A Machine Learning-Based Strategy Predicts Selective and Synergistic Drug Combinations for Relapsed Acute Myeloid Leukemia.

ABSTRACT: Combination therapies are one potential approach to improve the outcomes of patients with relapsed/refractory (R/R) disease. However, comprehensive testing in scarce primary patient material is hampered by the many drug combination possibilities. Furthermore, inter- and intrapatient heterogeneity necessitates personalized treatment optimization approaches that effectively exploit patient-specific vulnerabilities to selectively target both the disease- and resistance-driving cell populations. In this study, we developed a systematic combinatorial design strategy that uses machine learning to prioritize the most promising drug combinations for patients with R/R acute myeloid leukemia (AML). The predictive approach leveraged single-cell transcriptomics and single-agent response profiles measu

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12260508 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature