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Theoretical Biology and Medical Modelling

, 10:29

First Online: 01 May 2013Received: 22 February 2013Accepted: 17 April 2013DOI: 10.1186-1742-4682-10-29

Cite this article as: Ravanbakhsh, S., Gajewski, M., Greiner, R. et al. Theor Biol Med Model 2013 10: 29. doi:10.1186-1742-4682-10-29


BackgroundAs microtubules are essential for cell growth and division, its constituent protein β-tubulin has been a popular target for various treatments, including cancer chemotherapy. There are several isotypes of human β-tubulin and each type of cell expresses its characteristic distribution of these isotypes. Moreover, each tubulin-binding drug has its own distribution of binding affinities over the various isotypes, which further complicates identifying the optimal drug selection. An ideal drug would preferentially bind only the tubulin isotypes expressed abundantly by the cancer cells, but not those in the healthy cells. Unfortunately, as the distributions of the tubulin isotypes in cancer cells overlap with those of healthy cells, this ideal scenario is clearly not possible. We can, however, seek a drug that interferes significantly with the isotype distribution of the cancer cell, but has only minor interactions with those of the healthy cells.

MethodsWe describe a quantitative methodology for identifying this optimal tubulin isotype profile for an ideal cancer drug, given the isotype distribution of a specific cancer type, as well as the isotype distributions in various healthy tissues, and the physiological importance of each such tissue.

ResultsWe report the optimal isotype profiles for different types of cancer with various routes of delivery.

ConclusionsOur algorithm, which defines the best profile for each type of cancer given the drug delivery route and some specified patient characteristics, will help to personalize the design of pharmaceuticals for individual patients. This paper is an attempt to explicitly consider the effects of the tubulin isotype distributions in both cancer and normal cell types, for rational chemotherapy design aimed at optimizing the drug’s efficacy with minimal side effects.

KeywordsBioinformatics Drug design Cancer Tubulin Side effects Optimization AbbreviationsACAdenocarcinoma

EREstrogen receptor

IDCInfiltrating ductal carcinoma



mRNAMessenger ribonucleic acid


OTIPOptimal tubulin isotype profile

PDPoorly differentiated

PELAPeloruside A

SCSerous carcinoma

SCCSquamous cell carcinoma

WDWell differentiated.

Electronic supplementary materialThe online version of this article doi:10.1186-1742-4682-10-29 contains supplementary material, which is available to authorized users.

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Autor: Siamak Ravanbakhsh - Melissa Gajewski - Russell Greiner - Jack A Tuszynski


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