True DX in the Pharma R&D Lab Defined by Enthought

Enthought’s team in Japan exhibited at the Pharma IT & Digital Health Expo 2022 life sciences conference in Tokyo, to meet with pharmaceutical industry leaders gathering for technological insight and to revitalize market growth. 200 companies exhibited across the 3-day in-person event, which drew over 6,700 attendees.

With digital transformation a headline theme, the show offered a great opportunity to engage with our target audience and showcase Enthought innovations that are accelerating novel discovery in the pharmaceutical industry.

We were honored to have Mr. Atsushi Hyogo of Daiichi Sankyo with us to present with Enthought’s Senior Executive, Dai Ike. Mr. Hyogo presented real world applications of digital transformation realized with Enthought technology and deep scientific expertise. He showed that true DX—and partnering with Enthought—is empowering faster scientific discovery and continuous innovation at Daiichi Sankyo and supporting their 2030 vision to transition a data-driven model of management.

Dai Ike framed the true value of digital transformation that Enthought brings to customers across Japan.

Our work makes it possible for scientists to expand the capacity of their work within their companies because we concentrate on three critical areas of improvement: process, technology, and people.

Altogether, Enthought changes the workflow and eases the workload within R&D labs, allowing iterations of testing to continuously be brought into analysis and lead to faster drug discovery.

Daiichi Sankyo, one of the top twenty pharmaceutical manufacturers worldwide, has experienced game-changing results by partnering with Enthought to transform R&D. Mr. Hyogo, former Director of Oncology, led the way in establishing change that pushed his lab into a higher range of performance. Sensing an impending crisis and the danger of falling behind the market, Daiichi Sankyo began a DX initiative, but they knew that they needed support from an experienced partner, and Enthought met all the qualities they sought. “Enthought is clearly the best DX partner for us because they are both technologists and scientists, and they have experience in the life sciences domain,” stated Mr. Hyogo.

DX is a core theme at the moment in Japan, and it has taken on urgency in pharmaceuticals that is unparalleled in other industries. As companies are evolving through similar problems, they face misunderstandings of DX by top management. DX is much more than commonly thought. It goes far beyond digitizing and organizing data to connect scientists with technologies for automating iterative testing and analysis. Most companies have no idea where or how to start. At Enthought, we are encouraged by how eager companies are to start on a DX journey and we have a program to jumpstart the work together.

True DX addresses the business ecosystem, and starts with the scientists in the R&D lab.

Enthought occupies a valuable and unique role in the DX conversation. Many companies use the term DX when they talk about shifting the traditional business relationships over to new platforms or applications, such as connecting patients and doctors through mobile technology, developing social media for healthcare companies, or new health management systems. We deliver change at the level of science. We change the way companies are doing science.

There are opportunities to continue to expand in the area of life sciences in Japan and deliver value to companies that struggle with the many demands for DX and are seeking one, reliable and proven partner, such as Enthought.

Contact us to learn more.

Share this article:

Related Content

R&D イノベーションサミット2024「研究開発におけるAIの大規模活用に向けて – デジタル環境で勝ち残る研究開発組織への変革」開催レポート

去る2024年5月30日に、近年注目のAIの大規模活用をテーマに、エンソート主催のプライベートイベントがミッドタウン日比谷6FのBASE Qで開催されました。

Read More

科学研究開発における小規模データの最大活用

多くの伝統的なイノベーション主導の組織では、科学データは特定の短期的な研究質問に答えるために生成され、その後は知的財産を保護するためにアーカイブされます。しかし、将来的にデータを再利用して他の関連する質問に活用することにはあまり注意が払われません。

Read More

科学研究開発リーダーが知っておくべき AI 概念トップ 10

近年のAIのダイナミックな環境で、R&Dリーダーや科学者が、企業の将来を見据えたデータ戦略をより効果的に開発し、画期的な発見に向けて先導していくためには、重要なAIの概念を理解することが不可欠です。

Read More

科学における大規模言語モデルの重要性

OpenAIのChatGPTやGoogleのBardなど、大規模言語モデル(LLM)は自然言語で人と対話する能力において著しい進歩を遂げました。 ユーザーが言葉で要望を入力すれば、LLMは「理解」し、適切な回答を返してくれます。

Read More

ライフサイエンス分野におけるデジタル化拡大の課題

研究開発におけるイノベーションの規模拡大は、ラボか…

Read More

ITは科学の成功にいかに寄与するか

科学と工学の分野においてAIと機械学習の重要性が高まるなか、企業が革新的であるためには、研究開発部門とIT部門のリーダーシップが上手く連携を取ることが重要になっています。予算やポリシー、ベンダー選択が不適切だと、重要な研究プログラムが不必要に阻害されることがあります。また反対に、「なんでもあり」という姿勢が貴重なリソースを浪費したり、組織を新たなセキュリティ上の脅威にさらしたりすることもあります。

Read More

Top 5 Takeaways from the American Chemical Society (ACS) 2023 Fall Meeting: R&D Data, Generative AI and More

By Mike Heiber, Ph.D., Di…

Read More

Life Sciences Labs Optimize with New Digital Technologies and Upskilling

Labs are resetting the tr…

Read More

From Data to Discovery: Exploring the Potential of Generative Models in Materials Informatics Solutions

Generative models can be used in many more areas than just language generation, with one particularly promising area: molecule generation for chemical product development.

Read More

The Importance of Large Language Models in Science Even If You Don’t Work With Language

OpenAI's ChatGPT, Google's Bard, and other similar Large Language Models (LLMs) have made dramatic strides in their ability to interact with people using natural language....

Read More