Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning
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| Publicado en: | Drug Design, Development and Therapy vol. 19 (2025), p. 3631 |
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| Otros Autores: | , , , , |
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Taylor & Francis Ltd.
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3204750504 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1177-8881 | ||
| 024 | 7 | |a 10.2147/DDDT.S515535 |2 doi | |
| 035 | |a 3204750504 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Zhao, J | |
| 245 | 1 | |a Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning | |
| 260 | |b Taylor & Francis Ltd. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Introduction: High-dose methotrexate is a typical chemotherapy that is widely used in the treatment of osteosarcoma. However, the unique dose-response relationship of methotrexate makes its treatment window relatively narrow, and its clinical use is in a dilemma: either the drug concentration in the patient’s body cannot reach the effective concentration level, or adverse reactions may occur due to drug overdose. For this circumstance, monitoring and predicting the drug concentration in the patient’s body is well founded and necessary. While pharmacokinetic models exist, they often oversimplify patient-specific covariates. This study addresses the unmet need for early-exposure prediction through interpretable machine learning, enabling data-driven decisions before toxicity manifestation.Methods: In this article, 68 osteosarcoma patients’ information including demography, administration and assay was gathered. We analyzed medical data and selected 10 important features using a random forest, including hydration status, red blood cell distribution width coefficient of variation, platelet distribution width, creatinine, γ-glutamyl transferase, large platelet ratio, serum potassium, lactate dehydrogenase, weight, and prealbumin. Then, cross-validation and SHAP has been conducted to confirm the robust and interpretation of the model.Results: On this basis, 7 machine learning regression models was built to predict the blood concentration of methotrexate. R2, MSE, RMSE, MAE are the evaluation metrics. Finally, LightGBM was selected as the best prediction model with a performance of R2=0.87, MSE=0.020, RMSE=0.141, MAE=0.065.Discussion: This machine learning framework addresses a critical gap in high-dose methotrexate therapeutic monitoring by achieving early and personalized blood drug concentration prediction, allowing for personalized dosing of patients based on predicted concentrations. The interpretability of SHAP-derived feature importance enhances clinical utility, offering a paradigm shift from reactive toxicity management to proactive precision dosing in osteosarcoma therapy. | |
| 653 | |a Erythrocytes | ||
| 653 | |a Datasets | ||
| 653 | |a Metastasis | ||
| 653 | |a Bone cancer | ||
| 653 | |a Regression analysis | ||
| 653 | |a Telemedicine | ||
| 653 | |a Machine learning | ||
| 653 | |a Toxicity | ||
| 653 | |a Prediction models | ||
| 653 | |a Methotrexate | ||
| 653 | |a Decision trees | ||
| 653 | |a Pharmacokinetics | ||
| 653 | |a Medical prognosis | ||
| 653 | |a Creatinine | ||
| 653 | |a Dosage | ||
| 653 | |a Hospitals | ||
| 653 | |a Platelets | ||
| 653 | |a Missing data | ||
| 653 | |a Algorithms | ||
| 653 | |a Chemotherapy | ||
| 653 | |a Patients | ||
| 653 | |a Dihydrofolate reductase | ||
| 653 | |a Regression models | ||
| 653 | |a Normal distribution | ||
| 653 | |a Data processing | ||
| 653 | |a Oncology | ||
| 653 | |a Blood levels | ||
| 653 | |a Health services | ||
| 653 | |a Monitoring | ||
| 653 | |a Demography | ||
| 653 | |a L-Lactate dehydrogenase | ||
| 653 | |a Hydration | ||
| 653 | |a Customization | ||
| 653 | |a Learning algorithms | ||
| 653 | |a Drug dosages | ||
| 653 | |a Overdose | ||
| 653 | |a Coefficient of variation | ||
| 653 | |a Osteosarcoma | ||
| 653 | |a Lactate dehydrogenase | ||
| 653 | |a Variables | ||
| 653 | |a Clinical decision making | ||
| 700 | 1 | |a Dai, S | |
| 700 | 1 | |a He J | |
| 700 | 1 | |a Liu, N | |
| 700 | 1 | |a Zhang, B | |
| 700 | 1 | |a Li S | |
| 773 | 0 | |t Drug Design, Development and Therapy |g vol. 19 (2025), p. 3631 | |
| 786 | 0 | |d ProQuest |t Nursing & Allied Health Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3204750504/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3204750504/fulltextwithgraphics/embedded/75I98GEZK8WCJMPQ?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3204750504/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch |