Data availability is a pressing challenge in the Cell and Gene Therapy (CGT) supply chain. While traditional pharmaceuticals benefit from large volumes of data, CGT programs face unique obstacles. This limits their ability to leverage artificial intelligence (AI) and machine learning (ML), which means data science within CGT is stunted. 

But is the issue down to a lack of data or a lack of data sharing among industry players? In this article, we explore the potential benefits of data sharing in cell and gene therapy, the role of regulatory bodies, and how collaborative data usage could unlock new efficiencies and innovations in the CGT supply chain, including data science. 

The Data Challenge in CGT Supply Chains 

Supply chain data volume for Cell and Gene Therapy (CGT) programs is minimal compared with high-volume, off-the-shelf products such as small molecule drugs and biologics. Based on the small number of therapies approved to date and the limited number of patients currently being treated, at an individual company level, data volume remains low. 

Data Requirements for AI in CGT Supply Chains 

Machine learning and artificial intelligence require a high volume of data to analyze for meaningful trends and effective data science. 

However, at a CGT company level, the absence of substantial data volume prevents companies from using artificial intelligence to effectively tackle key CGT supply chain challenges, such as apheresis bottlenecks, optimized utilization of manufacturing slots, and other potential points of failure or inefficiency 

Is Data Volume Really the Issue? 

We’re sure this is not the first time you have heard this! To us, it is nothing but a myth. The real issue is not data volume but rather data availability. The data already exists across CGT companies. 

With our shared goal of reducing the cost of goods through supply chain efficiency, why aren’t we collaborating to share non-proprietary data that benefits everyone? 

The Role of Regulatory Agencies in Data Sharing 

Regulatory agencies like the FDA or EMA could play a vital role in driving data-sharing mandates. While a range of CGT industry consortia already exist and actively discuss the importance of CGT data sharing, without regulatory involvement, nobody is willing to take the first step. 

The value delivered by supply chain technology platforms, such as OCELLOS, could be significantly enhanced through adoption of AI functionality, but without data sharing, meaningful analysis remains years away. The absence of data sharing is significantly slowing down the pace of innovation for CGT supply chains.   

Data Sharing in Healthcare 

Data sharing in healthcare already exists in other sectors. For instance, market access data is widely shared, and payers share claims data with peers. 

Organizations like IQVIA collect data from over 550 suppliers and 100 pharmaceutical manufacturers. Clinical trial data is shared as mandated by federal agencies. Could these data-sharing principles be applied to the CGT supply chain? 

Barriers to Data Sharing in the CGT Industry 

Despite the potential benefits, several barriers hinder data sharing in the CGT sector. Key issues may stem from competition, legal concerns, technological limitations, or data privacy regulations such as HIPAA and GDPR. 

Most of these barriers can be resolved through effective technology solutions and by implementing processes such as those used for other sensitive data sharing. 

How Data Sharing and Data Science Could Transform CGT Supply Chains 

Data sharing and data science will enable process efficiency improvements, resulting in a shorter supply chain lifecycle, lower cost, and ultimately shortening the time-to-treatment for seriously ill patients. 

Perhaps organizations like IQVIA can play a pivotal role in this transition, as they already have a strong reputation in the life sciences industry. Given their nature of business, they have robust data handling measures that have continuously evolved over the last few decades.   

Conclusion: The Future of Data Sharing and Data Science in CGT Supply Chains 

In summary, real-world data is key to solving many CGT supply chain problems. From manufacturing slot optimization to accelerated time-to-treatment, data science could have both a short-term and long-term impact on the efficiency of cell and gene therapies. 

Furthermore, improving supply chain performance will contribute to lowering the cost of advanced therapies, helping improve patient accessibility overall. This change could have a huge impact on us, worldwide. 

For any data-related queries, please don’t hesitate to reach out to Dr Matthew Lakelin (Co-Founder & Head of Consultancy) or Prakash Pandey (Chief Technology Officer at TrakCel).