ABSTRACT
Vancomycin has been widely prescribed as the first-line antibiotic in the treatment of methicillin-resistant Staphylococcus aureus and other serious Gram-positive infections. Due to its large pharmacokinetic (PK) variability and narrow therapeutic range, it requires optimization of dosage to achieve target exposure. In this study, SmartDose, a decision support system for individualization of vancomycin dosage is developed using the maximum a posterior Bayesian estimation (MAPB) by the open-source language R combined with the population PK characteristics of vancomycin in Chinese patients. It provides initial design and adjustment of dose regimens based on the therapeutic drug monitoring (TDM) results, as well as a user-defined module to facilitate optimal vancomycin therapy. SmartDose has a high computational reliability, which is validated by NONMEM, the golden standard PK software. Meanwhile, SmartDose is established as a web-based application and its operational flexibility makes it an efficient tool for vancomycin dose optimization in routine clinical settings.
ABSTRACT
Objective:To explore the role played by clinical pharmacists in the formulation and pharmaceutical care of individual-ized administration of warfarin. Methods:Clinical pharmacists adopted maximum a posteriori Bayesian method to formulate the dosage regimen of patients treated with warfarin and monitored the implementation process. The increase of INR was timely identified on ac-count of drug interactions to avoid bleeding events,as well as the decrease of INR caused by patients' medication mistakes to avoid re-currence of thrombosis. Results:The maximum a posterior Bayesian estimation method could be used to estimate and guide clinical medication,which showed great reference value for individualized drug regimen. Conclusion:Clinical pharmacists should formulate in-dividualized administration plan according to genetic testing results and choose optimal treatment for patients. Moreover,the implemen-tation of dosage regimen should be monitored during the whole progress,so as to timely find INR abnormal fluctuations due to disease interactions,drug interactions,poor treatment compliance and so on,and consequently improve anticoagulant effect and reduce adverse reactions such as bleeding and thrombosis etc.
ABSTRACT
OBJECTIVE:To provide reference for the study of optimal sampling points in clinical pharmacokinetics.METHODS:The literatures about optimal sampling points in clinical pharmacokinetics were searched from CNKI,Wanfang database,VIP,PubMed,Medline,ScienceDirect and other databases during Jan.2011-Jun.2016 using "Bayesian estimate(s)""Bayesian estimator(s) Bayesian analysis" "Limited" "Optimal" "Sparse" "Minimal sampling" as retrieval words.The systematic analysis and evaluation were conducted.RESULTS:A total of 1 Chinese literature and 13 English literatures were involved respectively.The drugs they focused on were mainly immunosuppressive agents,antiviral drugs,antibiotics,pediatric individualized medication,etc.Multiple linear regression (MLR) was still the most widely used method in China,while maximum a posteriori Bayesian (MAPB) method was more popular in foreign studies.MLR equation was simple and easy to use,but the sampling was very strict.MAPB method could be completed with less sampling points and sampling time;it was more suitable for clinical practice,but needed professional software.The precision and accuracy of the two methods were similar.The research methods of optimal sampling strategy were quite different but all included 4 steps as prior information ganining,reference value determination,sampling point optimization,prediction capability verification.CONCLUSIONS:MAPB method requires less sampling points and it results are relatively accurate and reliable.It is more suitable for clinical practice and optimal sampling study of clinical pharmacokinetics.