Publications

Combination therapy is a major strategy to circumvent the onset of treatment resistance in cancer patients; knowing which drugs to combine, however, can be difficult. Rashid et al. developed a computational platform to facilitate the discovery and optimization of drug combinations to treat multiple myeloma, a disease that often develops resistance to therapies containing the first-line drug bortezomib. The authors validated the combination treatments and refined the drug dosages in mouse models and ex vivo patient samples. Their platform requires no knowledge of which pathways to target and could more broadly aid drug repurposing efforts.

Multiple myeloma is an incurable hematological malignancy that relies on drug combinations for first and secondary lines of treatment. The inclusion of proteasome inhibitors, such as bortezomib, into these combination regimens has improved median survival. Resistance to bortezomib, however, is a common occurrence that ultimately contributes to treatment failure, and there remains a need to identify improved drug combinations. We developed the quadratic phenotypic optimization platform (QPOP) to optimize treatment combinations selected from a candidate pool of 114 approved drugs. QPOP uses quadratic surfaces to model the biological effects of drug combinations to identify effective drug combinations without reference to molecular mechanisms or predetermined drug synergy data. Applying QPOP to bortezomib-resistant multiple myeloma cell lines determined the drug combinations that collectively optimized treatment efficacy. We found that these combinations acted by reversing the DNA methylation and tumor suppressor silencing that often occur after acquired bortezomib resistance in multiple myeloma. Successive application of QPOP on a xenograft mouse model further optimized the dosages of each drug within a given combination while minimizing overall toxicity in vivo, and application of QPOP to ex vivo multiple myeloma patient samples optimized drug combinations in patient-specific contexts.