Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of
proteins encoded by 15,841 genes in 27
hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of
antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each
antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101
antimetabolites that could be effective in preventing
tumor growth in all HCC patients, and 46
antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted
antimetabolites have already been used in different
cancer treatment strategies, while the remaining
antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.