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

Mutations activating RAS/RAF/MEK/ERK signaling are associated with poor outcome in acute myeloid leukemia (AML), but therapeutic targeting of this pathway is challenging. Here, we employ a structure-based, dynamic RAS pathway model to successfully predict RAF inhibitor (RAFi) combinations which synergistically suppress ERK signaling inRAS-mutant AML.

Ourin silicomodels predicted therapeutic synergy of two iterations of conformation-specific RAF inhibitors: Type I½ + Type II and Type I + Type II. Predictions were validatedin vitroin AML cell lines and patient samples, with synergy verified by the Loewe Additivity model. Lifirafenib (Type II) + encorafenib (Type I½) was highly synergistic against bothNRAS– andKRAS-mutant lines, while synergy of lifirafenib + SB590885 (Type I) was specific toNRAS-mutants. Immunoblotting confirmed that combination efficacy correlated strongly with decreased RAS pathway activation.

Leveraging the pharmacokinetic predictions of ourin silicomodel, both combinations were then assessed in a pre-clinicalNRAS-mutant AML patient-derived xenograft (PDX) model, showing significantly improved leukaemia growth delay and event-free survival compared with single agent approaches. Assessment of leukemia burden in bone marrow and spleen during treatment further showed site-specific efficacy against circulating and spleen-resident blasts for both combinations.

In summary, we report that our structure based-modelling approach can effectively identify novel, non-obvious, and well-tolerated RAFi combinations that are highly effective againstin vitroandin vivomodels, thereby suggesting alternative potential therapeutic strategies for high-riskRAS-mutant AML.