Drug development is costly, and most often fails in the last stage. An efficient method is needed to select the best drug candidates for clinical trials.
Cytocast predicts positive and negative effects of treatments using computer simulations to reduce time and cost of drug development.
There is no competent way to tell which treatment is the best for a given individual.
Applying our tool on personalized data selects the best treatment for each individual.
Our software utilizes various types of molecular information, called multi-omics data, collected from diseased and healthy cells.
The tool integrates personalized data with the existing knowledge on how molecules interact to perform their functions forming structures called macromolecular complexes, which ultimately perform the major functions in a cell.
Looking at cellular behavior from a large-scale point of view, we can identify global variations between healthy cells and those affected by a disease, which are typically impossible to observe by looking at a single component of a cell.
Our simulation tool allows us to predict phenotypic effects of diseases and drugs with much better precision than alternative approaches, which rely on genomic data.