Prediction of Thiopurine Metabolite Levels Based on Haematological and Biochemical Parameters

• Published in: Journal of pediatric gastroenterology and nutrition Vol. 69; no. 4; p. e105
• Main Authors: Hradsky, Ondrej, Potuznikova, Kristyna, Siroka, Jitka, Lerchova, Tereza, Urbanek, Lubor, Mihal, Vladimir, Spenerova, Michaela, Velganova-Veghova, Maria, Karaskova, Eva, Bronsky, Jiri
• Format: Journal Article
• Language: English
• Published: United States 01.10.2019
• Subjects: Adolescent; Area Under Curve; Child; Drug Monitoring; Erythrocytes - metabolism; Female; Humans; Immunosuppressive Agents - administration & dosage; Immunosuppressive Agents - pharmacokinetics; Immunosuppressive Agents - therapeutic use; Inflammatory Bowel Diseases - blood; Inflammatory Bowel Diseases - drug therapy; Inflammatory Bowel Diseases - metabolism; Male; Medication Adherence; Mercaptopurine - administration & dosage; Mercaptopurine - analogs & derivatives; Mercaptopurine - pharmacokinetics; Mercaptopurine - therapeutic use; Models, Biological; Predictive Value of Tests; Sensitivity and Specificity; Thioguanine - metabolism
• ISSN: 1536-4801
• DOI: 10.1097/MPG.0000000000002436

Therapeutic drug monitoring of thiopurine erythrocyte levels is not available in all centers and it usually requires quite a long time to obtain the results. The aims of this study were to build a model predicting low levels of 6-thioguanine and 6-methylmercaptopurine in pediatric inflammatory bowel disease (IBD) patients and to build a model to predict nonadherence in patients treated with azathioprine (AZA). The study consisted of 332 observations in 88 pediatric IBD patients. Low AZA dosing was defined as 6-thioguanine levels <125 pmol/8 × 10 erythrocytes and 6-methylmercaptopurine levels <5700 pmol/8 × 10 erythrocytes. Nonadherence was defined as undetectable levels of 6-thioguanine and 6-methylmercaptopurine <240 pmol/8 × 10 erythrocytes. Data were divided into training and testing part. To construct the model predicting low 6-thioguanine levels, nonadherence, and the level of 6-thioguanine, the modification of random forest method with cross-validation and resampling was used. The final models predicting low 6-thioguanine levels and nonadherence had area under the curve, 0.87 and 0.94; sensitivity, 0.81 and 0.82; specificity, 0.80 and 86; and distance, 0.31 and 0.21, respectively, when applied on the testing part of the dataset. When the final model for prediction of 6-thioguanine values was applied on testing dataset, a root-mean-square error of 110 was obtained. Using easily obtained laboratory parameters, we constructed a model with sufficient accuracy to predict patients with low 6-thioguanine levels and a model for prediction of AZA treatment nonadherence (web applications: https://hradskyo.shinyapps.io/6TG_prediction/ and https://hradskyo.shinyapps.io/Non_adherence/).