Computational platform optimizes multiple myeloma treatments
Masturah Bte Mohd Abdul Rashid and colleagues have developed a new platform that optimizes drug combinations for the treatment of multiple myeloma (MM), an incurable blood cancer. The computational platform, named QPOP, could one day help improve clinical outcomes in patients with MM that has become resistant to standard therapies. Combination therapies have become a pillar of many cancer treatment plans because they can be more effective than single therapies that only disrupt one molecular pathway. MM – a blood cancer involving malignant plasma cells – is frequently treated with combination therapies that include bortezomib, a first-line drug with promising response rates. However, most MM patients end up relapsing due to the development of resistance against bortezomib, highlighting a need to identify secondary combination treatments that can overcome or forestall therapeutic resistance. Rashid and colleagues developed a tool named the quadratic phenotypic optimization platform (QPOP), which approximates biological responses to therapies using advanced mathematical equations. Unlike conventional models, QPOP doesn't require predetermined information about the mechanisms or composition of a drug to optimize treatments. The authors tested their platform with 128 different combinations of 14 FDA-approved anticancer drugs, and found QPOP was able to identify effective combinations and dosages against bortezomib-resistant MM. A combination of the approved drugs mitomycin C and decitabine decreased tumor size and prolonged survival in a mouse model of bortezomib-resistant MM, suggesting the platform could be a useful tool in efforts to identify promising drug combinations for MM patients.The authors also say that further studies will be needed to determine if QPOP could be applied to the treatment of other blood cancers.
Science Press Package team