At NIBR Biologics Center we aim to digitalize our biologics discovery processes and workflows to improve and speed up decision-making and to support the digital transformation of our organization. We aim to achieve this by implementing a state-of-the-art data and workflow platform, a central data management solution integrated with tools and applications ensuring seamless integration into NIBR’s existing IT landscape, helping the uninterrupted flow of data within the organization.

Like all R&D organizations in other top 20 biopharmas, AstraZeneca R&D is proceeding to our next-generation digitalization. However, the fragmented nature of early research brings additional challenges to the IT strategy. Morris will to share AstraZeneca’s approach, progress and lessons learned on how we modernize our ELN, LIMS, sample management, workflow management, in-lab data management, etc., and keep the balance between enterprise synergy and unique scientific needs.

For Research Informatics and IT professionals, building an integrated data platform with FAIR data is challenging, but these challenges are dwarfed when it comes to convincing the users to ensure that the right data is captured for downstream use.  We explore these change management challenges and potential solutions.

Quality and consistent biotherapeutics data is needed for biologics candidate selection and AI/ML. The biotherapeutics organization currently has multiple systems of record for management of biotherapeutics screening and assay data and different processes per site. Therefore, it has been very challenging for scientists to compile data packages to make informed decisions for selecting drug candidates, comparing molecules generated by different platforms/groups, and applying AI/ML technology for in silico prediction and drug development. We will show how we are simplifying the biotherapeutics data ecosystem and providing a single data capture experience with clear, consistent workflows across discovery biotherapeutics sites.

We are building an integrated Global Biologics Research Informatics Platform to improve cross-function collaboration and enhance data-driven decision-making in Takeda. This platform covers many aspects of biologics and novel modalities research from screening, registration and engineering for candidate selection and optimization. In silico immunogenicity and developability prediction platforms are also used to help scientists to reduce lab workload if possible. The goal is to enable scientists to make quick data-driven decisions through real-time data access with integrated systems. 

This talk introduces a simple and flexible framework that can be used to standardize and automate the digitalization of drug discovery experiments. The framework is centered on the idea of creating generic "research entity" data objects that correspond to the different types of elements involved in a lab experiment (cell lines, plates, molecules, batches, etc), define standard "actions" that can be performed with each of these entities, connect them into reusable "action blocks" that can be connected to create a digital-twin of a drug-discovery "experiment".  See an example of this framework in practice in the AutoLab platform, Roche's digital lab automation platform.

Key to scientific communication and information dissemination workflows surrounding the iterative drug discovery process are standardized methods for the unambiguous understanding of new molecular entities and efficient downstream data analysis capabilities. This presentation will reveal several new methods and intuitive interfaces Amgen has developed for the visual encoding and analysis of complex biologics and RNAi modalities.

The development of innovative biologics medicine is complex and costly. This session will explore informatics tools and methods used by Pfizer Global Biologics R&D to advance digitization in drug discovery.

Managing research data, and making it available for reuse by others in line with Open Research & FAIR principles, is a major issue for research-intensive organisations. In this case study we will outline an institute-wide review of data management, carried out at The Franklin. We will describe the processes involved in the review of current services, the learnings, and next steps.

Generate Biomedicines’ goal of leveraging machine-learning powered generative biology to discover and develop new drugs faster and cheaper is supported by an integrated and cohesive R&D data platform. This platform creates a common source of truth for the research organization with robust and nimble data modeling capabilities, flexible integrations with third-party software, and a core suite of powerful tools designed to streamline research and maximize the value of experimental results. FAIR to its core, this integrated data platform leverages industry best practices and lessons-learned from a talented team of informatics engineers to form the critical foundation of a unique company with equal parts “bio” and “tech”.

Digitalization is recognized as a key enabler in pharmaceutical R&D, yet the fully digital lab is in most places still a vision on paper and slides. The talk will summarize key digital capabilities most labs still need to evolve further, discuss realistic timelines to reach fully digital ways of working within and across labs, and present opportunities to speed up the path towards the vision of a fully digital lab on the example of R&D labs in Roche Pharma Research and Development.

Data escape from the wet lab via different modalities, ranging from sneakernet to automated data capture systems. We will present a decision tree to help wet lab scientists and IT decide how to get raw data from the laboratory into computational spaces in ways that support FAIR principles.

Successful FAIRification of lab data presents well-known challenges due to the nature of experimental workflows. We will present recent efforts in AstraZeneca early research to Digitize in lab workflows. We will discuss our work from policy, software selection, and solution implementation including obstacles we have faced.