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How Automation Is helping Pharma Companies Transform Their Manufacturing Processes
If there’s one thing the COVID-19 pandemic has highlighted, it’s the need for increased agility and scalability in the pharmaceutical industry.
The current pandemic and the prospect of future ones have required a much more offensive strategy than the industry has previously been using. The extreme spike in demand, both in terms of R&D and actual production, has spurred the industry to explore further and adopt automation to speed things up.
With the advent of COVID-19, the pharmaceutical industry was forced to test new products and produce, scale, and bring them to market at an unprecedented velocity. Demand has changed and increased due to supply chain disruptions in the wake of the pandemic.
Shortening the Timeline for Clinical Trials With RPA and Pharma Automation
One of the critical aspects of developing new drugs is the timeline for clinical trials. This is one of the areas where the experience with Covid has probably permanently reshaped expectations. We’ve now seen how innovation and technology can shorten the clinical trial timeline, many thanks to automation that enables testing on a larger scale in a shorter period of time. This, in turn, puts pressure on product development to also increase the speed of processes to ensure parallelization.
It’s almost two decades ago now that Electronic Data Capture (EDC) tech was first introduced in clinical trials. This was one of the first steps in automating trial processes. Today, with access to robotic process automation (RPA), cognitive analytics, and artificial intelligence (AI), the industry is now able to keep digitizing clinical development.
One forecast estimates that the global market for pharmaceutical robotic systems will grow from$64.37 million in 2016 to $119.46 million by 2021. This would be a compound annual growth rate (CAGR) of 13.2% from the year 2016 to 2021.
Rapid Batch Releases with Automation in Pharma
To be able to produce and deliver large quantities of pharmaceuticals quickly, rapid batch releases are essential. These require electronic batch records that can be verified instantaneously. Analytical techniques must be quick enough to enable corrective actions to be taken before the quality of the batch is affected. In a world where many workflows still remain manual and paper-oriented, and in an industry where much time is spent on processing, managing, and cleaning the data before acting on it, automation is a game-changer. Clean data means better insights, which equals better decisions.
Process Analytical Technology (PAT) Helps Quality Assurance
Even though regulatory bodies like the FDA have advocated the use of PAT for a long time, wide-scale practical adoption has been relatively slow. This is primarily due to the complexity of the technologies and infrastructure it entails. But now, process analytical technology (PAT) is starting to gain ground. An increasing number of manufacturers have implemented the sensors and the suitable data systems that enable multivariable processing to manage feedback online in real-time and thus close the information loop. Thanks to these technologies, the industry will produce high-quality products with more excellent reliability and consistency.
Optimized Processes and Improved Consistency
Automating tasks that previously had to be done manually not only saves time. It provides decision-makers with unprecedented insights on to base their strategies. Reducing manual labor frees up time so that researchers can focus on value-adding rather than administrative tasks. This allows them to reduce the number of iterations needed to validate the manufacturing process and enables them to cut costs.
Reduced Time-to-Market and Automating Late Phase Research
Automation contributes to more robust manufacturing processes and reduces delays. Many companies and contract research organizations (CROs) are today working on automating steps in Phase II and Phase III research programs. But so far, less emphasis has been on how to automate aspects of late phase research. However, these stages are also ready for digitalization; it just has to be done in different ways.
Cleaning the data in clinical trials is a time-consuming and expensive process. Much effort is needed to correct erroneous data on a Data Collection Tool (DCT). A DCT can cover data from Case Report Forms (CRFs), Quality of Life (QOL) questionnaires, or from patient reports and diaries. Typically, late-phase trials involve very large numbers of patients, which is a massive amount of data. Applying automation at this stage has a lot of potential to speed up the time-to-market for new drugs.
The manual process of cleaning and updating large amounts of data also has a high risk for human errors. The time and costs from data management staff manually working with potentially thousands of data pointsdo not seem feasible in the future. Automation helps create better quality data in less time.
Automation has a lot to bring to the pharmaceutical industry. The digitalization of this sector has been accelerated as a response to the CoVID-19 pandemic, and we are now seeing significant progress in a number of areas. This development affects every stage of pharmaceutical processes and brings a lot of new opportunities.
Some of the areas where automation is bringing important progress are the following:
- Shortening the Timeline for Clinical Trials With RPA
- Rapid Batch Releases with Automation
- Optimized Processes and Improved Consistency
- Process Analytical Technology (PAT) Helps Quality Assurance
- Reduced Time-to-Market and Automated Late Phase Research
As an increasing part of pharmaceutical processes, from research and development to testing and production, become automated and digitized, we will see more rapid progress in many fields. In a world facing numerous health challenges, the adoption of these technologies holds a lot of promise when it comes to solving the medical issues of today – and of tomorrow.