Abstract

Do clinical and laboratory parameters predict thiopurine metabolism and clinical outcome in patients with inflammatory bowel diseases?

Frick S1, Müller D2, Kullak-Ublick GA1, Jetter A3. Eur J Clin Pharmacol. 2019 Jan 4. doi: 10.1007/s00228-018-02616-7. [Epub ahead of print]
 
     

Author information

1 Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Rämistrasse 100, CH-8091, Zürich, Switzerland.

2 Institute of Clinical Chemistry, University Hospital Zurich, University of Zurich, Zurich, Switzerland.

3 Department of Clinical Pharmacology and Toxicology, University Hospital Zurich, University of Zurich, Rämistrasse 100, CH-8091, Zürich, Switzerland. alexander.jetter@usz.ch.

Abstract

PURPOSE: The thiopurines azathioprine and 6-mercaptopurine are frequently used for remission maintenance in patients with inflammatory bowel diseases. However, there are therapy failures, and it is unclear whether clinical and laboratory parameters can be used to predict thiopurine metabolite concentrations (as a surrogate for adequate remission maintenance therapy) and clinical outcome in these patients.

METHODS: In this retrospective analysis of clinical routine patient data, multivariate statistical models based on Linear Mixed Models regression and Generalized Estimating Equations logistic regression were developed. The adequacy of the models was assessed using Pearson's correlation and a receiver operating characteristic curve.

RESULTS: This study included 273 patients and 1158 thiopurine metabolite measurements as well as routine laboratory and clinical data. In the statistical models, thiopurine metabolite concentrations and the odds of non-remission based on different clinical and laboratory parameters were computed. Correlation (r2) between predicted and measured thiopurine metabolites were 0.40 (p < 0.001) for 6-thioguanine nucleotides and 0.53 (p < 0.001) for 6-methyl-mercaptopurine nucleotides, respectively. The model for remission classified data sets in remission and non-remission with a sensitivity of 63% and a specificity of 73%. The area under the receiver operating characteristic curve of the model was 0.72.

CONCLUSIONS: Although the models are not yet accurate enough to be used in clinical routine, model-based prediction of thiopurine metabolite concentrations and of outcome is feasible. Until more accurate models are developed and validated, traditional therapeutic drug monitoring of thiopurine metabolites in patients with inflammatory bowel diseases under thiopurine therapy stays the best tool to individualize therapy.

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