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CASE STUDY:

Pharma Giant Accelerates Drug Discovery Pipelines with Agile R&D Data Platform

Take-Aways

Greater flexibility in cloud infrastructure enables researchers to use a broader range of toolkits and technologies so they can make better and faster data-driven decisions

Faster prototyping of analytics applications and the underlying technology stack allows scientists both inside and outside the company to work more efficiently

Company

  • Pharmaceutical

  • Europe HQ

The Business

Based in Europe, one of the 10 largest pharmaceutical companies in the world researches, develops and manufactures innovative pharmaceutical medicines, vaccines and consumer healthcare products.

Challenges

As the world confronts the COVID-19 pandemic, the Global 500 pharma is intensely focused on innovating vaccines and treatments to lower the impact of the coronavirus and save lives. Drug discovery, however, is a complex process that requires huge amounts of data throughout multiple stages of research. Hundreds of drug formulations need to be created to take one successfully to market, because only 3% of small molecules entering human studies actually become medicines.

“We are expanding our data capabilities to support the new biopharma company, and evolving into a hybrid, multi-cloud platform is one of the many steps that we are taking to be future-ready.”

At the pharma, all of this data processing occurs on an R&D data platform, which centralizes, curates and rationalizes research data from the company’s 10,000+ scientists around the world. The data is used to drive strategic business value, such as through standardization of clinical trials, genome-wide association study analysis, and processing of real-world scientific evidence.

In 2020, the pharma began preparing to separate into a biopharma company and a consumer healthcare company. It needed to expand the R&D data platform’s data and computational capabilities for the biopharma, so it could better enable large-scale scientific experiments and bring complex specialty medicines to patients faster.

“We are expanding our data capabilities to support the new biopharma company, and evolving into a hybrid, multi-cloud platform is one of the many steps that we are taking to be future-ready,” said the company’s DevOps engineering manager. “Our key focus will still be making data-driven decisions better and faster.” Greater data capabilities were especially needed for research related to immunology, genetics, and cell therapies, which often require big data analytics, artificial intelligence and machine learning.

Moving from Swarm to Kubernetes

The pharma first began enabling its container platform in 2017 with multiple POCs, ultimately deploying Mirantis Kubernetes Engine (formerly Docker Enterprise) withSwarm orchestrationonto on-premises infrastructure. Due to many academic partnerships, acquisitions, and industry collaborations, infrastructure included a wide range of systems and platforms, andMirantis Kubernetes Engineprovided the flexibility they needed to support the diverse infrastructure. After achieving platform stability, they began onboarding data pipelines and user applications, including various web services.

By 2019, with applications already deployed in production, the company began evaluating Kubernetes orchestration. They enabled it through Mirantis Kubernetes Engine and decided to make Kubernetes their de facto orchestrator.

That year, the R&D technology team also began offering edge nodes on demand to provide self-service custom containers with seamless multi-cluster communication. Scientists use the secure, isolated sandbox environments for rapid prototyping of new software tools, data analytics solutions, and hardware emerging in the data science field, so that they can be approved for use.

By 2020, the company migrated all of its microservices-based applications and data pipelines to container platforms, where they currently run in production.

“We have made the container-first approach as an architecture standard across R&D technologies at our company,” the DevOps engineering manager said. “We also started deploying our AI/ML training models onto containers, and all this work is happening on our (Mirantis Kubernetes Engine) platform.”

Transitioning to Hybrid Cloud

In 2020, the pharma began expanding their R&D data platform beyond on-premises infrastructure. Utilizing Docker images as immutable artifacts in their build process made this flexibility possible.

“As part of our R&D platform’s hybrid, multi-cloud journey, we started enabling container and Kubernetes-based platforms on public clouds,” the DevOps engineering manager explained. “Now, going into 2021 and the future, enabling our R&D users to easily access data and applications in a platform-agnostic way is very crucial for our success,” he said.

Results

Early in their container journey, the pharma experienced clear benefits with Mirantis Kubernetes Engine. “By migrating all our web services into containers, we not only achieved horizontal scalability for those specific services, but also saved more than 50% in support costs for the applications which we have migrated,” the DevOps engineering manager said.

Additionally, with edge nodes on demand, prototyping has become significantly faster. Some tasks that previously required 3-6 months now take only a few minutes.

With a hybrid, multi-cloud platform in place, scientists at the pharma now have greater flexibility to conduct large-scale experiments within a world-leading data and computational environment, using a broader range of toolkits and technologies. The company has successfully implemented containers for artificial intelligence and machine learning and is now looking to use MIrantis Kubernetes Engine to securely deliver virtualized data in containers or Kubernetes volumes to scientists on demand.

Challenge

  • Expand data capabilities to support computation-intensive research, including big data analytics, artificial intelligence and machine learning

  • Accelerate prototyping of new data science tools, which was taking 3-6 months and slowing down drug discovery and clinical trials

  • Provide agile, scalable R&D data platform used by scientific teams worldwide

Solution

  • Hybrid, multi-cloud R&D data platform based on Mirantis Kubernetes Engine enables scientists to access data and applications in a platform-agnostic way

  • Implementing big data analytics, artificial intelligence and machine learning in containers provides efficient resource utilization for computation heavy workloads

  • Edge node on demand platform based on Mirantis Kubernetes Engine secures and isolates research environments for scientists to create prototypes

Results

  • Greater flexibility in cloud infrastructure enables researchers to use a broader range of toolkits and technologies so they can make better and faster data-driven decisions

  • Faster prototyping of analytics applications and the underlying technology stack allows scientists both inside and outside the company to work more efficiently

  • Horizontal scalability and 50% reduction in support costs for web services migrated to containers

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