Hormone-sensitive prostate cancers eventually stop responding to hormone therapy. A new treatment regimen may or may not work. That is why it is important to apply optimal therapy against hormone-sensitive prostate cancer at its earliest stage.
Finding the best therapy for cancer is difficult because of its extremely high complexity. However, deciphering this complexity has become possible today by using powerful supercomputers and sophisticated applications.
This is the reason why, in this project, we propose to use Vini, a biophysical model of cancer.
Vini has proven to be efficient in finding effective therapies against various cancer types. By using it, we hope to find more effective treatments than the currently known therapies against hormone-sensitive prostate cancer.
Considering that the same type of cancer in different patients has a different genomic fingerprint, we will let Vini search for the optimal therapy for each of our patients individually. Since Vini is computationally demanding, we intend it to run on the EuroHPC Vega supercomputer.
From Vini, proposed therapies will be finally examined for their toxicity, pharmacodynamics, and pharmacokinetic properties. We hope that the results of this project will make a meaningful contribution in the battle to find more effective cancer therapies.