"Bringing automation to every scientist starts with a sample centric approach"
Markus Gershater, CSO & Co-Founder at Synthace
SLAS US 2024, Boston MA | February 7th
There's a category of experiment that R&D biologists still have a hard time automating. Why?
Definition: by “robot” we mean the entire functionality of a liquid handler or dispenser, specifically its ability to move and manipulate liquids.
Life science R&D lives and dies on the success of its experimentation, and this experimentation often relies on automated liquid handling robots. But an entire category of experiment isn’t that easy to automate. Even if they do get automated, they typically miss out on key benefits of automation, such as reproducibility, transferability, and productivity gains. It’s like this because of how most automation processes work today-they focus on moving the robot.
While this approach works in many other scenarios, it fails when experiments that have at least 2 of these 4 characteristics:
- Variable: the experimental conditions or the overall protocol are frequently changed
- Multifactorial: they study the intricacies of interactions between different factors
- Small-scale: they are run in the early stages of an experimental campaign before protocols are scaled up
- Emergent: protocols may need to be adapted depending on the results
In our experience, these experiments are especially prevalent in process development for modern therapeutic modalities such as biologics, where processes are less established. They are also common in assay development, where a protocol must be designed and then optimized over a multitude of experimental factors.
Despite often being small-scale, these experiments can be highly impactful to the overall outcomes of a drug discovery campaign because they are run in its early stages.
More effective optimization of an assay for High Throughput Screening (HTS) can result in better candidate selection or cost savings on expensive reagents. That’s because a better Z’ can lead to fewer false positives and negatives, or conditions can be found which yield a good enough Z’ while using less material. Iterating on a protocol during process development faster and with greater flexibility can result in substantially faster time to market, or better yield of a pharmaceutical.
Why are experiments like this so hard to automate? What would make it easier?
In this presentation, originally recorded at SLAS US in Boston on February 7, 2024, Markus Gershater shares how abstraction can help us automate faster, and with higher levels of complexity.
14 фев 2024
