title: Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods.
abstract: BACKGROUND
  Computer-aided drug design has a long history of being applied to discover new molecules to treat various cancers, but it has always been focused on single targets. The development of systems biology has let scientists reveal more hidden mechanisms of cancers, but attempts to apply systems biology to cancer therapies remain at preliminary stages. Our lab has successfully developed various systems biology models for several cancers. Based on these achievements, we present the first attempt to combine multiple-target therapy with systems biology.
  
  
  METHODS
  In our previous study, we identified 28 significant proteins--i.e., common core network markers--of four types of cancers as house-keeping proteins of these cancers. In this study, we ranked these proteins by summing their carcinogenesis relevance values (CRVs) across the four cancers, and then performed docking and pharmacophore modeling to do virtual screening on the NCI database for anti-cancer drugs. We also performed pathway analysis on these proteins using Panther and MetaCore to reveal more mechanisms of these cancer house-keeping proteins.
  
  
  RESULTS
  We designed several approaches to discover targets for multiple-target cocktail therapies. In the first one, we identified the top 20 drugs for each of the 28 cancer house-keeping proteins, and analyzed the docking pose to further understand the interaction mechanisms of these drugs. After screening for duplicates, we found that 13 of these drugs could target 11 proteins simultaneously. In the second approach, we chose the top 5 proteins with the highest summed CRVs and used them as the drug targets. We built a pharmacophore and applied it to do virtual screening against the Life-Chemical library for anti-cancer drugs. Based on these results, wet-lab bio-scientists could freely investigate combinations of these drugs for multiple-target therapy for cancers, in contrast to the traditional single target therapy.
  
  
  CONCLUSIONS
  Combination of systems biology with computer-aided drug design could help us develop novel drug cocktails with multiple targets. We believe this will enhance the efficiency of therapeutic practice and lead to new directions for cancer therapy.
authors.forename: Yung-Hao
authors.initials: YH
authors.lastname: Wong
authors.forename: Chih-Lung
authors.initials: CL
authors.lastname: Lin
authors.forename: Ting-Shou
authors.initials: TS
authors.lastname: Chen
authors.forename: Chien-An
authors.initials: CA
authors.lastname: Chen
authors.forename: Pei-Shin
authors.initials: PS
authors.lastname: Jiang
authors.forename: Yi-Hua
authors.initials: YH
authors.lastname: Lai
authors.forename: Lichieh
authors.initials: L
authors.lastname: Chu
authors.forename: Cheng-Wei
authors.initials: CW
authors.lastname: Li
authors.forename: Jeremy J W
authors.initials: JJ
authors.lastname: Chen
authors.forename: Bor-Sen
authors.initials: BS
authors.lastname: Chen
mesh_terms: D014408:Biomarkers, Tumor / Q000737:chemistry* / Q000378:metabolism*; D017076:Computer-Aided Design; D015195:Drug Design; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D008024:Ligands; D062105:Molecular Docking Simulation; D058990:Molecular Targeted Therapy; D009369:Neoplasms / Q000188:drug therapy*; D017386:Sequence Homology, Amino Acid; D049490:Systems Biology / Q000379:methods*; D014584:User-Computer Interface
chemical_list: D014408:Biomarkers, Tumor; D008024:Ligands