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

Area of Science:

• Oncology
• Pharmacology
• Computational Biology

Background:

• Activating mutations in the RAS/RAF/MEK/ERK signaling pathway are linked to poor prognosis in acute myeloid leukemia (AML).
• Targeting this pathway presents therapeutic challenges due to pathway complexity and resistance mechanisms.

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 in silico predictions through in vitro and in vivo experiments.

Main Methods:

• Development of a dynamic RAS pathway model for predicting RAFi synergy.
• In vitro screening of conformation-specific RAFi combinations (Lifirafenib + Encorafenib, Lifirafenib + SB590885).
• In vivo testing of synergistic combinations in an NRAS-mutant AML patient-derived xenograft model, informed by in silico pharmacokinetic predictions.

Main Results:

• In silico models successfully predicted synergy for two RAFi combinations.
• Lifirafenib + encorafenib demonstrated high synergy against NRAS- and KRAS-mutant AML cells.
• Lifirafenib + SB590885 showed NRAS-mutant-specific synergy.
• Combination efficacy correlated with RAS pathway activity.
• In vivo studies showed improved leukemia growth delay and survival with combination therapy compared to single agents.

Conclusions:

• An integrated computational and experimental approach effectively identified novel synergistic RAFi combinations for RAS-mutant AML.
• These findings suggest promising alternative therapeutic strategies for patients with RAS-mutant AML.
• The study highlights the potential of structure-based modeling in drug discovery for complex signaling pathways.