Data, Informatics and Analytics

Enhancing Value through Data

The explosion of healthcare and life sciences data promises tremendous value for society as a whole. Thanks to data and insights generated through advanced analytic methods, organizations seeking to discover, develop and deliver new therapies are creating value for patients in amazing and everchanging ways. 

Astellas is committed to being at the forefront of healthcare change - turning innovative science into VALUE for patients. With the explosion of data, innovative science isn’t just happening in the laboratories. Valuable innovation is increasingly being driven by data and analytics, and Astellas takes those insights further through a division dedicated to these capabilities: Advanced Informatics and Analytics.

We collaborate with our partners to create and enhance the value of every idea, molecule and therapy across all of our operations, from discovery to development to commercialization and beyond. This integrated approach helps us have the deepest understanding of disease, biology, modality/technology and the outstanding need for treatment.

Many organizations talk about a data and analytics capability. They use words like information and insights. But those aren’t enough to make a meaningful impact for patients. Data, informatics and analytics are already elevating Astellas’ entire value chain and business operations by: 

  • Optimizing operational efficiencies and enhancing decision making.
  • Enhancing the value of our existing products and pipeline.
  • Changing the way we create value.

Data, informatics and analytics underpin Astellas’ Strategic Plan 2018. We continue to heavily invest in the field, and we are structured to accelerate the product lifecycle, from idea to new therapy (medicinal, biological, genetic, cellular, digital, etc.). That is how Astellas and our partners will harness big data in healthcare and life science to create meaningful value for patients in amazing and ever-changing ways.

Examples of Data Analysis

Here are the examples of our activities using data, informatics and analytics. These examples are about diversifying bioinformatics activities, AI-driven drug discovery, and the utilization of real-world data in commercial activity.

1) Diversifying Bioinformatics Activities
In pharmaceutical business, the mission of bioinformatics is to discover knowledge that will accelerate drug R&D from huge biological data. Until several years ago, the objectives were to contribute to early phase research by conducting omics analysis to identify a drug target or elucidate a drug’s mechanism of action. Recent technological innovation enabled us to acquire a variety of data including images, texts, sensors, electronic health records, and so on. Furthermore, AI or machine learning technologies to analyze these types of data are becoming matured. Because of these situations, our bioinformatics capability is contributing to diverse fields beyond conventional bioinformatics, such as digital biomarker development from image data, optimization of research by using image or sensor data, contribution to research strategy planning by analyzing other company activity/scientific literature/patent information, and so on. 

Examples of Data Analysis1

2) AI-Driven Drug Discovery
Advances in science and technology generate various kinds of huge amounts of data every day in recent drug discovery research. Pharmaceutical companies use those data and the past drug discovery research data, such as the activity, physicochemical properties, pharmacokinetics, and safety of compounds, to explore and identify drug candidates. With the increasing needs for data-driven drug discovery strategy utilizing machine learning and/or AI, we in Astellas are applying cutting-edge technologies such as deep learning to practical applications, for instance, conducting applied research on efficient molecular design and highly accurate compound property prediction. We are also working on the integrated operation of robots and AI that automates specific experiments to improve the productivity of drug discovery research.

Examples of Data Analysis2

3) The Utilization of Real-World Data in Commercial Activity
Real-world data is used in order to prepare the commercial organization for a product launch in a new therapeutic area. Gaining an enhanced understanding of patient and physician dynamics allows the company to better formulate strategies and tactics. Longitudinal patient data is used to estimate patient population sizes and segment patients to inform reimbursement strategies and understand resource requirements. It is also used to investigate scenarios for life cycle management, including new indications and formulations. Time series and machine learning analyses are used to examine patient journeys and to estimate market potential. Natural language processing and social media listening are leveraged to identify the language patients use and the burdens they face in order to inform patient education materials. Real-world data enhances the data-based decision making within Astellas, driving more informed strategic decisions.

Examples of Data Analysis3


Messages from the AIA VP Head

Messages from the AIA VP Head

Nate Crisel
VP Head, AIA

New discovery and insight generation have long been cornerstones for companies in the life sciences industry. Astellas has long been committed to turning those discoveries and insights into VALUE for patients. Recently, with the data and information explosion in healthcare and life sciences, myriad new opportunities to create value are possible. We recognize these possibilities, not only for generating new discoveries, but for maximizing the potential of existing opportunities (regardless of whether internally sourced or externally sourced) to become valuable therapies for patients. Astellas has invested heavily to create world-class capabilities in this field, because we clearly see the role that data-derived knowledge plays in turning innovative science into VALUE for patients.


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