This webinar explored how ๐ผ๐บ๐ถ๐ฐ๐ ๐ฏ๐ถ๐ผ๐ฎ๐ฐ๐๐ถ๐๐ถ๐๐ ๐ฑ๐ฎ๐๐ฎ ๐ฐ๐ฎ๐ป ๐ฏ๐ฒ ๐๐๐ฒ๐ฑ ๐๐ผ ๐ถ๐ป๐ฐ๐ฟ๐ฒ๐ฎ๐๐ฒ ๐ฐ๐ผ๐ป๐ณ๐ถ๐ฑ๐ฒ๐ป๐ฐ๐ฒ ๐ถ๐ป ๐ฐ๐ต๐ฒ๐บ๐ถ๐ฐ๐ฎ๐น ๐ด๐ฟ๐ผ๐๐ฝ๐ถ๐ป๐ด. It included both ๐ฟ๐ฒ๐ด๐๐น๐ฎ๐๐ผ๐ฟ๐ ๐ฎ๐ป๐ฑ ๐ถ๐ป๐ฑ๐๐๐๐ฟ๐ ๐ฝ๐ฒ๐ฟ๐๐ฝ๐ฒ๐ฐ๐๐ถ๐๐ฒ๐, highlighting the importance of grouping to support read-across and to enable a reduction in animal testing.
The session featured ๐๐ฒ๐๐ฒ๐ฟ๐ฎ๐น ๐ฐ๐ฎ๐๐ฒ ๐๐๐๐ฑ๐ถ๐ฒ๐ ๐ฑ๐ฒ๐บ๐ผ๐ป๐๐๐ฟ๐ฎ๐๐ถ๐ป๐ด ๐๐ต๐ฒ ๐ฎ๐ฝ๐ฝ๐น๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป ๐ผ๐ณ ๐บ๐ฒ๐๐ฎ๐ฏ๐ผ๐น๐ผ๐บ๐ถ๐ฐ๐ and transcriptomics data in chemical grouping, and ๐ถ๐ป๐๐ฟ๐ผ๐ฑ๐๐ฐ๐ฒ๐ฑ ๐๐ผ๐บ๐ฒ ๐ด๐๐ถ๐ฑ๐ฎ๐ป๐ฐ๐ฒ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐ฒ๐ฑ ๐ฏ๐ ๐๐ต๐ฒ ๐ข๐๐๐ ๐๐ผ ๐ฟ๐ฒ๐ฝ๐ผ๐ฟ๐ ๐ผ๐บ๐ถ๐ฐ๐ ๐๐๐๐ฑ๐ถ๐ฒ๐.
Finally, a case study was presented that expands beyond the primary use of metabolomics, here being used to discover chemical biotransformation products which can then be used to support chemical grouping based on metabolic similarity.
Thank you to everyone who contributed to this webinar Mark Viant, Thomas Lawson, Tomasz Sobaลski, Francesca Trivelloni, Anthony Reardon


