Biocomputing drug repurposing toward targeted therapies

Targeted inhibition of the oncogenic activation of signalling pathways represents the main goal of drug discovery in oncology. Selective inhibitors of oncogenes such as mutated kinases and phosphatases have been identified mainly through traditional small molecule drug screenings aimed at identifying inhibitors of catalytic activity. However, ever-increasing failure rates, high costs, unsatisfactory safety profile, and limited efficacy are often associated with such traditional drug screenings. Moreover, the escalating costs of anticancer-targeted therapies are generating serious issue of sustainability for all healthcare systems. These inhibitors, even when effective, show paradigms of primary or secondary resistance [1,2]. Thus, additional strategies to identify oncogenic pathway inhibitors should be implemented. An exciting alternative in drug discovery is to repurpose old, well-known, FDA-approved drugs for novel therapeutic indications, an approach defined as drug repurposing or drug repositioning. Repositioning takes advantage of available pharmacokinetic and toxicity data on existing drugs, limits risks and costs, and thus, accelerates the implementation of new therapies [3]. In-silico bio-computational prediction for novel therapeutic indications of FDA-approved drugs can further reduce time and cost efforts necessary for integrating drug repositioning. Our recent paper [4] demonstrated that a specific bio-computational approach [5] could be successfully implemented for repurposing therapeutics able to inhibit oncogenically activated molecular pathways that have a wellestablished impact on molecular pathogenesis of cancer. This approach is based on modelling specific molecular alterations in cell lines, followed by generating an oncogene-specific gene signature. This molecular signature allowed the inspection of drug networkassociated signatures to reposition drugs able to “revert” the oncogenic signature and that could, potentially, act as pathway inhibitors. As a proof of principle, we focused on oncogenic PI3Kdependent signalling, a molecular pathway frequently driving cancer progression as well as raising resistance to anticancer-targeted therapies. We showed that the implementation of “reverse” oncogenic PI3K-dependent transcriptional signatures combined with the interrogation of drug networks identified inhibitors of Editorial