The latest Business updates from the science industry
by Gwyneth Astles Platform supports growing liquid handling community
Japanese biotech PRISM BioLab has selected SPT Labtech’s fi refl y liquid handling platform to accelerate the design of small molecule inhibitors of protein-protein interaction (PPI) targets by automating screening workfl ows.
The fi rst of this system to be installed in Japan, fi refl y will be integrated into the company’s screening workfl ows to automate numerous pipetting and transfer steps across 96 and 384-well plates. Chosen for its speed, accuracy and cost effectiveness, as well as service and support from fi eld engineers, it dispenses low liquid volumes through automated positive displacement technology.
It will be used to identify small molecule inhibitors of PPI targets, thereby converting previously undruggable targets across cancer, autoimmune, fi brosis and other therapeutic areas intro readily druggable pathways.
“It’s fantastic to see fi refl y make its debut in Japan. The adoption of fi refl y technology by PRISM BioLab is a true validation of the dedication and expertise of our scientists and engineers in delivering a truly innovative and robust automated liquid handling platform,” said Joby Jenkins, Chief Technology Offi cer at Cambridge, UK-based, SPT Labtech. “fi refl y’s capabilities are well-suited to the repetitive nature of drug discovery screening workfl ows, empowering laboratories to maximise throughput in such critical areas of diseases with unmet needs,” he added.
“We chose fi refl y to expand our screening platform capability. It will be utilised in the screening of our PepMetics®
small molecule library and in the dose titration assays. Through the several demo opportunities, we realised it is easy to handle
Prism Biolab installs fi refl y platform
and is applicable to automation,” said Tatsuya Toma, Chief Technology Offi cer at PRISM BioLab. “We are confi dent fi refl y will minimise our assay optimisation steps and accelerate our research projects to identify novel drugs for the patients.”
More information online:
ilmt.co/PL/Mbmx 63029pr@reply-direct.com
Predicting polymer mechanical properties with machine learning A new study [1] shows that machine learning can predict the mechanical
properties of polymers, such as polypropylene, using X-ray diffraction data. Published in Science and Technology of Advanced Materials, the research offers a non-destructive method for determining how new polymers will perform under different conditions.
Traditionally, predicting properties like tensile strength and fl exibility requires costly physical tests. However, a team from Japan, led by Dr Ryo Tamura, Dr
Kenji Nagata, and Dr Takashi Nakanishi from the National Institute for Materials Science, has developed a machine learning approach to overcome this. The researchers applied the method to homo-polypropylenes, using X-ray diffraction patterns to gain detailed insights into their structure.
“Machine learning can use data from existing materials to predict the properties of unknown materials,” the researchers explain. The key challenge was selecting descriptors that best represent the polymers’ complex features, especially given how their structure changes during moulding.
The team analysed two datasets using Bayesian spectral deconvolution: one with X-ray diffraction data from 15 types of homo-polypropylenes, and another from four types that underwent injection moulding. Their fi ndings revealed that machine learning could accurately predict properties like stiffness, elasticity, and deformation temperature.
The study offers a potential non-destructive alternative to conventional polymer testing methods and could be extended to other materials. According to the researchers, this approach could set the stage for future data-driven innovations in polymer science.
More information online:
ilmt.co/PL/25WK and
ilmt.co/PL/Gz2D
1. Machine learning prediction of the mechanical properties of injection-molded polypropylene through X-ray diffraction analysis, Science and Technology of Advanced Materials Volume 25, Issue 1, 2024
Machine learning predicts the material properties of new polymers with high accuracy, providing a nondestructive alternative to conventional polymer testing methods. (Credit: Mike MacKenzie via Flickr/Wikimediacommons)
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