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Preclinical Predictors of Anticancer Drug Efficacy: Critical Assessment with Emphasis on Nanomolar Potency

Published: Friday, April 13, 2012
Last Updated: Friday, April 13, 2012
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This article looks at prediction of cancer drug efficacy in its ultimate user, the human patient and reviews current knowledge followed by illustrative examples that, in the absence of definitive data, provide an empirical perspective.

In the current paradigm of anticancer drug development, candidate compounds are evaluated by testing their in vitro potency against molecular targets relevant to carcinogenesis; their effect on cultured cancer cells; and their ability to inhibit cancer growth in animal models. We discuss the key assumptions inherent in these approaches. In recent years, great emphasis has been placed on selecting for development compounds with nanomolar in vitro potency, expecting that they will be efficacious and safer based on the assumption that they can be used at lower doses (“the nanomolar rule”). However, this rule ignores critical parameters affecting efficacy and toxicity such as physiochemical and ADMET properties, off-target effects and multi-targeting activities. Thus, uncritical application of the nanomolar rule may reject efficacious compounds or select ineffective or toxic compounds. We present examples of efficacious chemotherapeutic (alkylating agents, hormonal agents, antimetabolites, thalidomide and valproic acid) and chemopreventive (aspirin and sulindac) agents having millimolar potency, and of compounds with nanomolar potency (COX-2 inhibitors) that, nevertheless, failed or proved unsafe. The effect of candidate drugs on animal models of cancer is a better predictor of human drug efficacy; particularly useful are tumor xenografts. Given the cost of failure at clinical stages, it is imperative to keep in mind the limitations of the nanomolar rule and to employ relevant in vivo models early in drug discovery to prioritize candidates. Although in vivo models will continue having a major role in cancer drug development, more robust approaches that combine high predictive ability with simplicity and low cost should be developed.

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