Burghelea Dan, Moisoiu Tudor, Ivan Cristina, Elec Alina, Munteanu Adriana, Tabrea Raluca, Antal Oana, Kacso Teodor Paul, Socaciu Carmen, Elec Florin Ioan, Kacso Ina Maria
Clinical Institute of Urology and Renal Transplantation, Cluj-Napoca, Romania.
Department of Urology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca, Romania.
Med Pharm Rep. 2025 Jan;98(1):125-134. doi: 10.15386/mpr-2805. Epub 2025 Jan 31.
Tacrolimus, a widely used immunosuppressive drug in kidney transplant recipients, exhibits a narrow therapeutic window necessitating careful monitoring of its concentration to balance efficacy and minimize dose-related toxic effects. Although essential, this approach is not optimal, and tacrolinemia, even in the therapeutic interval, might be associated with toxicity and rejection within range. This study aimed to identify specific urinary metabolites associated with tacrolimus levels in kidney transplant patients using a combination of serum high-precision liquid chromatography-mass spectrometry (HPLC-MS) and machine learning algorithms.
A cohort of 42 kidney transplant patients, comprising 19 individuals with high tacrolimus levels (>8 ng/mL) and 23 individuals with low tacrolimus levels (<5 ng/mL), were included in the analysis. Urinary samples were subjected to HPLC-MS analysis, enabling comprehensive metabolite profiling across the study cohort. Additionally, tacrolimus concentrations were quantified using established clinical assays.
Through an extensive analysis of the HPLC-MS data, a panel of five metabolites were identified that exhibited a significant correlation with tacrolimus levels (Valeryl carnitine, Glycyl-tyrosine, Adrenosterone, LPC 18:3 and 6-methylprednisolone). Machine learning algorithms were then employed to develop a predictive model utilizing the identified metabolites as features. The logistic regression model achieved an area under the curve of 0.810, indicating good discriminatory power and classification accuracy of 0.690.
This study demonstrates the potential of integrating HPLC-MS metabolomics with machine learning algorithms to identify urinary metabolites associated with tacrolimus levels. The identified metabolites are promising biomarkers for monitoring tacrolimus therapy, aiding in dose optimization and personalized treatment approaches.
他克莫司是肾移植受者中广泛使用的免疫抑制药物，其治疗窗狭窄，需要仔细监测其浓度以平衡疗效并将剂量相关的毒性作用降至最低。尽管这一方法至关重要，但并非最佳选择，即使在治疗区间内，他克莫司血药浓度仍可能与毒性反应及排斥反应相关。本研究旨在结合血清高效液相色谱 - 质谱联用技术（HPLC - MS）和机器学习算法，识别肾移植患者中与他克莫司水平相关的特定尿液代谢物。
分析纳入了42名肾移植患者，其中19名他克莫司水平较高（>8 ng/mL），23名他克莫司水平较低（<5 ng/mL）。对尿液样本进行HPLC - MS分析，以全面分析研究队列中的代谢物谱。此外，使用既定的临床检测方法对他克莫司浓度进行定量。
通过对HPLC - MS数据的广泛分析，确定了一组与他克莫司水平显著相关的五种代谢物（戊酰肉碱、甘氨酰 - 酪氨酸、肾上腺酮、LPC 18:3和6 - 甲基泼尼松龙）。然后使用机器学习算法，以识别出的代谢物为特征建立预测模型。逻辑回归模型的曲线下面积为0.810，表明具有良好的区分能力，分类准确率为0.690。
本研究证明了将HPLC - MS代谢组学与机器学习算法相结合以识别与他克莫司水平相关的尿液代谢物的潜力。所识别的代谢物有望成为监测他克莫司治疗的生物标志物，有助于剂量优化和个性化治疗方案。
