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Mouse models of cancer have played an important role in the discovery and development of cytotoxic and targeted anticancer agents. Initially, discovery models used transplantable syngeneic tumors that were treated with doses maximally tolerated by the normal tissues of the mouse. Thus, experimental compounds were selected for development based on their selectivity for murine malignant tissue over murine normal tissue, and the discovery method assumed that a murine therapeutic index closely approximates a human therapeutic index for most compounds. When mouse modeling migrated to the use of xenografted human malignancies in order that drug efficacy assessment would be more relevant for clinical disease, there was not a corresponding transition to human normal tissue to determine the maximum tolerated dose (MTD) to use in treating the mouse. Consequently, drug discovery in these models has been based on selectivity for human malignant tissue over murine normal tissue. This ˵xeno-therapeutic index″ has an unknown relationship either to a mouse or human therapeutic index – these latter ones being determined by efficacy against cancer at the maximum dose tolerated by normal tissues from the same species of origin as the cancer. The biological process of producing new blood cells is termed hematopoiesis, and this process is a frequent target of toxicity of anticancer drugs, including toxicity that limits dose. Two recently established methods in experimental hematology and hematotoxicology are useful for determining the MTD of human hematopoiesis in the mouse. The methods are suitable for implementation in the drug discovery setting, and therefore could be used to discover new anticancer compounds that exhibit selectivity for malignant human tissue over normal, human hematopoiesis in mouse models. This chapter provides background information on hematopoiesis, explains why it frequently limits the dose of anticancer drugs, describes the two methods, and then proposes ways in which the methods might contribute to mouse modeling of cancer therapy to improve predictive accuracy for clinical outcome. [ABSTRACT FROM AUTHOR]