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: 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.
Objectives: This study aimed to evaluate the patterns of intraperitoneal (IP) antibiotic utilization for the treatment of peritonitis in CAPD patients.
Materials and Methods: This is a retrospective study conducted at a tertiary hospital setting in Malaysia. Medical records of CAPD patients who were diagnosed with peritonitis and registered with National Kidney Registry from 2013 to 2018 were reviewed. Types of antibiotics used and its dose and duration were recorded and reported using the anatomical therapeutic chemical/defined daily dose (ATC/DDD) system.
Results: A total of 105 peritonitis episodes were recorded from 72 patients. The most common first-line empirical antibiotic combinations used were ceftazidime/cefazolin (40%, n = 42), followed by cefepime/cefazolin (30.5%, n = 32) and ceftazidime/cloxacillin (25.7%, n = 27). The definitive therapy for culture-proven CAPD-related peritonitis (CAPD-P) showed that vancomycin was the most frequently prescribed antibiotic (31.7%, n = 26/82), followed by amikacin (14.6%, n = 12/82), meropenem (11%, n = 9/82) and ampicillin (11%, n = 9/82). Ciprofloxacin was among the least prescribed definitive antibiotics for CAPD-P (2.4%, n = 2/82) but the DDD/100 patient-days estimates showed that it had the highest therapeutic intensity.
Conclusion: There are various IP antibiotics used for CAPD-P and the most common empirical therapy was the combination of ceftazidime and cefazolin while vancomycin is predominantly used for definitive therapy. Future studies to evaluate the clinical outcomes of the antibiotic use should be conducted to have a better insight on the efficacy of the peritonitis treatment.
METHODS: MEDLINE and Embase databases were searched from inception up to September 2019 to identify all studies that compared the predictive performance of cystatin C- and/or creatinine-based eGFR in predicting the clearance of vancomycin. The prediction errors (PEs) (the value of eGFR equations minus vancomycin clearance) were quantified for each equation and were pooled using a random-effects model. The root mean squared errors were also quantified to provide a metric for imprecision.
RESULTS: This meta-analysis included evaluations of seven different cystatin C- and creatinine-based eGFR equations in total from 26 studies and 1,234 patients. The mean PE (MPE) for cystatin C-based eGFR was 4.378 mL min-1 (95% confidence interval [CI], -29.425, 38.181), while the creatinine-based eGFR provided an MPE of 27.617 mL min-1 (95% CI, 8.675, 46.560) in predicting clearance of vancomycin. This indicates the presence of unbiased results in vancomycin clearance prediction by the cystatin C-based eGFR equations. Meanwhile, creatinine-based eGFR equations demonstrated a statistically significant positive bias in vancomycin clearance prediction.
CONCLUSION: Cystatin C-based eGFR equations are better than creatinine-based eGFR equations in predicting the clearance of vancomycin. This suggests that utilising cystatin C-based eGFR equations could result in better accuracy and precision to predict vancomycin pharmacokinetic parameters.
MATERIALS AND METHODS: This study investigated the isolation of anaerobes from the clinical specimens of Hospital Sungai Buloh, Malaysia, from January 2015 to December 2015. All isolates were identified using the API 20A system (bioMérieux, France). Antimicrobial susceptibility testing was performed using the E-test (bioMérieux, France).
RESULTS: The proportion of obligate anaerobes isolated from the clinical specimens was 0.83%. The Gram-positive anaerobes were most susceptible to vancomycin and imipenem, showing 100% sensitivity to these antimicrobials, followed by clindamycin (86.3%), penicillin (76.7%), and metronidazole (48.9%). Meanwhile, Gram-negative anaerobes were most susceptible to metronidazole (96%) followed by imipenem (89%), clindamycin (79%), and ampicillin (32%). The present study also showed that 3 out of 12 Bacteroides fragilis isolates were resistant to imipenem.
CONCLUSION: This study demonstrated the differences in the susceptibility patterns of anaerobes towards commonly used antimicrobials for the treatment of anaerobic infections. In summary, continuous monitoring of antimicrobial resistance trends among anaerobes is needed to ensure the appropriateness of treatment.
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 '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.
METHODS: A pooled population-pharmacokinetic model was built in NONMEM based on data from 14 different studies in different patient populations. Steady-state exposure was simulated and compared across patient subgroups for two US Food and Drug Administration/European Medicines Agency-approved drug labels and optimised doses were derived.
RESULTS: The final model uses postmenstrual age, weight and serum creatinine as covariates. A 35-year-old, 70-kg patient with a serum creatinine level of 0.83 mg dL-1 (73.4 µmol L-1) has a V1, V2, CL and Q2 of 42.9 L, 41.7 L, 4.10 L h-1 and 3.22 L h-1. Clearance matures with age, reaching 50% of the maximal value (5.31 L h-1 70 kg-1) at 46.4 weeks postmenstrual age then declines with age to 50% at 61.6 years. Current dosing guidelines failed to achieve satisfactory steady-state exposure across patient subgroups. After optimisation, increased doses for the Food and Drug Administration label achieve consistent target attainment with minimal (± 20%) risk of under- and over-dosing across patient subgroups.
CONCLUSIONS: A population model was developed that is useful for further development of age and kidney function-stratified dosing regimens of vancomycin and for individualisation of treatment through therapeutic drug monitoring and Bayesian forecasting.