Are You Wasting Time In Your Bioprocessing Workflows?
Optimise Your Process Using Antha’s DoE Software
Challenge: Trying to do more with outdated tools
An article last December in R&D Magazine said one of the main challenges in bioprocessing beyond 2019 will be continuously delivering new, high quality therapeutics to patients . And to do so faster and cheaper, whilst meeting strict compliance standards.
But as a bioprocessing engineer, you know that handling complex development procedures to reach these goals isn’t possible using traditional methods for collecting, cleaning and analysing data.
For example, wasteful procedures using basic software like Excel can cost more than 52 days per scientist every year. And between 10-20% of development work has to be repeated because of problems relating to accessibility and unreliable data .
Delays in research and repeated experiments add extra costs onto an already expensive process.
And these costs and time delays don’t even need to be that large. Smaller expenses and hold-ups can have a significant impact if left unchecked over long periods.
So optimising productivity is critical in your bioprocessing workflow. It ensures you’re making the best use of your time and resources. In fact, of all the trends covered in the Annual Report and Survey of Biopharmaceutical Manufacturing by BioPlan Associates, an increase in productivity was cited as one of the most important factors .
"In a recent process optimisation study with Oxford Biomedica, design of experiments saved 40 hours FTE, and gave a 3 to 10-fold increase in vector titre, with an 81% reduction in pure error"
How experimental design can help
Most of these challenges will not be solved until companies eliminate the bottlenecks caused by unreliable data, data handling and throughput.
And they can do this using Computer-Aided Biology (CAB).
CAB is the intersection of biology and technology, where next generation technology can support and augment human capabilities in biological research. A full discussion of CAB is beyond the scope of this blog post, so for more information, you can download our white paper at https://synthace.com/computer-aided-biology-whitepaper.
But one area of CAB that deserves special attention is the ability to enable users to truly leverage a Quality by Design (QbD) approach earlier in the value chain (e.g. R&D). Tools that enable users to perform sophisticated and complex multi-factorial experiments, known as Design of Experiments (DoE) .
Instead of trying to optimise complex biological systems using a brute force “one factor at a time” approach commonly adopted in discovery, DoE leverages statistics to enable a multi-factorial approach.
DoE’s can be performed manually or on integrated software on robots, but the complexity of design is restricted by the the amount of time and effort it takes the operator to plan, program and then perform the experiments.
Taking a computer-aided biology approach means scientists can perform more complex DoE’s with greater efficiency, increasing the size of the design space they can realistically explore, and thus delivering more robust processes.
For example, DoE can help you develop the right experimental designs within a specific budget and timeframe, and ensure your analysis gives you reliable data. In a quality by design (QbD) framework, it can quickly optimise purification conditions and help you get the highest yield of active product throughout your downstream processing.
Bottom line: you shouldn’t be wasting time on arduous and repetitive jobs like pipetting, robotic programming or endlessly messing around with data in Excel. As this can lead to errors, issues with reproducibility and delays in experimental work. Instead, you can make your job (and your life) much easier with a CAB approach. You can also perform sophisticated multi-factorial DoE-based procedures, which allow for a bigger design space and ultimately more insight. This level of complex experiment is only possible by adopting tools in the CAB cycle.
DoE increases yield, increases process robustness, and reduces experimental error
For instance, bacterial cellulose is a natural and biodegradable type of cellulose that has applications in many industries. But a regular supply is delayed by sluggish manufacturing and the batch-to-batch variability of the yield.
Basu et al. developed an approach for characterising bacterial cellulose production using DoE methods. They used these methods to study the impact of different parameters on desired process attributes .
This experimental design approach resulted in a projected cellulose yield as high as ∼40 g/L for Gluconacetobacter hansenii 53582 grown on sucrose, compared to previously reported yields of ∼10 g/L.
Here’s another example: Oxford Biomedica used our experimental design software Antha – an easy-to-use cloud-based system that can implement multi-factorial DoE optimisation – to boost the efficiency and robustness of their in-house lentiviral vector development .
Optimising this development would have been challenging if Oxford Biomedica had tried to use manual methods, because of the sheer number of experimental runs and overall intricacy of the setup. But experimental design saved 40 hours on their experiments, and gave a 3 to 10-fold increase in vector titre, with an 81% reduction in pure error . You can learn more about this case study at https://synthace.com/oxford-biomedica-and-antha-case-study.
Ready to Learn More?
Want more information on how DoE can help you save time and improve your bioprocessing procedures? Click the link below to learn more about Antha - software that allows you to leverage the benefits of multi-factorial optimisation without strenuous planning or manual liquid handling.
Click the link below right now:
You’ll be taken to the DoE information page where you can enter you email address and sign up for more information. You’ll also be able to book a demo.
 15th Annual Report and Survey of Biopharmaceutical Manufacturing, BioPlan Associates, Inc., April 2018 http://bioplanassociates.com/wp-content/uploads/2018/07/15thAnnualBiomfgReport_TABLEOFCONTENTS-LR.pdf
Machine Learning: It’s All About the Data
How to build a strong data foundation for machine learning applications.
Synthace Unveils First Life Sciences R&D Cloud Addressing Complexity, Speed & Reproducibility for Scientists
First ever no-code platform lowers barriers to automated biological experimentation and insight sharing.