In population pharmacokinetics (PopPK), non-linear mixed effects (NLME) models are used to simultaneously describe a drug's pharmacokinetics (PK) and dynamics (PD) in a patient population using systems of ordinary differential equations. In this field, machine learning is mainly used for data preparation, hypothesis generation, predictive modeling, and model validation. Some approaches to integrate artificially generated information have already been explored, but real-world application is still limited. We therefore conducted a proof-of-concept study to analyze the ability of generative artificial intelligence (AI) to create artificial patient profiles to augment PopPK data sets and assess their influence on parameter estimates. We defined the pharmacokinetic parameters of a hypothetical drug and simulated the concentration curves of 20 patients. We then trained Wasserstein Generative Adversarial Networks (WGANs) with gradient penalty (GP) to generate artificial patients. The data distribution of original and artificial patients was statistically indistinguishable as shown by the Maximum Mean Discrepancy test. Therefore, the WGAN-GP is neither overfitted, that is producing only single instances of artificial patients, nor underfitted, that is producing unrealistic artificial patients. We then combined different shares of original and artificial patients in separate data sets to build and compare PopPK model estimates. Addition of artificial patients led to narrower confidence intervals, indicating more robust parameter estimates, and accentuated the allometric effect of weight on the volume of distribution. In conclusion, we provide a proof-of-concept that generative AI can be used to augment pharmacokinetic data sets, with preliminary evidence suggesting improved parameter estimation.
Schöning et al. (Wed,) studied this question.