Quantitative characterization of pharmacokinetics (PK) and pharmacodynamics (PD) of drugs is an essential component in pharmacology/toxicology education as well as drug discovery/development. PK determines the relationship between the dose of the administered drug and the concentration measured in the body, whereas PD characterizes the extent of pharmacological effect induced by drug concentrations. Simulation and modeling of such properties is a powerful tool to predict drug's efficacy and toxicity with the limited dose‐concentration‐response data. Several software tools, such as WinNonlin, Simcyp, GastroPlus and SAAM II, are traditionally used for PK/PD modeling and simulation. However, these tools have not demonstrated the dynamic flexibility in the generation of novel modeling approaches in both academic and research settings. While MATLAB has been extensively used in the field of Science and Engineering with a variety of applications, SimBiology provides an app to model, simulate, and analyze dynamic systems, focusing on PK/PD and quantitative systems pharmacology (QSP) applications. It also provides a block diagram editor for building models, or the user can create models programmatically using the MATLAB language. Moreover, SimBiology includes a library of common PK models, which one can customize and integrate with mechanistic systems biology models. Consequently, MATLAB/SimBiology provides a comprehensive tool that can help to train young scientists in the field of pharmaceutical sciences, including pharmacology and toxicology. While our graduate program at Northeastern University offers several important features of fundamental PK/PD principles and advanced PK/PD analyses, an opportunity to have a hands‐on experience on PK/PD simulation and modeling has not been provided. In Spring 2016, we incorporated SimBiology as a modeling and simulation tool to our graduate Advanced Pharmacokinetics and Toxicology course through a collaboration with MathWorks. We then assessed the benefits of SimBiology in students' learning based on class participation and problem sets/exams, as well as students' survey through Teacher Rating and Course Evaluation (TRACE). The results demonstrated that SimBiology improved students' learning in several TRACE sections, including “The classroom technology helped me to learn”, “I found this course intellectually challenging”, and “I learned a lot in this course”. Our results also indicate the benefits and importance of hands‐on experience in modeling software to better understand PK/PD in pharmacology.Support or Funding InformationThis project was supported by MathWorks Curriculum Development Grant.