Physiology-Informed Conditional Variational Autoencoder for Generating Pediatric Virtual Patients

Reliable pediatric virtual patients are essential for model-informed simulations, including physiologically based pharmacokinetic (PBPK) modeling, to support dose selections in children and to evaluate drug exposure across developmental stages. Despite the availability of extensive pediatric physiological data and age- or size-based models, there remains a lack of well-established, flexible, and scalable approaches for integrating these data into realistic pediatric virtual patients that preserve multivariate physiological correlations and whole-body coherence across diverse clinical conditions and population needs. In this proof-of-concept study, we developed a physiology-informed conditional variational autoencoder (cVAE) to address this challenge. The model was trained using real-world pediatric data augmented with mechanistically derived physiological information and conditioned on age and sex. It generated realistic physiological parameters, including body size, estimated glomerular filtration rate, organ weights, and blood flows, while biological plausibility was maintained through embedded physiological constraints. The trained model demonstrated high reconstruction accuracy, with a mean absolute error of 0.0043 and an R{superscript 2} of 0.998, and the generated distributions closely matched those of the training data. All synthesized physiological profiles satisfied predefined physiological constraints, with total organ mass remaining below body weight and the sum of organ blood flows not exceeding cardiac output. Latent-space analyses further revealed smooth developmental patterns, enabling targeted physiological profile generation. The applicability of the generated physiological data was further demonstrated through PBPK simulations conducted across the pediatric age range using vancomycin as a testbed. Overall, this physiology-informed generative framework supports coherent pediatric virtual patient generation for PBPK modeling and model-informed dosing applications development. Study HighlightsO_ST_ABSWhat is the current knowledge on the topic?C_ST_ABSModel-informed simulation, including physiologically based pharmacokinetic (PBPK) modeling, provides useful estimation of pediatric drug disposition across developmental stages and supports pediatric dose selection. However, constructing physiologically coherent pediatric virtual populations remains challenging. Although real-world pediatric measurements and physiologically derived, function-based information are available, these data are typically obtained from heterogeneous sources. Integrating them to generate multivariate physiological profiles at the individual level, while maintaining internal coherence across interconnected organ systems, remains an open challenge in pediatric pharmacometric modeling. What question did this study address?Can a conditional generative modeling and latent representation learning framework that integrates real-world pediatric data with mechanistically derived physiological constraints generate biologically coherent, multivariate pediatric physiological profiles that are suitable for downstream PBPK modeling across the full pediatric age range? What does this study add to our knowledge?This study introduces a physiology-informed conditional variational autoencoder that learns a smooth, interpretable latent space of pediatric physiology conditioned on age and sex. By embedding physiological constraints directly into the training objective, the model generates virtual pediatric patients with internally consistent body size, renal function, organ weights, and blood flows. The utility of these generated profiles was demonstrated through latent-space inversion and vancomycin PBPK simulations that reproduced reported age-dependent exposure trends and variability. How might this change clinical pharmacology or translational science?This framework provides a scalable approach for generating physiologically coherent pediatric virtual populations, laying a foundation for robust and flexible mechanistic PK simulations, virtual clinical trials, and digital twin applications. It offers a practical bridge between real-world pediatric data and mechanistic modeling, supporting model-informed dosing and translational decision-making in pediatric patient care and drug development.