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Petter Moree06 Jul, 2020

Working apart, together

As discussed in a previous blog post, one of the key challenges is to develop a process for producing a vaccine for the COVID-19 virus as quickly as possible. As of June 30th, there are 178 vaccines and 258 treatments in development.

The key objective is to have a superior process understanding, specifically the knowledge management for a successful scale up and technology transfer of manufacturing processes to meet the demand of the vaccine and treatment. It is important to fully understand the critical material attributes (CMA), the critical process parameters (CPP), and how these are affecting the critical quality attributes (CQA) as well as the economical attributes, like titer, production time and cost.

Pharmaceutical development is highly reliant on collaboration, which can be strained by social and physical distancing requirements as we navigate COVID-19.  Cooperation is critical at all stages of the new product life cycle, from process development to clinical trials to commercial operations.

Process development

We see an increase in remote work, with digital platforms still enabling strong social participation among team members. Some data says that as much as 25 to 75% of the workforce in these areas are now working remotely when possible.

All the vast amount of data generated can be made accessible and trustworthy with strong data integrity from any location without going into the plants and labs daily. To enable this, establishing a data vault or a single source of the truth is key. Consolidating of data is the first step; the second step is the ability to generate information from the data which requires data models and context. For example;

  • 1.A process model with ability to find the batches and phases in operations, often the industry standards like ISA S88/S95 are used. This is essential for compliance such as data integrity, batch recording and analytics like golden batch etc.
  • 2.An equipment and system model--often referred to as a digital twin--which helps in educational, SOP, maintenance management and ability to build an overview of a process line for KPI like OEE etc.

Merck and Kristin O'Neill presented a use case on their ability to combine data from multiple systems and tools by building a digital platform where they are able to monitor systems remotely while still collaborating and remaining productive.


Shire, now Takeda, presented a great use case in 2016 on their ability to integrate disparate data sources in their process R&D environment into a single source of the truth which enables analytics, data integrity, and increased performance.


On average, it takes more than 12 years and costs billions of dollars to take a drug to the market, and this journey consists of several milestones where the key objective is to get a safe and secure new drug to as many persons as possible in shortest time.

Clinical trials

One of the more expensive phases of the bringing a new product to market is clinical studies. For example, AstraZeneca has partnered up with University of Oxford and hope to include 30,000 participants in their upcoming clinical trials Phase III. Imagine trying to manage that vast number of participants across multiple locations to quickly assess the safety and efficacy of the product AZD1222 in the same way as we want to manage physical distancing. We have seen use cases from Parexel, a leading CRO (Clinical Research Organization), who has presented how they are improving the studies in terms of patient direct and active tracing where data is collected from the participants and made available for recording, analysis and visualization etc.


This digital transformation from Parexel will simplify the research journey so that safe new treatments can reach patients quicker by allowing decisions to become real time and collaborative, moving from a phased and costly drug development process to a lean and adaptive procedure. By shifting from data collection to risk mitigation, from silos of activity to customer-centric, and from reactive to proactive, clinical trials can be conducted in a safer, more efficient way.

The connected worker

Similar technologies are also being used to monitor the workforce, with companies equipping workers with sensors so that they can monitor their heart rate, temperature, peripheral oxygen saturation (SpO2), respiratory rate, etc. This shows that digital transformation isn't limited to just machinery and processes but expands to the people who run them as well.


Commercial and clinical manufacturing

Lastly, there is a trend that innovative companies who are doing research and discovery on new products including vaccines often partner with CDMO (Contract Development Manufacturing Organization) and CMO (Contract Manufacturing Organization).

Groundbreaking innovators like Moderna with its mRNA platform are partnering with Lonza, one of the world-leading CMO companies. Their collaboration aims to have one billion doses available annually which would possibly meet the demand for 500 million people.

These corporations are striving for new ways to cooperate with each other, such as Moderna handling materials produced and Lonza supplying data. These crucial collaborations are enabled by digital technologies which allow firms to seamlessly share critical data between different entities, which helps ensure quality, transparency and reduction of cost.

We see new ways the life sciences industry needs to consume and use digital technologies such as analytics, knowledge management, remote work and collaboration amidst the COVID-19 pandemic.

If we are to successfully create and distribute a cure or vaccine, data must be accessible, understandable, trustworthy, and shareable ways we have never seen before.

Related Articles

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Defending Against an Invisible Enemy: Improving Safety Amidst COVID-19

How COVID-19 Will Shape the Future of Manufacturing

Petter MoreeGlobal Industry Principal, Life Sciences and Food & BeveragesPetter is the Global Industry Principal, Life Sciences and Food & Beverages. Petter has a M.Sc. in technical chemistry with a specialization towards chemometrics and data science.
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