Last updated on April 16th, 2024 at 06:47 am

Biopharmaceutical companies are continually looking for technological disruptions to increase the efficiency of the production process. Automation and other specialized technologies like the Internet of Things (IoT) and analytics platforms are the future of scientific laboratory operations.

Automation in the biotechnology and life sciences sector is always correlated with interdisciplinary cooperation. It involves leaders from diverse disciplines who maintain work together. These include engineers and experts even from the fields like systems and mechanical engineering. Life sciences industry experts and medical professionals guide about dealing correctly with biomaterials and other living systems. 

 Like any other sector, the biotechnology sector is making its manufacturing processes automated as far as possible. This automation produces products quickly and in large quantities, with the lowest possible cost. This trend is in line with standardization, i.e., developing and implementing technical standards that improve the quality of goods and services. In biotechnology, clients expect products of consistently supreme quality. 

Product manufacturers or service providers consistently stick to quality management guidelines (example, GMP guidelines) and certifications that demonstrate compliance with all the relevant standards like DIN and ISO. Automation and standardization for biotechnological research, especially in living cells and tissues, are much more crucial than inanimate products.

 Large-scale industrial production has become competitive by employing automated production processes. These processes require organisms that can adapt to large-scale production processes. This requirement is dealt with effectively by experts using highly specialized knowledge about the metabolism of the involved microorganisms used and the various tools that affect these processes. 

Why is automation needed?

Automation in biotechnology has two excellent benefits. 

• One, processes are made more efficient and at the same time more objective. It makes research processes qualitatively and quantitatively similar; this way, methods are assessed more efficiently for quality management. This way, automation is a considerable step when laboratories plan to perform operations in conformance with GMP (good manufacturing practice) standards.
• The second advantage is that automation helps the people involved with designing and employing these automated processes. In biotechnology, there is a requirement of expert knowledge for all activities, and hence having well-trained personnel is a need. 

In R&D, it is a practice for the scientists to conceive the work and subsequently carrying out the experiments. This labor led to physical and mental exhaustion. Automation has dramatically helped to reduce this required time and energy, thus providing opportunities to use these resources in other activities.

Biotechnology labs and biopharmaceutical development labs often study the differences between sophisticated scientific experiments and repetitive manual tasks. In addition to efficiency improvement, another critical objective of automation is to improve experimental results’ reproducibility. It’s noteworthy how eliminating sources of human error through automation has improved the reproducibility of laboratory processes.

An automated laboratory replaces humans with robots or other electrically-driven devices and uses computers to monitor experiments and integrate the obtained data. This upgrade enhances productivity, increases reproducibility, and also overall operational accuracy. 

The prominence of lab robots

The first thing that is considered when it comes to lab automation is applying robots for laboratory experiments. The use of robots in the lab is an old concept; commercial robotic systems first came into use in the 1980s. The purpose of these systems was to mainly replace manual tasks like liquid handling or pipetting and managing the culture plates. Further technological advances in this field connected robots to corporate inventories, sample management, and dispensing systems; this meant that scientists could now design their experiments and let automation do the rest of the work.

Many updated instruments entering the market are now IoT enabled. There is a rise in vendors adapting to these concepts to deliver better services. These have been improvements in services like preventative maintenance and on-time delivery of reagents. IoT enabled instruments to make it possible to deliver the right information to the right audience. These are easy to use and ensure data integrity and provide early warnings for potential problems.

Most of the robots designed for lab use were very complicated to operate and expensive for most research groups. This problem has gradually improved with more affordable technologies such as Opentrons. Vendors are now designing robotic systems that are more user-friendly. Earlier, programming a robot required specialized skills and was typically dependent on one or more computer-savvy scientists and IT support staff. In the current scenario, it is so easy to perform that almost everybody in the lab can develop robust and reproducible workflows that enable automated systems to execute sufficiently.

Robots	Application
Cobots	Performs both simple pick-and-place tasks and more complex assembly tasks.
Autonomous mobile robots (AMRs)	They are used to transport materials from a receiving to a storage location in a warehouse and from a warehouse to a staging area or material airlocks. These are also used in cleanrooms for transporting buffer bags from buffer preparation areas to purification suites.
Neo robots	Used for cleaning floors in warehouses and cleanrooms in biomanufacturing facilities.

What is next in line with automation?

After establishing a fully connected and automated environment, the focus then shifts onto the next phase of development: self-monitoring and regulating a closed system. Based on the current status, this system can make decisions on what task is to be undertaken. As analytical instruments become even more sophisticated and the data increases at each experimental stage, informatics developments need to keep pace so for the data to be turned into something useful.

At this stage, real-time analytical instrument monitoring, data gathering, and performing automated analysis and feedback loops will be considered. These models can be developed to allow systems to control themselves based on real-time patterns. Lab Genius and Ginkgo Bioworks use machine learning algorithms in protein engineering and synthetic biology. This application holds great potential for both therapeutic and consumer product development.

The basis of this technical revolution, i.e., Industry 5.0 are, IoT and robotics. Their normalized application in laboratory operations and manufacturing contexts requires considerable optimization and testing. The last component of the system that is considered for automation is data analysis and its reporting, both of which are crucial to the scientific decision-making process. 

Despite the automation of the instruments, data aggregation, research, and reporting are manual in most organizations. Significant initiatives are taken up to accelerate the automation of the analysis and reporting processes. These initiatives involve alerting, building automated decision trees based on business rules, and machine learning. For analysis, all the different system parts need to be integrated at the data level, allowing the automation and analysis to occur smoothly.

The need is to enhance efficiency and reduce the time for the market launch of the products. These requirements are pushing companies to consider the ways they have been using to leverage technology. Several companies like Ingenious, etc., conduct Technology landscape studies for various companies, including Biopharma and Biotech companies. These companies provide information about the market’s technological trends, thus ensuring the client companies have a stance in the competitive market ecosystem. When considering automation in this holistic manner, companies see many great opportunities to streamline research and development. This possibility of making new products cost-effectively, faster, and improved quality is an exciting prospect for the entire life sciences industry.