Few industries were not impacted by the COVID-19 pandemic, with the life sciences among the most directly affected. The pandemic saw unprecedented levels of demand for sample testing, as well as a global effort to produce a vaccine. In the wake of this chaos, the Biotech industry has seen a renewed industry push for increased urgency in bringing new drugs and therapies to market. Traditionally, Biotech has moved much more slowly due to being tied down by stringent regulations; it can take upwards of ten years and $1 billion to bring a new drug to market. With this increased pressure to produce more, get better results, and to do so more quickly, Biotech labs are concerned about keeping pace with demand. This is where automation comes in – automation systems help labs do more, with less. Benchtop automation can be used, for example, to set up experiments and perform experiments without any need for human observation or management, thereby reducing the opportunity cost of valuable staff time used for menial operational tasks. Automation is a promising value-add for Biotech firms by helping manage ever-increasing throughput volumes and improving the quality and reliability of results.
The Case for Automation in Biotech Labs
The relevance of laboratory automation is greater than ever, as evidenced by a Nature survey. 1,576 researchers were polled on the reproducibility of experimental data with discouraging results – 70% of respondents have been unable to reproduce the results of a peer’s experiment, with more than 50% unable even to reproduce their own results. This report highlights the need for a greater level of reproducibility across the industry, which can be achieved through reducing the incidence of human error. The introduction of automation systems helps not only to reduce the need for direct human involvement in manual experimental processes, but also enhances overall lab productivity while yielding more replicable and accurate results.
The State of Automation in Biotech
The Biotech industry deals with huge swathes of data and ever-growing volumes of samples, presenting many opportunities to generate efficiencies through the implementation of automation systems. McKinsey & Company reports on the future of automation in US BioPharma, providing a quotient that rates the digital maturity of several industries for comparison. A score of 70-80 is awarded to industries recognized as digital leaders. Perhaps surprisingly, the pharmaceutical sector scores 29, thus ranking lower in terms of digital maturity than the banking, media, telecom, retail, and hospitality sectors. These statistics suggest low rates of adoption of cutting-edge technologies, and higher associated costs. The same report estimates general and administrative expenses for biological and pharmaceutical companies as 7% of revenue, 1.5-2% higher than comparable sectors. This provides a compelling case for the audit of lab processes to identify bottlenecks and potential time and cost-savings through the implementation of automation equipment. The upfront cost of automation technologies is quickly offset by long-term labor savings and should be regarded as a tool to “break the linear relationship between workload growth and cost.”
Looking to the future, the lab of tomorrow is likely to rely much more heavily on machine learning and benchtop automation systems. Increasing uptake of automation equipment is being spurred on by the growing accessibility and advancement of automation technologies, as well as the falling costs of data storage and processing. McKinsey & Company estimate that the lab of the future will automate between 40 and 70% of manual case-processing tasks, with data entry, coding, and patient intake as the areas representing the highest potential gains if automated. Automation can also be used as a tool to remedy skill deficits. Around 45% of companies globally report that their organization is currently dealing with skill gaps, and 41% will encounter shortfalls in the next five years. In this instance, implementing automation systems can provide staff with additional and much-needed bandwidth, and can also provide a new area in which researchers can skill up.
As throughput in Biotech labs continues to rise and staff shortages loom, the case for automating manual lab processes is compelling. The WHO projects strong continued growth in the pharmaceutical industry at 5% annually, with the potential for market size to exceed $1.8 trillion by 2024. In alignment with the expanding pharma market, the demand for lab automation is expected to grow 7% CAGR through 2031. Despite Biotech’s low level of digital and automation maturity relative to other sectors, there is plenty of room for growth and numerous areas in which labs stand to benefit from implementing some level of automation.
Benchtop Automation for BioTech
As a first use case, the discipline of synthetic biology involves the design and execution of complex workflows. The use of automation instruments in this instance has been shown to reduce the assembly time of large DNA molecules in a workflow from twelve to three hours. Winning back this time opens up numerous possibilities for researchers, ultimately allowing labs to design and execute on a higher volume of experiments – helping to get results faster and advance business outcomes.
The case for automation is also strong in the area of DNA sequencing. Using automation systems, the pooling of DNA fragments from sample libraries becomes substantially faster. Without the help of automation, the total time for the process can take upwards of six hours, but with the use of automation instrumentation, transferring each sample takes less than a second. This reduces the processing time to six minutes. The use of liquid handling systems represents similar opportunities in staff time-savings and reduced churn from carrying out repetitive manual tasks. Advanced systems are being innovated, making use of soundwaves to cause tiny amounts of liquid to jump from one container to the destination receptacle, thereby using a tiny fraction of the samples and reagents that a conventional liquid handler would use. This would be of great benefit in high throughput labs, allowing staff to do more with less.
Labs may be discouraged by high buy-in costs, however, and the resources needed to train personnel to program and operate the systems. Increasingly, low-cost options are populating the market. These machines are cheaper than the industry standard setup and simple to use, for example, giving users control through a web browser. This enables researchers to simply download operational protocols, and then run them.
AI and Machine Learning
As well as benchtop automation, AI and machine learning are producing innovations with great potential for Biotech companies. For instance, the UK-based firm LabGenius uses AI and automation to trawl through trillions of genetic designs, searching for new biological solutions and compounds. Machine learning can be used to generate a number of efficiencies for researchers, even with the possibility of autonomously planning and running experiments. Machine learning gains knowledge of how various combinations of reagents perform and can predict novel combinations which may yield better results. The system could then check in with the inventory register to check the availability of reagents in the lab, order more if needed, and then schedule the synthesis and testing of new compounds. Automating these processes would free up valuable staff time and streamline workflows. As automation systems become more advanced, the level of communication and interaction between instruments will increase, allowing more complex research to be carried out with less manual input from lab scientists.
The Lab of Tomorrow, Today
Many repetitive tasks crucial to lab processes can be advantageously performed by automation systems. This frees up valuable scientific resources for higher value objectives – with staff relieved of these duties, more time is available for planning and performing experiments. As well as increasing bandwidth, the implementation of automation equipment reduces the incidence of experimental errors, improving the reproducibility and accuracy of data and helping to comply with stringent regulations.
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