In the drug discovery and development setting, the ability to accurately predict the human pharmacokinetics (PK) of a candidate compound from preclinical data is critical for informing the effective design of the first-in-human trial. PK prediction is especially challenging for monoclonal antibodies exhibiting nonlinear PK attributed to target-mediated drug disposition (TMDD). Here, we present a model-based method for predicting the PK of PF-03446962, an IgG2 antibody directed against human ALK1 (activin receptor-like kinase 1) receptor. Systems parameters as determined experimentally or obtained from the literature, such as binding affinity (k(on) and k(off)), internalization of the drug-target complex (k(int)), target degradation rate (k(deg)), and target abundance (R(0)), were directly integrated into the modeling and prediction. NONMEM 7 was used to model monkey PK data and simulate human PK profiles based on the construct of a TMDD model using a population-based approach. As validated by actual patient data from a phase I study, the human PK of PF-03446962 were predicted within 1- to 2-fold of observations. Whereas traditional approaches fail, this approach successfully predicted the human PK of a monoclonal antibody exhibiting nonlinearity because of TMDD.