Why Industry Resists FDA Quality Management Efforts

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An online Clinical Leader guest column raises the question of why the drug industry is resisting quality management changes that FDA so clearly supports. PDC Pharma Strategy CEO Penelope Przekop, a quality management expert, says that at a recent meeting of agency representatives and stakeholders to discuss the challenges and successes of integrating quality by design and risk-based monitoring into the design and conduct of clinical studies, three industry barriers came up:

  • the drug industry does not understand the concepts and how to apply them, which perpetuates a low perception of value;
  • industry is not aligned in its understanding and definition of risk as applied to the quality management concepts; and
  • there is a pervasive overfocus on timelines and a deeply entrenched associated reward system.

The author lists 10 things that speakers and panelists at the session said the industry needs:

  • critical to quality processes, process steps, documentation and data must be properly identified;
  • thresholds for decisions and taking actions must be properly set;
  • those responsible for identifying criticality and thresholds must have the knowledge to do it correctly;
  • awareness and understanding of these concepts must be driven cross-functionally so that each function knows how it applies to their work and the value it brings not only in the long run but to their function;
  • quality by design can improve diversity in clinical trials, which is another advantage;
  • the healthcare industry needs to also understand how the concepts can improve the quality of natural history data;
  • that which is critical to quality must be specified in contractual agreements;
  • sponsors should be closely involved in the risk process;
  • the overfocus on how risk is scored is slowing progress and the industry must better understand the underlying point, which is to get cross-functional experts together as early as possible to brainstorm what can go wrong and make decisions;
  • 100% source data verification is no longer expected and is inefficient; and
  • protocols need to be simplified.

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