Can Artificial Intelligence Platform Optimize Combination Therapy for Multiple Myeloma?
Posted: Wednesday, September 26, 2018
A quadratic phenotypic optimization platform (QPOP), developed by a team of researchers from Singapore and Los Angeles, may help clinicians to identify the most effective combination therapy for a patient. By applying this computational platform to bortezomib-resistant multiple myeloma cell lines, the investigators were able to optimize the efficacy of combination therapies. According to the study, published in Science Translational Medicine, this artificial intelligence platform may provide optimal treatments for individual patients from an expanding range of possible drugs.
“QPOP revolutionises the way in which drug combinations are designed and represents a key area in healthcare that can be transformed with [artificial intelligence]. The efficiency of this platform in utilising small experimental data sets enables the identification of optimal drug combinations in a timely and cost-efficient manner, which marks a big leap forward in the field of personalized medicine,” noted principal investigator Edward Kai-Hua Chow, PhD, of the National University of Singapore, in a recent press release.
From a pool of 114 approved drugs, the computational platform identified a series of effective drug combinations, including an unexpected combination that outperformed the standard-of-care in relapsed myeloma. The effectiveness of the novel combination was validated against 13 patient samples, and QPOP was used to fine-tune dosage ratios of the selected treatments for optimal effectiveness.
In four patient samples, QPOP had the capacity to rank the novel combination against two other clinically used drug regimens. For two of the patients with myeloma, the novel drug combination was the most effective treatment. QPOP also identified the most effective combination for the other two patients.
