Tailoring combinational therapy with Monte Carlo method-based regression modeling.

ABSTRACT: 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 re

SUBMITTER: Wang B 

PROVIDER: S-EPMC12744531 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature