Development and Validation of Open-Source R Package HMCtdm for Therapeutic Drug Monitoring

Sooyoung Lee, Moonsik Song, Woojae Lim, Eunjung Song, Jongdae Han, Bo Hyung Kim

Research output: Contribution to journalArticlepeer-review

Abstract

Most therapeutic drug monitoring (TDM) packages are based on the maximum a posteriori (MAP) estimation. In this study, HMCtdm, a new TDM package, was developed using a Hamiltonian Monte Carlo (HMC) simulation. The estimation process of HMCtdm for the drugs amikacin, vancomycin, theophylline, and phenytoin was based on the R package Torsten. The prior pharmacokinetic (PK) models of the drugs were derived from the Abbottbase® pharmacokinetics systems (PKS) program. The performance of HMCtdm for each drug was assessed through internal and external validations. The internal validation results of the HMCtdm were compared with those of a MAP-based estimation. The developed open-source HMCtdm package is user friendly. The validation results were reviewed and interpreted using the mean percentage error and root mean squared error. The successful transplantation of the prior PK structures (used in PKS) was confirmed by comparing the validation results with a MAP estimation. An open-source HMC-based TDM package was also successfully developed in this study, and its performance was evaluated. This package can be operated by users unfamiliar with C++ and can be further developed for various applications.

Original languageEnglish
Article number127
JournalPharmaceuticals
Volume15
Issue number2
DOIs
Publication statusPublished - Feb 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Bayesian method
  • HMC
  • MAP
  • Pharmacokinetic
  • Simulation

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