Automated Biotech Solutions Through Industry 5.0

Increasing the efficiency of biopharmaceutical production is a constant goal of biopharmaceutical companies. The future of scientific laboratory operations will be largely automated and centered around specialized technologies such as IoT and analytics platforms.

It is always associated with interdisciplinary cooperation in the biotechnology and life sciences sectors. Various disciplines are represented by leaders who work together. Even from mechanical and systems engineering, there are engineers and experts involved.

Biomaterials and other living systems can be handled correctly by professionals in the life sciences industry and medical professionals

Biotechnology makes use of automation as much as possible in its manufacturing processes. Automated production produces large quantities of products quickly and at low costs.

As a result, there is an increase in the standardization of products and services, which includes establishing and enforcing technical standards. Product quality is of the utmost importance to clients in biotechnology

In accordance with all relevant standards like DIN and ISO, manufacturers and service providers comply with quality management guidelines (for example, GMP guidelines). It is much more important to automate and standardize biotechnological research, especially in living cells and tissues.

With automated production processes, industrial production at a large scale has become competitive. The organisms should be able to adapt to large-scale production processes in order to perform these tasks.

Using highly specialized knowledge about the metabolism and various tools that affect the metabolism of the microorganisms involved, experts effectively deal with this issue.


Two excellent advantages can be derived from biotechnology automation.

  • By improving processes, we are able to make them more efficient while also making them more objective. It improves quality management by making qualitative and quantitative research processes similar. Automating operations in accordance with Good Manufacturing Practice (GMP) standards is therefore a significant step towards making laboratories more efficient.
  • Automated processes have the second advantage of helping the people involved with their design and implementation. It is imperative to have well-trained personnel in biotechnology because all activities require expert knowledge.

The scientists are responsible for conceiving the research and conducting experiments in R&D. Physical and mental exhaustion resulted from this labor. Due to automation, this time and energy can be used for other purposes, thus resulting in a significant reduction in the amount of time and energy required.

The different between sophisticated scientific experiments and repetitive manual tasks is often studied in biotechnology and biopharmaceutical development laboratories. The improvement of reproducibility of experimental results is another important objective of automation, in addition to improving efficiency

The reproducibility of laboratory processes has been improved by eliminating human error sources through automation.

Robots or electrically powered devices replace humans in automated labs, which use computers to control experiments and integrate data. By upgrading, productivity will be enhanced, reproducibility will be increased, and accuracy will increase overall.

A prominent role for lab robots

Using robots for laboratory experiments is one of the first steps towards lab automation. Commercial robotic systems first appeared in the 1980s, and robots have been used in laboratories on and off ever since.

These systems were intended primarily to eliminate manual tasks like handling liquids or pipetting and managing culture plates. As technology advanced, robots were connected to corporate inventories, sample management systems, and dispensing systems so scientist could now design their experiments and automate the rest.

It is now common to find IoT-enabled instruments in the market. In order to deliver better services, vendors are adapting these concepts. A number of services have improved, such as on-time reagent delivery and preventative maintenance.

With the help of IoT, it is possible to deliver the right information to the right audience. Data integrity is ensured and early warnings of potential issues are provided with these easy-to-use systems.

It was extremely difficult for most research groups to operate most lab robots due to their complexity and cost. In recent years, new technologies such as Opentrons have made this problem gradually easier to solve.

Users are now able to interact with robotic systems more easily thanks to the new vendor designs. It was previously necessary for a computer-savvy scientist and IT staff to program a robot requiring specialized skills.

Nowadays, automated systems are capable of executing adequately because processes that are robust and reproducible are so easy to develop.



  • Neo robots
  • It is used in warehouses and biomanufacturing facilities to clean floors and cleanrooms.
  • Cobots
  • Picks and places items and performs more complicated assembly tasks.
  • Autonomous mobile robots (AMRs)
  • They are used to transport materials from a receiving area to a storage location and from a warehouse to a staging area or material airlocks. These are also used in cleanrooms to transport buffer bags from buffer preparation areas to purification areas.

Is automation about to take off?

In the next phase of development, we shift our focus on self-monitoring and regulating a closed system once we have established a fully connected, automated environment. A system can make decisions based on the current status based on the task that needs to be accomplished.

In order to make the data useful, informatics developments need to keep pace with the sophistication of analytical instruments and the increasing amount of data at every experimentation stage.

This stage involves monitoring and gathering data in real-time from analytical instruments, automated analysis, and feedback loops. By developing such models, systems can learn from real-time patterns and control themselves accordingly.

The synthetic biology algorithms used by Ginkgo Bioworks and Lab Genius are based on machine learning algorithms. Consumer and therapeutic products can both benefit from this application.

Industrial 5.0 is the result of the Internet of Things and robotics. Optimisation and testing are required before they can be applied in laboratory operations and manufacturing contexts. To make scientific decisions, it is necessary to analyze and report the data, both of which are considered for automation.

Although the instruments are automated, most organizations still rely on manual processes for aggregating, researching, and reporting data. Analysis and reporting processes are being automated in a significant way.

A number of initiatives are being implemented to improve business performance, including alerts, automated decision trees based on business rules, and machine learning. In order for the automation and analysis to occur smoothly, all the different parts of the system must be integrated at the data level.

To reduce market launch times and enhance efficiency, new products need to be launched more quickly. Companies must rethink how they leverage technology in order to meet these requirements.

The Technology landscape study is conducted by several companies, including Ingenious, etc., for companies within the Biopharmaceutical and Biotech sectors. In this way, these companies ensure that their client companies have a competitive edge in the market ecosystem by providing them with information about the current technological trends

Companies can streamline research and development when they consider automation holistically. All in the life sciences industry look forward to the possibility of making new products more cost-effectively, faster, and with better quality

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