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For contract laboratory managers navigating the complexities of selecting the right automation system, and ensuring the chosen solution is future-proofed, is a challenge.
A diverse client base and the need for reliable, high-quality results, data transparency and the flexibility to respond to demand means that many traditional solutions may fall short on delivering against both short-term needs and long-term goals.
To help CROs and CDMOs find a flexible automation solution that aligns with their evolving needs, we’ve compiled some tips and guidance to help you ensure the provider you choose is the right one for you.
This article is an extended version of a chapter featured in our latest whitepaper.
Research phase
1. Analyse your existing capabilities
Assess your lab’s existing workflows, challenges, and automation requirements and identify specific pain points that automation can address, such as repetitive tasks, sample processing bottlenecks, or data management inefficiencies.
From there, it’ll be easier to rule certain types of automation – and therefore suppliers – in or out, and it’ll give you a benchmark on which to improve.
For contract labs, research may also involve exploring missed opportunities – times when you could have delivered more for the client or taken the contract if lab processes were faster, for example.
2. Look to the future
Whether you’re automating an existing workcell/flow or designing a new one, future-proofing is critical. Envision the future trajectory of your lab, considering potential expansions or mergers, changes in research or client focus, and scalability requirements over the coming years.
Look for modular, flexible automation platforms that allow incremental expansion and integration of new functionalities as your lab’s needs evolve. Avoid vendor-specific solutions if possible.
Scalability through modular, flexible solutions like LINQ ensures that your automation investment remains relevant and effective in accommodating increased workloads and emerging technologies – the latter of which will be an unavoidable part of the evolution of your lab and practises.
3. Decide how to fund your purchase
Flexible automation solutions support evolution, but, particularly when monitoring return on investment, both short and long-term goals should be considered.
You’ll also need to decide whether your budget comes from OPEX or CAPEX, and some providers, like Automata, have financing options based on improvement results, such as pay-per-plate.
Implementation phase
1. Consider a change management programme
Depending on the usage of and attitudes towards automation within your current organisation, the first implementation stage may be rolling out an internal change management programme. This should seek to alleviate concerns but, more importantly, highlight the features and benefits of the incoming solution and how it will support day-to-day lab life as well as business-wide goals.
Your supplier should be able to help with this, and they should provide you with training and documentation to help existing and future employees.
Most labs operate across a variety of teams and skill sets so ensure everyone, regardless of their experience with automation, can understand the project and the intended results.
2. Make sure you can accommodate downtime
There will, inevitably, be a time when lab operations need to be paused for migration to the new system – unless you’re moving to a new space. While this may be slightly easier for contract labs to schedule, there may still be revenue implications, so build that shortfall into your budget.
A phased implementation may be an option; relocating some must-run processes to other facilities could be planned for a short time, and some of our clients choose to run the non-automated solution alongside the automated one for a more staggered transition.
Automata has a low-downtime implementation process, and our solution has been designed to be as intuitive as possible to ensure high levels of adoption and immediate impact – critical for CROs and CDMOs who have no time to waste!
3. Find a supportive partner
What sets some providers apart from others is:
The period right after the physical components are in place
Implementation isn’t just about getting the hardware set up and the software installed; it’s how quickly the solution gives a return on investment, how well adopted the solution is, and how easy it is to optimise it.
Automata has a period of hypercare post-implementation, where we’ll work to optimise your system and support your new users. We’ll discuss how long this period will be during project scoping because post-project care isn’t one size fits all.
What happens when system changes are needed
Choosing a user-friendly automation platform is paramount for contract labs that may not employ full-time automation specialists, at least initially, but the provider should still support change requests too.
Your Automata customer success team will also include scientists and hardware and software engineers who can help you tweak the system, from workflow design to execution, to instrument calibration and data flow.
Optimisation phase
Once your automation has been executing live workflows, delivering real results and transferring quality data for some time, further chances to optimise instruments and instructions will become apparent.
Many solutions still don’t provide end-users with the ability to interrogate audit logs, which makes proactive maintenance, optimisation, and troubleshooting next to impossible.
Platforms like LINQ unlock users’ ability to assess and optimise automated processes across the lab quickly and easily because all the information, from run instructions to audit logs to error reports, is available.
For contract labs, access to this data can expedite identifying operational efficiencies, supporting cost-savings and strengthening that all-important profit margin.
By prioritising flexibility, scalability, user experience, and vendor support, contract lab managers can benefit from immediate competitive advantage while laying the foundations facility-wide or even infrastructure-level automation – the ultimate way to deliver high-quality, high-throughput, truly innovative scientific laboratory services.
See how the path to automation, from system selection to exponential expansion, can be made simple for contract research, development and manufacturing labs in our latest whitepaper: Infrastructure-level lab automation for changing client demands: delivering flexibility for CROs.
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Enzyme-Linked Immunosorbent Assay (ELISA) is a cornerstone technique in immunology for detecting and quantifying various substances such as peptides, proteins, antibodies, and hormones.
While manual execution of ELISA assays is still common in many research and diagnostic labs, it is prone to human error, leading to inconsistent results.
This blog post discusses frequent mistakes made during manual ELISA assays and highlights how automation can mitigate these issues.
Preparation errors
Inaccurate mixing of reagents and pipetting and cross-contamination
Manually executing critical tasks like reagent mixing and pipetting leaves assays open to mistakes, inaccuracies and inconsistencies from the beginning.
For example, improper mixing of reagents can lead to uneven distribution, while manual pipetting often results in inconsistent volumes. Cross-contamination between wells can also occur due to poor pipetting practices or splashing.
These kinds of pipetting errors affect assay sensitivity and specificity, increase waste and associated costs, and compromise repeatability, while executing manual tasks like this is the leading cause of repetitive strain injuries in the lab
Pipetting is one of the easiest, most cost-effective processes to automate, and as such, is one of the first to be switched from manual to automated.
Read our complete guide to automated pipetting for more information about choosing an automated system and going beyond pipetting to automating full liquid handling processes or even entire workflows end-to-end.
Automated full liquid handling systems speed up the movement of small and precise volumes of liquids and can be programmed to set protocols for aliquoting, mixing, and serial dilution of liquid samples. Compared to their traditional manual counterparts, they increase efficiency, productivity, and cost savings while supporting the delivery of highly accurate and repeatable assay results without researcher-to-researcher variability.
Timing challenges
Inconsistent incubation and reagent addition times
Timing is crucial in ELISA assays because each step — coating, blocking, washing, incubation with samples and detection antibodies, and substrate reaction — requires precise durations to ensure optimal binding and reaction conditions.
Small, often undetectable inconsistencies in incubation start and stop times, reagent addition and even plate-washing processes can result in variations in antigen-antibody binding and enzymatic reactions.
Automating ELISA allows every process step to be controlled, with systems adhering to strictly prescribed incubation parameters and actions for uniform timing across all samples.
And while automating these processes removes manual errors and strain injuries, it also frees scientists from the frustration of closely monitoring assays.
Throughput and productivity restrictions
Too much scientist dead time
ELISA systems can be restricted by incubation time requirements, washing and immunostaining requirements, and shift availability.
Automated ELISA systems don’t necessarily work faster than a person, but they allow machines to be tended to concurrently and enable complete hands-off time as the system can run independently of human interactions.
Because of this, automation is an excellent way for labs to increase productivity and reliably and flexibly scale throughput.
Data inaccuracy problems
Reproducibility, misinterpretation and validation consequences
Manual data recording within any setting is prone to transcription errors, and given their sensitivity and specificity, maintaining the integrity of the data throughout the ELISA process is paramount.
Consequences of inaccurate data recording in ELISA include:
Misinterpretation of results
Incorrectly recorded data can lead to the wrong conclusions about the presence or concentration of a target molecule, affecting downstream applications, such as diagnosing diseases, evaluating vaccine efficacy, or conducting basic research.
Reduced reproducibility
If data is inaccurately recorded, other researchers or even the original team may struggle to replicate the findings, undermining the study’s credibility.
Compromised quality control
ELISA assays often include controls to ensure they are working correctly. Inaccurate recording of control data can obscure issues with the assay, such as reagent problems or procedural errors, leading to erroneous results being accepted as valid.
Erroneous statistical analysis
Statistical analysis relies on accurate data input. Incorrect data can skew statistical results, leading to false positives or negatives and ultimately flawed scientific conclusions.
Wasted resources
Inaccurate data recording can necessitate repeating experiments, leading to unnecessary consumption of resources and time.
Regulatory and compliance issues
Accurate data recording is often a requirement for regulatory compliance, especially in clinical settings. Inaccurate records can result in regulatory non-compliance, potentially leading to legal consequences and loss of certification or accreditation.
Automated LIMS
Companies are already implementing Lab Information Management Systems (LIMS) alongside automated laboratory machinery to revolutionise workflows and reduce the potential for data errors.
The LINQ Cloud software bridges the physical and digital aspects of automated ELISA on our lab automation platform, LINQ. It integrates with all the instruments in use, transferring data to any LIMS in real-time without manual transcription. This enables labs to produce high-quality, reproducible data sets for contextualisation, standardisation, collaboration, and easier validation.
How can automation help with ELISA validation?
Validation is a crucial part of the ELISA process and is required to ensure the workflow’s precision, accuracy, and reproducibility. It assures users and regulators that assay results are consistent and reliable.
Automated ELISA platforms can help validation with precision, standardisation, speed and efficiency, throughput, data collection and analysis, and compliance with regulatory requirements. Read more about ELISA validation and automation here.
Conclusion
While manual execution of ELISA assays is common, it is fraught with potential errors that can compromise results. Automation offers a robust solution to these issues, enhancing accuracy, consistency, and efficiency in ELISA assays. By integrating automation into your laboratory workflow, you can significantly improve the reliability of your results, ultimately advancing the quality of your research and diagnostic capabilities.
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The dawn of lab automation has brought with it many efficiencies. From increasing walkaway time for scientists to increasing throughput in response to demand, life in the lab has become easier to plan, predict and optimise.
The key to this lies in creating a platform that delivers both robotically and digitally, connects instruments, streamlines data flows, and supports dynamic lab scheduling at both a hardware and software level.
Efficient experiment scheduling software offers a multitude of benefits, from ensuring time, instruments and people are being used to their full potential, to improving reliability, consistency and experiment success rates.
However, finding a scheduler that can assign tasks to a set of heterogeneous resources with efficiency in mind, taking into account both the constraints of each resource and responding to these parameters if they change in order to deliver a complete workflow, is a whole new challenge.
Why are schedulers important?
Experiment scheduling software helps streamline processes and workflows in the lab.
They can:
Support the creation of new and unique workflows and protocols
Help analyse performance, optimising instruments and workflows
Allow virtual workflow simulations for testing timing and experiment design
Facilitate the execution and tracking of multiple workflows when instruments, robots and transport systems are fully integrated
Most labs have moved on from simply tracking experiments on paper or with spreadsheets, and we now see two popular types of schedulers in use: static, and dynamic. Their algorithms are typically either rule-based, considering specific scheduling problems or goals, or mathematical optimisation algorithms that seek to minimise end-to-end workflow execution time.
Static schedulers
Static schedulers make decisions based on known constraints. They typically allocate tasks before execution starts, and they do not have the functionality to change this during the run time in response to information from task-tracking events.
How a static scheduler may handle an instrument error during a run
Benefits and drawbacks of static schedulers
Because known parameters are used, static schedulers are good for simple predictable processes, are robust when the data in use is accurate, and don’t involve multiple threads being created, synchronised and working in parallel. While this may reduce risk, time efficiencies in generalised applications may be lost.
The predictability offered by static schedulers can help labs with resource allocation, dictating who needs to be available to monitor or support the workflow, what instruments will be available for other uses, and when results can be expected.
The main disadvantage, like with lots of lab automation solutions, is that experiments often don’t follow simple routes and rules, with each variable representing a potential issue that could cause the workflow to pause, delay or fail completely. Fault tolerance is low, load balancing is difficult, and adaptability is limited with static scheduling.
Dynamic schedulers
Dynamic schedulers adapt their decisions based on information provided during runtime. They take into account information from multiple real-time events and allocate resources based on real-time status and workload information.
How a dynamic scheduler may handle time constraints during a run
Benefits and drawbacks of dynamic schedulers
This built-in adaptability reduces the risk of workflow failure as the scheduler seeks to find an alternative route to successful execution.
Dynamic schedulers can reassign tasks that are failed or delayed without waiting for a master node or operator to intervene, improving reliability, removing the need for manual monitoring, and providing detailed data for use in experiment design and instrument optimisation.
Due to its complexity, dynamic scheduling requires sophisticated programming and can increase initial set-up times, with multiple threads needing to be created and synchronised. Moreover, losing the ability to pre-plan executions for optimality can limit experiment throughput or introduce variables that impact the consistency and quality of results.
Dynamic replanning schedulers
Our LINQ Cloud scheduler takes the benefits of static and dynamic schedulers and offers users the best of both worlds.
It has the ability to consider known constraints for effective workflow planning and resource allocation while using state-of-the-art solving algorithms to keep workflow delivery on track.
LINQ Cloud’s dynamic replanner scheduling engine considers:
Time constraints
Known conditionals
Data transfer events
…allowing time to completion and expected results to be predicted while facilitating:
Batch parallelisation
Real-time error handling
Dynamic rerouting
Deadlock prevention
…enabling full confidence in successful workflow completion.
How LINQ Cloud’s scheduler handles run errors and constraints
Why LINQ Cloud and dynamic replanning scheduling
Our scheduler concentrates on allowing labs to maximise their instrumentation while reducing reliance on scarce resources like people to benefit the workflow and, ultimately, its results.
By taking the best bits of static and dynamic schedulers, and combining that with our workflow builder software, we’ve been able to develop something that’s easy to use, makes an impact immediately, and supports labs on their journey to automation adoption.
We have created a scheduler that outperforms across applications featuring explicit constraints handling and multi-assay workcells. By combining static solving with dynamic response capabilities, more labs will be able to automate and reap the benefits of reliable automated workflow execution.
Daniel Siden, Director of Product, Automata
Interested in learning more about the benefits that dynamic replanning scheduling can bring to your lab?
Speak with one of our automation experts today.