title: Mathematical modeling for novel cancer drug discovery and development.
abstract: BACKGROUND
  Mathematical modeling enables: the in silico classification of cancers, the prediction of disease outcomes, optimization of therapy, identification of promising drug targets and prediction of resistance to anticancer drugs. In silico pre-screened drug targets can be validated by a small number of carefully selected experiments.
  
  
  METHODS
  This review discusses the basics of mathematical modeling in cancer drug discovery and development. The topics include in silico discovery of novel molecular drug targets, optimization of immunotherapies, personalized medicine and guiding preclinical and clinical trials. Breast cancer has been used to demonstrate the applications of mathematical modeling in cancer diagnostics, the identification of high-risk population, cancer screening strategies, prediction of tumor growth and guiding cancer treatment.
  
  
  CONCLUSIONS
  Mathematical models are the key components of the toolkit used in the fight against cancer. The combinatorial complexity of new drugs discovery is enormous, making systematic drug discovery, by experimentation, alone difficult if not impossible. The biggest challenges include seamless integration of growing data, information and knowledge, and making them available for a multiplicity of analyses. Mathematical models are essential for bringing cancer drug discovery into the era of Omics, Big Data and personalized medicine.
authors: CSIRO Computational Informatics , Marsfield, NSW , Australia.
authors: Ping
authors: P
authors: Zhang
authors: Vladimir
authors: V
authors: Brusic
mesh_terms: D000970:Antineoplastic Agents / Q000009:adverse effects / Q000737:chemistry / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000175:diagnosis / Q000188:drug therapy / Q000209:etiology; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D055808:Drug Discovery / Q000379:methods*; D005260:Female; D006801:Humans; D008954:Models, Biological; D012307:Risk Factors
chemical_list: D000970:Antineoplastic Agents
keywords: cancer; computational models; drug discovery; mathematical modeling