A mechanistic effect modeling approach to the prioritization of hidden drivers in chemical cocktails

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.

A novel equal frequency sampling of factor levels (EFSFL) method is applied to identify the dominant factor inducing the combined toxicities of 13 factors

The research framework combining global sensitivity analysis (GSA) with quantitative high-throughput screening (qHTS), called GSA-qHTS, provides a potentially feasible way to screen for important factors that induce toxicities of complex mixtures. Despite its value, the mixture samples designed using the GSA-qHTS technique still have a shortage of unequal factor levels, which leads to an asymmetry in the importance of elementary effects (EEs). In this study, we developed a novel method for mixture design that enables equal frequency sampling of factor levels (called EFSFL) by optimizing both the trajectory number and the design and expansion of the starting points for the trajectory. The EFSFL has been successfully employed to design 168 mixtures of 13 factors (12 chemicals and time) that each have three levels. By means of high-throughput microplate toxicity analysis, the toxicity change rules of the mixtures are revealed. Based on EE analysis, the important factors affecting the toxicities of the mixtures are screened. It was found that erythromycin is the dominant factor and time is an important non-chemical factor in mixture toxicities. The mixtures can be classified into types A, B, and C mixtures according to their toxicities at 12 h, and all the types B and C mixtures contain erythromycin at the maximum concentration. The toxicities of the type B mixtures increase firstly over time (0.25 ∼ 9 h) and then decrease (12 h), while those of the type C mixtures consistently increase over time. Some type A mixtures produce stimulation that increases with time. With the present new approach to mixture design, the frequency of factor levels in mixture samples is equal. Consequently, the accuracy of screening important factors is improved based on the EE method, providing a new method for the study of mixture toxicity.