Development of Farnesyl Transferase Inhibitors: A Review

Abstract

Learning Objectives: After completing this course, the reader will be able to: Describe the potential mechanisms by which farnesyl transferases inhibit tumor growth.Explain possible mechanisms by which tumor cells may develop resistance to this class of agents.Discuss the scientific requirements for developing targeted cancer treatments that will actually be useful in patients. Access and take the CME test online and receive 1 hour of AMA PRA category 1 credit at CME.TheOncologist.com Farnesyl transferase inhibitors are a new class of biologically active anticancer drugs. The exact mechanism of action of this class of agents is, however, currently unknown. The drugs inhibit farnesylation of a wide range of target proteins, including Ras. It is thought that these agents block Ras activation through inhibition of the enzyme farnesyl transferase, ultimately resulting in cell growth arrest. In preclinical models, the farnesyl transferase inhibitors showed great potency against tumor cells; yet in clinical studies, their activity was far less than anticipated. Reasons for this disappointing clinical outcome might be found in the drug-development process. In this paper, we outline an algorithm that is potentially useful for the development of biologically active anticancer drugs. The development of farnesyl transferase inhibitors, from discovery to clinical trials, is reviewed on the basis of this algorithm. We found that two important steps of this algorithm were underestimated. First, understanding of the molecular biology of the defective pathway has mainly been focused on H-Ras activation, whereas activation of K-Ras or other farnesylated proteins is probably more important in tumorigenesis. Inhibition of farnesylation is possibly not sufficient, because geranylgeranylation might activate K-Ras and suppress the effect of farnesyl transferase inhibitors. Furthermore, a well-defined proof of concept in preclinical and clinical studies has not been achieved. Integrating the proposed algorithm in future studies of newly developed biologically active anti-cancer drugs might increase the rate of success of these compounds in patients.