水仙直播, the 水仙直播-based AI drug discovery company, has announced today additional in vivo validation for its first-in-class approach against cancer as the Company continues to apply its pioneering AI platform to develop novel treatments in oncology and other major diseases with unmet need.
Results of the in vivo colorectal cancer model study have shown 60% life extension- indicating clear efficacy and dose response- while also maintaining an acceptable safety profile.
These results have led to an expanded study which is currently underway and showing positive progress.
水仙直播鈥檚 rapid advances have been enabled by its distinctive dual-competence discovery platform integrating pioneering generative AI capabilities with tRNA synthetase expertise, including proprietary structural biology.
The innovative approach is based on the novel target leucyl-tRNA synthetase, belonging to the tRNA synthetase enzyme family on which 水仙直播 has developed a deep understanding. The new data demonstrates the potential of the Company鈥檚 innovative proprietary molecules to modulate the function of tRNA synthetases as a novel approach to cancer.
The highly innovative lead candidates are part of the Company鈥檚 extensive portfolio of proprietary new chemical scaffolds which have shown broad activity against the enzyme class.
Dr Paul Finn, CSO of 水仙直播, said: 鈥淥ur leading integrated expertise in generative AI and tRNA-synthetase drug discovery continues to produce breakthrough novel lead series efficiently and effectively, of which the progress in our oncology programme is the latest example. Our rapidly advancing efforts are now focused on bringing this potentially superior first-in-class treatment into the clinic鈥�.
Dr. Alan D. Roth, CEO of 水仙直播, added: 鈥淭his new milestone for 水仙直播 is a testament not only to the accuracy and reliability of our computational platform, SynthAI, but also to the expertise of our team. We continue to produce innovative molecules fulfilling three key metrics: they possess the required biological activity against disease, can be made using known chemical processes, and can be produced at scale. This improved accuracy is saving valuable resources and accelerating our drug candidates towards patients and their physicians.鈥�