Literature DB >> 23831857

Mathematical modeling in cancer drug discovery.

Zhihui Wang1, Thomas S Deisboeck2.   

Abstract

Mathematical models have the potential to help discover new therapeutic targets and treatment strategies. In this review, we discuss how the latest developments in mathematical modeling can provide useful context for the rational design, validation and prioritization of novel cancer drug targets and their combinations. We give special attention to two modeling approaches: network-based modeling and multiscale modeling, because they have begun to show promise in facilitating the process of effective cancer drug discovery. Both modeling approaches are integrated with a variety of experimental methods to ensure proper parameterization and to maximize their predictive value. We also discuss several challenges faced in modeling-based drug discovery.
Copyright © 2013 Elsevier Ltd. All rights reserved.

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Year:  2013        PMID: 23831857     DOI: 10.1016/j.drudis.2013.06.015

Source DB:  PubMed          Journal:  Drug Discov Today        ISSN: 1359-6446            Impact factor:   7.851


  25 in total

Review 1.  Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers.

Authors:  Robert Clarke; John J Tyson; Ming Tan; William T Baumann; Lu Jin; Jianhua Xuan; Yue Wang
Journal:  Endocr Relat Cancer       Date:  2019-06       Impact factor: 5.678

Review 2.  [Personalization in the medicine of the future : Opportunities and risks].

Authors:  N P Malek
Journal:  Internist (Berl)       Date:  2017-07       Impact factor: 0.743

Review 3.  Integrated PK-PD and agent-based modeling in oncology.

Authors:  Zhihui Wang; Joseph D Butner; Vittorio Cristini; Thomas S Deisboeck
Journal:  J Pharmacokinet Pharmacodyn       Date:  2015-01-15       Impact factor: 2.745

4.  Evaluation of uptake and distribution of gold nanoparticles in solid tumors.

Authors:  Christopher G England; André M Gobin; Hermann B Frieboes
Journal:  Eur Phys J Plus       Date:  2015-11-19       Impact factor: 3.911

5.  Editorial: Special Section on Multiscale Cancer Modeling.

Authors:  Zhihui Wang; Philip K Maini
Journal:  IEEE Trans Biomed Eng       Date:  2017-02-22       Impact factor: 4.538

Review 6.  Simulating cancer growth with multiscale agent-based modeling.

Authors:  Zhihui Wang; Joseph D Butner; Romica Kerketta; Vittorio Cristini; Thomas S Deisboeck
Journal:  Semin Cancer Biol       Date:  2014-05-02       Impact factor: 15.707

Review 7.  Toward a science of tumor forecasting for clinical oncology.

Authors:  Thomas E Yankeelov; Vito Quaranta; Katherine J Evans; Erin C Rericha
Journal:  Cancer Res       Date:  2015-01-15       Impact factor: 12.701

8.  Understanding Drug Resistance in Breast Cancer with Mathematical Oncology.

Authors:  Terisse Brocato; Prashant Dogra; Eugene J Koay; Armin Day; Yao-Li Chuang; Zhihui Wang; Vittorio Cristini
Journal:  Curr Breast Cancer Rep       Date:  2014-06-01

Review 9.  Multiscale Modeling in the Clinic: Drug Design and Development.

Authors:  Colleen E Clancy; Gary An; William R Cannon; Yaling Liu; Elebeoba E May; Peter Ortoleva; Aleksander S Popel; James P Sluka; Jing Su; Paolo Vicini; Xiaobo Zhou; David M Eckmann
Journal:  Ann Biomed Eng       Date:  2016-02-17       Impact factor: 3.934

10.  Development of a Physiologically-Based Mathematical Model for Quantifying Nanoparticle Distribution in Tumors.

Authors:  Prashant Dogra; Yao-Li Chuang; Joseph D Butner; Vittorio Cristini; Zhihui Wang
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2019-07
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