Increasing throughput and agility is key for Janssen Biotherapeutic Organization (JBIO) in delivering our pipeline while adding new cell and gene therapy modalities. New modalities add complexity while there is an increasing need to push more volume at a higher quality. Significant investments in key technology platforms along with digitization, automation, data science, and standardization provide the tools needed to meet the goal of delivering life-changing medicine to patients.

FAIR (Findable, Accessible, Interoperable, and Reusable) data are critical to scientific discovery. We have implemented a FAIR data platform for cell therapy research and early development by logically and semantically modeling biological entities to enable registration, inventory, and workflows. A further integration with the enterprise data lake enables semantic search with internal and external data sources beyond cell therapy.

Biologics drug discovery and development require joint efforts from many scientists with very specific knowledge of biologics. We are building an integrated Global Biologics Research Informatics Platform to ensure data quality and transparency, improve cross-function collaboration, and enhance data-driven decision-making. This platform will cover many aspects of biologics research from screening, registration, candidate selection, engineering, and optimization, with a focus on in silico prediction for developability and immunogenicity. The goal is to accelerate biologics drug design and development with combined in silico prediction and lab experiment data to enable decision-making through real-time data access with integrated systems. This platform will help Discovery Research to improve efficiency and productivity across the lifecycle of biologics innovation.

R&D is becoming more specialized and distributed with the explosion of new modalities, but legacy systems and manual processes aren’t designed to support complex collaboration. Organizations that work in modern software to orchestrate efforts across teams, automation, and partners make progress more quickly and efficiently - ultimately increasing chance or success and accelerating time to market. 

Pharma is experiencing a renaissance in science driven by innovations in cell and gene-therapy, omics, systems biology approaches and in technology, driven by economical/ scalable cloud computes, AI/ML products and more. In this discussion, we’ll focus on how this evolution can be supported by R&D data operations that function with a ‘team science’ mindset at the interface of technology, business, data-science and research functions, breaking organizational barriers to create business impact.

Protein expression and purification drives early-stage drug discovery of biomedical research with protein production being a major rate-limiting step. The next-generation of biologic therapies require increased capacity to produce high-quality, protein batches. Janssen is tackling this problem with an integrated platform combining automation, robotics, and a web-based Laboratory Information Management System (LIMS) with the goal of increasing protein production capacity by 200-300%.

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.

The development of innovative biologics medicine is complex and costly. Pfizer uses the ‘Begin with the End in Mind’ assessment strategy to screen molecules that can succeed in late-stage clinical development. To support the strategy, Pfizer Research and Digital teams build informatics and data platforms to streamline assessment design, experiment data capture and analysis, and enable comprehensive reporting and advanced analytics.

In March 2021, Dotmatics joined forces with Insightful Science, holder of best-of-breed desktop software such as Geneious, SnapGene, and GraphPad Prism. This combination gives biopharma organizations the compelling benefits of centralized data management provided by Dotmatics, while still allowing end-users to innovate in tools with which they are familiar and productive. This presentation will highlight integrations amongst these applications to accelerate digitalized drug discovery research.

At Novartis Research 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. This platform will be comprised of a central data management solution integrated with a set of diverse tools and applications ensuring seamless integration into NIBR’s existing IT landscape, helping the uninterrupted flow of data within the organization, and providing the data layer for data science and AI-based applications.

BioTherapeutics R&D requires a system that can accurately and timely register biological samples and manage their inventory. The ideal system should be highly configurable to accommodate the great diversity of biological samples, including types of samples and their associated properties. The system should be integrable with workflow systems or instruments. Boehringer Ingelheim has implemented a global system to support biological sample management in both human pharma and animal health R&D.         

Through embedding digital ways of working, AstraZeneca continues to drive scientific innovation and streamline working practices to deliver life-changing medicines to patients. Digitalization of early drug discovery requires that we leverage our technology platforms, provide frictionless access to data, and expand AI/ML insights to augment decision-making. This presentation discusses the inherent challenges and enabling activities in building a digital capability that accelerates future drug discovery.

Data is the lifeblood of any enterprise and laboratory in our digitized world. We have automatized workflows that support fulfilling analysis requests and answering scientific questions about the incoming biological or chemical samples. But what about all the data generated in this process? How can we obtain extra insights by deeper analyses or machine learning and artificial intelligence methods using this data? This presentation shares our vision, work, and solution based on cloud computing and open-source tool and standards, which support us obtaining more value from the analytical data in early pharma research.

In a large organization such as Sanofi, to better leverage predictive models developed by data scientists it is essential to have an operation model that allows us to move from “experiments” to impactful models leveraged by any. Sanofi is investing in developing key approaches that enable global operationalization of our predictive models with the goal of accelerating the discovery of large molecules.

The speed at which vaccines were launched in the past year has raised the bar, demanding accelerated innovation. The biopharma industry has leveraged innovative technologies ranging from the use of GPU-powered transformer models, federated learning platforms, and data fabrics, and has disrupted conventional models fueling co-innovation. AI is being used to design precision-engineered drugs and innovative strategies involving the use of computer vision are being leveraged to identify lead candidates for multiple diseases. Specific process analytical technologies (PAT) and intelligent supply chains are being developed for the manufacturing of highly complex and time and cost-intensive cell and gene therapies. While cloud-native platforms and high-performance computing are hyperscaling innovation, there are still significant concerns around data security and data ownership.