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Virtual patients (VPs) are widely used within quantitative systems pharmacology (QSP) modeling to explore the impact of variability and uncertainty on clinical responses. In one method of generating VPs, parameters are sampled randomly from a distribution, and possible VPs are accepted or rejected based on constraints on model output behavior. This approach works but can be inefficient (i.e., the vast majority of model runs typically do not result in valid VPs). Machine learning surrogate models offer an opportunity to improve the efficiency of VP creation significantly. In this approach, surrogate models are trained using the full QSP model and subsequently used to rapidly pre‐screen for parameter combinations that result in feasible VPs. The overwhelming majority of parameter combinations pre‐vetted using the surrogate models result in valid VPs when tested in the original QSP model. This tutorial presents this novel workflow and demonstrates how a surrogate model software application can be used to select and optimize the surrogate models in a case study. We then discuss the relative efficiency of the methods and scalability of the proposed method. [ABSTRACT FROM AUTHOR]

The pharmacokinetics (PKs) and safety of medications in particular groups can be predicted using the physiologically‐based pharmacokinetic (PBPK) model. Using the PBPK model may enable safe pediatric clinical trials and speed up the process of new drug research and development, especially for children, a population in which it is relatively difficult to conduct clinical trials. This review summarizes the role of pediatric PBPK (P‐PBPK) modeling software in dose prediction over the past 6 years and briefly introduces the process of general P‐PBPK modeling. We summarized the theories and applications of this software and discussed the application trends and future perspectives in the area. The modeling software's extensive use will undoubtedly make it easier to predict dose prediction for young patients. [ABSTRACT FROM AUTHOR]