Enhancing Pharmacokinetic Modeling with Fractional-Order Kinetics and Deep Learning

In this study, we present an innovative pharmacokinetic modeling that combines fractional order kinetics with deep learning techniques to improve the prediction accuracy of drug concentration distribution in biological systems. Traditional pharmacokinetic models rely on ordinary differential equations, which often fail to accurately predict complex drug behaviors. To address these limitations, we develop a fractional-order pharmacokinetic model that can more accurately represent the memory and genetic properties of biological systems. To complement this, we combine fractional-order pharmacokinetics with the predictive capabilities of Long Short-Term Memory (LSTM) models in recurrent neural networks (RNNs). Synthetic datasets were first generated by selecting appropriate pharmacokinetic parameters to simulate a range of drug behavior scenarios. The dataset was then used to train and optimize the LSTM model, aiming to improve the prediction of the fractional-order model and thus achieve higher accuracy. Compared to traditional models, our results show significantly better prediction accuracy, with lower root mean square error (RMSE) and higher coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) values across time. This study not only highlights the potential of combining fractional-order kinetics with deep learning to improve pharmacokinetic models, but also opens avenues for more personalized and accurate drug therapy planning.