A team at Glaxo Smith Kline, GSK, has carried out several studies using Raman spectroscopy and multivariate data analytics in order to monitor upstream cell culture processes. The studies show that Raman spectroscopy is a valuable analytical tool for enabling real-time monitoring and control of production bioreactors and that improved control can lead to improved product quality. Below is a summary of the studies.
An important step on the road to creating treatments for illnesses like COVID-19, which has caused the recent global pandemic, may start with understanding the similarities and differences between the various strains of coronavirus known to exist today. Making sense of large and complex sets of data, especially those that require novel interpretation, calls for a powerful analytics toolset to speed up the process.
Out of control processes in pharma manufacturing are not something to take lightly. If your production runs are seeing frequent deviations, leading to expensive batch losses or frequent rework, it’s time to take a look at ways to correct any process deviations in a more expedient manner. Uncorrected deviations or processes that vary from approved process parameters can lead to costly and dangerous mistakes.
Keeping your pharmaceutical manufacturing processes under control is important not only to ensure a quality product, but also for regulatory compliance. Process or raw material deviations can affect the downstream quality of a product and could mean tossing out an entire batch or end product if process corrections aren’t made soon enough — or if you can’t document that a correction was made before it affected your critical quality attributes.
For pharmaceutical manufacturers, a process deviation may not only mean a bad batch that affects a downstream process, it can also risk a regulatory violation that leads to fines or expensive market setback, or worse, it could endanger the health of the patient.
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.
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.
For pharmaceutical companies facing challenges such as rising costs, sterner regulations and declining profit margins, innovative new technologies like artificial intelligence (AI) and digital twins have become part of an essential strategy to future-proof their businesses. A digital twin is the next evolution of machine learning combining advanced data analytics and equipment simulation with comprehensive system models that blend historical information with real-time data to predict the future of a process. According to Gartner, the digital twin concept was one of the top 10 strategic technology trends in 2019.
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.
Optimizing the function of boilers, turbines and other capital equipment used to generate power requires a careful balance of fuel, heat, pressure, operator proficiency and many other variables. Managing the process on a day-to-day, or minute-to-minute basis, is like performing a skilled and well-orchestrated dance—partly based on data, but also based on operator expertise. Yet, adding more accurate information to the equation can potentially save millions of dollars, cut emissions significantly and even expand the working life of your equipment.
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.
Over the last several years, the use of artificial intelligence (AI) in the pharma and biomedical industry has gone from science fiction to science fact. Increasingly, pharma and biotech companies are adopting more efficient, automated processes that incorporate data-driven decisions and use predictive analytics tools. The next evolution of this approach to advanced data analytics incorporates artificial intelligence and machine learning.