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Endpoints for Determination of Efficacy of Antiangiogenic Agents in Clinical Trials

  • William J. Gradishar
Part of the Cancer Drug Discovery and Development book series (CDD&D)

Abstract

The fiield of angiogenesis research has undergone dramatic growth in recent years, as evidenced by the massive number of citations appearing in the medical literature (1). Until very recently, the focus of most work has been on defining the steps in the angiogenesis cascade, and the naturally occurring factors that both induce and inhibit the process. Over 20 years ago, Judah Folkman postulated that if tumor angiogenesis could be inhibited, then a potentially novel and effective treatment strategy could be developed for solid tumors (2). That hope is now coming to fruition as novel agents that inhibit tumor angiogenesis are entering clinical trials. As outlined in previous chapters, many of the compounds that are known to inhibit angiogenesis behave in a cytostatic fashion. Unlike classic cytotoxic chemotherapy agents, inhibitors oftumor angiogenesis may not cause tumor shrinkage but instead maintain a stable tumor size. The challenge facing clinical investigators and pharmaceutical companies developing these drugs is to carefully and thoughtfully design clinical trials that take into consideration the cytostatic behavior of angiogenesis inhibitors (AI) (3). Assessments of efficacy of AIs must avoid the trap of requiring tumor shrinkage
Table 1

Original Fibonacci Number Series Used for Dose Escalation of a Drug in a Phase I Clinical Trial

Mulitiole of initial number

%Increase over previous dose

1n

-

2n

100

3n

50

5n

67

8n

60

13n

63

21n

62

34n

61

in order to continue clinical development. As a result, end points other than tumor regression must be incorporated into clinical trial design.

Keywords

Breast Cancer Vascular Endothelial Growth Factor Hepatocyte Growth Factor Overall Response Rate Angiogenesis Inhibitor 
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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© Springer Science+Business Media New York 1999

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  • William J. Gradishar

There are no affiliations available

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