The global biologics market is one of the fastest growing segments of the pharmaceutical industry. Annual revenues are expected to exceed $300 billion in the next 2-3 years.¹ But development and production can’t keep pace with discovery. That’s one reason outsourcing to contract development and manufacturing organizations (CDMOs) continues to increase. CDMOs, which currently represent 20% of the industry’s manufacturing capacity, are expected to grow to 30% of global manufacturing volume by 2025. ¹
For pharmaceutical and biopharma companies, building quality into your products from an early stage is a key factor in regulatory approval and market success. Design of Experiments (DOE) is an essential tool for achieving both regulatory compliance and faster time to market.
Consumers expect a certain consistency in quality and taste from the food and beverage brands they love. But many factors can influence the way a product tastes when it reaches the consumer – ranging from the manufacturing process to seasonality of ingredients to storage temperatures. Similarly, a number of other factors may influence the overall quality attributes that matter, such as alcohol content of beer or stability of the whiskey aging process.
In the midst of a global COVID-19 pandemic, a top priority for many pharma and biopharma companies is to get a vaccine developed, produced and delivered to the public as quickly as possible. Ushering a vaccine through rigorous testing protocols and regulatory approvals is not an easy (or quick) effort, but incorporating advanced data analytics could help accelerate the process. Data analytics has proven effective in speeding vaccine development both by enabling more efficient Design of Experiments (DOE) and by creating rapid-scale production rollout processes.
Digital transformation in biopharma promises to deliver exponential results and make new discoveries and solutions to complex problems a reality, but it requires companies to make big changes to get there—changes in processes as well as adoption of new technologies. For some companies and facilities, this is a bigger leap than for others. Depending on the level of digitalization and integration that currently exists within a company, the process can take from months to years.
Looking for ways to improve the efficiency of its power plant operations while reducing costs and environmental emissions, the Department of Power and Water at Michigan State University (MSU) began a study using multivariate data analytics that led to some surprising findings. The results have implications that could help other operators of large-scale power facilities reduce their carbon footprint and improve power plant operations.
Finding the right balance between efficient power output from boilers and other energy producing equipment while also reducing environmental emissions is an important objective for power plant operators. Governments and environmental agencies around the world establish emission standards as part of air pollution regulations, but finding the right way to meet the standards can vary greatly depending on location, equipment and other operating factors.
In the midst of a global crisis, many industrial manufacturing operations— including those in the chemical industry— are faced with shortages of supplies and equipment, or staff reductions, and finding it difficult to keep operations working as normal. Are there process improvements or tools that can be used to manage production more efficiently during this time of COVID-19 (and moving forward)?
While many other industries have implemented multivariate data analysis software for process optimization and control, it is still not very common in the pulp and paper industry. However, multivariate data analysis has a very promising potential for both cost reductions and quality improvements in pulp and paper mills. No capital investments are needed, the implementation can be done remotely, and the software typically requires no permits.
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.
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.