Enhancing Pharmacokinetic Modeling with Fractional-Order Kinetics and Deep Learning

Junting Zou*, Mohd Rizal Arshad, Ziyan Wang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

Abstract

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 (R2) 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.

Original languageEnglish
Title of host publication14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages46-51
Number of pages6
ISBN (Electronic)9798350364507
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024 - Penang, Malaysia
Duration: 23 Aug 202424 Aug 2024

Publication series

Name14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024 - Proceedings

Conference

Conference14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024
Country/TerritoryMalaysia
CityPenang
Period23/08/2424/08/24

Keywords

  • Deep learning
  • Fractional-order kinetics
  • LSTM
  • Pharmacokinetic

Fingerprint

Dive into the research topics of 'Enhancing Pharmacokinetic Modeling with Fractional-Order Kinetics and Deep Learning'. Together they form a unique fingerprint.

Cite this