Our customer Evotec is an internationally leading biotechnology company that is committed to advancing drug discovery and development. The company engages in pipeline co-creating and technology partnerships, offers specific solutions and products as well as provides CRO/CDMO services. Specifically, Evotec focuses on data-driven ‘precise’ disease understanding and early disease relevance to bring the probability of success up.
A key building block for this is a central platform for data reuse, for which OSTHUS, a Pharmalex company, has been commissioned to provide strategic advice and implementation.
A large company like Evotec has many sites, many teams, and many IT systems, all with their own specialties. The various specialist areas have first-class data systems, but these have limited semantic interoperability. Scientific projects are conducted across sites, teams, and systems. Large amounts of experimental data are generated. Scientists need to obtain this data in a consolidated, standardized, and aggregated form and in context with data from other data domains to efficiently gain insights. In order to get these data to the right place, many data flows have been created in the past. Evotec is a growing company, which leads to a further increase in complexity.
Machine learning and artificial intelligence have become important and require consistent data for reuse. Translational science requires data generated in one context and linked to data from another context. For both, it is essential that the data is standardized, well governed, and structured in a common data model.
A large company like Evotec has many sites, many teams, and many IT systems, all with their own specialties. The various specialist areas have first-class data systems, but these have limited semantic interoperability. Scientific projects are conducted across sites, teams, and systems. Large amounts of experimental data are generated. Scientists need to obtain this data in a consolidated, standardized, and aggregated form and in context with data from other data domains to efficiently gain insights. In order to get these data to the right place, many data flows have been created in the past. Evotec is a growing company, which leads to a further increase in complexity.
Machine learning and artificial intelligence have become important and require consistent data for reuse. Translational science requires data generated in one context and linked to data from another context. For both, it is essential that the data is standardized, well governed, and structured in a common data model.
The team started with documenting the current situation, pain points and wishes. Together with the customer, we brought together perspectives from across the company. High level use cases were defined and prioritized.
We developed a shared vision, agreed on project goals, and created a plan to reach these goals.
Now that we knew what was to be achieved, a future data and IT architecture was designed. Solution components were carefully selected by bringing in know-how, conducting market research and methodically guiding vendor selection.
The project is run in Agile mode to adapt to the circumstances and growing insights and to be continuously improved through feedback loops. PharmaLex / OSTHUS staff work closely with the client's stakeholders.
A single data access platform was created. Initially, a Minimum Viable Product (MVP) was released, from which the platform will be expanded.
The platform provides:
This is done with advanced technical components:
Some of the uses are:
Evotec will use a global platform for data reuse across the pharmaceutical R&D continuum and across different modalities. Data are standardized and structured in context with other data.
Data is generated and managed by subject matter experts in their respective fields. For data consumers and data managers, the data landscape is much less complex than before.
Technically, the new platform is relatively easy to maintain, can be easily extended to include new data types and new technical functions, and is scalable for large data volumes.
A single platform supports a unified way of working across all sites, while allowing each department to use the data they need in a secure and convenient way.
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