Extending the boundaries of cancer therapeutic complexity with literature text mining

Danna Niezni, Hillel Taub-Tabib, Yuval Harris, Hagit Sason, Yakir Amrusi, Dana Meron-Azagury, Maytal Avrashami, Shaked Launer-Wachs, Jon Borchardt, M. Kusold, Aryeh Tiktinsky, Tom Hope, Yoav Goldberg, Yosi Shamay*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageAmerican English
Article number102681
JournalArtificial Intelligence in Medicine
Volume145
DOIs
StatePublished - Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

  • Combination therapy
  • Crowdsourcing
  • Drug synergy
  • Literature-based discovery
  • Personalized therapy
  • Text mining

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