Digital Labs: AI, IoT, and Robotics Offer Better Efficiency and Insights

Digital transformation in pharma, biopharma, and biotech.

The COVID-19 pandemic accelerated intelligent digital automation in labs.
In 2020, pharma and biotech labs raced against time to analyze the new virus, develop, and deliver vaccines – while keeping employees safe. As a result, there was greater buy-in for digital transformation from senior leadership in these industries. In particular, we saw a surge of investments in intelligent, connected Internet-of-Things (IoT) technology.

Connected labs and AI-powered analytics can enhance the efficiency and capabilities of skilled professionals in the field. Buffers can be made ahead of time, experiments can be monitored remotely, and analysts can access incredible insights to improve efficiency, reliability, and results.

Pharma 4.0: Digital Labs Will Be the New Normal

77% of respondents in a 2021 Forrester survey of global decision-makers in R&D strategy and decision-making confirmed that IoT supports some portion of their lab environment. Over two-thirds of survey respondents supported the use of Robotics and IoT in their labs. 37% indicated that they use automated analysis of experimental results.

The effects of the pandemic will continue to impact how the pharma and biotech world operates long after COVID-19 is in our rearview mirror. In the Forrester survey, 69% of the decision makers surveyed felt their organization would lose out to the competition without connecting and automating their labs. Digitalization is the new normal for labs.

The pandemic may have turbocharged the demand for precision-based laboratory automation solutions, but the trend towards lab digitalization had already begun before. While some labs may be on the fence, others with a head start are already reaping the benefits of AI analytics, robotics, and connected technologies. After all, emerging infectious diseases do not work a standard nine to five. Additionally, there is a fiery race to provide patients relief from their chronic illnesses. Connected labs use cloud technology to collaborate with global teams. IoT helps remote teams monitor and control experiments round the clock. This flexibility empowers teams to be more agile and optimize their work for peak performance. Others may need to get on board quickly or get left behind.

For pharmaceutical and biotech players today, survival depends on:

  • faster time-to-market,
  • the ability to reproduce scientific results reliably, and
  • future-proofing against uncertainty and adversity.

Faster Time-To-Market and Efficiency with Digitalization

The pressure to deliver drugs, vaccines and therapies faster has never been higher. From drug development to production and rollout, a shorter time-to-market is now a necessity. The market is fiercely competitive for big pharma, mid-sized, and smaller biotech firms and the chain of vendors that feed into these industries.

By continuously learning and adapting, AI algorithms and machine learning can guide researchers as they identify, process, and test novel molecules and therapies. In IoT-powered labs, real-time data collected throughout the drug’s journey can be used to gain AI-driven insights. This information can help companies discover and develop new drugs while keeping costs low, which is significant.

The cost of developing new drugs and vaccines is on the rise. FDA approvals are a lengthy and expensive process. Developing and bringing a new drug to market takes ~US $2.6 billion. This cost is extremely high while, at the same time, new regulations and market demands are driving down pricing. Even if a company is among the first few to launch, maintaining market share is highly challenging when competing with biosimilars and generics. Also, biopharma executives are increasingly concerned about Medicare benchmarking physician-administered drugs using international prices as a reference. The only way for the industry to continue to innovate and stay competitive is through greater process efficiencies and advanced analytics.

Reliability and Reproducibility to Scale with Quality Control

Reproducibility is the defining cornerstone of scientific research and quality control. However, over US $28 billion is spent annually on non-reproducible preclinical research.

To improve reliability, reproducibility, efficiency, and safety in our labs, we need to reduce variability; we need data to do this. Intelligent automation and robotic technology in labs can be operated remotely, can collect real-time data, and offer in-depth analytics and insights like never before.

For example, it is well-documented that even experienced researchers can vary from standardized protocols. Reducing the potential for human error throughout the entire lab workflow involves better process monitoring through real-time data analytics and automating steps at a high risk of variability – like solution and buffer preparation.

Biotech labs must also keep up with a significant paradigm shift in business models. While in the past, many firms focused on niche therapies or getting orphan drugs approved for unmet needs in the market first, this business model is no longer sustainable. Precious time and resources are potentially lost waiting for sequential approvals for orphan drugs and then expand indications.

Now, biotech firms are beginning to cater to multiple disease conditions simultaneously through portfolio management. A key driving factor is developing advanced targeted and gene therapies to treat cancers, auto-immune disorders, and other inter-related chronic conditions. But with this level of specificity for patient populations, there is an added urgency to reduce variability and increase the reproducibility of results to build customer trust in these products.

Greater Resilience for the Labs of the Future

“…we are only a plane ride away from any global infectious disease.” This statement by Dr. Philip Huang, the Director of the Dallas County Health and Human Services, was about a Monkeypox case discovered in Texas. However, this sums up the broader issue at stake in our global world today and how labs need to be resilient to adapt to future uncertainties,
outbreaks, and other disasters.

Intelligent automation fosters adaptability. Digitalizing can help labs future-proof, not just to protect essential data in the face of a disaster. Automation through connected systems can help labs be agile and adapt business models in the face of adversity. For example, in 2020, as some companies raced to identify and develop antibody therapies against COVID-19, they partnered with many other pharma and biotech firms to facilitate faster time-to-market. From sharing antibody drug discovery libraries to all aspects of research, testing, and manufacturing, strong collaborations helped get vaccines in the hands of people who needed them.

In this data-driven world, labs that invest in digitalization today will place ahead of their competition. Millions of individuals worldwide suffer from multiple chronic illnesses, infectious diseases, and other complex conditions waiting for more effective diagnostic and therapeutic solutions to offer relief. Labs and organizations that use intelligent, connected systems to get approved therapies to market faster, with reliability and robust quality control, will be the winners.

Anita Santos

Anita Santos

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