High drug attrition rates remain a critical issue in oncology drug development. A series of steps during drug development must be addressed to better understand the pharmacokinetic (PK) and pharmacodynamic (PD) properties of novel agents and, thus, increase their probability of success. As available data continues to expand in both volume and complexity, comprehensive integration of PK and PD information into a robust mathematical model represents a very useful tool throughout all stages of drug development. During the discovery phase, PK/PD models can be used to identify and select the best drug candidates, which helps characterize the mechanism of action and disease behavior of a given drug, to predict clinical response in humans, and to facilitate a better understanding about the potential clinical relevance of preclinical efficacy data. During early drug development, PK/PD modeling can optimize the design of clinical trials, guide the dose and regimen that should be tested further, help evaluate proof of mechanism in humans, anticipate the effect in certain subpopulations, and better predict drug-drug interactions; all of these effects could lead to a more efficient drug development process. Because of certain peculiarities of immunotherapies, such as PK and PD characteristics, PK/PD modeling could be particularly relevant and thus have an important impact on decision making during the development of these agents.