Fullness Area Under The Curve Research Articles

Beneficial effects of a high protein breakfast on fullness disappear after a night of short sleep in nonobese, premenopausal women

As insufficient sleep and obesity become more widespread, finding strategies to overcome changes in appetite and food cravings after sleep reduction is imperative. This study examined the effects of a high-protein (HP) and high-carbohydrate (HC) breakfast on appetitive sensations, food cravings, and dietary intake after nights of habitual (HS) and curtailed sleep (CS). Twenty-seven non-obese, premenopausal women who reported routinely eating breakfast participated in this randomized crossover study. Participants completed 4 laboratory visits with different combinations of sleep and breakfast conditions. Sleep was reduced by 33% on curtailed nights. At each visit, appetitive sensations were measured before breakfast and every 30 min thereafter throughout the 4-hour visit; area under the curve (AUC) was calculated. Food cravings were assessed before and 3.5 h after breakfast. Intake of ad libitum lunch and daily dietary intake were measured. Regardless of the breakfast condition, CS increased hunger (p = 0.043) and desire to eat (p = 0.044) and decreased fullness (p = 0.035). The HP breakfast increased fullness AUC after HS (p = 0.022) but not CS. Regardless of the sleep condition, the changes in food cravings scores were significantly different based on breakfast condition (p = 0.009), with food cravings increased after the HC breakfast and decreased after the HP breakfast. However, breakfast condition did not influence hunger or desire to eat AUC after either sleep condition. Neither the breakfast condition nor the sleep condition influenced lunch and daily energy intake. In conclusion, it appears protein reduces food cravings regardless of sleep condition in this population but obtaining sufficient sleep is necessary to benefit from the effects of high protein intake on fullness.

Food Type and Lentil Variety Affect Satiety Responses but Not Food Intake in Healthy Adults When Lentils Are Substituted for Commonly Consumed Carbohydrates

Lentils have potential to increase satiety and may contribute to a body weight management strategy; however, the effects on satiety of replacing common food ingredients with lentils within food products remain largely unknown. The aim of this study was to determine the effects of replacing wheat and rice with 2 lentil varieties within muffins and chilies on satiety, test-meal food intake, and 24-h energy intake. Healthy adults consumed muffins or chilies in which wheat or rice was substituted with green (61.8 g) or red (54 g) lentils in 2 randomized crossover studies (muffin study: n=24, mean±SE age: 25.4±0.9 y, BMI (in kg/m2): 23.2±0.5; chili study: n=24, age: 25.7±1.0 y, BMI: 23.2±0.5), with ≥1-wk washout periods between study visits and studies. Subjective appetite sensations measured over 180 min were summarized with total area under the curve (AUC), food intake was measured at an ad libitum test meal, and 24-h energy intake was measured using weighed food records. Treatment effects were compared within each study using repeated-measures ANCOVA (subjective appetite sensations) and ANOVA (food intake, 24-h energy intake). Green, but not red, lentil chili significantly increased fullness AUC (17.5%, P=0.02) and decreased desire to eat AUC (20.1%, P=0.02) and prospective food consumption AUC (16.7%, P=0.04) compared with rice chili, with no significant differences between chili treatments for test-meal food intake or 24-h energy intake. Muffin treatments did not significantly differ for any outcomes. Replacing rice with green, but not red lentils within chili increases satiety but does not decrease food intake, whereas replacing wheat with lentils within muffins does not increase satiety or decrease food intake in healthy adults. Further study of the role of lentil replacement in food products in body weight management is warranted. This trial was registered at clinicaltrials.gov as NCT03128684.

Morning and afternoon appetite and gut hormone responses to meal and stress challenges in obese individuals with and without binge eating disorder.

Eating late in the day is common, and stress can induce eating. Little is understood about how time of day and stress interact to affect appetite and thereby body weight. These may be particularly important influences in binge eaters, who tend to binge in the evening, and in response to stress. Obese participants with (n=16) and without (n=16) binge eating disorder (BED) participated in two identical test protocols beginning either in the morning or the afternoon (AM condition/PM condition), each following an 8 h fast. For each protocol, they first received a standardized liquid meal (0900/1600 hours), then a stress test (Socially Evaluated Cold Pressor Test, 1110/1810 hours), and then a multi-item ad libitum buffet meal (1140/1840 hours) while rating appetite and stress and having blood drawn for hormone measures. Appetite at baseline was greater in the PM than in the AM condition (higher hunger, lower fullness). Following the liquid meal, area under the curve (AUC) values for hunger and ghrelin were greater and AUC values for peptide YY lower in the PM than in the AM condition. Only those with BED showed lower fullness AUC in the PM condition, as well as a pattern of higher initial PM and lower initial AM ghrelin. Following the stress test, cortisol and ghrelin increased in both the AM and PM conditions, but higher ghrelin AUC and lower cortisol AUC were observed in the PM condition. Again, only participants with BED showed lower fullness AUC in the PM condition. Buffet meal intake was similar across groups and conditions but those with BED reported greater loss of control and binge resemblance than those without. Afternoon/evening may be a high-risk period for overeating, particularly when paired with stress exposure, and for those with binge eating.

Comparison of appetite responses to high– and low–glycemic index postexercise meals under matched insulinemia and fiber in type 1 diabetes

Patients with type 1 diabetes face heightened risk of hypoglycemia after exercise. Subsequent overfeeding, as a preventative measure against hypoglycemia, negates the energy deficit after exercise. Patients are also required to reduce the insulin dose administered with postexercise foods to further combat hypoglycemia. However, the insulin dose is dictated solely by the carbohydrate content, even though postprandial glycemia is vastly influenced by glycemic index (GI). With a need to control the postexercise energy balance, appetite responses after meals differing in GI are of particular interest. We assessed the appetite response to low-glycemic index (LGI) and high-glycemic index (HGI) postexercise meals in type 1 diabetes patients. This assessment also offered us the opportunity to evaluate the influence of GI on appetite responses independently of insulinemia, which confounds findings in individuals without diabetes. Ten physically active men with type 1 diabetes completed 2 trials in a randomized crossover design. After 45 min of treadmill exercise at 70% of the peak oxygen uptake, participants consumed an LGI (GI ∼37) or HGI (GI ∼92) meal with a matched macronutrient composition, negligible fiber content, and standardized insulin-dose administration. The postprandial appetite response was determined for 180 min postmeal. During this time, circulating glucose, insulin, glucagon, and glucagon-like peptide-1 (GLP-1) concentrations and subjective appetite ratings were determined. The HGI meal produced an ∼60% greater postprandial glucose area under the curve (AUC) than did the LGI meal (P = 0.008). Insulin, glucagon, and GLP-1 did not significantly differ between trials (P > 0.05). The fullness AUC was ∼25% greater after the HGI meal than after the LGI meal (P < 0.001), whereas hunger sensations were ∼9% lower after the HGI meal than after the LGI meal (P = 0.001). Under conditions of matched insulinemia and fiber, an HGI postexercise meal suppresses feelings of hunger and augments postprandial fullness sensations more so than an otherwise equivalent LGI meal in type 1 diabetes patients.

Effects of Oatmeal and Corn Flakes Cereal Breakfasts on Satiety, Gastric Emptying, Glucose, and Appetite-Related Hormones

Objective: The extent to which different types of breakfasts affect appetite and food intake is unclear. To assess the satiety effects of a high-fiber cereal, we compared oatmeal, isocaloric corn flakes, and water. Subjects/Methods: Thirty-six subjects (18 lean, 18 overweight) were assigned to three conditions in a randomized sequence on different days. Ratings of hunger and fullness were obtained concurrently with blood samples for measuring concentrations of glucose, insulin, glucagon, leptin, and acetaminophen (gastric emptying tracer). Appetite was assessed by calculating the area under the curve (AUC) for fullness and hunger, and by measuring food intake of an ad libitum lunch meal at 180 min. Results: Lunch meal intake was lowest after consuming oatmeal (p < 0.00001), which was lower for overweight subjects than lean subjects (p = 0.007). Fullness AUC was greatest (p = 0.00001), and hunger AUC lowest (p < 0.001) after consuming oatmeal. At 180 min, blood glucose was lowest after the corn flakes (p = 0.0001). Insulin AUC was greater for both cereals than water (p < 0.00001). Leptin AUC and glucagon AUC values did not differ between conditions. Acetaminophen concentrations peaked latest after consuming oatmeal (p = 0.046), reflecting slower gastric emptying. Conclusions: Satiety was greater and ad libitum test meal intake lower after consuming oatmeal than after corn flakes, especially in the overweight subjects.

On the use of partial area under the ROC curve for comparison of two diagnostic tests.

Evaluation of diagnostic performance is typically based on the receiver operating characteristic (ROC) curve and the area under the curve (AUC) as its summary index. The partial area under the curve (pAUC) is an alternative index focusing on the range of practical/clinical relevance. One of the problems preventing more frequent use of the pAUC is the perceived loss of efficiency in cases of noncrossing ROC curves. In this paper, we investigated statistical properties of comparisons of two correlated pAUCs. We demonstrated that outside of the classic model there are practically reasonable ROC types for which comparisons of noncrossing concave curves would be more powerful when based on a part of the curve rather than the entire curve. We argue that this phenomenon stems in part from the exclusion of noninformative parts of the ROC curves that resemble straight-lines. We conducted extensive simulation studies in families of binormal, straight-line, and bigamma ROC curves. We demonstrated that comparison of pAUCs is statistically more powerful than comparison of full AUCs when ROC curves are close to a "straight line". For less flat binormal ROC curves an increase in the integration range often leads to a disproportional increase in pAUCs' difference, thereby contributing to an increase in statistical power. Thus, efficiency of differences in pAUCs of noncrossing ROC curves depends on the shape of the curves, and for families of ROC curves that are nearly straight-line shaped, such as bigamma ROC curves, there are multiple practical scenarios in which comparisons of pAUCs are preferable.

Item-based analysis of delayed reward discounting decision making

Delayed reward discounting (DRD) is a behavioral economic index of time preference, referring to how much an individual devalues a reward based on its delay in time, and has been linked to a wide array of health behaviors. It is commonly assessed using a task that asks participants to make dichotomous choices between two monetary rewards, one available immediately and the other after a delay. This study sought to shorten an extended iterative DRD assessment to increase its versatility and efficiency. Data were drawn from two young adult samples, an exploratory sample (N=130) and a confirmatory sample (N=247). In the exploratory sample, eight items were identified as predicting the majority of the variance in the full task area under the curve (AUC) (R2=.821; p<.001). In the confirmatory sample, the same eight items similarly predicted the majority of variance in the full task AUC (R2=.844, p<.001). These results provide initial support for the validity of a brief 8-item assessment of DRD. Priorities for further validation and potential applications are discussed.

Satiety following Intake of Potatoes and Other Carbohydrate Test Meals

Background and Aims: The study investigated satiation and satiety following intake of starch-rich side dishes representing a range of glycemic indices (GIs). Methods: Twelve normal-weight (BMI = 22.4 ± SD 2.0) participants (6 male, 6 female, 22–30 years) received one of four side dishes or white bread (GI reference) in randomized order on five mornings, followed by an ad libitum lunch. Blood draws prior to test meal and during the 2 h before lunch measured plasma glucose and insulin concentrations. GI was calculated from glucose incremental area under the curve (AUC). Hunger, fullness, desire to eat and prospective consumption were rated just before blood draws. Results: No significant difference was found in hunger or fullness AUCs between test meals. Both potato meals yielded lower desire to eat compared to pasta throughout the 2-hour period (p = 0.002). Total lunch energy intake did not differ. No significant correlations were found between test meal GI and ratings of hunger, fullness or energy intake at lunch meal. Conclusions: GI of energy-equivalent test meals did not predict satiety or lunch meal intake. There was evidence of reduced appetite following both potato meals relative to the other carbohydrate side dishes but no differences in subsequent intake.

Pharmacokinetic Drug Interaction of Mycophenolate With Co-Amoxiclav in Renal Transplant Patients

Pharmacokinetic Drug Interaction of Mycophenolate With Co-Amoxiclav in Renal Transplant Patients

Proton Pump Inhibitor Co-medication Reduces Mycophenolate Acid Drug Exposure in Heart Transplant Recipients

Proton pump inhibitors (PPIs) are often prescribed for gastrointestinal discomfort after heart transplantation (HTx). This study investigates the impact of PPI use on mycophenolate acid (MPA) pharmacokinetics in heart transplant recipients receiving mycophenolate mofetil (MMF) and tacrolimus (Tac). MPA post-dose plasma concentrations at 0 to 2 hours were obtained by high-performance liquid chromatography (HPLC) in 21 patients on pantoprazole 40 mg/day (PPI group) and 12 patients not on pantoprazole (control group). In a subgroup, MPA plasma concentrations at 0 to 12 hours were measured to evaluate full MPA area-under-the-curve (AUC) measurements. In the PPI group, the mean daily MMF dose was 1,912 +/- 1,023 mg with mean pre-dose serum concentrations (C(0)) of 1.9 +/- 1.4 mg/liter, without a significant difference from controls. Mean post-dose MPA concentrations at 30 minutes (C(0.5h)) were significantly higher in the control group (control, 17.9 +/- 11.5 mg/liter; PPI, 6.7 +/- 4.6 mg/liter; p < 0.001). One- and 2-hour (C(1h, 2h)) MPA concentrations were persistently higher in the control group (1 hour: control, 13.8 +/- 9.1 mg/liter; PPI, 7.7 +/- 4.1 mg/liter; 2 hours: control, 7.6 +/- 4.2; PPI, 5.0 +/- 2.8 mg/liter; p < 0.05). In the subgroup with full AUC measurements, the control group had higher MPA levels 12 hours after dosing (C(12h): control, 3.3 +/- 2.4 mg/liter; PPI, 1.6 +/- 1.3 mg/liter; p < 0.05) and a significantly higher dose-adjusted AUC C(0-3h) (control, 59.5 +/- 20.7 mg/liter; PPI, 41.7 +/- 23.4 mg/liter; p < 0.05). In the subgroup, the maximum plasma concentration of mycophenolic acid (MPA C(max)) in the PPI group was significantly lower (10.1 +/- 4.7 mg/liter) than in the control group (45.5 +/- 53.5 mg/liter, p < 0.0001), whereas t(max) revealed no differences (control: 1.1 +/- 0.77 hours; PPI: 1.1 +/- 0.97 hours). Furthermore, a clear trend for more acute rejection episodes (AREs) and more transplant vasculopathy (TVP) was found in the PPI group. Patients with PPI co-medication show significantly lower MPA plasma concentrations, possibly due to decreased absorption. Therapeutic drug monitoring allows identification of patients with decreased MMF drug exposure who might be at risk for acute rejection.

Food intake, appetite, gut hormones, and resting energy expenditure in resistance trained vs. sedentary older adults

ObjectiveTo assess the effects of chronic resistance training (RT) on food intake, appetite, fasting gut hormones, and energy expenditure (EE) in 16 RT and 35 sedentary (Sed) older persons (62‐84 y).MethodsFree‐living energy intake and appetite sensations (hunger, fullness and desire to eat) were assessed on 3 days (2 weekdays, 1 weekend day) and fasting‐state resting EE, plasma glucose and gut hormones (Cholecystokinin, CCK; glucagon‐like peptide‐1, GLP‐1; insulin; ghrelin) were measured.ResultsMaximum strength was higher for RT (8.3 ± 0.3) vs. Sed [6.9 ± 0.2 kg/kg fat free mass (*, p&lt;0.05); mean ± SE]. RT did not affect total energy (RT 2079 ± 79 vs. Sed 1995 ± 66 kcal/d), protein, carbohydrate and fat intakes. Hunger and desire to eat area under the curve (AUC) values were not different between groups. Fullness AUC was lower in RT (0.4 ± 0.2) vs. Sed (0.9 ± 0.3* AU·720 min). EE was not different between groups, and respiratory exchange ratio tended to be lower in the RT (0.87 ± 0.00) vs. Sed (0.89 ± 0.01, p=0.06). CCK was higher in RT (0.71 ± 0.38) vs. Sed (0.51 ± 0.05* ng/mL), and GLP‐1 was lower in RT (0.31 ± 0.32) vs. Sed (1.45 ± 0.19* ng/mL). RT did not affect glucose, insulin, and ghrelin.ConclusionsRT alters fasting concentrations of CCK and GLP‐1. However, despite the isolated differences in appetitive sensations and gut hormones, RT did not influence EI.Grant Funding SourceNIH R01 AG021911, Purdue Bilsland Fellowship

Effects of food form on postprandial plasma glucose and insulin, energy expenditure, and appetite in resistance trained vs. sedentary older adults

This study assessed the effects of food form and recreational resistance training (RT) on postprandial (PP) plasma glucose and insulin, energy expenditure (EE), and indices of appetite. 16 RT and 18 sedentary persons, age 62–84 y; physical activity 425 ± 33 vs. 272 ± 28 kcal/d* (*, p≤ 0.05); BMI 24.0 ± 0.6 vs. 25.7 ± 0.5 kg/m2*; and maximum strength 8.3 ± 0.3 vs. 6.7 ± 0.3 kg/kg FFM*, were studied. On separate days, each subject consumed 12.5% of their energy need as macronutrient‐matched meal replacement products in solid (S) or beverage (B) form. Parameters were assessed over 4 h. Independent of RT, glucose and insulin area under the curve (AUC) were higher for S (503 ± 292; 2809 ± 273) vs. B (−266 ± 240 mg/dL•240 min*; 1865 ± 179 uU/mL•240 min*). PPEE AUC was not different between S vs. B, and the respiratory exchange ratio AUC was lower for S (−11.54 ±−2.04) vs. B (−5.36 ± 3.07 •240 min*). Fullness AUC was higher for S (475 ± 96) vs. B (218 ± 94 AU•240 min*) and sedentary (450 ± 106) vs. RT (230 ± 118 AU•240 min*); these responses were independent and additive. Food form and RT did not influence hunger AUC, while desire to eat AUC was lower for S vs. B* and not influenced by RT. Meal replacement products consumed in solid and beverage forms should not be considered as metabolic and appetitive equivalents in older people who are sedentary or more physically active and use resistance training to achieve higher strength.Support: NIH R01 AG021911, The Solae Company

Effects of food form and portion size on postprandial appetite, ghrelin, and energy expenditure in healthy, older adults

Objective: To examine the independent and combined influences of food form and portion size on appetite and metabolic responses. Methods: 43 older men and women (age: 72 ± 1 y; BMI: 25.6 ± 0.3 kg/m2) consumed, on 2 separate days, preloads in solid (S) vs. beverage (B) form as either 12.5% (264 ± 8 kcal) or 25% (533 ± 14 kcal) of estimated daily energy need. Postprandial (PP) appetite (hunger, fullness, desire to eat), plasma ghrelin concentration, and energy expenditure (EE) were measured over 4 h. Results: Main effects of food form (p&lt;0.05) and portion size (p&lt;0.05) were observed for appetite; PP hunger and desire to eat area under the curve (AUC) values were higher and fullness AUC values were lower for B vs. S and between 12.5% vs. 25%. Post‐hoc analyses revealed no difference in appetite between the S‐12.5% vs. S‐25%. However, B‐12.5% led to higher hunger and desire to eat and lower fullness AUC values vs. the B‐25% (p&lt;0.05). No difference in 4‐h ghrelin was observed between S vs. B and 12.5% vs. 25%. Concerning PPEE, the B treatment led to lower overall PPEE vs. S when consuming 25% (p&lt;0.01) but not 12.5%. Conclusion: Several key parameters of appetite appear to be independently and additively influenced by food form and portion size with particular insensitivity to lower energy beverages in healthy, older adults.Support: NIH R01 AG021911; The Solae Company; IBRC, Purdue University

Change in hunger, fullness, ghrelin, PYY and GLP-1 in relation to a fixed test meal pre and post Rou-en-Y gastric bypass (RYGBS).

Bariatric surgery is the most effective treatment for achieving long-term weight loss in clinically severe obese patients. This study examines changes in hunger, fullness and appetite hormones in relation to a fixed meal in patients pre and 5 months post-RYGBS. There were 10 severely obese, otherwise healthy premenopausal women, BMI=45.3±6.8 SD, % body fat (BODPOD)=53.6±6.0, age=35±9.3. Ratings of hunger and fullness immediately preceded blood draws at 0, 15, 30, 60 min in relation to a fixed complete liquid meal of Glytrol, 250 ml, 1kcal/ml, consumed at 0–10 min. The meal was presented at 2 pm following an overnight 18-h fast. Results revealed a decline in hunger prior to the meal ( p =.013) and an increase ( p =.009) in area under the curve (AUC) for fullness from pre to post 5 months. However, fasting ghrelin and AUC for ghrelin did not change from pre to post-5 months. AUC for PYY ( p =.009) and GLP-1 ( p =.004) increased from pre- to 5 months post-surgery. However, the change in hunger did not correlate with the change in fasting ghrelin and the change in fullness AUC did not correlate with the changes in AUC for GLP-1 or PYY. Moreover, the reduction in stomach capacity itself would not be able to help explain the reduction in premeal hunger post-surgery. Thus, other biological mechanisms to explain the changes in hunger and fullness post-RYGBS should be explored. Supported by NIH DK 61519 and MO1 RROO64529.

Limited Sampling Strategy for the Estimation of Systemic Exposure to the Protease Inhibitor Nelfinavir

Therapeutic drug monitoring (TDM) of antiretroviral drugs has been proposed as a means of optimizing response to highly active antiretroviral therapy (HAART) in HIV infection because suboptimal exposure to these agents may lead to the development of resistant viral strains and subsequent therapeutic failure. The area under the curve (AUC), though considered to make the best estimate of total drug exposure, requires repeated blood sampling. The authors investigated the predictability of individual nelfinavir (NFV) concentrations at different time points for the AUC and tried to find the best sampling time for the abbreviated AUC to predict NFV total body exposure. A total of 99 NFV AUC0-12h values were measured in 99 patients receiving a 1250-mg oral dose twice a day. Venous blood samples were collected at baseline (predose, 0) and 1, 2, 3, 4, 5, 6, 8, and 12 hours postdose. A stepwise forward-selection, multiple-regression technique was chosen to assess the relative importance of single and combination concentration time points to predict the AUC calculated from the entire pharmacokinetic profile. Data were split into a development set and a validation set. The development set contained 49 randomly selected HIV patients. Of these, 22 HIV patients were coinfected with HCV, 7 with and 15 without cirrhosis. One-point predictors provided the lowest prediction precision, but predictive performance improved after the first 2 hours postdose. Plasma concentrations at 0 and 4 hours after the oral dose were most predictive if 2 variables were used in the regression equation. The AUC could be estimated from data for these 2 samples by using the following equation: AUC0-12 = 3.0 + 2.7 (C0) + 6.4 (C4), r = 92. The predictive performance of 2-point predictors at 0 and 4 hours (C0 + C4) was validated by comparing their ability to predict the full AUC in a validation set representative of HIV/HCV patients (n = 28) and HIV/HCV patients, with (n = 8) and without (n = 14) cirrhosis. The results showed a mean bias ranging from +2.7% in HIV/HCV patients to -6.0% in HCV coinfection with cirrhosis. The authors conclude that this result is clinically significant. The limited sampling strategy (LSS) described could be used in clinical practice for the easy assessment of the total exposure to NFV in HIV/HCV patients, both with and without cirrhosis.

The partial area under the summary ROC curve

The area under the curve (AUC) is commonly used as a summary measure of the receiver operating characteristic (ROC) curve. It indicates the overall performance of a diagnostic test in terms of its accuracy at various diagnostic thresholds used to discriminate cases and non-cases of disease. The AUC measure is also used in meta-analyses, where each component study provides an estimate of the test sensitivity and specificity. These estimates are then combined to calculate a summary ROC (SROC) curve which describes the relationship between-test sensitivity and specificity across studies. The partial AUC has been proposed as an alternative measure to the full AUC. When using the partial AUC, one considers only those regions of the ROC space where data have been observed, or which correspond to clinically relevant values of test sensitivity or specificity. In this paper, we extend the idea of using the partial AUC to SROC curves in meta-analysis. Theoretical and numerical results describe the variation in the partial AUC and its standard error as a function of the degree of inter-study heterogeneity and of the extent of truncation applied to the ROC space. A scaled partial area measure is also proposed to restore the property that the summary measure should range from 0 to 1. The results suggest several disadvantages of the partial AUC measures. In contrast to earlier findings with the full AUC, the partial AUC is rather sensitive to heterogeneity. Comparisons between tests are more difficult, especially if an empirical truncation process is used. Finally, the partial area lacks a useful symmetry property enjoyed by the full AUC. Although the partial AUC may sometimes have clinical appeal, on balance the use of the full AUC is preferred.

A limited sampling strategy for the estimation of 12-hour SangCya and neoral AUCs in renal transplant recipients.

Neoral and SangCya are new cyclosporine formulations with more consistent pharmacokinetic profiles than the older formulation of cyclosporine, Sandimmune. Limited sampling strategies have been derived to estimate the full area under the curve (AUC) for both Neoral and Sandimmune. In addition, no limited sampling strategy has been derived for SangCya, and no rigorous prospective testing has been done on any of these formulas. The authors studied 32 renal transplant patients who received Neoral and then SangCya during a formulation conversion study. Full AUCs were drawn on all patients (twice while on Neoral, once while on SangCya). Abbreviated formulas were derived using linear regression models and then tested for the prediction error. The authors found that several abbreviated formulas offer excellent estimations of the full AUC for both SangCya and Neoral. The generated formulas worked equally well with either formulation. In addition, the authors found that limited sampling strategies using a 1.5-hour sample may predict a full AUC more accurately than those that use a 2-hour sample. The use of these abbreviated formulas allows for a convenient and accurate estimate of CsA exposure.

A limited sampling strategy for the estimation of eight-hour neoral areas under the curve in renal transplantation.

Neoral, the microemulsion formulation of cyclosporine (CsA), demonstrates more consistent bioavailability than the corn oil formulation Sandimmune. Because of Neoral's rapid peak and metabolism, 8-hour dosing has to be used in many pediatric and some adult patients to maintain adequate CsA peak-to-trough ratios. Although the area under the curve (AUC) is considered the best estimate of total drug exposure, it requires repeated blood sampling. Abbreviated AUC profiles yielding excellent estimates of Neoral AUC with twice daily dosing have been described, but no such abbreviated strategy exists for 8-hour dosing. One hundred fifty-two pharmacokinetic profiles in 23 patients were used to derive and prospectively test a limited two-sample strategy to predict Neoral AUCs in pediatric and adult patients on 8-hour dosing regimens of Neoral. The formula was derived from 69 full 8-hour CsA pharmacokinetic profiles in nine children who underwent renal transplantation. Stepwise forward linear and multiple-curve regression techniques assessed the relative importance of single and combination concentration time points to predict AUC. The abbreviated profiles were validated by comparing the mean prediction error for each regression equation. The abbreviated profile calculated by second (C2)- and fourth (C4)-hour levels (AUC = 129 + 1.84 x C2 + 4.39 x C4) correlated well with the full AUC (r2 = 0.96; p < 0001). Mean prediction error was -0.4% +/- 5.48%, and no values fell outside the clinically acceptable 15% prediction error limit. Prospectively applying the formula to 83 AUCs of 14 adults who underwent renal transplantation and were taking Neoral three times a day demonstrated an excellent fit (r2 = 0.93; p < 0.001), with 94% of predicted values falling inside the +/-15% limit. The authors describe the development of a clinically acceptable, limited sampling strategy to predict 8-hour Neoral AUCs in children and adults who underwent renal transplantation.

Cyclosporine monitoring in patients with renal transplants: two- or three-point methods that estimate area under the curve are superior to trough levels in predicting drug exposure.

The recent introduction of a cyclosporine microemulsion demonstrating less pharmacokinetic variability than the conventional formulation offers the potential for accurately and precisely predicting area under the curve (AUC) with a limited-sampling monitoring strategy. This was studied based on the pharmacokinetic profiles from 55 stable patients with renal transplants who were observed on two occasions at steady state on both formulations. Multiple linear regression analyses were performed on a training dataset from 27 patients, in which combinations of cyclosporine concentrations drawn from 0 to 4 hours postdose were regressed against the full AUC over the dosing interval. Predictor regression equations used concentration combinations ranging from one-point (concentrations at 0, 1, 2, 3, or 4 hours) through five-points (all five concentrations 0 to 4 hours). The predictive performance of these equations was then assessed in the training group with data from a subsequent profiling occasion and in the remaining 28 patients who constituted an independent test group. Prediction bias (mean prediction error) and prediction precision (absolute prediction error) were quantified and compared between formulations. Correlations between predicted and actual AUC were consistently stronger for the microemulsion, suggesting the possibility of more accurate and precise predictions of exposure than from the conventional formulation. For both formulations, the one-point predictors rendered the lowest prediction precision, and predictive performance improved considerably when multiple-point predictors were used. Significantly higher precision and lower variability were observed with the microemulsion for most predictors in the both training and test groups. For the microemulsion, two-point (C0 + C1 or C0 + C2) and three-point (C0 + C1 + C2) predictors yielded relatively unbiased and precise exposure predictions, inasmuch as mean absolute prediction error was less than 10% and 5%, respectively. Hence, a two- or three-point method may provide a clinically important improvement over the use of trough levels in monitoring cyclosporine therapy in patients with renal transplants.