Continuous manufacturing is one of the key trends within the pharmaceutical industry, both for the production of ‘classical’ drugs as well as large molecules. Companies are looking for ways to shift from traditional batch processing to a continuous method of operation. The main advantages associated with these processes are more room for modularity, automation and flexibility due to a smaller footprint, as well as more consistent quality of the drug product.
Biosimilars are an exciting route to increasing access to the highly effective therapy made possible by biologics, but ensuring a biosimilar meets the critical quality attributes (CQA) of the original biologic is a major challenge. Optimizing production at full scale is impractical, which makes a quality by design (QbD) approach using a reliable scale down model of the process an attractive alternative. A process development team at Zhejiang Hisun Pharmaceuticals, Taizhou, China, therefore developed a scaled down model of the cell culture process used to produce the biosimilar adalimumab. They qualified the model using multivariate data analysis (SIMCA), and explored the design space for key process attributes (KPA) and CQAs using MODDE.
There is a strong demand for devices such as mobile phones, tablets and large screen TVs all over the globe. The business is competitive, which puts pressure on prices. At the same time, production costs are fairly high due to complex production processes. Consequently, a high yield becomes paramount for good profit margins. Multivariate data analysis (MVDA) is being employed by an increasing number of manufacturing companies to increase yield, and the electronics industry is no exception. This article provides examples of where and how real-time data analytics can be used in the electronics industry.
The key to being able to innovate, improve and streamline your processes often lies in gaining as many insights as you can from a variety data sources scattered throughout your operations. Making sense of all that data can be difficult. But it's not an impossible dream.
Most biopharma manufacturing companies are keen to adopt new methods that would streamline production, reduce errors and ensure product quality. That was the goal of Bristol-Myers Squibb when they implemented a complex real-time process monitoring system that involved integrating data from a number of different technologies, systems and vendors to gain greater control over complex batch processes.
In the last few years, many pharmaceutical companies have started investing in continuous production, and some have already succeeded in filing new pharmaceuticals using a continuous flow manufacturing process. This article summarizes a study at GlaxoSmithKline, GSK, where real-time multivariate monitoring added value to the development of a continuous production process of an active pharmaceutical ingredient (API).
In agrochemical, pharmaceutical and other industries that manufacture complex chemicals, finding ways to reduce waste and improve inefficiencies often hinges on selecting the right chemical compounds. Data analytics can help manufacturers find alternative compounds that meet complex requirements, decrease raw material usage or enable more cost-effective, sustainable processes.
A challenge for the regenerative medicine industry is to develop cell culture processes that can be scaled up for high volume production. Finding a better way to scale up commonly used research cells like HEK293T (used for protein expression and the production of recombinant retroviruses or lentiviral vectors) would be beneficial for biologists in many fields of medicine. Dr. Franziska Bollmann, virus scientist at Sartorius Stedim Biotech in Germany, recently conducted two experiments to find out if micro bioreactor systems can help facilitate the transition from the traditional shake flask process to a more improved method optimizing process control.
The 2019 Umetrics User Meeting drew more than 102 engineers, operations managers, process experts, researchers, and data scientists in industries ranging from biopharma to food and beverage to chemicals who gathered to share ideas and insights into new methods for streamlining their processes, reducing waste and cost of goods sold.
The potential for Artificial Intelligence (AI) is enormous and the applications seemingly unlimited. One subset of AI, deep learning, offers the promise of efficiently solving a large range of challenges involving unstructured data by harnessing neural networks to save time and money, and even perform seemingly impossible tasks.
Deep learning has revolutionized the fields of artificial intelligence, computer vision, speech recognition, and more in recent years. Deep learning can draw information from unstructured data such as images or text in a way that was unthinkable a decade ago. In industries like Pharma and Biopharma, deep learning can help all the way from understanding how cells work using live cell imaging to monitoring manufacturing using audio.
SIMCA 16 offers improved ribbons, tours, wizards, data merging, multiblock analysis and more.
SIMCA is a multivariate data analytics tool that helps users make sense of complex data by transforming numbers and statistics into visual information for easy interpretation and understanding. Across many industries ranging from pharmaceuticals and chemicals to food and beverage manufacturers to academia, SIMCA helps production managers and researchers a like make better decisions in order to take action quickly and with confidence.
One key to reducing R&D costs in the biopharmaceutical market is streamlining and speeding up process data flow for Design of Experiments (DOE). Now, a direct integration of Genedata Bioprocess® platform and Umetrics Suite MODDE® software enables seamless data flow and facilitates the design, execution and evaluation of experiments in large-molecule process development.