A simulation study of sampling time point designing for therapeutic drug monitoring based on a population pharmacokinetic model

Aim To develop a method to estimate population pharmacokinetic parameters with the limited sampling time points provided clinically during therapeutic drug monitoring. Methods Various simulations were attempted using a one-compartment open model with the first order absorption to determine PK parameter estimates with different sampling strategies as a validation of the method. The estimated parameters were further verified by comparing to the observed values. Results The samples collected at the single time point close to the non-informative sampling time point designed by this method led to bias and inaccurate parameter estimations. Furthermore, the relationship between the estimated non-informative sampling time points and the values of the parameter was examined. The non-informative sampling time points have been developed under some typical occasions and the results were plotted to show the tendency. As a result, one non-informative time point was demonstrated to be appropriate for clearance and two for both volume of distribution and constant of absorption in the present study. It was found that the estimates of the non-informative sampling time points developed in the method increase with increases of volume of distribution and the decrease of clearance and constant of absorption. Conclusion A rational sampling strategy during therapeutic drug monitoring can be established using the method present in the study.

Pharmacologic mechanisms mining and prediction of Xiaoer Qixing Cha Formulae in the treatment of infantile functional dyspepsia based on chemical analysis by UPLC-QTOF/MS and interactive network pharmacology

Objective: To explore the main chemical compounds in Xiaoer Qixing Cha Formulae (XQCF), and investigate its mechanisms for the treatment of infantile functional dyspepsia (IFD). Methods: The chemical components were identified by UPLC-QTOF/MS analytic technique. Targets of the compounds were screened from TCMSP and SWISS database, and disease targets were screened from OMIM and TTD online database. Candidate targets of compounds were mapped to the disease targets as predict therapeutic targets for XQCF. Several networks were constructed and analyzed by Cytoscape ver. 3.2.1. Meanwhile, prescription compatibility in XQCF was interpreted from the network perspective based on distribution of the number of targets. Furthermore, Gene Ontology (GO) enrichment analysis and KEGG pathway analysis were operated via Clue Go to illustrate complex relationships between the potential targets and pharmacological mechanisms. Results: A total of fifty-three compounds were recognized or tentatively characterized belonging to XQCF based on MS data and online chemical database. Sixty-three therapeutic targets were screened. AKT1, FOS, SLC6A4, COMT and 5-HT receptors were focused as therapeutic targets of XQCF. Pathways including carbohydrate digestion and absorption, serotonergic synapse, calcium signaling pathway and cAMP signaling pathway were predicted as significant regulatory pathways. The results indicated that the predicted targets and pathways related in brain-gut axis to a great extent, which could be potential pharmacological mechanism of XQCF for the treatment of IFD. Conclusions: The findings in this study provided the experimental and theoretical basis for further research for XQCF. Those also illustrated a reasonable method worth intensive study on pharmacodynamic mechanisms of TCM Formulae.

Statistical Methods for Classification of Medicinal Plants

Statistical classification methods are frequently applied to analyze metabolomics data, especially from medicinal plants. Combined with variable selection techniques, we are able to identify marker candidates, which can be used to discriminate the group to which unknown subjects belong. After preprocessing, such as outlier checking, normalization, missing value imputation and transformation, we then mainly utilized four novel classification methods： RF （random forest）, NSC （nearest shrunken centroid）, PLS-DA （partial least square discriminant analysis） and SAM （significant analysis ofmicroarrays）. Each method has its own device to measure the importance of single metabolite, so that, it is probable to choose highly ranked metabolites, which show the best prediction accuracy. Adapting above strategy, we have successfully analyzed several kinds of metabolomics data including Panax ginseng, Lespedeza species, Anemarrhean asphodeloides and Gastrodia elata.

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