Mathematical modeling for novel cancer drug discovery and development

Ping Zhang, Vladimir Brusic

Research output: Contribution to journalArticle

15 Citations (Scopus)

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 languageEnglish
Pages (from-to)1133-1150
Number of pages18
JournalExpert Opinion on Drug Discovery
Volume9
Issue number10
DOIs
Publication statusPublished - Oct 1 2014
Externally publishedYes

Fingerprint

Drug Discovery
Neoplasms
Computer Simulation
Precision Medicine
Pharmaceutical Preparations
Theoretical Models
Expert Testimony
Early Detection of Cancer
Immunotherapy
Clinical Trials
Breast Neoplasms
Therapeutics
Growth
Population

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 journalArticle

Zhang, Ping ; Brusic, Vladimir. / Mathematical modeling for novel cancer drug discovery and development. In: Expert Opinion on Drug Discovery. 2014 ; Vol. 9, No. 10. pp. 1133-1150.
@article{2615481f8c624ec5a44ae8343272c993,
title = "Mathematical modeling for novel cancer drug discovery and development",
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.",
keywords = "Cancer, Computational models, Drug discovery, Mathematical modeling",
author = "Ping Zhang and Vladimir Brusic",
year = "2014",
month = "10",
day = "1",
doi = "10.1517/17460441.2014.941351",
language = "English",
volume = "9",
pages = "1133--1150",
journal = "Expert Opinion on Drug Discovery",
issn = "1746-0441",
publisher = "Informa Healthcare",
number = "10",

}

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

UR - http://www.scopus.com/inward/record.url?scp=84907442845&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907442845&partnerID=8YFLogxK

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 -