Mechanistic models position ceritinib as a nuclear integrity disrupting therapy in pediatric liver tumors

Salih Demir1, Thomas Kessler2, Alina Hotes1

  • 1Department of Pediatric Surgery, Dr. Von Hauner Children's Hospital, LMU University Hospital, LMU Munich, Lindwurmstr. 2a, Munich, 80337, Germany.

Abstract

Insights

Computational models identified ceritinib as a promising treatment for high-risk pediatric liver tumors. This drug targets key kinases, disrupts nuclear integrity, and shows efficacy in preclinical models.

Area of Science:

  • Computational biology and oncology
  • Translational medicine
  • Drug discovery

Background:

  • High-risk pediatric liver tumors present significant therapeutic challenges.
  • Targeted treatment strategies are crucial for improving patient outcomes.
  • Computational modeling offers a novel approach to identify effective therapies.

Purpose of the Study:

  • To leverage patient-specific mechanistic cell models to identify alternative treatments for pediatric liver tumors.
  • To utilize computational drug response simulations for predicting therapeutic options.

Main Methods:

  • Generated digital twins of pediatric liver tumor patients using clinical, genetic, and transcriptomic data.
  • Performed in silico drug response simulations with mechanistic models.
  • Validated the efficacy of ceritinib in vitro and in vivo using patient-derived xenograft models.

Main Results:

  • Mechanistic models identified ceritinib as the most effective treatment via in silico simulations.
  • Ceritinib inhibited tumor growth by targeting non-canonical kinases, unlike other ALK inhibitors.
  • Ceritinib suppressed nucleoporin expression, disrupted nuclear integrity, and induced apoptosis, reducing tumor burden in mouse models.

Conclusions:

  • Mechanistic models applied to virtual patients successfully positioned ceritinib as a promising therapeutic agent for high-risk pediatric liver tumors.
  • Ceritinib demonstrates efficacy by targeting kinases involved in tumor aggressiveness and compromising nuclear integrity.
  • This approach highlights the potential of computational modeling in accelerating drug discovery for challenging pediatric cancers.

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