https://www.nature.com/articles/d41586-026-00911-3
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15 April 2026
How effective is the personalized off-label use of targeted cancer treatment If general cancer treatment fails, a tumour-type-specific therapy might be tried for other cancers with genetic changes in the targeted pathway. How well does this work?
By Funda Meric-Bernstam
https://www.nature.com/articles/d41586-026-01596-4
QT:{{” In one experiment, Natarajan and his colleagues used Google’s Co-Scientist to look for approved drugs that could be repurposed to treat a form of blood cancer called acute myeloid leukaemia1. The system identified a list of candidate drugs, from which human researchers selected five for further study. Three of these showed promise in preliminary studies on cells grown in the lab.
FutureHouse, a non-profit AI research lab in San Francisco,
California, developed the second system, called Robin, and instructed it to find drugs to treat an eye condition called dry age-related macular degeneration2. “}}
Gottweis, J. et al. Nature https://doi.org/10.1038/s41586-026-10644-y (2026).
Ghareeb, A. E. et al. Nature https://doi.org/10.1038/s41586-026-10652-y (2026).
frontdesk
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Customize the Launcher (Hide the App): Go to your phone’s Settings > Connected devices > Connection preferences > Android Auto > Customize launcher. Uncheck Telegram to remove it from the car screen. “}}
1. Paulsson (2005) – “Models of stochastic gene expression”
https://www.sciencedirect.com/science/article/abs/pii/S1571064505000138 Nice pedagogical review of the master equation framework – covers the conceptual foundations and different analytical approaches.
2. Shahrezaei & Swain (2008) – “Analytical distributions for stochastic gene expression”
https://pubmed.ncbi.nlm.nih.gov/18988743/
This one derives the exact analytical solutions to the master equation for protein/mRNA distributions.
The Paulsson paper is probably better as a tutorial.
However, it’s a bit difficult to connect to protein & mRNA.
linkstream2 microblog 