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A structure-based modelling approach identifies effective drug combinations for RAS-mutant acute myeloid leukemia.

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Researchers developed a computational model to identify effective RAF inhibitor combinations for RAS-mutant acute myeloid leukemia (AML). These combinations synergistically suppress ERK signaling, showing promise in preclinical models for treating high-risk AML.

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Area of Science:

  • Oncology
  • Molecular Biology
  • Pharmacology

Background:

  • Activating mutations in RAS/RAF/MEK/ERK signaling pathways are linked to poor prognosis in acute myeloid leukemia (AML).
  • Targeting this pathway therapeutically in AML presents significant challenges.

Purpose of the Study:

  • To utilize a structure-based, dynamic RAS pathway model to predict synergistic RAF inhibitor (RAFi) combinations for RAS-mutant AML.
  • To validate the efficacy of predicted RAFi combinations in vitro and in vivo models.

Main Methods:

  • Development of a structure-based, dynamic RAS pathway model for *in silico* prediction of RAFi synergy.
  • Validation of predicted synergistic combinations (Type I½ + Type II, Type I + Type II) using AML cell lines and patient samples.
  • Assessment of combination efficacy in a pre-clinical *NRAS*-mutant AML patient-derived xenograft (PDX) model, evaluating leukemia growth delay and survival.

Main Results:

  • The *in silico* model successfully predicted synergistic RAFi combinations, including Lifirafenib (Type II) + encorafenib (Type I½) and Lifirafenib (Type II) + SB590885 (Type I).
  • Both combinations demonstrated significant synergy *in vitro* and reduced RAS pathway activation.
  • In *in vivo* PDX models, the combinations significantly improved leukemia growth delay and event-free survival compared to single agents, with site-specific efficacy observed.

Conclusions:

  • A structure-based modeling approach can effectively identify novel and synergistic RAFi combinations for RAS-mutant AML.
  • These identified combinations show potent anti-leukemic activity *in vitro* and *in vivo*, offering potential therapeutic strategies for high-risk AML.
  • The study highlights the utility of computational modeling in drug discovery for targeted cancer therapies.