Issue link: https://www.e-digitaleditions.com/i/1173734
18 October 2019 Tablets & Capsules Supply chain visibility remains a very big challenge for pharma and biotech. The ability to anticipate failures or address excursions in real time has always been the end game. As with any process, the supply chain has its own unique sources of variability. Whether that is a result of human interaction or mechanical failure, the ability to monitor, measure, and ultimately predict excursions that are not part of the normal process control requires real- time or near real-time measurement capability. Presently, IoT solutions include sensor network tech- nology coupled with intelligent data analysis. Com- pliance with the FDA's Unique Device Identification (UDI) system and the Drug Supply Chain Security Act (DSCSA) is a significant driver for deploying IoT within the supply chain. Manufacturers, including both drug sponsors and contract manufacturing organizations (CMOs), needed to comply with the DSCSA by Novem- ber 2018, a delay of one year from the original target. Compliance was defined as an implemented solution to create a unique Global Trades Item Number (GTIN), serial number, lot number, expiry date in human-readable format, and GS1-compliant data matrix code. Looking only at the US market, this is a significant technical challenge, especially from a database manage- ment perspective. When you look at the global market- place and supply chain, with more than 70 different seri- alization standards and regulations, it's easy to see how a patchwork solution architecture would not be viable in the long term. Accessing data, unlocking information One of the first challenges the pharmaceutical indus- try has faced in attempting to step into big data analytics is that, currently, disparate data are largely trapped in iso- lated silos of automation and databases. This dramatically complicates predictive analysis and severely restricts the potential for any analysis that is new and innovative. If the goal is to have a complete 360-degree view of all relevant data and their relationships to each other across your business, patients, supply chain, and development pipeline, then the industry needs an architecture that can easily handle all types of data. With data in silos, the problem has become worse instead of better, despite many attempts at solving it. Data integration has proven to be the most challeng- ing problem in IT, and existing data-integration products and strategies are not working. Most organizations have a similar-looking IT architecture—a bunch of operational "run the business" systems utilizing a suite of extract, transform, and load (ETL) tools to feed data to respective "observe the business" data warehouses. In recent years, new sources of data such as IoT feeds, message feeds, artificial intelligence (AI), and machine learning tools have made the problem more complicated. Today, ontological databases have matured to a point where they can address the challenges of managing and analyzing siloed, disparate data. Technical solutions exist that let you bring in data from disparate sources "as is," operators to make smarter decisions that increase oper- ational efficiencies, improve yields and engineering pro- ductivity, and substantially drive business performance. Within modular smart factories, cyber-physical systems monitor physical processes, create a virtual copy of the physical world, and help make decentralized decisions. With the connected devices of the Internet of Things (IoT), cyber-physical systems communicate and interop- erate with each other—and with humans—for real-time control and data collection, contributing usable infor- mation that's shared among participants of the overall pharma manufacturing value chain. This enhanced business performance revolves around three basic elements: • Broad deployment of IoT: Data are gathered from across the global supply chain via smart sensors and smart devices; • Engineering systems: Data are integrated with intelligence to detect, analyze, and predict out- comes to everyday manufacturing challenges; • Integrated intelligence: All data, including enter- prise-level systems, are completely interconnected across the entire ecosystem, allowing for enter- prise-wide intelligence. The objectives of Pharma 4.0 are ambitious in that the intent is to make the leap from a reactive framework, his- torically achieved using automation strategies and tech- nologies, to a predictive framework based on analytics, allowing companies to anticipate and address potential challenges in the overall supply chain. While the focus of Pharma 4.0 is the manufacturing supply chain, the prin- ciples are being applied in a much broader fashion across the entire drug development life cycle. IoT across the supply chain IoT is one area where we are seeing an expansion of the principles as early as drug discovery. Table 1 sum- marizes some of the key areas where IoT is deployed across the drug product development life cycle and sup- ply chain extending from drug discovery all the way to post-commercial pharmacovigilance. Table 1 Applications of IoT across the drug product development life cycle and supply chain Drug discovery and development Manufacturing and supply chain Patient access • Wearable devices for subjects to do real-time reporting • Monitoring and reporting of data from clinical sites, subject screening, and real-time reporting • Serialization (AIDC) • Real-time logistics visibility • Smart warehousing and route management • Predictive maintenance • Yield optimization • Wearable devices • Smart pills • Compliance and usage tracking