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Related Concept Videos

Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance01:07

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Drug transporters are critical in drug absorption, distribution, and excretion processes. They should be included in physiological-based pharmacokinetic (PBPK) models, which help predict human drug disposition. However, predicting this is challenging during drug development, especially when liver transport is involved. However, with a realistic representation of body transport processes, an accurate model may be possible.
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Related Experiment Video

Updated: Apr 20, 2026

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
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Machine learning-driven prognostication in liver transplantation: A tacrolimus intrapatient variability enhanced

Yao-Xing Ren1, Yan Wang2, Jun-Xi Xiang3

  • 1National Local Joint Engineering Research Center for Precision Surgery & Regenerative Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China; School of Future Technology, Xi'an Jiaotong University, Xi'an 710061, China.

Hepatobiliary & Pancreatic Diseases International : HBPD INT
|April 18, 2026
PubMed
Summary

A new index, the Liver Transplantation Prognosis Predictor (LTPP), accurately predicts long-term survival after liver transplantation by integrating tacrolimus intrapatient variability. LTPP outperforms existing scores, aiding personalized post-transplant care.

Keywords:
Intrapatient variabilityLiver transplantationMachine learningSurvival prediction modelTacrolimus

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Area of Science:

  • Hepatology and Transplant Surgery
  • Clinical Prognostics
  • Immunosuppression Management

Background:

  • Accurate prediction of long-term survival post-liver transplantation (LT) is clinically challenging.
  • Tacrolimus intrapatient variability (Tac-IPV) shows prognostic potential but is underutilized.
  • Current prognostic markers lack sufficient accuracy for long-term outcomes.

Purpose of the Study:

  • To develop and validate a novel composite index for predicting long-term survival after LT.
  • To integrate Tac-IPV with other clinical factors into a predictive model.
  • To compare the prognostic performance of the new index against established scoring systems.

Main Methods:

  • Retrospective analysis of 381 adult LT recipients from two centers.
  • Development of the Liver Transplantation Prognosis Predictor (LTPP) integrating Tac-IPV, total bilirubin, and donor age.
  • Evaluation using survival analysis, random forest modeling, and external validation.

Main Results:

  • The random forest model showed robust predictive performance for 1-, 2-, and 3-year survival (AUCs ranging from 0.68 to 0.76).
  • LTPP significantly outperformed MELD and Child-Pugh scores in risk stratification (log-rank P < 0.0001).
  • LTPP demonstrated reliable prognostic accuracy in both internal and external validation datasets.

Conclusions:

  • The LTPP is a novel, clinically accessible composite index incorporating Tac-IPV.
  • LTPP offers superior prognostic accuracy for long-term survival post-LT compared to existing scores.
  • LTPP represents a promising tool for individualized post-transplant management and patient care.