Considerations to ensure lab automation truly streamlines drug manufacturing and QC testing
Drug manufacturers are under growing pressure to expedite the development and market launch of new medicines, vaccines, and diagnostic testing services.
They are hindered in achieving this goal by the ongoing use of legacy and unconnected lab systems in quality control (QC) testing and drug manufacturing, as well as by the slow uptake of advanced integrated data management technologies designed to automate data analysis. All of this has implications for both time efficiency and data accuracy.
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Many QC labs still have few—if any—integrated instruments and applications, meaning technicians must manually do data transcription and input before being transmitted by hand to other systems. This not only wastes time but also introduces the need for secondary verification, resulting in further delays and increasing the risk of human error. These inefficiencies have implications for effective and efficient commercial production of finished drug products.
QC lab automation technology has the power to address these issues, enabling companies to collate and store their data with greater ease and accuracy while also allowing them to analyze it more effectively to extract its full potential and shorten QC test cycle time frames.
The power of lab automation in QC testing
A growing number of QC labs feature advanced lab informatics systems to reduce data input time and facilitate long-term data storage. If these technologies are not fully connected with each other or with the core IT processes of the wider organization, valuable lab data can remain siloed in the systems to which it was input. This makes it harder to gather information in a way that makes it possible to draw valuable business or drug manufacturing insights that could benefit operational efficiency.
The “human middleware” between the labs and core systems is often the problem. As team members must input information by hand or track datasets from other equipment, there is an increased risk of missing valuable information or errors creeping in that could negatively impact outputs. Individuals must investigate anomalies and verify new data inputs by another person. This wastes personnel’s time that could be invested in analyzing the data for actionable and beneficial outputs.
By integrating all these separate QC lab systems through applications and instrumentation, it’s possible to automate all data flows within a lab and the wider organization, making it easier for drug manufacturing stakeholders to access data from across the lab and the business.
Increasing data accessibility across an organization can also deliver better insights to help accelerate QC test cycles and commercial product launches. It can also improve manufacturing efficiency post-commercialization. Eliminating manual data transcription and workflows boosts regulatory compliance by reducing error rates and making it easier to investigate deviations and take corrective action and preventive action (CAPA).
The barriers to lab automation
Concerns around cloud security, data integrity, regulatory compliance, and perceived difficulties in the transformation process have historically held companies back from investing in lab automation despite the potential productivity gains from harnessing the technology. Business value, as well as a shortage of skilled labor to implement the new technology and train lab technicians, have also been seen as obstacles.
Other issues making the prospect of upgrading seem daunting to smaller organizations, in particular, include the complexity of integrating the latest connectivity solutions with legacy lab and business systems. Older equipment may not be connected to an organization-wide system, undermining the ability to effectively source and analyze valuable data from across the business. Existing systems may not be compatible with the new technology or other equipment already in place.
Finally, change management is an obstacle companies must overcome to achieve true testing lab automation. For larger organizations, efforts to automate lab data processes could impact hundreds of team members. All affected employees need to be made aware of what is happening, why, and how it will benefit them and the wider business, and receive training in using the new technology, requiring additional time and resources.
Four things to consider to achieve effective QC lab automation
Before setting out on their lab automation journey, business leadership teams should first have a clear idea of their end state and take a value-first approach to digital transformation. Leadership should communicate its vision to the broader organization, so all affected workers appreciate the value of the upgrades.
To create this vision, organizations should focus on these four key factors, which will help them overcome obstacles in implementing QC lab automation:
Consideration 1: Your organization’s digital lab maturity
Assessing how much progress the organization has already made in adopting new technology and integrating data management systems during the planning stage is crucial to developing a practical and tailored action plan that addresses its unique needs. This allows organizations to take a systematic approach, starting with fundamental upgrades, such as linking lab systems to core business networks, and gradually progressing toward more advanced automation, including method execution.
This assessment can also reveal opportunities to retain existing technology, potentially reducing costs. Equipment with digital capabilities might need reconfiguration to enable cloud access and control. Any equipment that cannot be integrated into the new system will need to be replaced.
Consideration 2: How long you are willing to wait for the “perfect” solution to enter the market?
The speed of technological advancement can deter organizations from automating their labs and pursuing digital transformation. Business leaders may feel compelled to postpone investments until the next innovation enters the market, delaying important progress that can compromise operational efficiency. In most cases, it is preferable for a company’s long-term efficiency and productivity to invest in the solutions that are currently available, and to take steps to ensure that the resulting system can be easily upgraded in the future, rather than waiting for the arrival of the perfect technology.
Consideration 3: How to get buy-in from the team
Effective change management is one of the most crucial contributing factors for ensuring digital transformation success. Engaging employees across all levels and sites of the organization from the outset ensures they are completely invested in the process. Employees must grasp the upcoming changes so they can explain them to their peers and gain buy-in for the change rather than fearing possible negative impacts on how they work. Involving employees in discussions about the company’s vision and the rollout of new technologies, coupled with regular updates, will reinforce their engagement and enthusiasm throughout the transformation process.
Consideration 4: How expert support can expedite and simplify the lab automation journey
Many lab automation systems and solutions are available on the market for the life science industry—so many that it can be daunting for any organization to select the best technology for their unique needs.
To ensure the success of a lab automation journey, accessing specialized contract partners’ expert support can be pivotal. With such support, organizations can fully understand the nature and location of all their data, as well as its inherent value, and identify critical areas where technology and data can have the most impact in improving efficiency.
Time to act to embrace the full potential of lab automation
A new generation of automation technologies is available to QC testing labs to maximize the value of their data. Advancements, such as generative artificial intelligence (gen AI), Bots, CoBots, augmented and virtual reality, robotic process automation (RPA), and cloud computing and Internet of Things (IoT) systems.
These technologies have a lot to offer companies looking to optimize lab asset performance management, thanks to their unique ability to connect lab systems with other equipment across multiple sites.
Investing in them now can allow organizations to enhance integration of enterprise data across their operations, not just to optimize general day-to-day working but also to help better utilize data from manufacturing in lab processes to positively impact productivity post-commercialization.
