Relevance-Based Score for MIPD Model Selection: Systematic Review and External Evaluation of popPK Tacrolimus Models in Adult Transplant Recipients

IF 2.8 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Samuel Baroudi, Bénédicte Franck, Jean-Baptiste Woillard, Ntobe-Bunkete Béni, Emmanuelle Comets, Florian Lemaitre
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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.

基于相关性评分的MIPD模型选择:成人移植受者popPK他克莫司模型的系统评价和外部评价。
为模型信息精确给药(MIPD)选择最相关的模型仍然具有挑战性,特别是当大量人群药代动力学(popPK)模型可用时,使得所有候选模型的外部评估既耗时又耗费资源。我们的目的是开发和评估一种适应性强的基于相关性的评分方法,以成人移植受者的他克莫司为例。候选标准来源于已发表的指南和外部评价。开发了一个23项相关性评分,涵盖训练数据集,研究设计,评估方法,协变量和参数精度。对于他克莫司,系统的文献综述确定了28种模型,使用来自雷恩大学医院(86例患者)的独立数据集进行相关性评分和外部评估。使用偏差和不精确指标在个体和群体水平上评估预测性能。将预测和观察到的auc与治疗阈值进行比较。然后使用外部评估分数(最多14分)对模型进行排名,该评分基于预定义的偏差和不精确的接受范围。使用Pearson和Spearman相关性评估基于相关性和外部评分和排名之间的相关性。平均相关性评分为22.3/56(范围:6-38)。较高的评分与更大、更丰富的数据集、前瞻性或多中心设计、临床相关协变量的纳入以及精确的参数估计相关。个体预测显示出可接受的偏差和不精确,而总体预测则一贯不准确。AUC一致性(56%-94%)主要受高估限制。最高外部评分为6/14。相关性评分与外部评分之间的相关性为中等(Pearson = 0.56; Spearman = 0.61)。这项工作为popPK模型选择和确定适合MIPD应用的他克莫司模型提供了结构化的基础。
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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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