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.
METHODS: Herein, we have engineered antibiotic-loaded (doxycycline or vancomycin) LPHNPs with cationic and zwitterionic lipids and examined the effects on their physicochemical characteristics (size and charge), antibiotic entrapment efficiency, and the in vitro intracellular bacterial killing efficiency against Mycobacterium smegmatis or Staphylococcus aureus infected macrophages.
RESULTS: The incorporation of cationic or zwitterionic lipids in the LPHNP formulation resulted in a size reduction in LPHNPs formulations and shifted the surface charge of bare NPs towards positive or neutral values. Also observed were influences on the drug incorporation efficiency and modulation of the drug release from the biodegradable polymeric core. The therapeutic efficacy of LPHNPs loaded with vancomycin was improved as its minimum inhibitory concentration (MIC) (2 µg/mL) versus free vancomycin (4 µg/mL). Importantly, our results show a direct relationship between the cationic surface nature of LPHNPs and its intracellular bacterial killing efficiency as the cationic doxycycline or vancomycin loaded LPHNPs reduced 4 or 3 log CFU respectively versus the untreated controls.
CONCLUSION: In our study, modulation of surface charge in the nanomaterial formulation increased macrophage uptake and intracellular bacterial killing efficiency of LPHNPs loaded with antibiotics, suggesting alternate way for optimizing their use in biomedical applications.
METHODS: A total of 28 critically ill patients were included in this study. All data were collected from medical, microbiology and pharmacokinetic records. The clinical response was evaluated on the basis of clinical and microbiological parameters. The 24-h area under the curve (AUC0-24) was estimated from a single trough level using established equations.
RESULTS: Out of the 28 patients, 46% were classified as responders to vancomycin treatment. The trough vancomycin concentration did not differ between the responders and non-responders (15.02 ± 6.16 and 14.83 ± 4.80 μg mL-1; P = 0.929). High vancomycin minimum inhibitory concentration (MIC) was observed among the non-responders (P = 0.007). The ratio between vancomycin trough concentration and vancomycin MIC was significantly lower in the non-responder group (8.76 ± 3.43 vs. 12.29 ± 4.85 μg mL-1; P = 0.034). The mean ratio of estimated AUC0-24 and vancomycin MIC was 313.78 ± 117.17 μg h mL-1 in the non-responder group and 464.44 ± 139.06 μg h mL-1 in the responder group (P = 0.004). AUC0-24/MIC of ≥ 400 μg h mL-1 was documented for 77% of the responders and 27% of the non-responders (c2 = 7.03; P = 0.008).
CONCLUSIONS: Ratio of trough concentration/MIC and AUC0-24/MIC of vancomycin are better predictors for MRSA treatment outcomes than trough vancomycin concentration or AUC0-24 alone. The single trough-based estimated AUC may be sufficient for the monitoring of treatment response with vancomycin.
METHODS: The antimicrobial activity was tested against the planktonic S. aureus cells using the microdilution broth assay, while the antibiofilm activity were evaluated using the crystal violet and resazurin assays. The cytotoxicity of the SBDs was assessed on MRC5 (normal lung tissue), using the MTT assay.
RESULTS: The individual SBDs showed significant reduction of biomass and metabolic activity in both S. aureus strains. Combinations of the SBDs with OXA and VAN were mainly additive against the planktonic cells and cells in the biofilm. Both the compounds showed moderate toxicity against the MRC5 cell line. The selectivity index suggested that the compounds were more cytotoxic to S. aureus than the normal cells.
CONCLUSION: Both the SBD compounds demonstrated promising antimicrobial and antibiofilm activities and have the potential to be further developed as an antimicrobial agent against infections caused by MRSA.