Predicting tacrolimus exposure in kidney transplanted patients using machine learning

TL;DR

This paper introduces a machine learning approach to predict tacrolimus exposure in kidney transplant patients, offering a faster alternative to traditional pharmacokinetic models with comparable accuracy.

Contribution

The study presents a novel machine learning method for estimating tacrolimus levels, reducing development time and knowledge requirements compared to existing models.

Findings

Predictive errors comparable to established pharmacokinetic models

Faster development and less pharmacokinetic knowledge needed

Potential for improved clinical dose adjustments

Abstract

Tacrolimus is one of the cornerstone immunosuppressive drugs in most transplantation centers worldwide following solid organ transplantation. Therapeutic drug monitoring of tacrolimus is necessary in order to avoid rejection of the transplanted organ or severe side effects. However, finding the right dose for a given patient is challenging, even for experienced clinicians. Consequently, a tool that can accurately estimate the drug exposure for individual dose adaptions would be of high clinical value. In this work, we propose a new technique using machine learning to estimate the tacrolimus exposure in kidney transplant recipients. Our models achieve predictive errors that are at the same level as an established population pharmacokinetic model, but are faster to develop and require less knowledge about the pharmacokinetic properties of the drug.