药物联合治疗是癌症治疗的主要支柱。随着可能的组合候选药物数量的增加，由于组合爆炸，开发最佳的高复杂性组合疗法（每次治疗涉及 4 种或更多药物）如 RCHOP-I 和 FOLFIRINOX 变得越来越具有挑战性。在本文中，我们提出了一种基于文本挖掘（TM）的工具和工作流程，用于快速生成高复杂性组合治疗（HCCT），以扩展癌症治疗复杂性的界限。我们的主要目标是：（1）描述联合治疗的现有局限性； （2）开发并引入Plan Builder（PB），有效利用现有文献进行药物组合； (3) 评估PB在加速HCCT计划制定方面的潜力。我们的结果表明，与传统方法相比，使用 PB 的研究人员和专家能够以更快的速度和更高的质量创建 HCCT 计划。通过发布 PB，我们希望能够让更多的研究人员参与 HCCT 规划并展示其临床疗效。版权所有 © 2023 Elsevier B.V. 保留所有权利。

Drug combination therapy is a main pillar of cancer therapy. As the number of possible drug candidates for combinations grows, the development of optimal high complexity combination therapies (involving 4 or more drugs per treatment) such as RCHOP-I and FOLFIRINOX becomes increasingly challenging due to combinatorial explosion. In this paper, we propose a text mining (TM) based tool and workflow for rapid generation of high complexity combination treatments (HCCT) in order to extend the boundaries of complexity in cancer treatments. Our primary objectives were: (1) Characterize the existing limitations in combination therapy; (2) Develop and introduce the Plan Builder (PB) to utilize existing literature for drug combination effectively; (3) Evaluate PB's potential in accelerating the development of HCCT plans. Our results demonstrate that researchers and experts using PB are able to create HCCT plans at much greater speed and quality compared to conventional methods. By releasing PB, we hope to enable more researchers to engage with HCCT planning and demonstrate its clinical efficacy.Copyright © 2023 Elsevier B.V. All rights reserved.