RT Journal Article SR Electronic T1 Preclinical Predictors of Anticancer Drug Efficacy: Critical Assessment with Emphasis on Whether Nanomolar Potency Should Be Required of Candidate Agents JF Journal of Pharmacology and Experimental Therapeutics JO J Pharmacol Exp Ther FD American Society for Pharmacology and Experimental Therapeutics SP 572 OP 578 DO 10.1124/jpet.112.191957 VO 341 IS 3 A1 C. C. Wong A1 Ka-Wing Cheng A1 Basil Rigas YR 2012 UL http://jpet.aspetjournals.org/content/341/3/572.abstract AB 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 absorption, distribution, metabolism and excretion properties, off-target effects, and multitargeting 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 compounds with nanomolar potency (cyclooxygenase-2 inhibitors) that, nevertheless, failed or proved to be 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 use 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.