METHODS: A 'meta-model' with 4894 concentrations from 1631 neonates was built using NONMEM, and Monte Carlo simulations were performed to design an optimal intermittent infusion, aiming to reach a target AUC0-24 of 400 mg·h/L at steady-state in at least 80% of neonates.
RESULTS: A two-compartment model best fitted the data. Current weight, postmenstrual age (PMA) and serum creatinine were the significant covariates for CL. After model validation, simulations showed that a loading dose (25 mg/kg) and a maintenance dose (15 mg/kg q12h if <35 weeks PMA and 15 mg/kg q8h if ≥35 weeks PMA) achieved the AUC0-24 target earlier than a standard 'Blue Book' dosage regimen in >89% of the treated patients.
CONCLUSIONS: The results of a population meta-analysis of vancomycin data have been used to develop a new dosing regimen for neonatal use and to assist in the design of the model-based, multinational European trial, NeoVanc.
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.
OBJECTIVE: The present study examines the antibacterial properties of 18 medicinal plants used by the Khyang tribe in day-to-day practice against human pathogenic bacteria.
MATERIALS AND METHODS: Leaves, bark, fruits, seeds, roots and rhizomes from collected plants were successively extracted with hexane, ethyl acetate and ethanol. The corresponding 54 extracts were tested against six human pathogenic bacteria by broth microdilution assay. The antibacterial mode of actions of phytoconstituents and their synergistic effect with vancomycin and cefotaxime towards MRSA was determined by time-killing assay and synergistic interaction assay, respectively.
RESULTS AND DISCUSSION: Hexane extract of bark of Cinnamomum cassia (L.) J. Presl. (Lauraceae) inhibited the growth of MRSA, Enterococcus faecalis, Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae and Acinetobacter baumannii with MIC values below 100 µg/mL. From this plant, cinnamaldehyde evoked at 4 × MIC in 1 h an irreversible decrease of MRSA count Log10 (CFU/mL) from 6 to 0, and was synergistic with vancomycin for MRSA with fractional inhibitory concentration index of 0.3.
CONCLUSIONS: Our study provides evidence that the medicinal plants in Bangladesh have high potential to improve the current treatment strategies for bacterial infection.