A novel mixture sampling strategy combining latin hypercube sampling with optimized one factor at a time method: A case study on mixtures of antibiotics and pesticides

Global sensitivity analysis in conjunction with quantitative high-throughput screening presents a novel technique for identifying the key components that induce the toxicities of mixtures. However, the mixtures currently designed with this method suffer from unequal frequency sampling, repeated mixtures, and only odd factor levels being considered. Accordingly, we use latin hypercube sampling to generate the starting points of the trajectories to achieve equal frequency sampling and non-repeated mixtures, as well as apply different one factor at a time methods for factors with odd and even levels to achieve suitability for factors with both odd and even levels. This method is called LHS-OAT. LHS-OAT was successfully applied to design 110 equal-frequency and non-repeated mixtures consisting of six antibiotics and four pesticides. It was found that four factors, roxithromycin (A5), tetracycline (A6), dichlorvos (P1), and demeton-S (P3), induce the toxicities of mixtures, and A5 and P1 in the Shaying River Basin have risk quotients >= 1. Additionally, we developed the toxicity deviation ratio to correct the risk quotients of interacting mixtures for effective risk assessments. This study provides a rational and effective method for mixture design that accurately identifies the important factors that induce the toxicities of mixtures.

Automated summary

Global sensitivity analysis combined with quantitative high-throughput screening provides a novel technique for identifying the key components responsible for the toxicities of mixtures. However, the current designs suffer from uneven frequency sampling, repeated mixtures, and only considering odd factor levels. To address these issues, the study proposes a method called LHS-OAT, which uses latin hypercube sampling to achieve equal frequency sampling and non-repeated mixtures, and applies different one factor at a time methods for factors with odd and even levels. The method was successfully applied to design mixtures of antibiotics and pesticides, accurately identifying the important factors inducing the toxicities of the mixtures.