ANALYTICAL & LABORATORY EQUIPMENT
to increase effi ciencies and cut costs.
LIMS: GENERATING DATA FROM EVERYDAY ACTIVITIES
LIMS gather information from many processes digitally across the lab, such as the dates samples are sampled, received, or completed. Logging this data for every sample provides a wealth of information that can be used to make the lab more effi cient. Obtained data can be used in many ways – like for data mining –
allowing the user to gain additional insight on
lab performance indicators such as the number of samples
Fig. 2. LIMS data analytics showing univariate analysis of the variables (left) and the results evaluation (right)
run over a given time frame. Furthermore, users can easily identify bottlenecks, allowing them to fi x issues, and, ultimately, reduce costs. T e LIMS data goes well beyond samples and can include aspects from training records through to unstructured data recorded in electronic analyst notebooks. T e collected data can be broadly grouped into three distinct categories: lab operations, system administration and scientifi c insights. T ese combined areas lead to a highly detailed data set ready for further analytics.
ACTIONABLE INSIGHTS WITH DATA ANALYTICS Data analytics solutions can turn data collected by LIMS into actions. And to simplify matters, some LIMS have data analytics integrated – meaning that there is no need to export data, and data governance is maintained. Automated data analytics can assist with two main areas: business intelligence (BI) and machine learning (ML).
BUSINESS INTELLIGENCE T e ability to rapidly identify operational and administrative bottlenecks is essential to the smooth running of the lab. BI dashboards are tools that enable lab managers to gain a deeper understanding and turn data insights into actions. Depending on the LIMS used, diff erent business intelligence capabilities will be available. One of the crucial aspects of the biopharmaceutical lab, for instance, is
ensuring a suffi cient supply of reagents and consumables. Running out of these vital components can cause signifi cant delays, forcing labs to miss project deadlines. Some LIMS come with a stock overview dashboard (Fig. 1), allowing users to view any consumable’s location and availability – and even order new stocks. T is information leads to a better understanding of consumables usage and distribution to help manage lab workloads. Stock overview is just one example, though – dashboards cover many other aspects critical to the business, including instrument uptime, analyst workload and instrument maintenance.
MACHINE LEARNING
ML can be applied to LIMS data in several ways to provide the biopharmaceutical lab with crucial scientifi c insight, including making predictions.
PREDICTING RESULTS Training an ML model using high quality historic data can enable the prediction of future result values. Insights such as this provide a foundation to prioritise projects as the results data is still being obtained.
FAILING EARLY Progressing projects that are less likely to fail saves money. But what if this could be done with fewer tests? Labs can apply ML to LIMS data to fi nd the relative importance of each test on the overall outcome. T is allows labs to run the
most important tests fi rst, subsequently progressing the candidates with the highest promise. In one test study, ML was used to
predict drug activity. Investigating a dataset of 1,700 small molecules, the research team looked at 32 diff erent chemical properties (Fig. 2). Training the ML model found the variables that are most important to the compound’s activity: LogP, number of rotatable bonds, polar surface, and molecular weight – all of which, unbeknownst to the system, are key Lipinski descriptors.
USE RICH DATA FOR PRODUCTIVE, COST-EFFECTIVE WORKFLOWS Data is absolutely vital to the success of a lab, but only when it is collected properly and connected digitally. LIMS and their associated data analytics solutions eff ectively gather data and enable actionable insights to support lab operations, system administration and scientifi c insight. Only by embracing these capabilities can labs across the world drive decision making for increased effi ciency, lower costs and ultimately, develop and deliver eff ective medicines more rapidly.
David Hardy, PhD, is senior manager, Data Analytics and AI Enablement at Thermo Fisher Scientific.
www.thermofisher.com
www.scientistlive.com 9
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