Personalized Medicine on the Way Thanks to 101 Liver Cancer Drug Candidates

Research indicates that the heart disease drug perhexiline is one of 101 compounds predicted to prevent cancer growth in most patients suffering from our most common liver cancer. This is an outcome from a novel simulation-based approach using personal sets of proteins of six HCC patients.

"This is the first time personalized models have been used to find and evaluate new potential drugs," says Professor Jens Nielsen at Chalmers University of Technology.--

Our most common liver cancer, Hepatocellular carcinoma, HCC, causes more than half a million deaths worldwide every year. If the cancer cannot be surgically removed the disease is usually deadly within 3-6 months. Only 30 percent of the patients respond to the best existing drug, sorenafib. This new research now identifies 101 drug candidates predicted to prevent the cancer growth in all six studied patients, which raises hope of developing a drug helping all HCC-patients.

Cancer cells modify their metabolism in order to breed. To understand these diverse mechanisms, what metabolic enzymes are involved, when and how, has been a major focus in medicine in order to identify novel drug targets. However, this is quite a challenging task, since HCC not only involves a large number of interplays between different biological pathways, but also significant individual variations.

Finding the candidates

Average models don't give sufficiently good answers. To take the individual variations into account the researchers generated personalized proteomics data for HCC patients using the antibodies produced in the Human Protein Atlas project (read more below). The researchers then generated individual computer models for six HCC patients based on their entire, personal set of proteins and a generic map of human metabolism, which had been produced in an earlier project.

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"I am excited to see how we have managed to successfully transfer our modelling approaches on yeast to study cancer metabolism," says Dr Rasmus Ågren, shared first author of the paper.

The six personal models were then used to find potential new anticancer drugs. One of the most common types of anticancer drugs is so called antimetabolites. Antimetabolites prevent the use of one or more metabolites (the small molecules that act and are produced, and whose interplay together constitute a full metabolism) by stopping the catalyzing enzymes. By simulating the effect of all possible antimetabolites – more than 3000 compounds – the computer generated potential anti-cancer drugs which could be effective in inhibiting tumor growth.

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Source-Eurekalert