Population pharmacokinetic (PK) analyses have been successfully incorporated into drug dosing optimization; however, these analyses necessitate relatively large patient cohorts that many clinical trials do not have the luxury of affording. To address this problem, we developed an approach that utilizes physiologically based pharmacokinetic (PBPK) modeling coupled with Monte Carlo simulation to generate a virtual population, complete with associated patient characteristics and PK data, for population PK analysis. For this work, we used a previously published PBPK model for docetaxel and found that the systemic clearance of this drug was significantly affected by blood volume, slowly perfused tissue volume, and two liver metabolic parameters--the maximum rate of liver metabolism and the Michaelis constant for liver metabolism. These findings, as well as the PK variability predictions, are consistent with those previously associated with docetaxel clearance in population PK analyses performed with actual patient populations, namely plasma protein levels, body size, and hepatic function. Thus, this in silico exercise demonstrates the utility of simulation modeling coupled to population PK analysis for the estimation of PK variability and the identification of patient characteristics that affect a drug's PK in the absence of data assembled from large clinical trials.