AI Enables Personalized Treatment For Myeloma

A multidisciplinary team of researchers has developed an artificial intelligence platform that could enable doctors to prescribe personalized treatments for myeloma patients.

AsianScientist (Aug. 20, 2018) – A research group in Singapore has used artificial intelligence (AI) to identify optimized combinatorial drug treatments for myeloma, a type of blood cancer. Their methods and findings are published in Science Translational Medicine.

Existing methods for designing drug combinations typically involve testing arbitrary combinations of commonly used drugs or incorporating new targeted therapies into established drug combinations. Bortezomib-containing drug combinations are currently used as the first- and second-line treatments for multiple myeloma.

However, most patients inevitably become resistant to these drugs and new combinations need to be established. While certain newer combinations have been shown to be effective for some patients, rapid identification of an optimal personalized treatment for a specific patient from an infinite span of possible drug combinations remains a challenge.

In this study, scientists at the National University Singapore (NUS) have developed an AI technology platform, which they call the Quadratic Phenotypic Optimization Platform (QPOP), to speed up drug combination design, using small experimental data sets to identify the most effective drug combinations targeted at individual patients. With just a small amount of blood or bone marrow sample from patients, the platform is able to map the drug response that a set of drug combinations will have on a specific patient’s cancer cells.

From an initial pool of 114 FDA-approved drugs, QPOP was able to identify a series of effective drug combinations, including a novel and unexpected drug combination that outperformed the standard of care regimen for relapsed myeloma. The combination was validated against 13 patient samples. QPOP was also used to fine-tune dosage ratios of the novel combination for optimal effectiveness.

Using four other patient samples, the research team further demonstrated that QPOP was able to evaluate and rank the novel drug combination against two other drug combinations currently used in the clinic. The novel drug combination identified by the AI platform was found to be the most effective treatment option for two of the myeloma patient samples tested. QPOP was able to match the ideal drug combination to each patient, hence demonstrating proof-of-concept for personalized medicine.

“QPOP revolutionizes the way in which drug combinations are designed and represents a key area in healthcare that can be transformed with AI. The efficiency of this platform in utilizing 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,” said Dr. Edward Chow Kai-Hua, principal investigator at the Cancer Science Institute of Singapore, NUS, who led the study.

The team is now working towards translating this work into the clinic. In 2019, they will look into recruiting patients for prospective clinical trials related to QPOP and other AI platforms, as well as expand the application of the platform into other disease areas.

The article can be found at: Rashid et al. (2018) Optimizing Drug Combinations Against Multiple Myeloma Using a Quadratic Phenotypic Optimization Platform (QPOP).


Source: National University of Singapore; Photo: Shutterstock.
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