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Is Your Company Ready for Digital Transformation and Biopharma 4.0?

May 19, 2020

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.

digital transformation using data analytics rv

Tools that support advanced data analytics, including multivariate data analysis, can help your company succeed with digital transformation and biopharma 4.0.

Whether it’s embracing the principles of Quality by Design (QbD), incorporating process analytical technology (PAT) or optimizing your supply chain, data analytics plays a significant role in fostering digital transformation in the biopharma, healthcare and life science industries.  

Consider these statistics:

 Companies use only 4% of the data they have available

 Only 20% of biopharma companies are digitally mature, whereas 80% are in the early stages or still developing

■ The IDC estimates that by 2024 over 50% of all IT spending will go toward digital transformation

■  Across all industries, 70% of companies either have a digital transformation strategy in place or are working on one, according to a Tech Pro Research survey

■  The 2020 State of Tech Spend report showed that digital transformation was expected to be the top budget item for IT expenditures in 2020.

 

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What Is Digital Transformation?

Digital transformation is the integration of digital technology into all areas of a business, fundamentally changing how companies operate, typically with a goal to make processes more efficient or effective.

Digital transformation can involve many different technologies but often incorporates areas such as cloud computing, the Internet of Things (IoT), big data, advanced analytics or predictive analytics, and artificial intelligence (AI). It goes beyond just changing business processes, and requires a careful adjustment to corporate culture and priorities to be successful.

For some companies, digital transformation is focused on innovation, for others, on efficiency. Companies that take an agile approach to digital transformation are able to capture more value from their data in order to improve processes, reduce costs, and become more efficient and ultimately more profitable.

Other companies focus on using digital transformation in order to create disruptive products and services, build capabilities, and create a differentiated ecosystem. The one thing we know for sure is that data is the common denominator in digital transformation across commercial, operational and scientific areas of any business.

From Supply Chain to Process Improvements

Digital transformation offers a mechanism for companies to revise their business models, to improve production processes, and even to design and discover innovative drugs and treatments faster by using artificial intelligence to screen compounds. From process automation to using data analytics in clinical trials, leveraging digital transformation helps to create more efficient processes as well create services that fully engage and provide value to key stakeholders.

In a PTC survey of executives across verticals, the top benefits of digital transformation were found to be:

survey resuls ptc sartorius

Supply Chain Improvement

Businesses rely on supply chain planning to steer the movement of goods, services, and information throughout the value chain in an optimal way. Taking digital transformation to the supply chain level helps companies improve their production processes, forecasting and profitability.

Digital technologies enable companies to utilize algorithms, machine learning, and AI to balance supply with demand, and automate purchasing and inventory management. But to get there, companies must have fundamentals in place: processes, organization, governance, data quality, performance metrics and employee buy-in.

End-to-end biopharma supply chain visibility is a major challenge and digital technologies have not been fully embraced yet. However, without having access to the best tools and data, how can the best decisions be made about the products that directly impact the health of customers? Multivariate tools (like SIMCA and SIMCA-online) can be used to improve operational functionality. Whether it’s for an upstream fermentation processes or a final fill-and-finish line, being able to pinpoint non-obvious issues that cause production or final product loss is critical when it comes to ensuring the supply of medicine.

Digital Analytics for Process Improvement

Supply chain improvements rely on having quality data and robust processes to manage the data in useful ways. Using data analytics to better understand the relationship between raw material variability and process performance during manufacturing can have a huge impact on critical quality attributes and help manufacturers control their processes.

In many biopharma companies cell culture media is outsourced to external suppliers. Cell culture media supports cell survival and proliferations, as well as cellular functions, meaning the quality of the medium directly effects the research results, the production rates, and ultimately the treatment outcomes. It is essential therefore, that companies ensure that the quality of the media is always high and formulations of the incoming lots don’t vary.

Multivariate data analytics provides an efficient method to map out process parameters and cell culture media characteristics to specific quality attributes, allowing manufacturers to adjust production processes in a consistent way to ensure product efficacy and quality.

An example of this is determining how amines or metal ions might affect a cell culture. 

two practical examples with SIMCA

Read more: Discover how Janssen Biologics identifies how lot-to-lot variation of cell-culture media influences end-product quality.

Predictive Process Control

Another area where digital transformation helps biopharma companies improve manufacturing is using the vast quantities of data generated from bioprocessing to help predict (and prevent) future process deviations. By using advanced data analytics to create accurate models of cause and effect from past production runs or batches, companies are able to create models for how future batches should operate, predict outcomes based on key performance indicators and set process parameters.

For example, Janssen Biologics builds real-time and predictive monitoring models allowing them to forecast processes and detect process faults before they happen. Since the program started, more than 15 unique models have been built and over $1.5 million has been saved. Predictive models have allowed Janssen to save on time-to-patient, cost of materials, and operational planning —ultimately maximizing efficiency and profitability.

janssen monoclonal antibody

Read more: Use PI system and SIMCA online to build a Real-time Multivariate monitoring System

Continuous Process Manufacturing

From quality by design (QbD) to continuous process manufacturing, regulatory agencies have shown their support and indeed promotion for the benefits of digital transformation in the pharma and biopharma space. The FDA encourages manufacturers to adopt continuous manufacturing using advanced multivariate data models, because it improves process control, reduces variability and improves product quality and consistency.

From a business perspective, continuous manufacturing promises to reduce costs and help products get to market faster. Better control means improved product quality and more standardization, making it easier, for example, to develop and manufacture biosimilars. It also becomes the foundation for a robust quality by design (QbD) strategy.

One example of how data analytics tools improves continuous processes within the biopharmaceutical industry can found in continuous downstream processes. An industry challenge when it comes to continuous chromatography steps is risk of changing behavior in each cycle.

Boehringer Ingelheim uses multivariate data analysis to detect column-to-column variations where monovariate analysis’s detected no significant results . MVDA also helped to reveal batch-to-batch variations where protein A elution peaks show no significant variations.

Looking into the future economic pressure, flexibility, and real time release  (RTRT) considerations will be the driving force to implement continuous manufacturing strategies.

monovarite vs multivarite analysis case study example

Read more: Capturing the value of Continuous Bioprocessing through MVDA

What Does the Future Look Like?

In a survey report, “Survey finds biopharma companies lag in digital transformation,” Deloitte points out several key ways that life sciences and pharma companies will incorporate digital transformation in the future:

■  Aggregate and derive insights from a vast array of stored data
■  Operate smart and connected devices and products that furnish automated and pervasive screening and testing
■  Delivering just-in-time drugs and other personalized therapies

“Although life sciences companies today may already be doing some of this, the future will focus on these competencies to a much greater extent. Investment in digital technologies and the organizational transformation needed to realize their promise is critical for not only success but for survival,” the Deloitte report says.

They key to a successful digital transformation lies in being able to use data in robust and useful ways to create forecasting models, manage processes, and make decisions based on past events and predictions.

Digital transformation is no longer something you can consider to be part of a future plan, but must become an essential initiative that informs every element of your organization.

This was part one of a three-part series on digital transformation. Subscribe to our blog to so you don’t miss the next one.

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Want to Know More?

Find out more about how data analytics supports digital transformation in this recorded webinar.

The Use of Multivariate Models in a Regulated Environment Within Continuous Manufacturing for Pharmaceutical Products

Continuous manufacturing is one of the key digital transformation trends within the pharmaceutical industry, both for the production of ‘classical’ drugs as well as large molecules. The regulatory bodies such as the FDA see the promise of these processes and actively promote companies to implement this manufacturing strategy.

Watch Recorded Webinar

 

Topics: Pharmaceutical manufacturing, Continuous Process Monitoring, SIMCA-online, Digital transformation

Tiffany McLeod

Written by Tiffany McLeod

Tiffany McLeod is the (Bio)pharma Market Manager within the Sartorius Stedim Data Analytics marketing team. She has been employed by Sartorius since 2017. Within this function Tiffany acts as the teams’ subject matter expert for life science market trends and requirements. She also works to addresses and develop data analytics solutions to solve industry challenges. She is passionate about biopharma 4.0 and helping businesses pursue digital transformations. Tiffany holds a degree in Bioengineering and Bioinformatics from the University of California, San Diego.

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