Optimal sampling design with nonparametric population modeling offers the opportunity to determine pharmacokinetic parameters for patients in whom blood sampling is restricted. This approach was compared to a standard individualized modeling method for meropenem pharmacokinetics in febrile neutropenic patients. The population modeling program, nonparametric approach of expectation maximization (NPEM), with a full data set was compared to a sparse data set selected by D-optimal sampling design. The authors demonstrated that the D-optimal sampling strategy, when applied to this clinical population, provided good pharmacokinetic parameter estimates along with their variability. Four individualized and optimally selected sampling time points provided the same parameter estimates as more intensive sampling regimens using traditional and population modeling techniques. The different modeling methods were considerably consistent, except for the estimation of CL(d) with sparse sampling. The findings suggest that D-optimal sparse sampling is a reasonable approach to population pharmacokinetic/pharmacodynamic studies during drug development when limited sampling is necessary.