Preserving the quality, safety, and effectiveness of pharmaceutical products has never been as challenging as it has been during the present turbulent times. The wave of new complications arising from issues like supply chain disruptions, clinical trial setbacks, and remote work inconveniences are no longer exceptions to the norm — they’re the new reality of doing business in the pharma sector. Quality professionals, in addition to carrying out their day-today tasks, are now responsible for adapting to and mastering change in an unsettled and rapidly evolving industry.
As the need for flexibility becomes more urgent industrywide, quality experts are perfectly positioned to help their organizations overcome the challenges presented by a world in disarray and serve as guides into a new era of data and system connectivity. Informed quality leaders who stay abreast of the trends shaping the industry’s future will be critical drivers of transformation, versatility, and success within their organizations.
In the full article linked below, SeerPharma's business partner MasterControl explores the following key topics:
Risk Management Expectations Continue to Evolve
The pandemic resulted in an overall decrease in the number of FDA inspections conducted in 2020, but it was still a record year for the number of Form 483 Inspectional Observations the agency issued to pharma companies for inadequately controlling their digital systems and data. Beyond the upsurge in Form 483s, the FDA has also recently begun reprimanding clinical organizations for data integrity issues and asking for their studies to be repeated.
Success Hinges on Access to Good Data
Once widely viewed as having revolutionary potential but few practical applications in traditional industry settings, artificial intelligence (AI) and other advanced technologies like deep learning and predictive analytics are yielding an increasing number of impressive, tangible results every day.
AI Shifts From Perk to Survival-Critical Tool
AI is venerated in the pharma world for its R&D applications and potential to revolutionize drug discovery, but the industry has only seen the tip of the iceberg in terms of what the emerging technology can do for quality. In recent case studies involving two companies’ use of machine learning (ML) for deviation resolution and critical process parameter range recommendations, AI-enabled systems helped them achieve a 70% reduction in deviation closure times and allowed them to identify root causes with 100% accuracy.
Optimized Quality Event Management Drives Efficiency
Imagine your supervisor saying, “I need you to precisely identify everything that’s going wrong right now, tell me who is affected by those problems, and then prioritize each issue.” That’s the kind of up-to-the-minute knowledge pharma companies expect their quality managers to have readily available. And that job is getting harder as the industry accelerates and more resilience is expected of the quality
function. Quality professionals are growing increasingly overwhelmed by the mounting tasks of tracking all quality events throughout the enterprise in real time, understanding their impact on other functions, and knowing if and when they should be escalated.
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