Early research and development units in the Pharmaceutical industry have scaled their throughput by creating or leveraging several specialized units or laboratories (including external partners or CROs) that each deal with a particular step in the drug discovery process. This in turn creates the need for advanced planning and tracking of those activities, i.e., by capturing operational data, from idea to result. Due to the increasing level of automation in the specialized laboratories, a robust yet flexible information flow between different units is essential to leverage the potential of lab data automation. This talk will introduce a ‘Scientific Operational Data management platform’ for the life science research space and demonstrate how it can digitally augment the innovation process by enabling just-in-time operations and supporting data-driven decision-making.

Development Sciences at AbbVie has embarked upon a digital transformation. A use-case driven approach, focusing on the key scientific challenges that can be addressed through improvements to the data landscape has been taken to drive this transformational effort. One of the initial use cases driven to completion resulted in the automated creation of a parameter map for impurities determined by HPLC analysis in drug substance development. Through the definition of a future state and leveraging three existing platforms in use by scientists at AbbVie, the team created an automated workflow combining data from automated lab reactors and offline chromatograms. Review of the data was completed in a curation platform prior to publishing of this information to a data warehouse for web-based visualization.

The iHEX system bridges the gap between conventional chemical and biological laboratories which are static and usually only one setup matches one work process. iHEX adapts to a wide variety of work processes found in both worlds that can be executed on the installed devices. This maintains full efficiency because of the flexible layout. The innovative hexagon design of the tables enables a new way to form your lab. Changes in lab architecture can be done simply by attaching hexagonal static and mobile iHEX elements with the relevant device to form a new working process – iHEX takes care of the workflow process management. iHEX is an interface between the users, devices, control software and the infrastructure itself while it provides for the physical connection between iHEX elements and devices on top, inside or even function integrated combined with the necessary software infrastructure.

LabTwin is creating the next generation of digital lab tools for the lab space, starting with the world's first voice-activated lab assistant. With LabTwin, scientists can collect data, access information, manage experiments and streamline documentation simply by speaking. Using voice recognition and machine learning technology, LabTwin’s smart assistant simplifies data capture, structures valuable information, and integrates with lab instruments and informatic systems.

With rapid advancement in automation across all industries, the pharmaceutical industry has been challenged to scale appropriately with the incorporation of AI to help discover new treatments for unmet medical needs. While automation itself has a strong place in the pharmaceutical industry, there is very little software that supports the complexity that each company requires. The Neurocrine solution is intuitive, user friendly, and accurate. Software simplifies the steps required to control robotics, integrate instrumentation, increase automation, accelerate cycles, and provide accurate data to enable scientific decisions.

No matter where you are in the automation and integration process for your lab, learn more about the essential questions you need to answer when preparing to make decisions on investing in software, hardware, data, and AI solutions. Automated and integrated laboratory solutions should be intuitive, easy-to-use, and fit your specific environment, processes, and equipment. This, with AI, will give your company the flexibility to grow and scale for the future.

The process of developing new therapeutics is costly and time-consuming, and generates considerable amounts of data from all of the associated lab experimental test results. A key goal of digital transformation is to better leverage this data, guiding scientists in their research through AI and machine learning. BIOVIA latest informatics solutions utilize a knowledge graph-based data model for experimental data, which enables scientists to gain unique insights based on existing work. We will discuss examples such as HPLC column selection and targeted querying for novel substances, and show how this technology has wide applicability across the lab. Now instead of using generic starting points, scientists can let the data do the work, guiding new research while increasing efficiency and productivity.

The “The Lab Of The Future” is a place where scientists can step around the mundane aspects associated with having a lab—that is, planning, performing maintenance, scheduling, and preparing—and get straight to the process of experimentation. This requires that all of the aspects of a lab, from automated devices to research informatics tools are seamlessly integrated and streamlined, to increase efficiency and speed to generate new research insights. In this talk, I will introduce you to a digital lab automation platform called AutoLab, that we have developed at Roche and that is truly transforming the way our scientists design, share and execute lab workflows, getting us closer to the Lab Of The Future dream. I will present the concepts behind the platform, how it is solving common lab automation problems and how an open and collaborative design has democratized access to digital capabilities in our research laboratories.

We report on advances achieved by the introduction of next-generation laboratory automation systems in our core chemistry and hematology laboratories including faster turnaround time, improved patient safety through positive identification, and streamlined process flow. We also describe the implementation process using existing laboratory space and we discuss IT challenges. 

In 2020 I created 2 babies, a flesh one and an electronic one, and only now with some distance do I realize how much they have in common. I would like to share our experience with this particular data and system migration, what went well and improved and the lessons learned with this project.