Automated laboratories (automated labs), facilities equipped with advanced systems and technologies that streamline and optimize various laboratory processes, have made a significant impact on clinical research and practice. Automation in a laboratory can be classified as stand-alone (i.e., modular) or total laboratory automation (TLA). The first laboratory automation systems were introduced 30 years ago. Today, fully automated labs powered by artificial intelligence (AI) are hyper-scalable and able to produce AI-designed drugs in a matter of days.

This article provides a brief description of automated laboratories, outlines their benefits, presents challenges and opportunities, and looks at what might come next in the automation of laboratories.

Automated Laboratories: An Overview

An automated lab is a facility that utilizes sophisticated technologies, such as robotics, AI, and advanced software systems, to perform a wide range of laboratory tasks with minimal human intervention. The five key benefits of automated laboratories include increased efficiency, improved accuracy, enhanced productivity, cost savings, and advanced data analysis.

Types of automation in a lab include pre-analytical, analytical, and post-analytical automation. Workflows can be classified as manual, stand-alone automation, and TLA. Labs consider size, complexity, and testing volumes when deciding how best to automate a lab and ensure long-term cost savings and growth capacity.

While the first automation systems were introduced in microbiology labs 30 years ago, fully automated labs like Battery Bio have more recently been recognized as a valuable option. The challenges associated with laboratory automation include the need for substantial initial investment, integrating automation devices with diverse interfaces from different solution providers, and sound change management strategies to fully benefit from automation.

The choice of automation depends on factors and requirements such as throughput, accuracy and precision, flexibility and modularity, control center setup, washing and decontamination requirements, durability, cost, and options for semi-automation.

The estimated value of the global laboratory automation market was US$5.1 billion (in revenue) in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 5.8% from 2022 to 2028 to reach US$7.1 billion in 2028. Analysts segment the market by product, e.g., automated workstations, off-the-shelf automated work cells, robotic systems, and automated storage and retrieval systems. Automated workstations reduce costs while improving efficiency and turnaround times. This segment represents the largest share of the global market. By application, market segments include drug discovery, diagnostics, genomics solutions, proteomics solutions, and microbiology. Finally, by end-user, the market segments include research labs and academic institutions, forensic labs, life science research, pharma and biotech companies, and environmental / food testing labs.

Benefits of Automated Laboratories

Automation, robots, and AI contribute to quality and productivity gains through speeding up of tasks, increased standardization, improved lab efficiency, higher throughput, reduced waste, reduced long-term costs, increased workplace safety, reduced repetitive motion injuries, elimination of errors associated with manual processes, and improved accuracy and reliability of data.

1. Quality and productivity gains

Robots provide consistency and accuracy and can operate faster for extended periods. A recent study by Fontana et al. (2023) demonstrated that the benefits of automated laboratories include improved efficiency, standardization of procedures, and staff requalification.

In the first multicenter study in North America to assess the benefits of automated laboratories from an efficiency standpoint, Culbreath et al. (2021) measured full-time equivalents (FTE), FTE reallocation, productivity, cost per specimen, and cost avoidance. They found that post-full automation audits of four laboratories had vastly different culture volumes and improved efficiencies regardless of the size of the facility. Using TLA, Culbreath et al. (2021) observed that productivity increased by up to 90% and cost per specimen reduced by up to 47%.

2. Speed

Studies evaluating automation in clinical microbiology vs. manual laboratories report fast turnaround times for results, including the identification of pathogens. A recent study by Fontana et al. (2023) on turnaround time found that laboratory automation improves laboratory performance. In turn, a reduction in reporting time can impact clinical choices and result in improved patient outcomes.

3. Automated laboratory equipment and safety

Provided that they are programmed correctly, automated systems can function 24 hours a day without error while maintaining quality and consistent levels of precision. Workers benefit from improved safety through a reduced need to perform potentially harmful tasks and exposure to biohazards.

4. Reducing the strain on workers

Workers are subjected to repetitive tasks like capping, uncapping, filling, and labeling that can be laborious and time-consuming, and put them at risk of repetitive strain injury and carpal tunnel syndrome. Automated systems for these repetitive tasks decrease the risk of strain injuries.

5. High throughput Next-generation sequencing (NGS)

To address bottlenecks in sample preparation, particularly in high-throughput next-generation sequencing (NGS) experiments, clinical genomics laboratories turn to liquid-handling automation to improve sequencing workflows. Robotic liquid-handling systems can be completely independent once the experiment begins, working tirelessly and consistently while maintaining performance and accuracy.

The COVID-19 pandemic underlined the importance of NGS in diagnostic testing and surveillance-based screening and presented an opportunity to automate workflows. According to Socea et al. (2023), automation could address barriers such as the lack of expertise in sequencing, limited staff to routinely prepare, test, and analyze samples, and difficulties in assay standardization.

6. Regulatory

Biopharma companies are under pressure to comply with stringent regulatory guidelines for preclinical and clinical testing. Ensuring compliance with regulatory requirements such as documentation and audit trails from R&D and testing to production and quality control is time-consuming and arduous. Industry players increasingly choose to automate to ensure streamlined processes, consistent laboratory behaviors across sites, and regulatory compliance.

On a related note, the European Medicines Agency (EMA) actively encourages process improvements using technologies that have already been established in other sectors. Tanzini et al. (2023) predict that robotics in pharma will significantly change within the next five years.

Challenges & Opportunities of Automated Laboratories

Christensen et al. (2021) caution that automation isn’t automatic and requires a careful assessment of critical procedural requirements and the determination of tools for effective execution.

1. Unrealized demand

Even with the demonstrated benefits of automated laboratories, some have yet to invest in automation. Due to cost constraints and the inflexibility of existing solutions, small laboratories, in particular, have yet to automate many processes.

Rupp et al. (2022) suggest that a flexible and inexpensive option is to automate existing laboratory equipment with suitable robotic arms. These processes include pipetting, autosampling for an atomic absorption spectroscopy instrument, and a complex process of inoculation of bacterial cultures. The researchers tested a system comprising a 4-axis robot and freely available software. They found it to be suitable for flexible automation in research and teaching, with the potential for more complex lab processes. From a global perspective, market analysts report that refurbished laboratory automation equipment is available and can meet the needs and demands of a segment of end-users.

2. Diversity of automation devices

Integrating automation devices from different solution providers into an end-to-end automation system is challenging due to the diversity of interfaces. Wolf et al. (2023) present a plug-and-play framework, which is an over-arching concept for the integration of robots in laboratories. In this scenario, neither manual configuration nor teaching robots is required.

Looking Forward to an Automated Future

Process mining for continuous improvement in lab efficiency

First, highlighting the increase in laboratory automation and concurrent increase in logged events, Tsai et al. (2023) conducted a study transforming raw laboratory data to data suitable for process mining. They concluded that process mining can provide valuable insights to improve laboratory efficiency further.

Chemical automation

Combining automation technology and machine learning (ML) in unmanned systems enables autonomous discovery systems. The autonomous system workflow goes from generating a hypothesis to testing and adjusting the hypothesis based on feedback from automated experiments.

Auto-verification and quality control

The increasing complexity of automated laboratories, coupled with the growing application of molecular genetics in the core laboratory space, leads Brown & Badrick (2022) to anticipate a need for enhanced real-time quality control measures. The authors stress the importance of ensuring that automation processes are implemented simultaneously with enhanced, real-time auto-verification of samples and quality control measures.

Summary

Advancements in robotics and computer science have given rise to automated systems that execute common lab procedures with demonstrated benefits of improved quality, efficiency, and productivity, increased workplace safety, and improved accuracy and reliability of data. However, automation does not happen automatically and requires a careful assessment of critical procedural requirements and the identification of necessary tools for execution.

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