We have proposed a novel method by population pharmacokinetics analysis for forecasting the drug concentration time-course in humans. This method is based on the non-linear mixed effect model (NONMEM) combined with in vitro-in vivo extrapolation (IVIVE). Eleven clinically tested compounds were selected for retrospective analysis. The in vivo pharmacokinetic (pk) profiles (rats, dogs, monkeys, and humans) and in vitro ADME data [intrinsic clearance (CLint), plasma unbound fraction (fp), and blood-plasma partition ratio (Rb)] for each compound was routinely tested via a screening system to account for inter-compound differences in pk properties. When evaluating the pk parameters, the hepatic plasma flow (Qph) and plasma volume (Vp) were taken into account to compensate for differences in body size among species. All these data were used to conduct population pk (PPK) analyses under the hypothesis that all species constituted one population. The two-compartment model (ADVAN4 TRANS3) and NONMEM software were used for this analysis. The fixed effect model for total body clearance (CL) and central distribution volume (Vd) were constructed as theta(CL)Qph x Eh and theta(Vd) x Vp, respectively, where the hepatic extraction ratio Eh was calculated using the traditional dispersion model. NONMEM generates both fixed and random effects (eta). The key point of this concept was to substitute the eta values of each species (rats, dogs, and monkeys) into the human PPK model to simulate three kinds of pk profiles, compound by compound, for use as a general scaling factor. The NONMEM post hoc option was used to perform the simulation, after which the concentration vs. time courses were compared with actual clinical pk data. The true values were almost within the dynamic range. Thus, the advantage of this concept is that it can generate time-courses without the detail of drug-specific parameters, from which the elimination half time can be determined. This proposed exploratory population pharmacokinetic (e-PPK) approach is a useful and progressive tool that can be applied during the early stages of drug discovery research.