In this study, in silico modeling was designed to examine the antagonistic effect of mercuric chloride (HgCl2) and silver nanoparticles (AgNPs) on the mortality rate of zebrafish (Danio rerio) based on response surface methodology (RSM). Adult zebrafish (Danio rerio) with an average weight of 0.75 ± 0.16 g were used in this study. An interaction between HgCl2 and AgNPs was evaluated using DLS, TEM, and EDX mapping. In addition, RSM was applied to determine and predict the mortality rate of zebrafish induced by HgCl2 in the presence of non-lethal concentrations of AgNPs and to optimize dependent and independent variables. Following exposure to HgCl2, in vitro observations showed an increase in the hydrodynamic size of AgNPs and the formation of irregular nanoparticles. EDX mapping analysis also demonstrated the deposition of Hg ions on the surface of AgNPs, indicating the interaction between HgCl2 and AgNPs (i.e., the amalgamation of Hg and AgNPs). Moreover, in silico and in vivo findings illustrated that the mortality rate of zebrafish increased significantly in a concentration-dependent manner; however, the mortality rate reduced greatly in the presence of AgNPs during 96-h exposure. Statistically significant correlation and regression were also observed for the mortality rate between the actual and predicted values based on the ANOVA results, showing that the proposed model fits well. The most critical conditions of mortality rate were occurred by HgCl2 concentration of 0.23 mg L−1 and AgNP concentration of 0.04 mg L−1 that yielding maximum fish mortality rate of 96.541%. Additionally, the obtained value for model desirability was equal to 1.000 (i.e., the highest possible value). In conclusion, this statistical model could accurately describe the relationship between independent and dependent variables, and consequently boost substantially the experimental design of ecotoxicological studies by reducing the number of model organisms, toxic and chemical substances, time, and budget.
