A new publication in Scientific Reports shows that it is possible to inhibit glucose metabolism in a disease-causing cell without severe side-effects to healthy human cells. To avoid side-effects of drug treatment to healthy cells, drug target selection studies often focus on protein targets that are only found a disease-causing cell. However, many disease-causing cells, like parasites and cancer cells, are biochemically very similar to their host and therefore the number of proteins unique to such cells are scarce.
Nevertheless, due to subtle, quantitative differences between the biochemical reaction networks of disease-causing cell and healthy host cells, a drug can affect the same essential process in one cell-type more than in another. Jurgen Haanstra and co-workers of a research team led by prof. Barbara Bakker (UMCG Groningen; Molecular Cell Physiology, AIMMS, VU) and prof. Hans Westerhoff (Molecular Cell Physiology, AIMMS, VU), showed in a proof-of-principle study how quantitative differences in cellular networks can be exploited to selectively hit the disease-causing cells.
They combined computational and experimental approaches to compare energy metabolism in the causative agent of deadly sleeping sickness, Trypanosoma brucei, with that of human erythrocytes. The computational analysis revealed that inhibitors of the uptake of glucose would affect energy metabolism in T. brucei stronger than in erythrocytes. Computational predictions were validated experimentally in a novel parasite-erythrocytes co-culture system. The authors furthermore showed that glucose-transport inhibitors killed trypanosomes without killing neurons or liver cells.
This study shows that very promising and selective drug targets can exist outside the realm of the unique proteins and thereby extends the pool of putative, selective drug targets. The important next step is to translate this knowledge to actual drugs: to design and synthesise drug-like molecules that inhibit the glucose transporter of T. brucei and stay active inside the human body. Furthermore, this network-based approach to drug target selection can also be applied to other diseases like cancer and diabetes.