Department of Clinical Pharmacy, Institute of Pharmacy, University of Bonn, An der Immenburg 4, 53121, Bonn, Germany.
Faculty of Medicine and University Hospital Cologne, Institute of Public Health, University of Cologne, Cologne, Germany.
Clin Pharmacokinet. 2024 Sep;63(9):1221-1237. doi: 10.1007/s40262-024-01409-9. Epub 2024 Aug 17.
In the last decade, various Machine Learning techniques have been proposed aiming to individualise the dose of anticancer drugs mostly based on a presumed drug effect or measured effect biomarkers. The aim of this scoping review was to comprehensively summarise the research status on the use of Machine Learning for precision dosing in anticancer drug therapy.
This scoping review was conducted in accordance with the interim guidance by Cochrane and the Joanna Briggs Institute. We systematically searched the databases Medline (via PubMed), Embase and the Cochrane Library for research articles and reviews including results published after 2016. Results were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist.
A total of 17 relevant studies was identified. In 12 of the included studies, Reinforcement Learning methods were used, including Classical, Deep, Double Deep and Conservative Q-Learning and Fuzzy Reinforcement Learning. Furthermore, classical Machine Learning methods were compared in terms of their performance and an artificial intelligence platform based on parabolic equations was used to guide dosing prospectively and retrospectively, albeit only in a limited number of patients. Due to the significantly different algorithm structures, a meaningful comparison between the various Machine Learning approaches was not possible.
Overall, this review emphasises the clinical relevance of Machine Learning methods for anticancer drug dose optimisation, as many algorithms have shown promising results enabling model-free predictions with the potential to maximise efficacy and minimise toxicity when compared to standard protocols.
在过去的十年中，已经提出了各种机器学习技术，旨在根据假定的药物作用或测量的作用生物标志物来个性化抗癌药物的剂量。本综述的目的是全面总结使用机器学习进行抗癌药物治疗精准剂量的研究现状。
本综述按照 Cochrane 中期指南和 Joanna Briggs 研究所的指南进行。我们系统地检索了 Medline（通过 PubMed）、Embase 和 Cochrane 图书馆的数据库，以查找包括 2016 年后发表的结果的研究文章和综述。结果根据系统评价和荟萃分析扩展的首选报告项目（PRISMA-ScR）清单进行报告。
共确定了 17 项相关研究。在纳入的 12 项研究中，使用了强化学习方法，包括经典、深度、双深度和保守 Q 学习以及模糊强化学习。此外，还比较了经典机器学习方法的性能，并使用基于抛物线方程的人工智能平台前瞻性和回顾性地指导剂量，尽管仅在有限数量的患者中进行。由于算法结构明显不同，因此无法对各种机器学习方法进行有意义的比较。
总体而言，本综述强调了机器学习方法在抗癌药物剂量优化方面的临床相关性，因为许多算法已经显示出有希望的结果，与标准方案相比，能够实现无模型预测，从而最大限度地提高疗效并最小化毒性。
