Personalized Prediction of Venetoclax-Associated Tumor Lysis Syndrome in Acute Myeloid Leukemia Using Drug Exposure, Genetic Polymorphisms, and Clinical-Laboratory Characteristics.

@article{wang2026personalized,
  title = {{Personalized Prediction of Venetoclax-Associated Tumor Lysis Syndrome in Acute Myeloid Leukemia Using Drug Exposure, Genetic Polymorphisms, and Clinical-Laboratory Characteristics.}},
  author = {Chanchan Wang and Xiaoyan Lin and Yonggong Yang and Miaoxin Peng and Fang Wu and Jie Zhou and Han Xie and Siliang Wang and Mengying Liu},
  journal = {Drug design, development and therapy},
  year = {2026},
  month = oct,
  doi = {10.2147/dddt.s627579},
  url = {https://europepmc.org/article/MED/42974127},
  note = {CC BY-NC},
}

TY  - JOUR
TI  - Personalized Prediction of Venetoclax-Associated Tumor Lysis Syndrome in Acute Myeloid Leukemia Using Drug Exposure, Genetic Polymorphisms, and Clinical-Laboratory Characteristics.
AU  - Chanchan Wang
AU  - Xiaoyan Lin
AU  - Yonggong Yang
AU  - Miaoxin Peng
AU  - Fang Wu
AU  - Jie Zhou
AU  - Han Xie
AU  - Siliang Wang
AU  - Mengying Liu
PY  - 2026
DA  - 2026/10/29
JO  - Drug design, development and therapy
DO  - 10.2147/dddt.s627579
UR  - https://europepmc.org/article/MED/42974127
AB  - Background  Tumor lysis syndrome (TLS) is a life-threatening complication of venetoclax (VEN) for acute myeloid leukemia (AML). Existing TLS risk tools were developed for CLL/SLL and need validation in AML. This study aims to integrate drug exposure, genetic polymorphisms and clinical indicators to build a personalized machine learning model for TLS risk in AML patients on VEN, and establish a standardized management flowchart.Methods  186 AML patients on VEN therapy were prospectively enrolled. Key predictors were identified via univariate and multivariate regression. Four algorithms-logistic regression (LR), random forest (RF), support vector machine (SVM), and XGBoost-were compared to select the optimal predictive model. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, F1 score, calibration curves, and decision curve analysis (DCA). SHAP interpretability analysis was employed to reveal feature contributions. Finally, an online TLS risk calculator was developed, and a standardized management workflow was established.Results  Among 186 patients, 39 (21.0%) developed TLS. The final model included four core covariates: VEN peak plasma concentration (Cmax), VEGFA rs3025039 genotype, ECOG performance status, elevated baseline WBC count. Among algorithms, XGBoost performed best, with validation set AUC=0.861, accuracy=0.811, specificity=0.831, sensitivity=0.7, F1=0.667, and Brier score=0.124, indicating good calibration. DCA and PRC curves confirmed high clinical utility. Based on this model, an online TLS calculator for risk stratification and personalized intervention, as well as a standardized management protocol, was successfully developed.Conclusion  The interpretable XGBoost model developed in this study can effectively predict TLS risk associated with VEN therapy in AML patients. The online prediction tool developed based on this model facilitates precise risk stratification, helping to avoid overtreatment of low-risk patients while optimizing resource allocation and significantly improving the safety of targeted therapy for AML.
SN  - 1177-8881
ER  -