METHODS: fDTP2 was prepared by mounting fWGA on DTX-loaded nanoparticles (DTP2) using the two-step carbodiimide method. Morphology of fDTP2 was examined using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). Dynamic light scattering (DLS) study was carried out to determine the mean diameter, polydispersity index (PDI) and zeta potential of fDTP2. Cellular uptake efficiency was examined using fluorescence microplate reader. Biocompatibility and active internalization of fDTP2 were conducted on HT-29.
RESULTS: fDTP2 was found to exhibit a DTX loading efficiency of 19.3%. SEM and TEM tests revealed spherical nanoparticles. The in vitro DTX release test showed a cumulative release of 54.7%. From the DLS study, fDTP2 reported a 277.7 nm mean diameter with PDI below 0.35 and -1.0 mV zeta potential. HT-29 which was fDTP2-treated demonstrated lower viability than L929 with a half maximal inhibitory concentration (IC50) of 34.7 µg/mL. HT-29 (33.4%) internalized fDTP2 efficiently at 2 h incubation. The study on HT-29 active internalization of nanoparticles through fluorescence and confocal imaging indicated such.
CONCLUSION: In short, fDTP2 demonstrated promise as a colonic drug delivery DTX transporter.
METHOD: This retrospective study involved SLE patients who attended the Rheumatology Clinic at the Hospital Kuala Lumpur from January 2014 to December 2016. Vitamin D was categorised as normal, insufficient or deficient, and the clinical variables were compared across vitamin D categories with chi-squared tests and Pearson correlation coefficient.
RESULTS: We included 216 patients. The mean 25(OH)D concentration was 51.3(Standard Deviation; SD 14.8) nmol/L. Fifty (23.1%) patients had vitamin D deficiency, 120 (55.6%) had vitamin D insufficiency, while 46 (21.3%) had adequate vitamin D levels. There were statistically significant associations between vitamin D status and ethnic group, lupus nephritis and hypertension. No correlations were observed between vitamin D status with SLEDAI score (Pearson correlation coefficient -0.015, p=0.829) as well as SDI score (Pearson correlation coefficient -0.017, p=0.801).
CONCLUSION: SLE patients should be screened for vitamin D concentrations and their levels optimised.
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.
MATERIALS AND METHODS: Five Malay patients receiving warfarin maintenance therapy were investigated for their CYP2C9*2, CYP2C9*3, and VKORC1-1639G>A genotypes and their vitamin K-dependent (VKD) clotting factor activities. The records of their daily warfarin doses and international normalized ratio (INR) 2 years prior to and after the measurement of VKD clotting factors activities were acquired. The mean warfarin doses were compared with predicted warfarin doses calculated from a genotypic-based dosing model developed for Asians.
RESULTS: A patient with the VKORC1-1639 GA genotype, who was supposed to have higher dose requirements, had a lower mean warfarin dose similar to those having the VKORC1-1639 AA genotype. This discrepancy may be due to the coadministration of celecoxib, which has the potential to decrease warfarins metabolism. Not all patients' predicted mean warfarin doses based on a previously developed dosing algorithm for Asians were similar to the actual mean warfarin dose, with the worst predicted dose being 54.34% higher than the required warfarin dose.
CONCLUSION: Multiple clinical factors can significantly change the actual required dose from the predicted dose from time to time. The additions of other dynamic variables, especially INR, VKD clotting factors, and concomitant drug use, into the dosing model are important in order to improve its accuracy.
METHODS: C0 were retrieved from a large neonatal vancomycin dataset. Individual estimates of AUC0-24 were obtained from Bayesian post hoc estimation. Various ML algorithms were used for model building to C0 and AUC0-24. An external dataset was used for predictive performance evaluation.
RESULTS: Before starting treatment, C0 can be predicted a priori using the Catboost-based C0-ML model combined with dosing regimen and nine covariates. External validation results showed a 42.5% improvement in prediction accuracy by using the ML model compared with the population pharmacokinetic model. The virtual trial showed that using the ML optimized dose; 80.3% of the virtual neonates achieved the pharmacodynamic target (C0 in the range of 10-20 mg/L), much higher than the international standard dose (37.7-61.5%). Once therapeutic drug monitoring (TDM) measurements (C0) in patients have been obtained, AUC0-24 can be further predicted using the Catboost-based AUC-ML model combined with C0 and nine covariates. External validation results showed that the AUC-ML model can achieve an prediction accuracy of 80.3%.
CONCLUSION: C0-based and AUC0-24-based ML models were developed accurately and precisely. These can be used for individual dose recommendations of vancomycin in neonates before treatment and dose revision after the first TDM result is obtained, respectively.