Multiple target drug cocktail design for attacking the core network markers of four cancers using ligand-based and structure-based virtual screening methods.

Yung-Hao Wong,Yi-Hua Lai,Bor-Sen Chen,Chien-An Chen,Ting-Shou Chen,Lichieh Chu,Jeremy J W Chen,Pei-Shin Jiang,Cheng-Wei Li,Chih-Lung Lin

doi:10.1186/1755-8794-8-s4-s4

Abstract

BackgroundComputer-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.MethodsIn 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.ResultsWe 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.ConclusionsCombination 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.

Highlights

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
Inspired by the above ideas, based on the result of our previous systems biology studies [17], we have developed a novel multitarget cocktail therapy to focus on common core network markers of four different cancers
We developed a carcinogenesis relevance value (CRV) for each protein in the protein-protein interactions (PPI) differential network based on the total alternations of PPI interaction abilities with other proteins to approve the critical PPI changes during tumorigenesis process

Summary

Introduction

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. 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. Cancer is the leading cause of human death worldwide It is a complex set of diseases, and people have tried to reveal its underlying mechanisms to guide the development of novel therapy strategies. CADD methods can be divided into structurebased and ligand-based methods [9] Methods in the former category analyze both the structures of the target protein and the small molecule inhibitors to design drugs: examples include the docking method and molecular dynamics simulations. Methods in the latter category use only the structures of the small molecule inhibitors (drugs) to do statistical calculations to determine the relationship between a drug’s IC50 and its corresponding molecular properties: examples include HypoGen pharmacophore modeling, COMFA (and COMSIA) [10], and many other machine learning and regression methods [11,12]

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Conclusion