Abstract
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 language | English |
|---|---|
| Pages (from-to) | 1133-1150 |
| Number of pages | 18 |
| Journal | Expert Opinion on Drug Discovery |
| Volume | 9 |
| Issue number | 10 |
| DOIs | |
| Publication status | Published - Oct 1 2014 |
| Externally published | Yes |
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Keywords
- Cancer
- Computational models
- Drug discovery
- Mathematical modeling
ASJC Scopus subject areas
- Drug Discovery
- Medicine(all)
Cite this
Mathematical modeling for novel cancer drug discovery and development. / Zhang, Ping; Brusic, Vladimir.
In: Expert Opinion on Drug Discovery, Vol. 9, No. 10, 01.10.2014, p. 1133-1150.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Mathematical modeling for novel cancer drug discovery and development
AU - Zhang, Ping
AU - Brusic, Vladimir
PY - 2014/10/1
Y1 - 2014/10/1
N2 - 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.
AB - 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.
KW - Cancer
KW - Computational models
KW - Drug discovery
KW - Mathematical modeling
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U2 - 10.1517/17460441.2014.941351
DO - 10.1517/17460441.2014.941351
M3 - Article
VL - 9
SP - 1133
EP - 1150
JO - Expert Opinion on Drug Discovery
JF - Expert Opinion on Drug Discovery
SN - 1746-0441
IS - 10
ER -