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With the use of a new technique called Image-Based Drug Discovery Processor the wealth of data contained in biological images is condensed into a multidimensional profile, or a set of extracted image-based attributes.
For several steps in the drug development process, these profiles can be mined for pertinent patterns that suggest unexpected biological activity. Applications of this category include understanding disease mechanisms, discovering disease-associated screenable phenotypes, and forecasting the activity, toxicity, or mechanism of action of a medicine.
Many of these applications have recently entered production mode in both academia and the pharmaceutical sector after being validated. Some of them have had mixed outcomes in the real world, but interest in them has recently increased because of enhanced machine learning techniques that make greater use of image-based data.
Despite ongoing difficulties, new computational techniques like deep learning better capture the biological information in images hold promise for accelerating drug discovery.
The Global Image-Based Drug Discovery Processor market accounted for $XX Billion in 2022 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2023 to 2030.
Using the CSU confocal scanner, Yokogawa has been creating a prototype for an Image-Based Drug Discovery Processor. The most fundamental unit of all living organisms, cells, are administered chemical compounds that could be drug candidates.
This system then uses a highly sensitive CCD camera and the CSU confocal scanner to record changes in the amount and localization of target molecules inside cells, processing and quantifying the high-resolution image data. With the use of this screening technique\.
, drug candidates can be identified in living cells as well as drug efficacy and adverse drug reactions of chemical constituents. The image processing technique Yokogawa created for the first version of a genomic drug test assistance system.
Cultured cells are sown on a 96-well or 384-well micro well plate for genuine specimens. After being given different chemical ingredient concentrations, they are fluorescently coloured in order to detect morphological changes, etc.
The Image-Based Drug Discovery Processor from Yokogawa (using CSU) has the following characteristics. Confocal pictures are taken of the light-filled cross sections of cells.
This characteristic makes it possible to clearly see the minute cell architecture. So, it is possible to measure intracellular granules precisely.The photos of cell cross sections can be stacked to create three-dimensional images.
Neurites’ complex structure can also be seen clearly.The confocal system from Yokogawa offers little fluorescence photobleaching, allowing for continuous long-term monitoring. It is possible to watch how living cells change dynamically thanks to this function.
