When conducting a PK/pharmacodynamic (PD) analysis, information regarding the full timeconcentration profile of a drug in vivo is more essential than exposure-response data, as it highlights subtle changes due to time elapsed . Previous anti-bacterial research has revealed that three PK/PD parameters that consider time are important, and these parameters have been used extensively since the 1980s : the ratio of maximum concentration to minimum inhibitory concentration (MIC), ratio of area under the concentration-time curve (AUC) to MIC, and time concentration above MIC. This concept was recently developed further, such that during PK/PD analyses, the whole time-concentration profile is used . Here, it should be noted that most Kp,brain or Kp,uu,brain data are not appropriate for PK/PD analyses, because although the equilibrium of the compounds' concentrations between plasma and brain is assumed when calculating these values (Figure ), in reality, some compounds show different phasing of concentration-time profiles between the plasma and brain (brain penetration often introduces a delay in the plasma concentration profile [Figure ]) . Even in this case, Kp,brain or Kp,uu,brain can technically be calculated using the AUC for the plasma and brain. However, as too many animals must be sacrificed for brain sampling at each time point (for plasma sampling, the same animal can be used for each time point), the number of sampling time points must be limited, resulting in too few time points to accurately calculate the AUC (Figure ). Based on these practical considerations, predicted compound concentration-time profiles are needed, and modeling and simulation (M&S) approaches, such as PK modeling and/or physiologically based PK (PBPK) modeling, are frequently used for this purpose . However, M&S approaches are typically used for individual drugs, as these models require a wide variety of kinetic parameters for which the values are determined experimentally or by fitting to the observed drug concentration . We know of only a few groups that have proposed M&S studies for a series of compounds oriented toward the drug discovery stage, in which many congeneric compounds are synthesized and analyzed in a trial-and-error manner. In 2018, a full PBPK modeling approach incorporating QSAR models using machine learning (ML) of ADME (absorption, distribution, metabolism and excretion) factors such as permeability, solubility, and intrinsic clearance was proposed to simulate various compounds' concentration-time profiles for bioavailability . Moreover, in 2021 and 2022, simplified PBPK models were proposed that employ kinetic parameters predicted using ML models to simulate the concentration-time profiles in plasma (humans and rats) and tissues such as liver and kidney (rats) .