Saadia Chaudhry, Director, Clinical Supply Management
The production of investigational products (IPs) for clinical trials stands as one of the most financially burdensome tasks for biopharmaceutical companies of all sizes. The intricacies of protocol design, the emergence of patient-centric drugs and the decentralized nature of the supply chain pose formidable challenges for managing costs and minimizing waste. Across the globe, the pharmaceutical industry grapples with the inefficiencies inherent in this process that drive up trial costs, potentially leading to delays and leaving a substantial carbon footprint if not managed effectively.
One of the key issues contributing to waste in clinical trial supply management is the difficulty in accurately forecasting demand for IPs. Due to the unpredictable nature of clinical trials, organizations often resort to introducing excessive buffers, a suggestive number is as high as 70%, in their demand/supply strategies. Consequently, a large percentage of drugs end up being wasted in each trial, translating to hundreds of millions of dollars lost from development to distribution. To ensure long-term sustainability, proactive measures must be taken to improve the accuracy of IP usage calculations and reduce wastage.
Enter artificial intelligence (AI) and machine learning technologies, which offer a promising solution to the challenges plaguing clinical trial supply management. By leveraging these advanced tools, an experienced supply manager or subject matter expert can optimize their supply chain strategies for pharmaceutical companies, mitigate risks such as overproduction, overstocking and overdistribution, and ultimately enhance efficiency and cost-effectiveness.
Interactive Response Technology (IRT) systems represent one such innovation that is revolutionizing clinical trial supply management. These systems, offered by various vendors, feature built-in forecasting models, real-time inventory monitoring and automated resupply strategies, empowering trial sponsors to make data-driven decisions and mitigate supply chain challenges. Moreover, technology platforms developed by depots and CMOs offer enhanced visibility, traceability and communication between stakeholders, further streamlining supply chain operations and ensuring trial integrity.
However, bridging the gap between traditional supply chain management practices and cutting-edge AI technologies requires a specialized skill set that is often lacking in the industry. Many small pharma companies and emerging biotechs lack in-house expertise in both global supply chain management specific to clinical trials and the latest AI and machine learning techniques. As a result, they often rely on contract research organizations (CROs) or contract manufacturing organizations (CMOs) for guidance on manufacturing and clinical trial supply.
The solution lies in partnering with carefully selected vendors who possess the requisite expertise in both global clinical trial supply chain management and AI technology. These vendors can provide invaluable support in navigating the complexities of clinical trial supply management, offering tailored solutions to optimize supply chain processes and minimize waste.
In conclusion, the future of clinical trial supply management lies at the intersection of human expertise and technological innovation. By fostering a workforce equipped with both domain knowledge and proficiency in AI and machine learning, the pharmaceutical industry can unlock new efficiencies, reduce waste, and ultimately enhance patient safety and trial sustainability. Through strategic partnerships with vendors who possess the necessary expertise, biotech companies can harness the power of AI to transform clinical trial supply management and drive positive outcomes for patients and stakeholders alike.