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实例探究 > Gilead partners with Benchling to improve large molecule bioprocess development

Gilead partners with Benchling to improve large molecule bioprocess development

技术
  • 平台即服务 (PaaS) - 数据管理平台
适用行业
  • 药品
适用功能
  • 产品研发
服务
  • 云规划/设计/实施服务
挑战
Gilead, a global biopharmaceutical company, was facing challenges in managing data across its process development teams. The teams were using multiple systems and databases, managed through a mixture of email, Excel, and an on-premise electronic lab notebook (ELN), which resulted in data silos within and across teams. This made data capture challenging and required time-consuming manual handoffs between teams. Furthermore, aggregating data and conducting trend analysis was difficult and time-consuming, leading to less data-driven decision making.
关于客户
Gilead is a global biopharmaceutical company that has pursued and achieved breakthroughs in medicine for more than three decades. The company is committed to advancing innovative medicines to prevent and treat life-threatening diseases, including HIV, viral hepatitis and cancer. Gilead’s Biologics team is focused on supporting clinical manufacturing and process development to advance the development of Gilead’s novel therapies. The company has between 10,000 and 50,000 employees and is based in Foster City, California.
解决方案
Gilead partnered with Benchling to improve scientist productivity, collaboration, and access to data insights. Benchling's cloud-based platform allowed for data unification, which paved the way for more efficient collaboration. The platform also enabled standardized data capture and provided an intuitive interface, which increased scientist productivity. Furthermore, Benchling made it easier for Gilead scientists to aggregate data and spot trends, enabling more data-driven decision making.
运营影响
  • Data unification on a centralized platform paved the way for more efficient collaboration.
  • Scientist productivity increased with standardized data capture and intuitive interface.
  • Clear insights enabled more data-driven decision making.
数量效益
  • 2x improvement in ease of data sharing within and across teams
  • 63% reduction in time spent on data capture, search, and collection
  • >2x improvement in ease of study review and forecast approval

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