The rapidly evolving field of artificial intelligence (AI) and data analytics offers immense potential for enhancing clinical trial quality assurance within pharma R&D and GxP. In May this year, at Momentum Events’ Clinical Quality Oversight Conference, Paul Houri, former Vice President, Head of Bioresearch Quality Assurance at Johnson & Johnson (currently Vice President, R&D Quality at Bristol Myers Squibb), discussed how companies can incorporate AI in quality assurance.
Houri started by describing the advancements and increases in the digital world and its application in Pharma R&D, emphasizing the transformational opportunities from preclinical drug discovery to full clinical development and post-market safety surveillance. He then reflected on how Quality Assurance organizations could also benefit from the advancements in digital and data. Through machine learning and data analytics, AI can play a significant role in identifying potential quality risks early, allowing for swift mitigation and enhancing the overall quality assurance mechanism. The recurring theme was the need to integrate AI into these processes to manage pharma R&D and GxP risk better and ensure quality.
Houri then discussed fostering a culture of innovation and maintaining a growth mindset. A transformative journey requires a clear and compelling vision and a culture where employees are encouraged to learn, grow, and innovate. Celebrating wins and acknowledging failures are fundamental to nurturing this innovation culture. Another key enabler is culture…developing a culture of innovation and a growth mindset drives a level of curiosity in our people to want to learn, grow, and innovate,” said Houri.
Upskilling is another crucial aspect discussed during the session. Data and digital acumen were identified as key areas of focus. In this digital era, having a workforce proficient in data interpretation and digital tools is no longer a bonus – it’s a necessity. Equipping employees with the necessary skills to navigate the digital landscape is vital for companies to stay competitive and innovate continually. Houri said, “Data and digital acumen…I’ve even taken it upon myself to go through various digital and data training courses, not to be able to code, but to be able to engage in deeper conversations, propose solutions, and drive transformation.”
But it’s not just about a digital/data focused workforce. The importance of having strong GxP expertise in quality assurance was also highlighted. The experts who interpret and contextualize the data remain an integral part of the process, demonstrating that while AI and data analytics are important, they are tools that supplement, not replace, human expertise.
There are some challenges in the AI integration journey in Pharma R&D and GxP, and Houri highlighted a few. One challenge was funding for innovative approaches, which can apply to all-size companies, but especially for small
and mid-sized enterprises. This obstacle can be overcome through innovative measures, such as leveraging central IT/innovation funds, optimizing efficiencies, and reallocating resources toward innovation.
Access to data was recognized as a major enabler. Hurdles like organizational resistance, issues with access to data from source systems, and data quality are most common. A common data taxonomy was highlighted as a valuable tool to drive effective and efficient data analysis. However, companies still face challenges regarding data access from third parties (e.g., in outsourced trials).
The AI tools being developed are being evaluated for their effectiveness. Going forward, one of the main considerations is ensuring the tools benefit the quality assurance team and the entire organization, promoting the FDA’s recommendations to foster a ‘culture of quality.’ The ultimate goal is to integrate these tools into the overall business process, creating a broader governance risk model involving all stakeholders.
In Summary, the transformation journey to integrate AI in quality assurance is challenging but rewarding, and showcases the potential of AI and data analytics in managing risks, ensuring quality, and fostering a culture of innovation. With continued efforts in innovation and upskilling industry is set to enter a new era of quality assurance powered by AI.
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Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.