  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.
