External evaluation of population pharmacokinetic models for tacrolimus in kidney transplant recipients: implications for model-informed precision dosing.
9 affiliations
- 1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India.
- 2Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
- 3Department of Renal Transplant Surgery, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
- 4Department of Pediatric Biochemistry, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
- 5Department of Nephrology, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
- 6Department of Anatomy, Postgraduate Institute of Medical Education and Research, Chandigarh, India.
- 7Department of Nephrology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India.
- 8Department of Pharmacology, Postgraduate Institute of Medical Education and Research, Chandigarh, India. [email protected].
- 9Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, India. [email protected].
Abstract
Background
Tacrolimus, a primary immunosuppressant in kidney transplantation, exhibits variable pharmacokinetics. While model-informed precision dosing (MIPD) using population pharmacokinetic (PopPK) models improve dosing accuracy, models developed in specific populations lack applicability in other populations. We aimed to evaluate the predictive performance of published models integrated in clinical decision support systems (CDSS) in Indian adult renal transplant recipients.Methods
Predictive performance of four published PopPK models were evaluated using an independent dataset of 120 trough concentrations from 60 adult renal transplant recipients. Models were assessed using prediction based and simulation-based diagnostics. Maximum a posteriori (MAP) Bayesian prediction was also used to evaluate the model performance.Results
A priori predictions (PRED) for all four models demonstrated systematic underprediction, with relative mean prediction error (rME) ranging from - 25.0% to -64.0%, failing clinical acceptability thresholds. However, integrating patient-specific data via MAP Bayesian estimation corrected this bias and improved precision. Notably, all models achieved higher accuracy and precision, with more than 60% of individual predictions falling within [Formula: see text] ([Formula: see text]) of the observed values. Additionally, scaling down the clearance parameter by 30% mitigated the systematic bias observed in the initial a priori predictions.Conclusions
Published PopPK models exhibited significant systematic bias when applied to Indian renal transplant recipients, posing a substantial risk of overdosing if used without adjustment for initial dose selection. However, these models adapted using MAP Bayesian approach or scaling of clearance parameter demonstrate better predictive performance in this cohort, suggesting they can guide proactive precision dosing in the Indian cohort. Prospective studies evaluating the target attainment and clinical outcomes are required to establish their clinical applicability.
Related
- Model‐Based Tacrolimus Dosing Using the Population Pharmacokinetic Analysis Under the Nirmatrelvir/Ritonavir Therapy
- Relevance‐Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients
- Correlation Between Health Literacy and Tacrolimus Variability in Solid Organ Transplant Recipients
- Changes in Tacrolimus and Everolimus Trough Concentrations and Dose Requirements After Initiation and Discontinuation of Letermovir Prophylaxis in Kidney Transplant Recipients: A Single-Center Retrospective Study
- Calcineurin Inhibitors and Male Fertility in Renal Transplant Recipients: A Comprehensive Review of Molecular Pathomechanisms and Reproductive Toxicity of Cyclosporine A and Tacrolimus.
BibTeX
@article{raju2026external,
title = {{External evaluation of population pharmacokinetic models for tacrolimus in kidney transplant recipients: implications for model-informed precision dosing.}},
author = {Arun Prasath Raju and Priyanka Naithani and Deepesh Kenwar and Sarbpreet Singh and Shiva Kumar Sp and Savita Verma Atri and Vivek Kumar and Anjali Agarwal and Ravindra Prabhu Attur and Mahadev Rao and Ashish Sharma and Smita Pattanaik and Surulivelrajan Mallayasamy},
journal = {BMC nephrology},
year = {2026},
month = jul,
doi = {10.1186/s12882-026-05214-3},
url = {https://europepmc.org/article/MED/42897489},
note = {CC BY},
}
RIS
TY - JOUR
TI - External evaluation of population pharmacokinetic models for tacrolimus in kidney transplant recipients: implications for model-informed precision dosing.
AU - Arun Prasath Raju
AU - Priyanka Naithani
AU - Deepesh Kenwar
AU - Sarbpreet Singh
AU - Shiva Kumar Sp
AU - Savita Verma Atri
AU - Vivek Kumar
AU - Anjali Agarwal
AU - Ravindra Prabhu Attur
AU - Mahadev Rao
AU - Ashish Sharma
AU - Smita Pattanaik
AU - Surulivelrajan Mallayasamy
PY - 2026
DA - 2026/07/01
JO - BMC nephrology
DO - 10.1186/s12882-026-05214-3
UR - https://europepmc.org/article/MED/42897489
AB - Background Tacrolimus, a primary immunosuppressant in kidney transplantation, exhibits variable pharmacokinetics. While model-informed precision dosing (MIPD) using population pharmacokinetic (PopPK) models improve dosing accuracy, models developed in specific populations lack applicability in other populations. We aimed to evaluate the predictive performance of published models integrated in clinical decision support systems (CDSS) in Indian adult renal transplant recipients.Methods Predictive performance of four published PopPK models were evaluated using an independent dataset of 120 trough concentrations from 60 adult renal transplant recipients. Models were assessed using prediction based and simulation-based diagnostics. Maximum a posteriori (MAP) Bayesian prediction was also used to evaluate the model performance.Results A priori predictions (PRED) for all four models demonstrated systematic underprediction, with relative mean prediction error (rME) ranging from - 25.0% to -64.0%, failing clinical acceptability thresholds. However, integrating patient-specific data via MAP Bayesian estimation corrected this bias and improved precision. Notably, all models achieved higher accuracy and precision, with more than 60% of individual predictions falling within [Formula: see text] ([Formula: see text]) of the observed values. Additionally, scaling down the clearance parameter by 30% mitigated the systematic bias observed in the initial a priori predictions.Conclusions Published PopPK models exhibited significant systematic bias when applied to Indian renal transplant recipients, posing a substantial risk of overdosing if used without adjustment for initial dose selection. However, these models adapted using MAP Bayesian approach or scaling of clearance parameter demonstrate better predictive performance in this cohort, suggesting they can guide proactive precision dosing in the Indian cohort. Prospective studies evaluating the target attainment and clinical outcomes are required to establish their clinical applicability.
SN - 1471-2369
ER -