Leveraging digital tools and analytics for temperature management in cold chain systems for gene therapies

Abstract

Emerging advanced therapies at Johnson & Johnson Innovative Medicine, such as a new retina gene therapy, require maintaining ultra low temperatures within the cold supply chain from the manufacturing plant and throughout distribution to the customer. In comparison to traditional cold chain medicines such as most vaccines, gene therapies are high-value, low-volume products and assurance of the product quality requires visibility into the full time-temperature history. This thesis describes the requirements for an end-to-end, digitally-enabled temperature management system for gene therapies. First, we establish a baseline understanding of the location, incidence, and severity of temperature excursions across the cold chain, based on current practices managing traditional drugs, through descriptive statistics on real-time temperature data, historical excursion records, and product complaints. While J&J has digital temperature monitoring solutions in place today, tracing the temperature history of a product across multiple legs of the supply chain, as required for a gene therapy, has to be done through manual review of disparate temperature records. To fill this gap in the existing infrastructure, we define the requirements for integrating temperature data across 6 enterprise data systems, including sensor data, ERP systems for shipments and warehouse management, and serialization records. Lastly, we build a Monte Carlo simulation to inform performance requirements for the system by modeling the trade-offs in system reliability and the cost of product loss.