Machine learning decreases experimental costs of drug combination screening for translational applications

Posted by Jean Ho Jean covers medicine, wellness, and mental health. Combination therapies have become a standard treatment of several complex diseases. High-throughput screening (HTS) makes it possible to profile phenotypic effects of thousands of drug combinations in patient-derived cells and other pre-clinical model systems. However, due to the massive number of potential drug and dose combinations, large-scale multi-dose combinatorial screening requires extensive resources and instrumentation, beyond the capability of most academic laboratories. Testing of hundreds of combinations is also impossible in limited cell numbers from patient samples.