Feb 14, 2021
From start-ups to big brands, businesses are offering personalized product options to extend their product lines and increase sales.
Pharmaceuticals are no different.
Cellular biology and gene therapy advancements have opened the door for smaller-volume and more personalized drugs. In order to meet increased global competition and the demands of customers, the pharmaceutical industry is also undergoing a rapid shift towards a more “distributed” model of building a large number of smaller batches of personalized medicines rather than focusing on large volumes of a few “blockbuster” ones.
When producing small quantities, filling, process design, and quality concepts present pharmaceutical companies with new technical and economic challenges. In spite of the need to get to market faster at competitive costs, compliance and data integrity becomes more critical now than ever. Manufacturers need greater process management, smarter operations, and more efficient R&D operations to accelerate regulatory approval and production. This results in an increasing need for data gathering, analysis, tracking, control, and automation.
Pharma 4.0 leverages connectivity, advanced analytics, and artificial intelligence technologies to increase both immediate and long-term productivity and quality while reducing costs.
The first real-life use cases have delivered 30 to 40 percent increases in productivity within already mature and efficient lab environments, and a full range of improvements could lead to reductions of more than 50 percent in overall quality-control costs. (https://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/digitization-automation-and-online-testing-the-future-of-pharma-quality-control)
Immediate Value: immediate benefits for Pharma companies relate to the health and operation of assets: asset performance management, optimization of energy consumption for sustainability, and operations intelligence.
Technology Transfer: advanced analytics using predictive technologies accelerates technology transfer from labs to production. Modeling can be used to better understand condition differences between sites so as to optimize tech transfer.
Process Intensification: is finding increased use in both biologics and small-molecule manufacturing. Migrating from batch to continuous processing offers several benefits, including higher throughput, reduced variability in product quality and efficacy, smaller equipment footprint, less waste, and lower energy consumption. Integration of IIoT based data with Industry 4.0 technologies provide benefits related to asset and quality monitoring.
Testing: costs can be reduced significantly by creating a digital twin of a lab to predict impacts before making physical changes. This virtual development can be used to determine the boundaries of continuous processing for process intensification, thereby reducing the risk, cost, and time associated with physical testing.