Tailoring combinational therapy with Monte Carlo method-based regression modeling

Combinatorial drug therapies are generally more effective than monotherapies in treating viral infections. However, it is critical for dose optimization to maximize the efficacy and minimize side effects. Although various strategies have been devised to accelerate the optimization process, their efficiencies were limited by the high noises and suboptimal reproducibility of biological assays. With conventional methods, variances among the replications are used to evaluate the errors of the readouts alone rather than actively participating in the optimization. Herein, we present the Regression Modeling Enabled by Monte Carlo Method (ReMEMC) algorithm for rapid identification of effective combinational therapies. ReMEMC transforms the sample variations into probability distributions of the regression coefficients and predictions. In silico simulations revealed that ReMEMC outperformed conventional regression methods in benchmark problems, and demonstrated its superior robustness against experimental noises. Using COVID-19 as a model disease, ReMEMC successfully identified an optimal 3-drug combination among 10 anti-SARS-CoV-2 drug compounds within two rounds of experiments. The optimal combination showed 2-log and 3-log higher load reduction than non-optimized combinations and monotherapy, respectively. Further workflow refinement allowed identification of personalized drug combinational therapies within 5 days. The strategy may serve as an efficient and universal tool for dose combination optimization.

Combinational therapy; Dose optimization; Monte Carlo method; Regression modeling; SARS-CoV-2.