A probabilistic cluster expansion based approach for predicting synergism of mixtures of compounds in treating cancers

Combination therapy has been one of the cornerstones for combating cancers. Even though the survival rate of cancer patients after receiving combination therapy is improved dramatically, there are still many unsolved issues to prevent us from claiming combination therapy is a cure for cancers. Those issues include it does not apply to all types of cancers; there are increased risks of side effects, and it requires long developmental time and huge costs. One approach to resolve these issues is applying multiple-compound multiple-target paradigm directly during discovery processes, which exactly is the backbone of the partner organization’s proprietary drug discovery technology. Yet the technology lacks an appropriate and efficient way to predict the effects of combinations. Therefore, the proposal aims not only to improve this technology but also resolve issues in combination therapy.

Faculty Supervisor:

Jack Tuszynski

Student:

Partner:

SinoVeda Canada Inc

Discipline:

Physics

Sector:

Agriculture; Manufacturing; Professional, scientific and technical services

University:

University of Alberta

Program: