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Development of Tumour-Selective and Endoprotease-Activated Anticancer Therapeutics

  • Jason H. Gill
  • Paul M. Loadman

Abstract

The therapeutic index for current chemotherapeutic agents is low based on the high frequency of systemic toxicities and the lack of selectivity between tumour and normal tissue. Our greater understanding both of the basis of cancer and the mechanisms that drive cancer growth has led to the design of therapeutics which exploit defined abnormalities responsible for the causation, maintenance, expansion or metastatic potential of the disease. Targeting enzymes centrally involved in the characteristic features of cancer is an attractive strategy for tumour-selective prodrug development since these will exploit the known phenotypic differences between ‘normal’ and tumour cells. One class of enzymes which satisfy all criteria for tumour-selective prodrug development and have been heavily implicated in tumour development and progression is the extracellular endopeptidases of the degradome. Tumour survival and expansion relies heavily upon the increased expression and activity of diverse extracellular endoproteases from multiple enzymatic classes, particularly the metallo-, serine, threonine, cysteine and aspartic proteases. In this chapter, studies utilising the increased endoprotease activity of tumours for selective drug delivery will be described. When considering this prodrug strategy for improvement of cancer treatment, it is important to remember that in addition to improving tumour-selective delivery of therapeutics, this whole strategy functions in parallel to reduce the restrictive toxicity of these agents against normal tissues, including liver, heart and bone marrow. In this sense, these prodrugs can be seen to have enormous clinical potential in any disease state involving increased activity of these endoproteases, including rheumatoid arthritis and other inflammatory diseases.

Keywords

Measle Virus Diphtheria Toxin Lethal Factor Anthrax Toxin Cysteine Cathepsin 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, LLC 2008

Authors and Affiliations

  • Jason H. Gill
    • 1
  • Paul M. Loadman
    • 1
  1. 1.Institute Cancer TherapeuticsUniversity of BradfordWest YorkshireUK

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