balance printout is a fixed record, and is also called static data. But how static are static data when the weight is used in a chromatographic analysis? Also, have some regulatory data integrity guidance documents failed to comply with their own regulations?

There is a hidden factory in plain sight in many laboratories. Not many people know of its existence, yet it stares back at you when you work. What is the output from this hidden factory? Paper.

This article uses case studies to explore the use and misuse of spreadsheet calculations in conjunction with a chromatography datasystem (CDS) in regulated GXP laboratories. What wonders of non-compliance will we find? How and when should spreadsheets be used in chromatographic analysis?

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This is the final part of a trilogy looking at the impact of the new USP on analytical instrument qualification (AIQ) on data integrity in a regulated chromatography laboratory. This part will focus on the performance qualification (PQ) portion of the 4Qs model and, specifically, monitoring and requalification of chromatographs.

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This is the second of three articles looking at the impact of the new United States Pharmacopeia (USP) on analytical instrument qualification (AIQ) on data integrity in a regulated chromatography laboratory. This part focuses on how the laboratory should supervise the execution of operational qualification (OQ) protocols by a third-party service provider. The principles described also apply to in-house metrology departments.

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This is the first of three articles looking at the impact of the new United States Pharmacopeia (USP) chapter on Analytical Instrument Qualification (AIQ) on data integrity in a regulated chromatography labora-tory. In part 1, user specifications for chromatography systems and the relationship between users and sup-pliers will be discussed.

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The United States Pharmacopeia general chapter on Analytical Instrument Qualification has been updated and became effective 1 August 2017. So, what has changed?

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Raw data is a term that is used in both good manufacturing practice (GMP) and good laboratory practice (GLP) laboratories but it can create misunderstanding. What exactly does raw data mean and what electronic records are within the scope of the term?

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One of the common threads in the six data integrity guidance documents published to date is the need to control any blank forms used in regulated GXP laboratories. This month’s “Questions of Quality” is focused on how to interpret the regulator’s requirements for this topic. We also pose the question: Is paper the best way to record regulated data?

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Data integrity issues are changing the way that we should be undertaking computerized system validation (CSV) of our chromatography data systems (CDSs). Do you understand what is required in the brave new world of CSV?

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Primary record is a term that was defined by the MHRA (Medicines and Healthcare products Regulatory Agency, the UK drug regulator) in data integrity guidance issued in 2015. In this instalment of Questions of Quality we explore what this term means in practice, and compare it with raw data in the European Union Good Manufacturing Practices (EU GMPs) and complete data in US Food and Drug Administration (FDA) GMPs. Why can’t we have harmonization of terms?

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Chromatography data systems (CDSs) have had a starring role in many regulatory citations involving falsification and fraud in analytical laboratories regulated by Good Manufacturing Practices (GMP). In this instalment of "Questions of Quality" we will examine the citations and identify the technical and procedural controls required to ensure data integrity within these systems. Although focused primarily on the pharmaceutical industry, the principles described here are applicable to all laboratories working to established quality standards.