This tutorial examines the relationship between CL, F, and hepatic blood flow (Q(H)) quantitatively at oral and i.v. administration as an answer to the quiz set for KSCPT members. In case of oral dosing, when hepatic blood flow increases, the hepatic clearance (CL) and bioavailability (F) increases in high-extraction ratio drugs according to the well-stirred model equations for hepatic clearance: CL(H) = Q(H)·ER = Q(H)·f(u)·CL(int)/(Q(H)+f(u)·CL(int)) and F = 1 - ER Despite such a clear relationship, many students may feel it rather paradoxical that the increased CL (thus decreasing the AUC) causes increased F and thus the AUC (F·Dose/CLH) remains unchanged. This tutorial clarifies that the degree to which CL increase fails to match that of the Q(H) increase, and thus the decreased ER (= CL/Q(H)) that results in the increased F. Contemplating this simple, but seemingly paradoxical phenomenon may help students gain a deeper understanding of the first-pass effect.
Area Under Curve ; Biological Availability ; Humans

PURPOSE: To investigate the usefulness of the measurement of disc-to-fovea distance to disc-diameter ratio (DF/DD ratio) in detecting large and small discs. METHODS: A total of 300 randomly selected subjects were included in the present study. All patients underwent stereoscopic disc photography and DF/DD ratio, which is the shortest distance between disc margin and fovea divided by mean disc diameter was determined by planimetry. The diagnostic accuracy of DF/DD ratio was evaluated using areas under the receiver operating characteristics curves (AUCs), sensitivity, and specificity. RESULTS: No significant differences in disc-to-fovea distance were observed among small and large disc groups. The DF/DD ratio was significantly lower in subjects with large discs (1.74 +/- 0.27) compared with subjects with small discs (2.70 +/- 0.15). AUCs of the DF/DD ratio were 0.942 and 0.947 in detecting large and small discs, respectively. In detecting disc size by a fixed DF/DD ratio of 2.0, sensitivity was 100% for both large and small discs, and specificity was 70.1% and 40.9% for the large and small discs, respectively. CONCLUSIONS: The DF/DD ratio may be a simple and useful clinical aid in detecting large and small discs. The 2.0 fixed DF/DD ratio, showed 100% sensitivity in detecting both large and small discs, although medium discs may be misdiagnosed as small discs more often than as large discs.
Area Under Curve ; Humans ; Photography ; ROC Curve ; Sensitivity and Specificity

Nasal step index(NSI) is an index of light sensitivity of the nasal step area, the early sign of visual field defects in glaucoma. It represents the asymmetry between the upper-nasal and lower-nasal quadrant within 30 degree field. We applied this index to 66 eyes with normal visual field and 204 eyes with mild-to-moderate glaucomatous visual field defect (mean defect(MD)Area under the curve(AUC) values of Receiver operating characteristic(ROC) curve in early glaucoma patients(MDor=1.04), respectively. AUC values related to the validity of this study were NSI-1:0.38, NSI-2;0.44 and MD;0.83 in early glaucoma patients. But AUC values in high NFBI patients were NSI -1;0.50, NSI -2;0.50, MD;0.82. Therefore, NSI is shown to have lesser diagnostic value than MD in detection of early glaucoma in this study.
Area Under Curve ; Glaucoma* ; Humans ; Nerve Fibers ; Photophobia ; Visual Fields

BACKGROUND: Effective oral loading of carbamazepine (CBZ) is very important as it is the most often administered drug for partial and generalized seizures. The pharmacokinetics and tolerability of a single oral loading of controlled-release form of carbamazepine (CBZ-CR) were assessed in 38 adult patients at risk for seizure. METHODS: CBZ-CR was administered to 38 adults (22 had had CBZ just before entry into the study and 16 had not) at a dosage of 20 mg/kg as a single loading. Side effects and serum levels of CBZ and CBZ-10,11-epoxide (CBZ-E) were evaluated at 0, 2, 4, 6, 8, 12, 18, 24 h after the loading. Correlations between the frequency of side effects and other parameters (maxium serum concentration : Cmax, time to maximum concentration Tmax and area under the concentration time curve (AUC) of CBZ and CBZ-E) were also assessed. RESULTS: Mean CBZ serum levels (percentage of subjects with level > 4 Mg/ml shown in parenthesis) were 0.0 (0%), 3.2 (30%), 6.1 (79%), 7.2 (92%), 7.7 (95%), 7.7 (95%), 7.7 (95%) and 7.1 Mg/ml (95%) at 0, 2, 4, 6, 8, 12, 18 and 24 h after loading. Cmax and Tmax were 8.42 Mg/ml and 13.2 h respective-ly. Although side effects developed in 15 patients (39%), there were no significant neurotoxic side effects. The frequen-cy of the history of CBZ use was not different (p<0.05) in the two groups (one had side effects, another had not). Cmax, Tmax, and AUC of CBZ and CBZ-E were also not different (p<0.05). CONCLUSIONS: A single oral loading dose of CBZ-CR provides therapeutic serum concentrations quickly (in most patients within 6h) and is well tolerated. Rapid loading with CBZ-CR appears to be a useful alternative for the management of patients with a high risk of seizures.
Adult ; Anticonvulsants ; Area Under Curve ; Carbamazepine* ; Humans ; Pharmacokinetics ; Seizures

The Cmax and AUC of rosuvastatin increase when it is coadministered with telmisartan. The aim of this study was to explore which of the pharmacokinetic (PK) parameters of rosuvastatin are changed by telmisartan to cause such an interaction. We used data from drug–drug interaction (DDI) studies of 74 healthy volunteers performed in three different institutions. Rosuvastatin population PK models with or without telmisartan were developed using NONMEM (version 7.3). The plasma concentration–time profile of rosuvastatin was best described by a two-compartment, first-order elimination model with simultaneous Erlang and zero-order absorption when given rosuvastatin alone. When telmisartan was coadministered, the zero-order absorption fraction of rosuvastatin had to be omitted from the model because the absorption was dramatically accelerated. Notwithstanding the accelerated absorption, the relative bioavailability (BA) parameter estimate in the model demonstrated that the telmisartan-induced increase in BA was only about 20% and the clearance was not influenced by telmisartan at all in the final PK model. Thus, our model implies that telmisartan may influence the absorption process of rosuvastatin rather than its metabolic elimination. This may be used as a clue for further physiologically based PK (PBPK) approaches to investigate the mechanism of rosuvastatin–telmisartan DDI.
Absorption* ; Area Under Curve ; Biological Availability ; Healthy Volunteers ; Plasma

The diagnostic value of GHRH in assessing GH secretion in biochemical GH sufficient short children was examined. GHRH (1microgram/kg i.v bolus) was given to three groups (upslope, trough, downslope) arbitrarily classified according to the basal pulsatile GH secretory pattern before GHRH administration. Cmax following GHRH administration were variable and overlapping. Two children in downslope group, three children in trough group, and one child in upslope group showed subnormal GH responses to GHRH administration despite of normal GH response to more than two classical GH provocative tests (Fig.1). The time of maximal GH response after GHRH administration (Tmax) in upslope group was significantly faster than those in other two groups (Fig.2). There was a highly significant correlation between AUC and Cmax (p<0.001) after GHRH administration (Fig.3) which suggests that AUC or Cmax can be used for parameters of GH response to GHRH each other. The AUC and Cmax after GHRH administration between three groups were significantly different (2764+/-579.1ng/ml min, 52.6 ng/ml, respectively in upslope group; 1645+/-383.9ng/ml min, 37.7+/-9.4ng/ml, respectively in downslope group; 1098+/-150.2ng/ml min, 26.3+/-4.5ng/ml, respectively in trough group)(p<0.005) (Fig.4, Table 1). In conclusion, GH responses to GHRH adminstration could be variable according to the basal GH secretory rhythm. Therefore, we should be cautious in interpreting the GH response to GHRH to evaluate the GH secretory capacity because subnormal GH response to GHRH administration could be observed even if normal GH response to classical GH provocative tests. In addition, the classification of these arbitary three groups (upslope, trough, and downslope) is remained to defined so as to promote the diagnostic value of GHRH in GH deficiency.
Area Under Curve ; Child* ; Classification ; Growth Hormone* ; Humans

The estimation of 90% parametric confidence intervals (CIs) of mean AUC and Cmax ratios in bioequivalence (BE) tests are based upon the assumption that formulation effects in log-transformed data are normally distributed. To compare the parametric CIs with those obtained from nonparametric methods we performed repeated estimation of bootstrap-resampled datasets. The AUC and Cmax values from 3 archived datasets were used. BE tests on 1,000 resampled datasets from each archived dataset were performed using SAS (Enterprise Guide Ver.3). Bootstrap nonparametric 90% CIs of formulation effects were then compared with the parametric 90% CIs of the original datasets. The 90% CIs of formulation effects estimated from the 3 archived datasets were slightly different from nonparametric 90% CIs obtained from BE tests on resampled datasets. Histograms and density curves of formulation effects obtained from resampled datasets were similar to those of normal distribution. However, in 2 of 3 resampled log (AUC) datasets, the estimates of formulation effects did not follow the Gaussian distribution. Bias-corrected and accelerated (BCa) CIs, one of the nonparametric CIs of formulation effects, shifted outside the parametric 90% CIs of the archived datasets in these 2 non-normally distributed resampled log (AUC) datasets. Currently, the 80~125% rule based upon the parametric 90% CIs is widely accepted under the assumption of normally distributed formulation effects in log-transformed data. However, nonparametric CIs may be a better choice when data do not follow this assumption.
Area Under Curve ; Confidence Intervals ; Phenothiazines ; Therapeutic Equivalency

It was recently reported that the C(max) and AUC of rosuvastatin increases when it is coadministered with telmisartan and cyclosporine. Rosuvastatin is known to be a substrate of OATP1B1, OATP1B3, NTCP, and BCRP transporters. The aim of this study was to explore the mechanism of the interactions between rosuvastatin and two perpetrators, telmisartan and cyclosporine. Published (cyclosporine) or newly developed (telmisartan) PBPK models were used to this end. The rosuvastatin model in Simcyp (version 15)'s drug library was modified to reflect racial differences in rosuvastatin exposure. In the telmisartan–rosuvastatin case, simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without telmisartan) ratios were 1.92 and 1.14, respectively, and the T(max) changed from 3.35 h to 1.40 h with coadministration of telmisartan, which were consistent with the aforementioned report (C(maxI)/C(max): 2.01, AUCI/AUC:1.18, T(max): 5 h → 0.75 h). In the next case of cyclosporine–rosuvastatin, the simulated rosuvastatin C(maxI)/C(max) and AUC(I)/AUC (with/without cyclosporine) ratios were 3.29 and 1.30, respectively. The decrease in the CL(int,BCRP,intestine) of rosuvastatin by telmisartan and cyclosporine in the PBPK model was pivotal to reproducing this finding in Simcyp. Our PBPK model demonstrated that the major causes of increase in rosuvastatin exposure are mediated by intestinal BCRP (rosuvastatin–telmisartan interaction) or by both of BCRP and OATP1B1/3 (rosuvastatin–cyclosporine interaction).
Area Under Curve ; Cyclosporine ; Drug Interactions* ; Rosuvastatin Calcium*

BACKGROUND: Since the diagnostic value of aldosterone to renin ratio (ARR) calculated by plasma renin concentration (PRC) or plasma renin activity (PRA) is still inconclusive, we conducted a meta-analysis by systematically reviewing relevant literature to explore the difference in the diagnostic efficacy of ARR calculated by PRC or PRA, so as to provide guidance for clinical diagnosis. METHODS: We searched PubMed, Embase, and Cochrane Library from the establishment of the database to March 2021. We included studies that report the true positive, false positive, true negative, and false negative values for the diagnosis of primary aldosteronism, and we excluded duplicate publications, research without full text, incomplete information, or inability to conduct data extraction, animal experiments, reviews, and systematic reviews. STATA 15.1 was used to analyze the data. RESULTS: The pooled results showed that ARR (plasma aldosterone concentration [PAC]/PRC) had a sensitivity of 0.82 (95% confidence interval [CI]: 0.78-0.86), a specificity of 0.94 (95% CI: 0.92-0.95), a positive-likelihood ratio (LR) of 12.77 (95% CI: 7.04-23.73), a negative LR of 0.11 (95% CI: 0.07-0.17), and symmetric area under the curve (SAUC) of 0.982, respectively. Furthermore, the diagnostic odds ratio (DOR) of ARR (PAC/PRC) was 180.21. Additionally, the pooled results showed that ARR (PAC/PRA) had a sensitivity of 0.91 (95% CI: 0.86-0.95), a specificity of 0.91 (95% CI: 0.90-0.93), a positive LR of 7.30 (95% CI: 2.99-17.99), a negative LR of 0.10 (95% CI: 0.04-0.26), and SAUC of 0.976, respectively. The DOR of ARR (PAC/PRA) was 155.52. Additionally, we conducted a subgroup analysis for the different thresholds (<35 or ≥35) of PAC/PRC. The results showed that the DOR of the cut-off ≥35 groups was higher than the cut-off <35 groups (DOR = 340.15, 95% CI: 38.32-3019.66; DOR = 116.40, 95% CI = 23.28-581.92). CONCLUSIONS The research results suggest that the determination of ARR (PAC/PRC) and ARR (PAC/PRA) was all effective screening tools for PA. The diagnostic accuracy and diagnostic value of ARR (PAC/PRC) are higher than ARR (PAC/PRA). In addition, within a certain range, the higher the threshold, the better the diagnostic value.
Aldosterone ; Area Under Curve ; Humans ; Hyperaldosteronism/diagnosis* ; Hypertension ; Renin

It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.
Blood-Brain Barrier ; Algorithms ; Area Under Curve ; Databases, Factual ; Permeability