50
Views
1
CrossRef citations to date
0
Altmetric
Reviews

Functional drug candidate profiling using complex human organotypic cell culture models: a promising way to reduce clinical drug failure

Pages 935-947 | Published online: 08 Aug 2007
 

Abstract

The human immune system is one of the most important causative factors in the pathogenesis of various chronic inflammatory diseases. The network of immunoregulatory signals ruling the course of such disorders is highly complex and does not only involve the cells of the immune system, but also those of the diseased organs. This makes it rather difficult to model such regulatory processes in vitro. Most existing cell culture systems suffer from a serious lack of complexity. This is one of the major reasons why the majority of drug candidates fail in patients. Drugs that are meant to mono-specifically counter-regulate a disease process tend to develop unpredictable responses, due to unknown crosslinks in signalling pathways that are not available in simple culture models. Thus, the candidate selection as it is used at present, can be improved substantially by using more elaborate, in-vivo-like human cell culture models. Such test systems will accelerate the drug development process and decrease the risk of candidate failure. This review describes a new set of human organotypic, reproducible routine cell culture models, developed to optimise the drug candidate selection process and to increase clinical success rates. It is expected that models like these will accelerate drug development in the future substantially.

Notes

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 99.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 1,340.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.