title: Exploring a structural protein-drug interactome for new therapeutics in lung cancer.
abstract: The pharmacology of drugs is often defined by more than one protein target. This property can be exploited to use approved drugs to uncover new targets and signaling pathways in cancer. Towards enabling a rational approach to uncover new targets, we expand a structural protein-ligand interactome () by scoring the interaction among 1000 FDA-approved drugs docked to 2500 pockets on protein structures of the human genome. This afforded a drug-target network whose properties compared favorably with previous networks constructed using experimental data. Among drugs with the highest degree and betweenness two are cancer drugs and one is currently used for treatment of lung cancer. Comparison of predicted cancer and non-cancer targets reveals that the most cancer-specific compounds were also the most selective compounds. Analysis of compound flexibility, hydrophobicity, and size showed that the most selective compounds were low molecular weight fragment-like heterocycles. We use a previously-developed screening approach using the cancer drug erlotinib as a template to screen other approved drugs that mimic its properties. Among the top 12 ranking candidates, four are cancer drugs, two of them kinase inhibitors (like erlotinib). Cellular studies using non-small cell lung cancer (NSCLC) cells revealed that several drugs inhibited lung cancer cell proliferation. We mined patient records at the Regenstrief Medical Record System to explore the possible association of exposure to three of these drugs with occurrence of lung cancer. Preliminary in vivo studies using the non-small cell lung cancer (NCLSC) xenograft model showed that losartan- and astemizole-treated mice had tumors that weighed 50 (p < 0.01) and 15 (p < 0.01) percent less than the treated controls. These results set the stage for further exploration of these drugs and to uncover new drugs for lung cancer therapy.
authors.affiliation: Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indiana 46202, USA.
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Peng
authors.forename: Fang
authors.initials: F
authors.lastname: Wang
authors.forename: Liwei
authors.initials: L
authors.lastname: Li
authors.forename: Khuchtumur
authors.initials: K
authors.lastname: Bum-Erdene
authors.forename: David
authors.initials: D
authors.lastname: Xu
authors.forename: Bo
authors.initials: B
authors.lastname: Wang
authors.forename: Anthony A
authors.initials: AA
authors.lastname: Sinn
authors.forename: Karen E
authors.initials: KE
authors.lastname: Pollok
authors.forename: George E
authors.initials: GE
authors.lastname: Sandusky
authors.forename: Lang
authors.initials: L
authors.lastname: Li
authors.forename: John J
authors.initials: JJ
authors.lastname: Turchi
authors.forename: Shadia I
authors.initials: SI
authors.lastname: Jalal
authors.forename: Samy O
authors.initials: SO
authors.lastname: Meroueh
mesh_terms: D000818:Animals; D000970:Antineoplastic Agents / Q000737:chemistry* / Q000378:metabolism / Q000494:pharmacology*; D045744:Cell Line, Tumor; D004195:Disease Models, Animal; D055808:Drug Discovery; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy / Q000378:metabolism* / Q000473:pathology; D051379:Mice; D008954:Models, Biological; D011485:Protein Binding; D020543:Proteome; D015398:Signal Transduction / Q000187:drug effects*; D047368:Tumor Burden / Q000187:drug effects; D023041:Xenograft Model Antitumor Assays
chemical_list: D000970:Antineoplastic Agents; D020543:Proteome