Real-time toxicity prediction of Aconitum stewing system using extractive electrospray ionization mass spectrometry

Due to numerous obstacles such as complex matrices,real-time monitoring of complex reaction systems(e.g.,medicinal herb stewing system)has always been a challenge though great values for safe and rational use of drugs.Herein,facilitated by the potential ability on the tolerance of complex matrices of extractive electrospray ionization mass spectrometry,a device was established to realize continuous sampling and real-time quantitative analysis of herb stewing system for the first time.A complete analytical strategy,including data acquisition,data mining,and data evaluation was proposed and implemented with overcoming the usual difficulties in real-time mass spectrometry quantification.The complex Fuzi(the lateral root of Aconitum)-meat stewing systems were real-timely monitored in150 min by qualitative and quantitative analysis of the nine key alkaloids accurately.The results showed that the strategy worked perfectly and the toxicity of the systems were evaluated and predicated accordingly.Stewing with trotters effectively accelerated the detoxification of Fuzi soup and reduced the overall toxicity to 68%,which was recommended to be used practically for treating rheumatic arthritis and enhancing immunity.The established strategy was versatile,simple,and accurate,which would have a wide application prospect in real-time analysis and evaluation of various complex reaction systems.

Manufactured nanoparticle:A prediction model for understanding PM2.5 toxicity to human

Epidemiological studies have demonstrated that chronic exposure to polluted concentration of fine ambient particulate matter(PM2.5)can induce markedly harmful effects on human health,however,an enormous research effort is still need to the comprehensive understanding of PM2.5 induction of new negative health outcomes.Recently,Maher and colleges[1]from Environmental Magnetism and Paleomagnetism at Lancaster University

Commentary: Unexpected Novel Chemical Weapon Agents Designed by Innocuous Drug-Development AI (Artificial Intelligence) Algorithm

Recent publications reveal the disturbing information that a minor edit to an algorithm being used for designing legitimate drug candidates redirected the program in a way that resulted in the surprising design of novel chemical warfare agent candidates. Although this outcome was not the result of nefarious intent, and appropriate chemical defense authorities were notified, the potential implications of some misapplication of a drug-design algorithm for nefarious purposes are clear. This Commentary summarizes how otherwise benign Artificial Intelligence (AI) algorithms used for drug discovery can be easily reversed to design novel chemical warfare agents for which no effective antidote will be available, or perhaps even envisioned.

Machine Learning-Based Quantitative Structure-Activity Relationship and ADMET Prediction Models for ERα Activity of Anti-Breast Cancer Drug Candidates

Breast cancer is presently one of the most common malignancies worldwide,with a higher fatality rate.In this study,a quantitative structure-activity relationship(QSAR)model of compound biological activity and ADMET(Absorption,Distribution,Metabolism,Excretion,Toxicity)properties prediction model were performed using estrogen receptor alpha(ERα)antagonist information collected from compound samples.We first utilized grey relation analysis(GRA)in conjunction with the random forest(RF)algorithm to identify the top 20 molecular descriptor variables that have the greatest influence on biological activity,and then we used Spearman correlation analysis to identify 16 independent variables.Second,a QSAR model of the compound were developed based on BP neural network(BPNN),genetic algorithm optimized BP neural network(GA-BPNN),and support vector regression(SVR).The BPNN,the SVR,and the logistic regression(LR)models were then used to identify and predict the ADMET properties of substances,with the prediction impacts of each model compared and assessed.The results reveal that a SVR model was used in QSAR quantitative prediction,and in the classification prediction of ADMET properties:the SVR model predicts the Caco-2 and hERG(human Ether-a-go-go Related Gene)properties,the LR model predicts the cytochrome P450 enzyme 3A4 subtype(CYP3A4)and Micronucleus(MN)properties,and the BPNN model predicts the Human Oral Bioavailability(HOB)properties.Finally,information entropy theory is used to validate the rationality of variable screening,and sensitivity analysis of the model demonstrates that the constructed model has high accuracy and stability,which can be used as a reference for screening probable active compounds and drug discovery.

(Q)SAR modelling of nanomaterial toxicity:A critical review

There is increasing recognition that some nanomaterials may pose a risk to human health and the environment. Moreover, the industrial use of the novel engineered nanomaterials （ENMs） increases at a higher rate than data generation for hazard assessment; consequently, many of them remain untested. The large number of nanomaterials and their variants （e.g., different sizes and coatings） requiring testing and the ethical pressure towards nonanimal testing means that in a first instance, expensive animal bioassays are precluded, and the use of（quantitative） structure-activity relationships （（Q）SARs） models as an alter- native source of （screening） hazard information should be explored. （Q）SAR modelling can be applied to contribute towards filling important knowledge gaps by making best use of existing data, prioritizing the physicochemical parameters driving toxicity, and providing practical solutions for the risk assessment problems caused by the diversity of ENMs. This paper covers the core components required for successful application of （Q）SAR methods to ENM toxicity prediction, summarizes the published nano-（Q）SAR studies, and outlines the challenges ahead for nano-（Q）SAR modelling. It provides a critical review of （1） the present availability of ENM characterization/toxicity data, （2） the characterization of nanostructures that meet the requirements for （Q）SAR analysis, （3） published nano-（Q）SAR studies and their limitations, （4） in silico tools for （Q）SAR screening of nanotoxicity, and （5） prospective directions for the development of nano-（Q）SAR models.

QSAR Models for Predicting Additive and Synergistic Toxicities of Binary Pesticide Mixtures on Scenedesmus Obliquus

Pesticides released into the environment may pose potential risks to the ecological system and hu-man health.However,existing toxicity data on pesticide mixtures still lack,especially regarding the toxic interac-tions of their mixtures.This study aimed to determine the toxic interactions of binary mixtures of pesticides on Scenedesmus Obliquus(S.obliquus)and to build quantitative structure-activity relationship models(QASR)for predicting the mixture toxicities.By applying direct equipartition ray method to design binary mixtures of five pes-ticides(linuron,dimethoate,dichlorvos,trichlorfon and metribuzin),the toxicity of a single pesticide and its mix-ture was tested by microplate toxicity analysis on S.obliquus.The QASR models were built for combined toxicity of binary mixtures of pesticides at the half-maximal effective concentration(EC_(50)),30%maximal effective concen-tration(EC_(30))and 10%maximal effective concentration(EC_(10)).The results showed that the single toxicity follows:metribuzin>linuron>dichlorvos>trichlorfon>dimethoate.The mixtures of linuron and trichlorfon,dichlorvos and metribuzin,dimethoate and metribuzin induced synergetic effects,while the remaining binary mixtures exhib-ited additive.The developed QSAR models were internally validated using the leave-one-out cross-validation(LOO),leave-many-out cross-validation(LMO),bootstrapping,and y-randomization test,and externally validated by the test sets.All three QSAR models satisfied well with the experimental values for all mixture toxicities,and presented high internally(R^(2)and Q^(2)>0.85)and externally(Q^(2)_(F1),Q^(2)_(F2),and Q^(2)_(F3)>0.80)predictive powers.The developed QSAR models could accurately predict the toxicity values of EC_(50),EC_(30)and EC_(10)and were superior to the concentration addition model's results(CA).Compared to the additive effect,the QSAR model could more ac-curately predict the binary mixture toxicities of pesticides with synergistic effects.

Study of Aldo-keto Reductase 1C3 Inhibitor with Novel Framework for Treating Leukaemia Based on Virtual Screening and In vitro Biological Activity Testing

Aldo-keto reductase 1C3(AKR1C3)is a potential target for the treatment of acute myeloid leukaemia and T-cell acute lymphoblastic leukaemia.In this study,pharmacophore models,molecular docking and virtual screening of target prediction were used to find a potential AKR1C3 inhibitor.Firstly,eight bacteriocin derivatives(Z1-Z8)were selected as training sets to construct 20 pharmacophore models.The best pharmacophore model MODEL_016 was obtained by Decoy test(the enrichment degree was 21.5117,and the fitting optimisation degree was 0.9668).Secondly,MODEL_016 was used for the virtual screening of ZINC database.Thirdly,the hit 83256 molecules were docked into the AKR1C3 protein.Compared to the total scores and interactions between compounds and protein,16532 candidate compounds with higher docking scores and interactions with important residues PHE306 and TRP227 were screened.Lastly,eight compounds(A1-A8)that had good absorption,distribution,metabolism,excretion and toxicity(ADMET)properties were obtained by target prediction.Compounds A3 and A7 with high total score and good target prediction results were selected for in vitro biological activity test,whose IC_(50) values were 268.3 and 88.94µmol/L,respectively.The results provide an important foundation for the discovery of novel AKR1C3 inhibitors.The research methods used in this study can also provide important references for the research and development of new drugs.