Where is the return on investment (ROI) from lab automation?
Automation in life-sciences comprises a mixture of informatics and lab equipment. These vary in adoption dependent upon the therapeutic modality, stage within the drug discovery pipeline and company size.
Given the importance of measuring ROI to the adoption of new software, here are several ways we’ve found that automation and informatics delivers a strong return to researchers across a variety of biological functions.
Cost savings are often an immediately realisable source of value from automation. Cost savings can be a direct cost saving (such as a reduction in reagent costs) or they can deliver an ‘economic’ cost saving (freeing up organisational resource to focus on other value creating exercises).
1. Empower employees & reduce costs
Finding, hiring, training and retaining technical talent is expensive in buoyant life-sciences ecosystems such as in the USA or UK. In London, the fully loaded cost for a laboratory scientist is often in the region of £75,000 per annum with competitive locations in the USA being much higher. In areas such as cell therapy bioprocessing and QC, an explosion in financing is also creating a war for talent. This in turn is draining talent from the traditional biopharma industry and driving up costs. It thus follows that in today’s competitive research environment:
“Highly qualified researchers should not be spending their time on laborious, low value and repetitive task such as pipetting or copying, pasting and aligning data in spreadsheets.”
Laboratory automation is an obvious choice to bear the brunt of the repetitive aspects of biological research. Often a blocking response to this transition is the inflexibility of software on existing pieces of lab equipment, requiring external engineers or in-house experts to program each different experiment. This is beginning to change allowing the benefits of in-house automation to be realised across the preclinical development chain even in areas such as bioprocessing.
Furthermore, often it’s not just the ‘in lab’ element that consumes scientist time. The planning of experiments, as well as the subsequent structuring, cleaning and analysis of experimental data is also a time-consuming bottleneck. Some integrated automation & informatics solutions (such as our modular end-to-end bioprocessing workflow), allows researchers to rapidly conduct complex experimental workflows. The integrated data and informatics capabilities mean that with increased experimental throughput a bottleneck in data analysis isn’t created downstream.
With many companies now choosing to outsource R&D & manufacturing to CDMOs/CROs, automation adoption by these organisations will allow them to compete on price. Automation solutions when coupled with advanced informatics and scheduling software, can run autonomously 24/7/365, allowing for an increase in throughput; reducing the need for excess headcount and thus reducing costs. This is also of interest for companies with high quality control (QC) burdens such as autologous cell therapy companies.
2. Reagent cost savings
Reducing reagent costs may have little material impact in an discovery environment, where projects are quickly started, stalled and killed frequently. However, in other areas such as process development, manufacturing and ATMP QC; reducing reagent cost can become an important Cost of Goods (COGS) driver. Often processes are developed within discovery teams with reagents (e.g. fluorescently labelled secondary antibodies), which then can become prohibitively expensive at scale. Hence, process development teams are now using automation not just to automate the repetitive assays and experiments, but also to optimise in-process and release assays within analytical development. The motive being to develop a deeper understanding of the product quality landscape, allowing a transition towards real-time release testing (RTRT) and more robust processes.
In contrast to biopharma, the service industries of synthetic biology and next-generation sequencing, are very much commodity markets focussed on the minimisation of unit cost. This can mean centralisation of facilities to achieve economies of scale. Or in the lab, the extensive use of automation and the miniaturisation (e.g. via acoustic liquid handling) to reduce reagent volumes. Often reagents such as primers or antibodies have a limited viable shelf life. Using automation combined with sample management systems to conduct more experiments in parallel can lead to less waste and small (but meaningful) cost savings.
3. Decrease Facility and Equipment Cost Footprints
Lab automation has typically been a serious capital investment, with prices typically ranging from $25,000 up to $200,000 for a high-performance liquid handling robot. Costs for an automated bioreactor or a work cell can be significantly more. Whilst some companies are focused on advanced robotics. Synthace is focused on developing software that empowers the existing lab hardware such as liquid handlers or bioreactors. This enables scientists to reap the benefits of advanced automation without driving a significant increase in Capex spend.
In addition, with many facilities a key limitation is often the available bench or plant space for new equipment. Solutions that work to empower existing systems allow teams to leverage the benefits of automation without the need for new facilities.
Productivity improvements are also a very realisable source of value from automation. These can be delivered through time compression, increased throughput and end-to-end quality improvements.
1. Time Savings
For a preclinical biologic candidate with peak sales of $500M, it can be approximated that for every week of preclinical time saved ~ $50K USD of risk-adjusted long-term value is created. Sources of immediate value creation such as: reducing errors and mix-ups, increasing product understanding, and accelerating technology transfer further helping to prevent delays in the development pipeline. This results in the realisation of more long-term strategic value from the patent exclusivity period.
In the lab, time saving can mean compression of the overall process or more commonly increasing walkaway time so highly skilled scientists are freed to work on additional projects or process steps.
At Synthace when looking into the time savings of an Antha automated qPCR set-up, we actually found that the two greatest drivers of value were a) massively increased walkaway time and b) a compression in the overall run time.
With more complex workflows such as a Design of Experiments (DOE), reductions in the experimental planning and data analysis times often dwarf the time savings obtained in the actual running of the experiment. Saving time per individual unit operation or experiment, when aggregated across multiple step processes or workflows, allows companies to do more with less – linking into the full-time equivalent (FTE) cost reduction above.
2. Accelerate Technology Transfer
An often-overlooked benefit of automation is that it can be used to accelerate technology transfer both internally (for example, from process development into manufacturing), and externally (to a CDMO/CRO). By digitally defining experimental methods, human to human operational variance is limited leading to an increase in reproducibility and a reduction in product variability. When integrated with leading informatics and Electronic Lab Notebook (ELN) packages, these methods can also be recorded and linked to end-results, accelerating filings and QA processes.
3. Increasing reproducibility and reducing error rates.
There has been a swathe of reports on the reproducibility crisis in preclinical biological research, with some reports suggesting that up to 50% of research is not reproducible. Irreproducibility can take many forms: sample mix-ups, human to human variances and additional uncontrolled factors. Fortunately, a solution to many of these can be found by the increased use of automation. Digitally encoded protocols should decrease sample mix-ups, as well as reduce human to human variances and errors. Systems which combine automation with the collection of environmental data will allow for the generation of structured biological datasets. This is important for the teams looking to leverage machine learning within their research. Whilst there is a huge cost associated with experimental error and irreproducible research, we believe the greatest cost is actually in the time lost in chasing false trails. Experiments which need to be repeated delay the development process, whilst experiments with false positives can lead to teams wasting months of precious time. Giving teams the option to conduct lower variance experiments, should lead to less time wasted further down the value chain. This represents a greater strategic benefit than the immediate cost savings from a failed experiment.
4. Increased process throughput
One of the key benefits of automation is it allows for 24/7/365 operation of equipment. This coupled with unit operation time savings allows teams to do more with less. One important caveat to throughput is the need to prevent bottlenecks. These bottlenecks often occur when a previous step is high throughput, but subsequent steps that a prior to a decision gate are done manually. A common example of this is with upstream PRD bioreactors. However, we have demonstrated that by combining bioreactor lab automation with automated analytics, data integration and centralised data warehousing -- this bottleneck can be fully removed.
Likewise, in cell and gene therapy, removing the manual QC bottleneck can increase throughput. Automating common assays such as ELISA, qPCR and Flow Cytometry can help prevent a release testing bottleneck and accelerate the delivery of products to patients.
5. Improving Product Quality
Linked to reproducibility, is the need to have a more in-depth and earlier understanding of the product and process. Currently, due to the time-consuming nature of statistical high dimensional experimentation the product/process landscape is often only explored partially within the process development groups. In the rush to the investigational new drug application (IND), development teams are under pressure to try and compress the 18-month Chemistry, Manufacturing and Control (CMC) window. Despite generating sufficient data for an IND filing, any process or product understanding omissions may necessitate expensive bridging studies later into clinical development. This is of particular importance in autologous cell therapy where high donor variability requires a deeper understanding of the product profile. Making high dimensional Design of Experiments (DOE) easier to set-up and run in the lab through automation, should help ease the experimental burden in CMC. This will allow teams to improve their product/process understanding and reduce later manufacturing challenges.
So how to get started with automation?
Rather than thinking – “Is automation and integrated informatics something we need now?” We’ve found many companies are now thinking:
“What is the consequence if we don’t invest in automation and digital solutions soon?”
Often standalone automation installations fail to deliver on the transformative promises – leading to the aforementioned ‘failed promise’ of lab automation, and the growth of an expensive equipment or software graveyard.
By introducing an easy to use and equipment agnostic software product we have worked with numerous partners to unlock latent ROI within bioprocessing, analytical development and discovery teams. By empowering their scientists to do more science in less time, without huge upfront costs, our partners finally are beginning to realise the true potential of their teams.
Schedule a quick call with Synthace to find out more about you can introduce lab automation and data integration into your own lab.
Return on Investment (ROI) is a standard approach to evaluating the value of a software or hardware product. Other methods include payback period, internal rate of return (IRR), net present value (NPV) and real options in order of increasingly complexity.
LinkedIn Analysis was conducted in mid-2018 and the values compared vs standard fully loaded costs for R&D. The values were obtained by searching for the term ‘senior scientist’ within the required industry, across 2 different locations and the median value used as the base. Other estimates have returned much larger figures (often in the region of $200-$300K per person/fully loaded). A 1.4 USD to GBP conversion was used.
A basic revenue model for a standard biologic drug asset in a major non-orphan indication was created. In this it was assumed that the peak sales were arbitrarily set at $500M USD. A 3-year ramp up/down in sales was modelled in, along with a 3% risk adjustment (assumed PTRS from preclinical to approval). The revenue was then discounted using a rate of 10% (e.g. a big pharma), to give a risk adjusted PV of the revenue in each year of the 10 years of patent exclusivity (terminal value assumed to be 0).
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