Mathematical modeling for novel cancer drug discovery and development

Ping Zhang, Vladimir Brusic

Research output: Journal PublicationReview articlepeer-review

20 Citations (Scopus)


Expert opinion: 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.

Introduction: 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.

Areas covered: 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.

Original languageEnglish
Pages (from-to)1133-1150
Number of pages18
JournalExpert Opinion on Drug Discovery
Issue number10
Publication statusPublished - 1 Oct 2014
Externally publishedYes


  • Cancer
  • Computational models
  • Drug discovery
  • Mathematical modeling

ASJC Scopus subject areas

  • Drug Discovery


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