The pharmaceutical industry, including R&D, manufacturing and also product sales and use, creates a lot of data. The question is, what can we do to understand our data better, get more out of it, and unlock its potential in the most rational way possible to get to the knowledge we need? And how can we gain control over our research, or the processes needed to generate a stable, reliable product that consistently meets regulatory requirements? The answer is Multivariate Data Analysis.
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.
Whether it’s fake olive oil, coffee bulked up with husks and twigs, or honey tainted with antibiotics, food fraud is a growing problem worldwide. The Australian research organization CSIRO states that the economic damage alone from food fraud has reached $35 billion (in US dollars) in 2018. The underlying cause is nearly always financial gain and economic pressures to save money by using inferior (or mislabeled) products. Predictive analytics is one tool manufacturers are using to combat food fraud.
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.
Recently the FDA issued a new draft guideline for continuous manufacturing of small molecule drugs. With these draft guidelines the FDA wants to engage more pharmaceutical manufacturers to shift from traditional batch/start-stop processing to continuous manufacturing. The main advantages associated with these processes are more room for modularity, automation and flexibility due to a smaller footprint, but also more consistent quality of the drug product. Of course the main incentive for the FDA to promote this way of processing is that it believes that this will have a positive impact on drug prices and prevent drug shortages.
Injection molding is the most important production method for manufacturing plastic components used in products ranging from cars to medical devices. Although the plastic components themselves are often inexpensive to produce, any defect can lead to expensive errors that can affect the performance or safety of the finished product. Creating a system of early fault detection and continuous process improvement can mean big payoffs for manufacturers.