Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays

Combination therapies enhance the therapeutic effect of cancer treatment;
however, identifying effective interdependent doses, durations, and
sequences of multidrug administration regimens is a time- and
labor-intensive task. Here, we integrated machine-learning, automation,
and large microfluidic arrays of cancer spheroids or patient-derived
organoids formed in a tissue-mimetic hydrogel to achieve drastic
acceleration of the discovery of effective multidrug administration
regimens. For the clinically approved drug combination, we discovered a
sequential administration regimen leading to a substantial reduction in
the total drug dose, in comparison with concurrent drug supply, both at
comparable drug efficacy. For the drugs that are currently under clinical
development, we found a synergistic effect of concurrently administered
drugs and showed that the synergy diminishes for the sequential drug
supply. The developed strategy holds promise for the discovery of
effective combination therapies for advanced cancer treatment, including
personalized chemotherapies.