Predicting and characterizing selective multiple drug treatments for metabolic diseases and cancer

Abstract

Background In the field of drug discovery, assessing the potential of multidrug therapies isa difficult task because of the combinatorial complexity (both theoretical andexperimental) and because of the requirements on the selectivity of the therapy.To cope with this problem, we have developed a novel method for the systematic insilico investigation of synergistic effects of currently available drugs ongenome-scale metabolic networks. Results The algorithm finds the optimal combination of drugs which guarantees theinhibition of an objective function, while minimizing the side effect on the othercellular processes. Two different applications are considered: finding drugsynergisms for human metabolic diseases (like diabetes, obesity and hypertension)and finding antitumoral drug combinations with minimal side effect on the normalhuman cell. The results we obtain are consistent with some of the availabletherapeutic indications and predict new multiple drug treatments. A clusteranalysis on all possible interactions among the currently available drugsindicates a limited variety on the metabolic targets for the approved drugs. Conclusion The in silico prediction of drug synergisms can represent an important tool forthe repurposing of drugs in a realistic perspective which considers also theselectivity of the therapy. Moreover, for a more profitable exploitation ofdrug-drug interactions, we have shown that also experimental drugs which have adifferent mechanism of action can be reconsider as potential ingredients of newmulticompound therapeutic indications. Needless to say the clues provided by acomputational study like ours need in any case to be thoroughly evaluatedexperimentally.