Looking to achieve scientific innovation faster? Latest life sciences trends point to rising technology spending in lab automation. Investments that enable reduced manual labor and increased data accuracy can help shorten your time to market.
In its Hype Cycle for Life Science Research and Development report, Gartner said that as the COVID-19 pandemic advanced the need for new products and medicines, life sciences organizations looked towards maturing technologies to help accelerate their research.
As a result, new niches of custom-made solutions, technology and support vendors, and service providers have popped up to meet this increased demand. Investments in automation are also on the rise. In fact, the global laboratory automation market (comprising robotics, software, data processes, and other technologies) is expected to reach US$8.84 billion by 2027, at a CAGR of 7.69% during 2022-2027, according to Research and Markets.
In this article, we delve into three key laboratory automation trends that are helping accelerate processes, productivity, and scientific discovery.
1. Increasing Diversity in Digital Lab Ecosystems
Automation unlocks lab efficiency, which is key to scientific innovation. For example, automating laboratory processes using robotics reduces the time scientists spend performing manual, repetitive tasks, so they can focus on breakthrough discoveries. Lab automation software also improves data integrity and accuracy by minimizing the risk of human error.
Life sciences research is evolving, with more therapeutic modalities, including mRNA, cell and gene therapies, and personalized medicines. Each of these new and emerging strategies and initiatives requires new instrumentation and data models to support drug discovery. As a result, labs will use many new complex processes involving multiple instruments, informatics software, etc.
With automation, laboratories can implement reliable and repeatable processes. Automation also allows laboratories to configure and connect a large number of technologies, including instruments and informatics applications. The most significant benefit is reducing the number of manual steps and freeing up time for lab scientists to work on value-added tasks.
Whether robotics or data-automation processes, these increased investments in laboratory equipment and software automation contribute to a growth in the size, diversity, and complexity of scientific data as well. Automation leads to increased throughput and productivity, which in turn leads to a larger volume of data, making it critical to manage scientific data effectively as part of a long-term strategy.
2. Unifying Data Silos
In order to maximize the benefits of a diverse digital ecosystem, labs must increase their use of data automation technology. Scientific data from multiple vendors implies the generation of multiple data formats, scattered across siloed data storage. For organizations to effectively access and gain insights from this scientific data, they must first integrate the various parts of the data ecosystem into a single, open, vendor-agnostic solution that centralizes data in the cloud.
These integrations — across instruments, software, and tools from different vendors — empower labs to make the most of their data, bridge silos, and foster collaboration. A vendor-agnostic (i.e. open) approach enables organizations to use a greater variety of technologies and automation capabilities, while still being able to leverage their data.
Making the data findable, accessible, interoperable, and reusable (FAIR) becomes more critical than ever. FAIR data provides a future-proof approach through data access across instruments, applications, and technologies.
For data to be FAIR, it must be standardized and accessible. Research suggests organizations are on the right path toward FAIR data principles. In fact, 74% of biopharma organizations are planning or implementing a FAIR data strategy, while 18% have already completed this journey, as the PharmaIQ and TetraScience study indicates.
How to reduce data silos
Here are some of the aspects of advanced approaches: