Systems multiple molecule drug design with less side-effects via drug data mining and genome-wide data identification

Background Drugs fail in the clinic for two main reasons; one is that they do not work and another is that they are not safe. As such, two of the most important steps in developing new drugs should be drug targets identification and side-effect validation. Aims The identification of drug targets and their restoration of cellular dysfunctions to normal cellular functions with less side-effects are considered as drug design specifications of systems medicine discovery. Since the effect on the normal expression of house-keeping genes and proteins is also considered as a restriction on drug design, the proposed multi-molecules drug strategy might be helpful for systems drug design with less-side effects. Methods By systems biology method, genetic and epigenetic networks (GENs) are constructed to identify network biomarker for drug targets of diseases by genome-wide high throughput data. An integration of computational networkbased approach for multiple drug targets with drug data mining is also proposed for systems drug discovery with more precise medicine and less side-effects. Finally, some systematic drug design specifications for drug design are proposed to restore to the normal functions of multiple drug targets with less side-effects. Results A systematic method is introduced to find multiple drug targets based on pathogenic mechanism investigated by network identification through genome-wide highthroughput data. Then a multi-molecule drug design strategy is also proposed to select a set of multi-molecule drugs with less side-effects via drug data mining method. Conclusion Systematic engineering design methods seem applicable to systems drug discovery and design.