In recent years, Physiologically-Based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for assessment of drug PK. It has demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio-pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug-drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism-based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism-based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp® software. The aim of this report was to establish confidence in each CYP-specific modulator file so they can be used in the future for prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned quinidine inhibitor model which requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.