Introduction of an artificial neural network-based method for concentration-time predictions.

ABSTRACT: Pharmacometrics and the application of population pharmacokinetic (PK) modeling play a crucial role in clinical pharmacology. These methods, which describe data with well-defined equations and estimate physiologically interpretable parameters, have not changed substantially during the past decades. Although the methods have proven their usefulness, they are often resource intensive and require a high level of expertise. We investigated whether a method based on artificial neural networks (ANNs) may provide an alternative approach for the prediction of concentration-time curve to supplement the gold standard methods. In this work, we used simulated data to overcome the requirement for a large clinical training data set, implemented a pharmacologically reasonable network architecture to impr

SUBMITTER: Bram DS 

PROVIDER: S-EPMC9197537 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature