Relevance‐Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients
Abstract
Selecting the most relevant models for model‐informed precision dosing (MIPD) remains challenging, particularly when numerous population pharmacokinetic (popPK) models are available, making external evaluation of all candidates time‐ and resource‐consuming. We aimed to develop and assess an adaptable relevance‐based scoring methodology, illustrated with tacrolimus in adult transplant recipients. Candidate criteria were derived from published guidelines and external evaluations. A 23‐item relevance score was developed, covering training dataset, study design, evaluation methodology, covariates, and parameter precision. For tacrolimus, a systematic literature review identified 28 models which were scored for relevance and externally evaluated using an independent dataset from Rennes University Hospital (86 patients). Predictive performances were assessed at individual and population levels using bias and imprecision metrics. Predicted and observed AUCs were compared against therapeutic thresholds. Models were then ranked using an external evaluation score (maximum 14 points) based on predefined acceptance limits for bias and imprecision. Correlations between relevance‐based and external scores and ranks were assessed using Pearson and Spearman correlations. The mean relevance score was 22.3/56 (range: 6–38). Higher scores were associated with larger and richer datasets, prospective or multicentric designs, inclusion of clinically relevant covariates, and precise parameter estimates. Individual predictions showed acceptable bias and imprecision, whereas population predictions were consistently inaccurate. AUC concordance (56%–94%) was mainly limited by overestimation. Highest external score was 6/14. Correlation between relevance‐based and external scores was moderate (Pearson = 0.56; Spearman = 0.61). This work provides a structured basis for popPK model selection and identified tacrolimus models suitable for MIPD applications.
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BibTeX
@article{baroudi2026relevancebased,
title = {{Relevance‐Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients}},
author = {Samuel Baroudi and Bénédicte Franck and Jean-Baptiste Woillard and Ntobe‐bunkete Béni and Emmanuelle Comets and Florian Lemaitre},
journal = {CPT: Pharmacometrics and Systems Pharmacology},
year = {2026},
month = aug,
doi = {10.1002/psp4.70315},
url = {https://hal.science/hal-05748903v1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Relevance‐Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients
AU - Samuel Baroudi
AU - Bénédicte Franck
AU - Jean-Baptiste Woillard
AU - Ntobe‐bunkete Béni
AU - Emmanuelle Comets
AU - Florian Lemaitre
PY - 2026
DA - 2026/08/12
JO - CPT: Pharmacometrics and Systems Pharmacology
DO - 10.1002/psp4.70315
UR - https://hal.science/hal-05748903v1
AB - Selecting the most relevant models for model‐informed precision dosing (MIPD) remains challenging, particularly when numerous population pharmacokinetic (popPK) models are available, making external evaluation of all candidates time‐ and resource‐consuming. We aimed to develop and assess an adaptable relevance‐based scoring methodology, illustrated with tacrolimus in adult transplant recipients. Candidate criteria were derived from published guidelines and external evaluations. A 23‐item relevance score was developed, covering training dataset, study design, evaluation methodology, covariates, and parameter precision. For tacrolimus, a systematic literature review identified 28 models which were scored for relevance and externally evaluated using an independent dataset from Rennes University Hospital (86 patients). Predictive performances were assessed at individual and population levels using bias and imprecision metrics. Predicted and observed AUCs were compared against therapeutic thresholds. Models were then ranked using an external evaluation score (maximum 14 points) based on predefined acceptance limits for bias and imprecision. Correlations between relevance‐based and external scores and ranks were assessed using Pearson and Spearman correlations. The mean relevance score was 22.3/56 (range: 6–38). Higher scores were associated with larger and richer datasets, prospective or multicentric designs, inclusion of clinically relevant covariates, and precise parameter estimates. Individual predictions showed acceptable bias and imprecision, whereas population predictions were consistently inaccurate. AUC concordance (56%–94%) was mainly limited by overestimation. Highest external score was 6/14. Correlation between relevance‐based and external scores was moderate (Pearson = 0.56; Spearman = 0.61). This work provides a structured basis for popPK model selection and identified tacrolimus models suitable for MIPD applications.
SN - 2163-8306
SN - 2163-8306
ER -