Researchers from the National University of Singapore (NUS) have developed an artificial intelligence (AI) platform that could change the way drug combinations are being designed. It enables doctors to determine the most effective drug combination for a patient quickly. Applying the platform towards drug resistant multiple myeloma, they were able to establish new effective drug combinations. They also identified the patients who may be more responsive to these treatments in under a week.
Existing methods for designing drug combinations typically involve testing arbitrary combinations of commonly used drugs. The other is to incorporate new targeted therapies into established drug combinations. Bortezomib-containing drug combinations are currently used as the first and second-line treatment for multiple myeloma. Most patients inevitably become resistant to these drugs and new combinations need to be established. Some newer combinations have shown to be effective for some patients. However, rapid identification of an optimal personalised treatment for a specific patient from an infinite span of possible drug combinations remains a challenge.
AI customizes optimal drug ‘cocktail’ for every patient
One goal of the Quadratic Phenotypic Optimisation Platform (QPOP) is to speed up drug combination design. The other is to identify the most effective drug combinations targeted at individual patients. With just a small amount of blood or bone marrow sample from patients, QPOP is able to map the drug response that a set of drug combinations will have on the 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. That included a completely novel and unexpected combination that outperformed the standard of care regimen for relapsed myeloma. The performance of the novel combination was validated against 13 patient samples. QPOP was also used to fine-tune dosage ratios of the novel combination for optimal effectiveness.
QPOP was able to evaluate and rank the novel combination against the other two current clinically used drug combinations. The novel combination was found to be the most effective treatment option for two patient samples tested. QPOP was able to match the best drug combination to each patient. Therefore demonstrating proof-of-concept for personalised medicine.
Dr Edward Kai-Hua Chow, Principal Investigator at the Cancer Science Institute of Singapore, NUS, said: “QPOP revolutionises the way in which drug combinations are designed. It represents a key area in healthcare that can be transformed with AI. The efficiency of QPOP in utilising small experimental data sets enables the identification of optimal drug combinations in a timely and cost-efficient manner. It marks a big leap forward in the field of personalised medicine.”
Source: National University of Singapore