During our 2nd annual SmartLabs Congress event, Angelika Fuchs (Lead Discovery Informatics Data Automation at Roche) gave an insightful presentation discussing the future of connected labs. Doctor Fuchs is leading a global Data Automation team in Roche Pharma Research and Early Development Informatics with the mission to catalyse digitalization for Roche’s preclinical labs with innovative and efficient digital solutions automating data capture, processing, analytics and upload into FAIR data systems. This article presents a condensed overview of her presentation, focusing on the basic building blocks of 21st-century lab workflows and how digitally augmented drug discovery opens the door to new possibilities and innovation.
Digitally Augmented Drug Discovery
Drug discovery faces a fundamental challenge; the cost of developing a new drug continues to increase over time. Researchers and companies are experiencing pressure to increase productivity and efficiency in R&D labs while at the same time still enabling innovation and flexibility, which is needed in a creative research environment. Lab automation and digitalisation are key factors in overcoming this challenge, allowing standardisation of processes and minimising manual and tedious work. Digitally augmented science allows more molecules to be tested in parallel and frees scientists to focus on science and innovation.
The Foundation of Smart / Digital Labs:
Creating the labs of the future starts with data. Modern experiments often create huge volumes of data; labs routinely run experiments on hundreds or thousands of samples. The ability to run research experiments, organise results and access information quickly is vital to the pharmaceutical industry. Additionally, in many cases, labs need to give a third party, for example, a regulatory body, access to information about samples and processes across the entire product’s lifecycle.
Modern data management systems are the basis to store data efficiently and accurately and making them accessible, which is necessary to identify new drugs and enable them to get to market or testing faster. In pharmaceutical R&D, the adoption of Laboratory Information Management Systems (LIMS) and Electronic Laboratory Notebooks (ELN) have helped labs achieve these targets. At the same time specialized data management systems such as molecule registration systems or study and sample registration systems are being put in place to ensure truly FAIR data.
LIMS automate and simplify the process of gathering information and scheduling tasks in laboratories. These systems allow users to track sample data from the first time they are used in the lab, through experimentation, and finally to the reporting stage. LIMS can automatically collect and sort information such as inspection number, batch material, times, dates, test results and more. LIMS are designed to catch human errors and guarantee that lab protocols are being followed correctly. As LIMS become more widespread, they are becoming mandatory for compliance with many country’s government regulations.
Electronic Laboratory Notebooks are often conflated with LIMS. However, there are some key differences. Most importantly, ELNs tend to be more malleable and adjustable, allowing more flexibility for data capturing across workflows and projects. Generally, LIMS are best suited for fixed repetitive tasks on structured data, while ELNs are more applicable for free form experimental testing and development.
Dependent on their work, labs need to carefully choose how to best capture and manage their data. For a R&D organization overall, however, it is important to ensure all data management systems can easily be extended and integrated into a seamless data and system landscape. To match the requirements of modern R&D, both commercial as well as in-house system need to ensure they are being built with the vision of a modular system architecture in mind where components can be exchanged and extended easily as the underlying research processes evolve.
Connected Labs and Data Transportation:
Many companies face issues stemming from different data formats of lab instruments, disconnected software systems and unintegrated workflows. Too often data is locked in proprietary formats or isolated in systems that are specialized towards a certain user group, hampering communication between individual labs, sites, regions, and external collaborators.
A critical aspect of digitalization in pharma research is the optimization and automation of workflows ensuring automated data flow, data collection, and data sharing. Workflow orchestration platforms promise great benefit in this area and start to be used more widely. With such platforms, digital twins of lab workflows can be created that automatize manual data transfer tasks and help identifying bottlenecks along the workflow execution. Business Process Model and Notation (BPMN) is starting to be adopted as a standard in this space.
The key for connected labs is that they enable scientists to work together in real-time on a scale that hasn’t been possible before. Cloud computing and IoT technologies are enablers for this goal but the uptake of such technologies will only happen if the actual users are put in the centre of any digitalization strategy. Carefully designed change management activities need to support the introduction of any new technology in order to achieve the goal of fully connected labs over the next decade.