Abstract
Drug repositioning identifies new indications for known drugs. Here we report repositioning of the malaria drug amodiaquine as a potential anti-cancer agent. While most repositioning efforts emerge through serendipity, we have devised a computational approach, which exploits interaction patterns shared between compounds. As a test case, we took the anti-viral drug brivudine (BVDU), which also has anti-cancer activity, and defined ten interaction patterns using our tool PLIP. These patterns characterise BVDU's interaction with its target s. Using PLIP we performed an in silico screen of all structural data currently available and identified the FDA approved malaria drug amodiaquine as a promising repositioning candidate. We validated our prediction by showing that amodiaquine suppresses chemoresistance in a multiple myeloma cancer cell line by inhibiting the chaperone function of the cancer target Hsp27. This work proves that PLIP interaction patterns are viable tools for computational repositioning and can provide search query information from a given drug and its target to identify structurally unrelated candidates, including drugs approved by the FDA, with a known safety and pharmacology profile. This approach has the potential to reduce costs and risks in drug development by predicting novel indications for known drugs and drug candidates.