MATERIALS AND METHODS: In this trial, a total of 56 eligible subjects were randomly assigned to the fasting group and the postprandial group. The two groups were given 250 mg of the test and reference preparation, respectively. Liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) was applied to determine the plasma concentration of cefalexin. PhoenixWinNonlin software (V7.0) was used to calculate the pharmacokinetic parameters of cefalexin using the non-compartmental model (NCA), and the bioequivalence and safety results were calculated by SAS (V9.4) software.
RESULTS: The main pharmacokinetic parameters of the test and reference preparations were as follows, the fasting group: Cmax 12.59 ± 2.65 μg/mL, 12.72 ± 2.28 μg/mL; AUC0-8h 20.43 ± 3.47 h×μg/mL, 20.66 ± 3.38 h×μg/mL; AUC0-∞ 20.77 ± 3.53 h×μg/mL, 21.02 ± 3.45 h×μg/mL; the postprandial group: Cmax 5.25 ± 0.94 μg/mL, 5.23 ± 0.80 μg/mL; AUC0-10h 16.92 ± 2.03 h×μg/mL, 17.09 ± 2.31 h×μg/mL; AUC0-∞ 17.33 ± 2.09 h×μg/mL, 17.67 ± 2.45 h×μg/mL.
CONCLUSION: The 90% confidence intervals of geometric mean ratios of test preparation and reference preparation were calculated, and the 90% confidence intervals of geometric mean ratios of Cmax, AUC0-10h, and AUC0-∞ were within the 80.00% ~ 125.00% range in both groups. Both Cmax and AUC met the pre-determined criteria for assuming bioequivalence. The test and reference products were bioequivalent after administration under fasting as well as under fed conditions in healthy Chinese subjects. This study may suggest that successful generic versions of cefalexin not only guarantee the market supply of such drugs but can also improve the safety and effectiveness and quality controllability of cefalexin through a new process and a new drug composition ratio.
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.
MATERIALS AND METHODS: A randomized, 2-treatment, 2-period, 2-sequence, single dose, crossover with a washout period of 2 weeks, was conducted in 24 healthy Thai male volunteers. Blood samples were collected at 0, 0.25, 0.5, 0.75, 1, 1.25, 1.5, 1.75, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10, 12, 24, 36, 48, 72 and 96 h following drug administration. Plasma concentrations of risperidone and 9-hydroxyrisperidone were determined using a validated LC-MS-MS method. The pharmacokinetic parameters of risperidone and 9-hydroxyrisperidone were determined using a non-compartmental model.
RESULTS: The geometric means ratios (%) and 90% confidence interval (CI) of the test and reference products for the log-transformed pharmacokinetic parameters, Cmax, AUC0-t and AUC0-inf of risperidone were 104.49 % (92.79% - 117.66%), 100.96 % (92.15% - 110.61 %) and 97.99 % (90.72% - 105.85%). The 90% CI of geometric means ratios of the test and reference products for the log-transformed pharmacokinetic parameters, Cmax, AUC0-t and AUC0-inf of 9-hydroxyrisperidone were 97.00%, 96.97% and 97.49%.
CONCLUSIONS: The 90% CI for the geometric means ratios (test/reference) of the log-trasformed Cmax, AUC0-t and AUC0-inf of risperidone and its major active metabolite were within the bioequivalence acceptance criteria of 80% - 125% of the US-FDA.
METHODS: The intrasubject coefficient of variation was estimated from the residual mean square error obtained from analysis of variance of the parameters AUC0-infinity, Cmax and Cmax/AUC0-infinity after logarithmic transformation. The test power in the analyses of the above parameters was subsequently estimated using nomograms provided by Diletti et al. [1991].
RESULTS AND CONCLUSION: Thirty products covering 16 drugs were studied in which 22 were immediate-release (including one dispersible tablet) and 8 were sustained-release formulations. The intrasubject coefficient of variation for the parameter AUC0-infinity was smaller than Cmax, and hence considerably more studies were able to attain a power of greater than 80% using 12 volunteers for the AUC0-infinity, compared to the Cmax. However, the variability in the Cmax could be reduced by using the parameter Cmax/ AUC0-infinity, and thus, provide a more realistic estimation of sample size, since the latter reflects only the rate of absorption and not both the rate and extent as in the case of Cmax [Endrenyi et al. 1991].
METHODS: Consecutive NAFLD patients who underwent liver biopsy were enrolled in this study and had two sets each of pSWE and TE examinations by a nurse and a doctor on the same day of liver biopsy procedure. The medians of the four sets of pSWE and TE were used for evaluation of diagnostic accuracy using area under receiver operating characteristic curve (AUROC). Intra-observer and inter-observer variability was analyzed using intraclass correlation coefficients.
RESULTS: Data for 100 NAFLD patients (mean age 57.1 ± 10.2 years; male 46.0%) were analyzed. The AUROC of TE for diagnosis of fibrosis stage ≥ F1, ≥ F2, ≥ F3, and F4 was 0.89, 0.83, 0.83, and 0.89, respectively. The corresponding AUROC of pSWE was 0.80, 0.72, 0.69, and 0.79, respectively. TE was significantly better than pSWE for the diagnosis of fibrosis stages ≥ F2 and ≥ F3. The intra-observer and inter-observer variability of TE and pSWE measurements by the nurse and doctor was excellent with intraclass correlation coefficient > 0.96.
CONCLUSION: Transient elastography was significantly better than pSWE for the diagnosis of fibrosis stage ≥ F2 and ≥ F3. Both TE and pSWE had excellent intra-observer and inter-observer variability when performed by healthcare personnel of different backgrounds.
METHODS: We performed a prospective study of consecutive adults with NAFLD who were scheduled for a liver biopsy at a tertiary hospital in Malaysia. Patients underwent VLFF and CAP measurements on the same day as their liver biopsy. Histopathology analyses of liver biopsy specimens were reported according to the Nonalcoholic Steatohepatitis Clinical Research Network scoring system. Stereologic analysis was performed using grid-point counting method combined with the Delesse principle.
RESULTS: We analyzed data from 97 patients (mean age 57.0 ± 10.1 years; 44.33% male; 91.8% obese; 95.9% centrally obese). Based on histopathology analysis, the area under receiver operating characteristic curve (AUROC) for VLFF in detection of steatosis grade ≥S2 was 0.92 and for CAP the AUROC was 0.65 (P < .001). Based on stereological analysis, the AUROC for VLFF for detection of steatosis grade ≥S2 was 0.92 and for CAP the AUROC was 0.63, (P = .002); for identification of steatosis grade S3, the AUROC for VLFF was 0.92 and for CAP the AUROC was 0.68 (P < .001).
CONCLUSIONS: In a prospective study of patients with NAFLD undergoing liver biopsy analysis, we found VLFF to more accurately determine grade of hepatic steatosis than CAP.