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Background and Objective <p>Systematic bias between therapeutic drug monitoring assays may lead to inappropriate treatment decisions in clinical practice. While such bias is well recognized, its impact on model-informed precision dosing remains unexplored. In this study, we evaluate how assay bias affects the predictive performance of population pharmacokinetic models, using ustekinumab in patients with Crohn’s disease as an example.</p> Methods <p>We repurposed data from 83 patients with Crohn’s disease. Ustekinumab concentrations were measured using both an homogeneous mobility shift assay and enzyme-linked immunosorbent assay. Two corresponding population pharmacokinetic models were developed. Bayesian forecasting was performed under matched and mismatched combinations of assay data and population pharmacokinetic models. Predictive accuracy and precision were assessed using relative bias and relative root mean square error, with predefined thresholds for clinical acceptability. Agreement between assays and clearance estimates was evaluated using Bland–Altman plots, Deming regression, and concordance correlation coefficients. Model prior flattening strategies were explored to mitigate mismatches between model priors and therapeutic drug monitoring data.</p> Results <p>Ustekinumab concentrations measured by the homogenous mobility shift assay were overall 8.1&#xa0;mg/L higher than those measured by an enzyme-linked immunosorbent assay (95%&#xa0;confidence interval −23.6, 39.7). Clearance estimates from the homogenous mobility shift assay-based population pharmacokinetic model were systematically lower (0.107&#xa0;L/day; relative standard error, 7.6%) compared with those from the enzyme-linked immunosorbent assay-based population pharmacokinetic model (0.235&#xa0;L/day; relative standard error, 5.4%). When assay data and population pharmacokinetic models were matched, Bayesian forecasting yielded clinically acceptable predictions across all scenarios (relative bias &lt;20%, 95% confidence interval including zero). Mismatched combinations led to reduced accuracy. Precision was highest using the homogenous mobility shift assay data, irrespective of the population pharmacokinetic model. Flattening strategies improved predictive performances in some mismatched scenarios but did not fully recover bias.</p> Conclusions <p>Assay bias has a clinically relevant impact on the predictive performance of model-informed precision dosing. Our findings underscore the importance of aligning the therapeutic drug monitoring assay format with the assay format used to build the population pharmacokinetic model to ensure accurate and clinically acceptable dosing predictions.</p>

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Why the Assay Matters in Model-Informed Precision Dosing: An Example of Ustekinumab in Crohn’s Disease

  • 期刊论文

  • 发表日期：

    2026年4月8日

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  • Wei Zhang,
  • Zhigang Wang,
  • Thierry Dervieux,
  • Nick Geukens,
  • Bram Verstockt,
  • Marc Ferrante,
  • Séverine Vermeire,
  • Erwin Dreesen

摘要

Background and Objective

Systematic bias between therapeutic drug monitoring assays may lead to inappropriate treatment decisions in clinical practice. While such bias is well recognized, its impact on model-informed precision dosing remains unexplored. In this study, we evaluate how assay bias affects the predictive performance of population pharmacokinetic models, using ustekinumab in patients with Crohn’s disease as an example.

Methods

We repurposed data from 83 patients with Crohn’s disease. Ustekinumab concentrations were measured using both an homogeneous mobility shift assay and enzyme-linked immunosorbent assay. Two corresponding population pharmacokinetic models were developed. Bayesian forecasting was performed under matched and mismatched combinations of assay data and population pharmacokinetic models. Predictive accuracy and precision were assessed using relative bias and relative root mean square error, with predefined thresholds for clinical acceptability. Agreement between assays and clearance estimates was evaluated using Bland–Altman plots, Deming regression, and concordance correlation coefficients. Model prior flattening strategies were explored to mitigate mismatches between model priors and therapeutic drug monitoring data.

Results

Ustekinumab concentrations measured by the homogenous mobility shift assay were overall 8.1 mg/L higher than those measured by an enzyme-linked immunosorbent assay (95% confidence interval −23.6, 39.7). Clearance estimates from the homogenous mobility shift assay-based population pharmacokinetic model were systematically lower (0.107 L/day; relative standard error, 7.6%) compared with those from the enzyme-linked immunosorbent assay-based population pharmacokinetic model (0.235 L/day; relative standard error, 5.4%). When assay data and population pharmacokinetic models were matched, Bayesian forecasting yielded clinically acceptable predictions across all scenarios (relative bias <20%, 95% confidence interval including zero). Mismatched combinations led to reduced accuracy. Precision was highest using the homogenous mobility shift assay data, irrespective of the population pharmacokinetic model. Flattening strategies improved predictive performances in some mismatched scenarios but did not fully recover bias.

Conclusions

Assay bias has a clinically relevant impact on the predictive performance of model-informed precision dosing. Our findings underscore the importance of aligning the therapeutic drug monitoring assay format with the assay format used to build the population pharmacokinetic model to ensure accurate and clinically acceptable dosing predictions.