Regulation of drug metabolism and toxicity by multiple factors of genetics,epigenetics,lncRNAs,gut microbiota,and diseases:a meeting report of the 21st International Symposium on Microsomes and Drug Oxidations(MDO)

Variations in drug metabolism may alter drug efficacy and cause toxicity;better understanding of the mechanisms and risks shall help to practice precision medicine.At the 21 st International Symposium on Microsomes and Drug Oxidations held in Davis,California,USA,in October 2-6,2016,a number of speakers reported some new findings and ongoing studies on the regulation mechanisms behind variable drug metabolism and toxicity,and discussed potential implications to personalized medications.A considerably insightful overview was provided on genetic and epigenetic regulation of gene expression involved in drug absorption,distribution,metabolism,and excretion(ADME) and drug response.Altered drug metabolism and disposition as well as molecular mechanisms among diseased and special populations were presented.In addition,the roles of gut microbiota in drug metabolism and toxicology as well as long non-coding RNAs in liver functions and diseases were discussed.These findings may offer new insights into improved understanding of ADME regulatory mechanisms and advance drug metabolism research.

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.

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.