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.
From employing artificial intelligence (AI) to identify drug candidates to using big data to support continuous process manufacturing, the prospects for digital transformation in the biopharma industry are huge. Yet, biopharma and life sciences lag behind many other industries when it comes to digital transformation.
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)?
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.
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.
Using Multivariate Models in a Regulated Environment for Continuous Manufacturing of Pharmaceuticals
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.
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.
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.
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.
Recently the FDA issued a new draft guidance 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.
