Prediction of drug targets using metabolic modelling

Cancer, as one of the leading causes of death worldwide, is a disease characterized
by the abnormal and uncontrolled proliferation of cells. Currently available anti-cancer
drugs come with a variety of different side effects reducing the quality of life of cancer
patients. Due to these severe side effects in anti-cancer therapy it is important to find
a compromise between killing the cancer cells (efficiency) and not affecting the healthy
cells (toxicity) to improve the quality of life of those patients. There exist different
methods of finding new drug targets in cancer such as the in vitro development of new
drugs which is very time consuming and expensive. The in silico prediction of targets,
on the other hand, is fast and cost effective and allows to make a pre-selection of drug
targets based on candidate genes.
In this work, I propose a new workflow which implements metabolic modelling for
finding metabolic drug targets in cancer. Therefore, context-specific models for cancer
(including primary and metastatic melanoma) and healthy controls were reconstructed
from Recon 2 (a genome scale metabolic model) using FASTCORMICS and two different
expression datasets. In silico single gene deletion was performed in the models
to search for potential candidate genes which are essential in cancer (reduce biomass
production by 50%) but not in healthy (do not affect ATP production). In a second step,
(approved) drugs targeting metabolic genes and their side effects, were extracted from
the DrugBank, STITCH and SIDER through data mining and mapped to the metabolic
network. A total of 65 possible drug targets have been found. These targets include
genes which are known targets for chemotherapeutic agents such as the thymidylate
synthase (TYMS), the fatty acid synthase (FASN) or dihydrofolate reductase (DHFR).
Furthermore, two anti-cancer agents have been predicted for FASN which have already
been proposed for the treatment of cancer.