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https://doi.org/10.1...arrow_drop_down
https://doi.org/10.1007/978-1-...
Part of book or chapter of book . 1999 . Peer-reviewed
Data sources: Crossref
https://doi.org/10.1385/0-8960...
Part of book or chapter of book . 2003 . Peer-reviewed
Data sources: Crossref
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Endpoints for Determination of Efficacy of Antiangiogenic Agents in Clinical Trials

Authors: William J. Gradishar;

Endpoints for Determination of Efficacy of Antiangiogenic Agents in Clinical Trials

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.

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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