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Related Experiment Video

Updated: Mar 21, 2026

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
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Towards Personalized Tacrolimus Dosing Using an Algorithm-Driven Prediction Pipeline for Kidney Transplant.

Jianliang Min1,2,3, Qihao Li1,3, Weijie Lai1,3

  • 1Department of Organ Transplantation Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People's Republic of China.

Drug Design, Development and Therapy
|March 20, 2026
PubMed
Summary

This study introduces an algorithm to personalize tacrolimus (TAC) dosing for kidney transplant patients, improving accuracy and reducing risks like nephrotoxicity. The novel approach enhances patient outcomes through precise drug management.

Keywords:
AIartificial intelligencecascaded deep forestpersonalized dosingrenal transplanttacrolimus

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Area of Science:

  • Pharmacogenomics and Precision Medicine
  • Computational Biology and Bioinformatics
  • Nephrology and Transplant Surgery

Background:

  • Tacrolimus (TAC) dosing is challenging due to its narrow therapeutic window and high inter-patient variability.
  • Suboptimal TAC exposure increases risks of nephrotoxicity and graft rejection in transplant recipients.
  • Algorithm-based personalized dosing strategies show promise for improving clinical decisions and long-term outcomes.

Purpose of the Study:

  • To develop a novel, versatile algorithm-driven strategy for predicting tacrolimus (TAC) doses.
  • To construct a cascaded deep forest model for predicting both initial and follow-up TAC doses.
  • To provide a practical, effective pipeline for automated drug dose analysis in clinical practice.

Main Methods:

  • A hybrid optimization method identified key clinical factors for TAC dose prediction.
  • A cascaded deep forest model was constructed using these key factors.
  • Leave-one-subject-out cross-validation and independent external validation were employed.

Main Results:

  • The algorithm achieved predictions within ±20% of actual TAC doses.
  • Prediction accuracy reached 89.8% for follow-up doses and 83.2% for initial doses.
  • Shapley additive explanation analysis confirmed significant correlations between input features and predicted doses.

Conclusions:

  • The developed approach offers a practical and effective pipeline for automated TAC dose prediction.
  • This algorithm-driven strategy can support clinical decision-making for personalized drug management.
  • An open-access web platform was provided to support real-time clinical use.