Researchers believe an artificial intelligence (AI) platform could help improve personalized drug combinations and treatments for patients with complex diseases.
In an auto-commentary published in SLAS Technology, authors Masturah Bte Mohd Abdul Rashin and Edward Kai-Hua Chow discuss a recent study that showed the effectiveness an AI platform that used small experimental datasets to design new drug combinations and identify the best drug combinations for specific patient samples.
“Because of the efficiency of this platform, QPOP (quadratic phenotypic optimization platform) was also able to identify patients that may be more sensitive to these optimized drug combinations from experimental data with patient samples,” the authors said. “QPOP overcomes hurdles associated with conventional drug combination design by identifying optimal drug-dose combinations in an experimental, data-driven deterministic manner independent of molecular mechanistic assumptions.”
According to the authors, the AI-platform’s designed drug combination outperformed the standard-of-care regimens when treating multiple myeloma—a cancer that forms in plasma cells and causes weakened bones. The authors also said the “robustness of QPOP in identifying and optimizing drug combinations that target drug-resistant multiple myeloma, as well as the potential to personalize combination therapy.”
“This study presents QPOP as a comprehensive optimization platform with a range of applications from drug development to personalized medicine. The ability to identify optimal drug combinations on the specified system of interest rapidly, in a rational and deterministic manner, with significant reductions in time and cost, is ideal for both the clinic and the laboratory,” the authors said.
“As drug development trends toward more specific molecularly targeted therapeutics, identifying the most effective drug combination for these new drugs will become increasingly vital to their success in getting clinical approval.”