越来越多的证据证明，治疗药物监测（TDM）在支持个体化医学方面应更广泛地使用，尤其是在毒性和疗效等关键问题非常重要的疗法中，如肿瘤学。然而，TDM检测方法的发展难以跟上新药物的快速引入。因此，需要新的方法来加快检测方法的开发速度，同时也可以轻松地包含新批准的药物，并根据科学或临床情况的需要进行定制。本文应用和评估了两种机器学习方法，即基于回归的方法和人工神经网络（ANN），用于液相色谱质谱（LC-MS）法快速发展一种可量化73种口服抗肿瘤药物（OADs）和五种活性代谢物的方法，并预测保留时间（RT）。各个步骤包括训练、评估、比较和应用优秀方法对RT进行预测，然后确定最佳梯度。两种方法均显示出很好的RT预测结果（平均差异±标准差：2.08%±9.44% ANN；1.78%±1.93% 基于回归的方法）。使用基于回归的方法，预测出最佳梯度（4.91% MeOH/min），总运行时间为17.92 min。该方法按照FDA和EMA的指导方针进行了全面验证。将基于回归的方法示范性地修改并应用于一组14种泌尿系统肿瘤药物，缩短了运行时间至9.29 min。使用基于回归的方法，高效地开发了一种多种药物LC-MS测定RT的方法，该方法可以轻松扩展到新批准的OADs，并根据需要定制到更小的子集中。© 2023 Walter de Gruyter GmbH, Berlin/Boston.

Accumulating evidence argues for a more widespread use of therapeutic drug monitoring (TDM) to support individualized medicine, especially for therapies where toxicity and efficacy are critical issues, such as in oncology. However, development of TDM assays struggles to keep pace with the rapid introduction of new drugs. Therefore, novel approaches for faster assay development are needed that also allow effortless inclusion of newly approved drugs as well as customization to smaller subsets if scientific or clinical situations require.We applied and evaluated two machine-learning approaches i.e., a regression-based approach and an artificial neural network (ANN) to retention time (RT) prediction for efficient development of a liquid chromatography mass spectrometry (LC-MS) method quantifying 73 oral antitumor drugs (OADs) and five active metabolites. Individual steps included training, evaluation, comparison, and application of the superior approach to RT prediction, followed by stipulation of the optimal gradient.Both approaches showed excellent results for RT prediction (mean difference ± standard deviation: 2.08 % ± 9.44 % ANN; 1.78 % ± 1.93 % regression-based approach). Using the regression-based approach, the optimum gradient (4.91 % MeOH/min) was predicted with a total run time of 17.92 min. The associated method was fully validated following FDA and EMA guidelines. Exemplary modification and application of the regression-based approach to a subset of 14 uro-oncological agents resulted in a considerably shortened run time of 9.29 min.Using a regression-based approach, a multi drug LC-MS assay for RT prediction was efficiently developed, which can be easily expanded to newly approved OADs and customized to smaller subsets if required.© 2023 Walter de Gruyter GmbH, Berlin/Boston.