Strategies to Control Human Health Risks Arising from Antibiotics in the Environment: Molecular Modification of QNs for Enhanced Plant–Microbial Synergistic Degradation

Abstract

In the present work, a comprehensive screening and evaluation system was established to improve the plant–microbial synergistic degradation effects of QNs. The study included the construction of a 3D-QSAR model, the molecular modification, environmental friendliness and functional evaluation of drugs, degradation pathway simulation, and human health risk assessment. Molecular dynamics was applied to quantify the binding capacity of QNs toward the plant degradation enzyme (peroxidase) and microbial degradation enzymes (manganese peroxidase, lignin peroxidase, and laccase). The fuzzy comprehensive evaluation method was used in combination with the weighted average method for normalization and assigning equal weights to the plant and microbial degradation effect values of the QNs. Considering the synergistic degradation effect value as the dependent variable and the molecular information of the QNs as the independent variable, a 3D-QSAR model was constructed for the plant–microbial synergistic degradation effect of QNs. The constructed model was then employed to conduct the molecular modification, environmental friendliness and functional evaluation, degradation pathway simulation, and human health risk assessment of transformation products using pharmacokinetics and toxicokinetics. The results revealed that the synergistic degradation effect 3D-QSAR (CoMSIA) model exhibited good internal and external prediction ability, fitting ability, stability, and no overfitting phenomenon. Norfloxacin (NOR) was used as the target molecule in the molecular modification. A total of 35 NOR derivatives with enhanced plant–microbial synergistic degradation effect (1.32–21.51%) were designed by introducing small-volume, strongly electronegative, and hydrophobic hydrogen bond receptor groups into the active group of the norfloxacin structure. The environment-friendliness and the functionality of NOR were evaluated prior to and after the modification, which revealed seven environment-friendly FQs derivatives exhibiting moderate improvement in stability and bactericidal efficacy. The simulation of the NOR plant and microbial degradation pathways prior to and after the modification and the calculation of the reaction energy barrier revealed Pathway A (D-17 to D-17-2) and Pathway B (D-17 to D-17-4) as the most prone degradation pathways in plants and Pathway A (D-17 to D-17-1) and Pathway B (D-17 to D-17-4) as the most prone degradation pathways in microorganisms. This demonstrated that the degradation of the modified NOR derivatives was significantly enhanced, with the hydroxylation and piperazine ring substitution reaction playing an important role in the degradation process. Finally, the parameters, including hepatotoxicity, mutagenicity, and rodent carcinogenicity, among others, predicted using the pharmacokinetics and toxicokinetics analyses revealed a significant reduction in the human health risk associated with the modified NOR, along with a considerable reduction in the toxicity of its transformation products, implying that the human health risk associated with the transformation products was reduced remarkably. The present study provides a theoretical basis for novel ideas and evaluation programs for improving the plant–microbial synergistic degradation of the QNs antibiotics for source control and drug design, thereby reducing the residues of these antibiotics and the associated hazard in the complex plant–soil environment, ultimately decreasing the potential risks to human health.

Triazine Herbicides Risk Management Strategies on Environmental and Human Health Aspects Using In-Silico Methods

“…Similar to our previous study, based on the constructed plant-microbial synergistic degradation CoMISA model of quinolones (QNs), the hydrophobic field and electrostatic field of this model were regarded as the main modifying force field. By introducing groups with hydrophobicity (-SH, -Cl, and -F), as well as groups with strong electronegativity (-CF 3 , -CH 3 , and -CH 2 F), QNs substitutes with enhanced plant-microbial synergistic degradation effect were designed reasonably [ 36 ].…”

“…The stacked training set molecules and docking scores were imported using the SYBYL-X2.0 software (where the comprehensive value was imported to construct the CoMSIA model [ 36 , 50 ] for the comprehensive effect of herbicidal properties, degradability and toxicity of S-THs). The Calculate Properties module output the calculated values for the hydrophobic (H), hydrogen bond acceptor (A), hydrogen bond donor (D), electrostatic (E), and steric fields (S) were output using the Calculate Properties module, and cross-verification and non-cross-verification [ 36 , 50 ]. The molecules from the superimposed test set were then imported into SYBYL-X2.0 software.…”

“…The molecules from the superimposed test set were then imported into SYBYL-X2.0 software. Based on the analysis results of the CoMSIA model constructed using the training set molecules, the predicted value of the test set molecule output was obtained using the Predict function under the Add a Computed Column module [ 36 , 50 ]. Finally, using the Calculate Properties module [ 36 , 50 ], the predicted values were externally validated against the original scoring values (the comprehensive model for the comprehensive values).…”

Discovery of novel tubulin polymerization inhibitors by utilizing 3D-QSAR, molecular docking and molecular dynamics simulation

“…A series of preparations were made for the compound with SYBYL-X2.0 software: first, the Gasteiger-Huckel charge and Tripos force field were added to the compound, and second, the Powell energy gradient method was used for optimization (optimization times up to 10 000 times, the convergence criterion is no more than 0.005 kcal mol À1 , and the other values are default). 19,20 We obtained the structure by using the above method for subsequent experiments. Molecular alignment is considered to be an important step in building the 3D-QSAR models.…”

“…21 Then, the effects of molecular active groups on biological activity were predicted by analyzing three-dimensional equipotential fields such as the electrostatic field, three-dimensional field, hydrophobic field, hydrogen bond donor field and hydrogen bond acceptor field, which provided the basis for designing new compounds. 19,22…”