title: Computable features required to evaluate the efficacy of drugs and a universal algorithm to find optimally effective drug in a drug complex.
abstract: BACKGROUND
  The H1N1 pandemic in 2009 and the H5N1 pandemic in 2005 demonstrated that the drugs approved to treat influenza A viruses have low efficacy. This provided a stimulus for new studies of influenza A viruses in the context of the methods used in drug design developed over the past 100 years. Finding new universal drugs is the ultimate goal but its long time horizon is incompatible with emergency situations created by reoccurring influenza outbreaks. Therefore, we propose a computer-aided method for finding efficacious drugs and drug complexes based on the use of the DrugBank database.
  
  
  METHODS
  (1) We start by assembling a panel of target proteins. (2) We then assemble a panel of drugs. (3) This is followed by a selection of benchmark binding pockets based on the panel of target proteins and the panel of drugs. (4) We generate a set of computational features, which measure the efficacy of a drug. (5) We propose a universal program to search for drugs and drug complexes. (6) A case study we report here illustrates how to use this universal program for finding an optimal drug and a drug complex for a given target. (7) Validation of the Azirchromycin and Aspirin complex is provided mathematically. (8) Finally, we propose a simple strategy to validate our computational prediction that the Azirchromycin and Aspirin complex should prove clinically effective.
  
  
  RESULTS
  A set of computable features are mined and then based on these features, a universal program for finding the potential drug &drug complexes is proposed. Using this universal program, the Azirchromycin and Aspirin complex is selected and its efficacy is predicted mathematically. For clinical validation of this finding, future work is still required.
authors.affiliation: College of Mathematical Sciences and LPMC, Nankai University, Tianjin, People's Republic of China.
authors.forename: Kui
authors.initials: K
authors.lastname: Wang
authors.forename: Wei
authors.initials: W
authors.lastname: Cui
authors.forename: Gang
authors.initials: G
authors.lastname: Hu
authors.forename: Jianzhao
authors.initials: J
authors.lastname: Gao
authors.forename: Zhonghua
authors.initials: Z
authors.lastname: Wu
authors.forename: Xingye
authors.initials: X
authors.lastname: Qiu
authors.forename: Jishou
authors.initials: J
authors.lastname: Ruan
authors.forename: Yi
authors.initials: Y
authors.lastname: Feng
authors.forename: Zhi
authors.initials: Z
authors.lastname: Qi
authors.forename: Yiming
authors.initials: Y
authors.lastname: Shao
authors.forename: Jack A
authors.initials: JA
authors.lastname: Tuszynski
mesh_terms: D000465:Algorithms; D000998:Antiviral Agents / Q000737:chemistry / Q000494:pharmacology*; D053118:Influenza A Virus, H1N1 Subtype / Q000187:drug effects; D053124:Influenza A Virus, H5N1 Subtype / Q000187:drug effects; D008958:Models, Molecular
chemical_list: D000998:Antiviral Agents