Umetrics Suite Blog

Which data analytics methods best support a quality by design approach for biosimilars?

November 12, 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.

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The trending role of artificial intelligence in the pharmaceutical industry

September 26, 2019

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.

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How one company integrated data to implement real-time bioprocess monitoring

September 4, 2019

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.

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How digital transformation helped Amgen implement real-time process control

August 6, 2019

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.

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Multivariate vs univariate analysis in the pharma industry: analyzing complex data

June 18, 2019

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.

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Using advanced data analytics to make the shift to continuous process manufacturing

April 2, 2019

Throughout the evolution of manufacturing, many industries have gradually shifted away from batch process to continuous process manufacturing as production technologies matured. This has included industries such as chemical, petroleum, steel, automobile, consumer goods and food manufacturing.

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Data analytics techniques help researchers identify promising breast cancer biomarkers

March 12, 2019

Breast cancer is the most commonly diagnosed cancer amongst women worldwide and a leading cause of cancer related deaths among females. It’s the second most common type of cancer overall. According to the International Agency for Research on Cancer Research, there were more than 2 million new cases in 2018.

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Reducing batch-to-batch variability of botanical drug products

January 14, 2019

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.

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Can data analytics improve biopharma manufacturing processes for autologous treatments like CAR-T?

December 13, 2018

Advancements in cell and gene therapy hold promise for the future of personalized medicine, especially for cancer treatments. However, bioprocessing methods for autologous cellular therapies, and CAR-T in particular, often present unique challenges in manufacturing due to the variability of the starting material and unique nature of each batch. Is there a way to create more efficient processes in order to bring down costs and make personalized medicine a viable option for more patients?

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Learn how one biopharma company relies on batch process MVDA to improve product consistency

September 20, 2018

In life science biopharma manufacturing, demonstrating consistent, repeatable processes is essential both for regulatory compliance and product quality. Being able to create data-driven, performance-based objectives, and aligning the process control strategies with compliance and business performance objectives, allows companies to take their data analysis to the next level: the level at which it becomes meaningful for the company’s bottom line.

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