A new diagnostic method for detecting a rare kidney stone disease has recently been developed at the University of Iceland. Instead of using urine microscopy, which has certain disadvantages, the diagnostic method is based on mass spectrometry of plasma samples. Preliminary clinical data shows very promising results both in terms of detecting the disease and therapeutic drug monitoring. Design of Experiments (DOE) was used as a chemometric approach to optimize the assay. Below is a summary of the assay development and optimization.
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.
In many manufacturing industries, variability in raw materials can lead to unexpected and undesirable changes in the final products. In regulated industries such as pharmaceuticals, this is especially problematic due to the need to maintain carefully controlled processes that stay within approved regulatory parameters for drug development and production. Embracing a total company-wide digital transformation enabled Amgen to align data across multiple systems to not only control, but also predict unacceptable deviations in time to make necessary adjustments. Read on to find out how they used data analytics to implement real-time process control.
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).
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.
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.
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.
In industries that depend on bioprocessing, achieving the highest possible yields in the shortest time frame, while keeping costs down and product quality high is often challenging. Meeting these goals requires having a well-designed, well-defined and well-controlled process. And at the core of any effective process control is a set of effective process modeling tools.
Speed and changing market conditions are significant challenges for businesses across many industries and markets. The need for improved efficiency, the ability to adapt to changing market conditions and digitalization are often key drivers of change. By focusing on data, a company can make significant improvements in processes or systems, and gain insights into the driving forces behind business operations. Data analytics is the key to business optimization.
The natural variability of botanical material often makes it difficult to ensure a consistent quality process for pharmaceuticals made from plant-based products. In addition, botanical drug products (BDPs) are often produced using a series of separate batch processes, which adds even more variability into the manufacturing process.