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In patients with renal disease, dosage adjustments are necessary to maintain therapeutic plasma drug concentrations and prevent toxicity or subtherapeutic exposure. Renal impairment alters drug pharmacokinetics, especially in conditions like uremia, where changes such as prolonged elimination half-life and altered apparent volume of distribution can significantly affect drug disposition. These changes require careful modification of the dosing regimen to achieve the desired clinical...
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Glomerular filtration rate (GFR) can be estimated from serum creatinine using the modification of diet in renal disease (MDRD) formula or the chronic kidney disease–epidemiology collaboration (CKD–EPI) equation. Both methods are widely used in clinical practice to assess kidney function and guide treatment decisions.The MDRD equation does not require weight or height measurements and is normalized to the body surface area of 1.73 m², considered the average adult surface area.
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A kidney transplant is a surgical approach that involves replacing a non-functioning kidney with a healthy one from a donor. This procedure is often a treatment option for end-stage renal disease (ESRD) patients. The method requires careful recipient selection, including evaluating various medical and psychosocial factors. These criteria vary between transplant centers but generally include assessments of the patient's overall health, adherence to medical recommendations, and lifestyle...
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In healthy individuals, serum creatinine levels remain stable due to a balance between its constant production—primarily from muscle metabolism—and renal excretion. Creatinine is freely filtered by the glomeruli, making it a valuable marker for estimating renal function. When the glomerular filtration rate (GFR) decreases, the kidneys can only eliminate less creatinine, causing serum levels to rise.Serum creatinine concentration is widely used to estimate creatinine clearance...
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Postoperative Nursing Management for Kidney Transplant PatientsPostoperative nursing management care includes monitoring the surgical site, encouraging early movement, and promoting lung health through breathing exercises. Nurses also administer prescribed medications like H2-blockers, such as famotidine, or proton pump inhibitors, like omeprazole, to help prevent gastrointestinal ulcers and bleeding. Fungal infections in the mouth and bladder can result from immunosuppressive and antibiotic...
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In patients with renal impairment, drugs undergo significant changes in their pharmacokinetics, which require dosage adjustments to ensure safe and effective therapy.
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Related Experiment Video

Updated: Jan 17, 2026

Quantification of the Immunosuppressant Tacrolimus on Dried Blood Spots Using LC-MS/MS
08:38

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Clinical-oriented tacrolimus dosing algorithms in kidney transplant based on genetic algorithm and deep forest.

Jianliang Min1,2,3,4, Qihao Li1,3,4, Weijie Lai1,3,4

  • 1Organ Transplantation Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Frontiers in Pharmacology
|September 15, 2025
PubMed
Summary

AI-powered personalized dosing of tacrolimus (TAC) improves precision for kidney transplant patients. This AI model predicts optimal TAC doses, reducing rejection risks and enhancing patient care.

Keywords:
deep forestgenetic algorithmkidney transplantmachine learningpersonalized dosingtacrolimus

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Area of Science:

  • Pharmacogenomics
  • Artificial Intelligence in Medicine
  • Transplant Medicine

Background:

  • Tacrolimus (TAC) is vital for preventing organ transplant rejection but has a narrow therapeutic window, posing dosing challenges.
  • Individual variability in TAC metabolism necessitates precise management for optimal patient outcomes.
  • AI-assisted personalized dosing offers a promising solution for automatic and accurate TAC management.

Purpose of the Study:

  • To develop and evaluate a clinical-oriented algorithm for predicting tacrolimus (TAC) doses in kidney transplant recipients.
  • To integrate genetic algorithms (GA) and deep forest (DF) for precise initial and follow-up TAC dose predictions.
  • To identify key clinical variables for accurate and user-friendly TAC dosing.

Main Methods:

  • A hybrid approach combining genetic algorithms (GA) and deep forest (DF) was employed.
  • GA with support vector regression (RBF kernel) optimized candidate variables from clinical factors.
  • Exhaustive feature selection identified key clinical variables for model input.

Main Results:

  • The developed DF model achieved 84.5% accuracy in initial TAC dose prediction.
  • The model demonstrated 91.7% accuracy for follow-up TAC dose prediction in a cohort of 288 recipients.
  • The integration of key clinical variables enhanced prediction performance.

Conclusions:

  • The proposed AI-driven algorithm provides a reliable method for personalized tacrolimus dosing.
  • This approach offers a potential reference for algorithm-based automatic drug dosing pipelines in clinical practice.
  • The study highlights the potential of AI to improve the management of immunosuppressants in transplant patients.