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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
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Related Experiment Video

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Quality in Non-GxP Research Environment.

Sandrine Bongiovanni1, Robert Purdue2, Oleg Kornienko3

  • 1Quality Assurance, Novartis Institutes for BioMedical Research (NIBR), Novartis Pharma AG, Basel, Switzerland. sandrine.bongiovanni@novartis.com.

Handbook of Experimental Pharmacology
|November 27, 2019
PubMed
Summary
This summary is machine-generated.

Life science research needs better data quality and reproducibility. This chapter proposes a risk-based quality system for non-regulated research to ensure reliable experimental results and efficient resource use.

Keywords:
ALCOA+ principlesData integrityData qualityEQIPDEuropean Quality in Preclinical DataEuropean Union’s Innovative Medicines InitiativeExperimental resultsGood research practiceIMINon-GxP research environmentQuality cultureReproducibilityResearch quality standardResearch quality systemRisk-based quality system assessmentTransparency

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Area of Science:

  • Life Sciences Research
  • Data Integrity
  • Research Quality Standards

Background:

  • Increasing evidence highlights reproducibility and data quality issues in life sciences research.
  • The evolving non-GxP research environment necessitates effective systems to enhance data integrity.
  • Existing quality systems may not be optimally adapted for all research settings.

Purpose of the Study:

  • To describe critical elements for implementing research quality standards in industry and academia.
  • To propose a pragmatic, risk-based quality system for non-GxP research.
  • To ensure reproducibility and data quality of experimental results efficiently.

Main Methods:

  • Foundation on data integrity principles and good research practices.
  • Inclusion of basic quality system elements common to most laboratories.
  • Development of an associated risk-based assessment process.

Main Results:

  • A framework for implementing research quality standards is presented.
  • A pragmatic and risk-based quality system is proposed.
  • An assessment process is outlined to ensure data quality and reproducibility.

Conclusions:

  • Implementing a risk-based quality system is crucial for improving data integrity in life sciences.
  • The proposed system aims to enhance reproducibility and data quality in non-GxP research.
  • This approach optimizes resource utilization while maintaining high research standards.