The Importance of Digitization in Biopharma

Automation in a vaccine lab is not enough 

The ultimate goal in the biopharma space, especially for vaccine manufacturers, is producing safe and effective therapies faster, and at a lower cost. Many professionals still believe that their competitive edge relies on technical improvement, when the reality is that implementing a digital data management platform can help achieve 40% gain in productivity. While automation can certainly cut costs and boost efficiencies to shorten the development timeframe, there’s only so much you can automate. Luckily, there’s more than one solution.  

A less evident one is digital transformation, where organizations re-evaluate how their data is captured, stored and managed. In fact, using this approach has the potential to improve far beyond the capabilities of simply digitizing workflows at the bench. 

With the amount of data hundreds of instruments and equipment generate every day, revolutionizing data management strategies is no longer a nice-to-have. Letting proper data management slide is always risky – not just for the study, but also for the organization as a whole – and even more so during COVID-19, when time is against us. Without the right IT infrastructure in place, processes are sub-optimal, preventing a lab from realizing its true potential.  

Your business is on the line 

Quality is another top concern among life sciences organizations, particularly vaccine manufacturers and third-party companies. A survey by Accenture revealed that more than 60% of contract organizations ‘struggle to get real-time inventory or manufacturing visibility’, while about 40% said they had no control over product quality. A lack of standardization in processes and technology, coupled with poor data management, get in the way of quick decision making and efficiency needed to make an impact in life sciences.  

Poor quality can start with data stuck in silos, or prone to error. Consider a typical scenario: the calibration data from a plate reader is noted in a paper lab notebook, then transferred to an Excel spreadsheet. But the decimal is accidentally moved over by one digit. As you can imagine, if this mistake is not caught early, it will have a cascading effect down the line, resulting in an unsafe product, such as a treatment for cancer, or a vaccine. Eventually, it’ll become evident that something has gone wrong, and the organization will have to launch an investigation, adding considerably to the project timeline and delaying patient access to much-needed medication. On top of that, the work will have to be redone, demanding personnel time and costly reagents.  

This case of simple human error can be avoided altogether by using an electronic system to extract the data automatically – this could be through integration or cloud technology, for example. Taking out the ‘human’ from ‘human error’ drastically reduces the chance of mistakes, encouraging a right-first-time approach that is traceable and fully-auditable – the perfect combination to circumvent compliance liability.  

Regulations are stricter than ever 

A poor data management layer can create safety and compliance risks, resulting in warning letters, withdrawal of market authorization and a damaged company reputation. 

Regulatory bodies have increased scrutiny on data integrity, possibly because the number of data integrity violations has increased considerably over the last decade or so. In 2018 alone, the Food and Drug Administration, or FDA, issued no less than 85 warning letters, and close to half of those were due to data integrity violations, including deviations not investigated, data not recorded contemporaneously, fully or accurately, and a lack of adequate access controls.  

It’s clear that something needs to change. In 2016, as a guideline, a consortium of scientists and organizations specified that scientific data should meet principles of findability, accessibility, interoperability, and reusability – the FAIR principles.  

Most companies believed that adding some more digital tools to their digital stack would be good enough.  Such an approach is counterproductive as it adds complexity, as well as interoperability and maintenance issues. 

If you don’t act now, you’ll likely fall behind your competition
 

The right approach starts with re-thinking the data strategy of the company. Leading firms, such as Lonza and Moderna, the latter of which is one of the forerunners for developing a vaccine against the viral agent behind COVID-19, have realized the potential of digitalization, and since implementing a holistic data management platform, have found it to be simple, streamlining their workflows and providing the speed and efficiency necessary to be at the forefront of the market. With the right IT infrastructure, organizations have reported that on average, they can gain 20% efficiencies in the lab, keep costs down, and ensure a quality product.  

The whole industry is moving toward biopharma 4.0: a holistic digital model based on real-time data and transparency that encourages collaboration, while preserving data-integrity and improving productivity. The first to leverage next-level innovations such as cloud enablement, integration, AI and machine learning, will gain a competitive advantage. Everyone would agree. But who is ready?

Such digital transformation requires a high level of expertise, starting with a rigorous data structure.
You will need to avoid half-baked solutions at any cost and implement a system that has been designed to scale with your business and streamline the whole data journey.
 

Looking for an expert to help you on your digital transformation journey? Look no further. Contact us today to see tangible results tomorrow.

References: 

Moderna, (2020). How building a digital biotech is mission-critical to Moderna. Available at: https://www.modernatx.com/sites/default/files/Moderna_Digital_WhitePaper_Final.pdf