Abstract
There has been increasing evidence in recent years that research in life sciences is lacking in reproducibility and data quality. This raises the need for effective systems to improve data integrity in the evolving non-GxP research environment. This chapter describes the critical elements that need to be considered to ensure a successful implementation of research quality standards in both industry and academia. The quality standard proposed is founded on data integrity principles and good research practices and contains basic quality system elements, which are common to most laboratories. Here, we propose a pragmatic and risk-based quality system and associated assessment process to ensure reproducibility and data quality of experimental results while making best use of the resources.
Highlights
There has been increasing evidence in recent years that research in life sciences is lacking in reproducibility and data quality
Research groups, which have the right quality culture/mind-set, could require less inputs from a quality organization. While these requirements are relatively easy to implement in a pharmaceutical setting, the current academic research environment presents a number of hindrances: usually, academic institutions transfer the responsibilities for data integrity to the principal investigators
In order to ensure data integrity and compliance with ALCOA+ principles, all scientific and business practices should underpin the research quality standard (RQS). This standard needs to contain a set of essential quality system elements that can be applied to all types of research, in a risk-based and flexible manner
Summary
Many activities performed in discovery phase and early development are not conducted under GxP standard but need to comply with a number of regulations. The GLP, for example, originate from the early 1970s, when the Food and Drug Administration (FDA) highlighted several compliance findings in preclinical studies in the USA, such as mis-identification of control and treated animals, suppressed scientific findings, data inventions, dead animal replacements and mis-dosing of test animals. These cases emphasized the need for better control of safety data to minimize risk, in study planning and conduct, in order to both improve the data reliability and protect study participant life. Risk-based and sciencedriven research quality standards could fit with the discovery activities’ scope and requirement of this research activity and ensure data integrity while saving resources
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