The role of partial area under the curve and maximum concentrations in assessing the bioequivalence of long-acting injectable formulation of exenatide_A sensitivity analysis.

To ensure therapeutic equivalence between the long-acting injectable (LAI) products, additional PK metrics other than Cmax and AUC were considered necessary. However, regarding the selection of additional PK metrics for bioequivalence (BE) assessment of exenatide LAI, a discrepancy existed between EMA's and USFDA's product-specific guidance. The EMA recommends that both the maximum plasma concentration in the initial-release phase (Cmax,1) and the extended-release phase (Cmax,2) should be determined. Nevertheless, the USFDA recommends the use of the partial area under the curve (i.e., the area under the curve from week 4 to the last sampling point; pAUC4w-t). The focus of this study was to compare the sensitivity of different PK metrics, including Cmax,1, Cmax,2, pAUC4w-t, early and late pAUC metrics truncated at different time points (three, four, five, six and seven weeks), to formulation-related parameters and pharmacodynamic (PD) markers of glycemic control. A sensitivity analysis was conducted using the published PK/PD model of exenatide LAI. The results indicated that Cmax,1 and Cmax,2 exhibited comparable sensitivities. AUC4w-t was sensitive to changes in detecting the differences in formulation-related parameters and PD markers of glycemic control, but did not provide superior sensitivity performance compared to Cmax,1 and Cmax,2. Among all tested PK metrics, AUC7w-t was found to be the most sensitive. The optimal cut-off time point for the pAUC should be set at the time of maximum plasma concentration in the extended-release phase (approximately 6-7 weeks). These results may provide useful insights into the selection of appropriate PK metrics for BE determination of exenatide LAI.

Distribution, abundance, and ecogenomics of the Palauibacterales, a new cosmopolitan thiamine-producing order within the Gemmatimonadota phylum.

The phylum Gemmatimonadota comprises mainly uncultured microorganisms that inhabit different environments such as soils, freshwater lakes, marine sediments, sponges, or corals. Based on 16S rRNA gene studies, the group PAUC43f is one of the most frequently retrieved Gemmatimonadota in marine samples. However, its physiology and ecological roles are completely unknown since, to date, not a single PAUC43f isolate or metagenome-assembled genome (MAG) has been characterized. Here, we carried out a broad study of the distribution, abundance, ecotaxonomy, and metabolism of PAUC43f, for which we propose the name of Palauibacterales. This group was detected in 4,965 16S rRNA gene amplicon datasets, mainly from marine sediments, sponges, corals, soils, and lakes, reaching up to 34.3% relative abundance, which highlights its cosmopolitan character, mainly salt-related. The potential metabolic capabilities inferred from 52 Palauibacterales MAGs recovered from marine sediments, sponges, and saline soils suggested a facultative aerobic and chemoorganotrophic metabolism, although some members may also oxidize hydrogen. Some Palauibacterales species might also play an environmental role as N2O consumers as well as suppliers of serine and thiamine. When compared to the rest of the Gemmatimonadota phylum, the biosynthesis of thiamine was one of the key features of the Palauibacterales. Finally, we show that polysaccharide utilization loci (PUL) are widely distributed within the Gemmatimonadota so that they are not restricted to Bacteroidetes, as previously thought. Our results expand the knowledge about this cryptic phylum and provide new insights into the ecological roles of the Gemmatimonadota in the environment. IMPORTANCE Despite advances in molecular and sequencing techniques, there is still a plethora of unknown microorganisms with a relevant ecological role. In the last years, the mostly uncultured Gemmatimonadota phylum is attracting scientific interest because of its widespread distribution and abundance, but very little is known about its ecological role in the marine ecosystem. Here we analyze the global distribution and potential metabolism of the marine Gemmatimonadota group PAUC43f, for which we propose the name of Palauibacterales order. This group presents a saline-related character and a chemoorganoheterotrophic and facultatively aerobic metabolism, although some species might oxidize H2. Given that Palauibacterales is potentially able to synthesize thiamine, whose auxotrophy is the second most common in the marine environment, we propose Palauibacterales as a key thiamine supplier to the marine communities. This finding suggests that Gemmatimonadota could have a more relevant role in the marine environment than previously thought.

Alternative Pharmacokinetic Metrics in Single-Dose Studies to Ensure Bioequivalence of Prolonged-Release Products at Steady State-A Case Study.

(1) Background: this article investigates which PK metrics in a single-dose study (concentration at the end of posology interval, Cτ, partial areas under the curve, pAUCs, or half-value duration, HVD) are more sensitive and less variable for predicting the failure of a prolonged-release product at steady-state that was the bioequivalent for Cmax, AUC0-t and AUC0-inf, in the single-dose study; (2) Methods: a cross-over study was performed in 36 subjects receiving desvenlafaxine 100 mg prolonged-release tablets. Conventional (Cmax, AUC0-t and AUC0-inf) and additional (Cτ, pAUCs and HVD) PK metrics were considered after single-dose conditions. Predicted PK metrics at steady state (AUC0-τ, Cmax,ss, and Cτ,ss) were derived using a population PK model approach; (3) Results: the existing differences in the shape of the concentration-time curves precluded to show equivalence for Cτ,ss in the simulated study at steady state. This failure to show equivalence at steady state was predicted by Cτ, pAUCs and HVD in the single-dose study. Cτ was the most sensitive metric for detecting the different shape, with a lower intra-subject variability than HVD; (4) Conclusions: conventional PK metrics for single-dose studies (Cmax, AUC0-t and AUC0-inf) are not enough to guarantee bioequivalence at steady state for prolonged-release products.

Prediction model for milk transfer of drugs by primarily evaluating the area under the curve using QSAR/QSPR.

PURPOSE: Information on milk transferability of drugs is important for patients who wish to breastfeed. The purpose of this study is to develop a prediction model for milk-to-plasma drug concentration ratio based on area under the curve (M/PAUC). The quantitative structure-activity/property relationship (QSAR/QSPR) approach was used to predict compounds involved in active transport during milk transfer. METHODS: We collected M/P ratio data from literature, which were curated and divided into M/PAUC ≥ 1 and M/PAUC < 1. Using the ADMET Predictor® and ADMET Modeler™, we constructed two types of binary classification models: an artificial neural network (ANN) and a support vector machine (SVM). RESULTS: M/P ratios of 403 compounds were collected, M/PAUC data were obtained for 173 compounds, while 230 compounds only had M/Pnon-AUC values reported. The models were constructed using 129 of the 173 compounds, excluding colostrum data. The sensitivity of the ANN model was 0.969 for the training set and 0.833 for the test set, while the sensitivity of the SVM model was 0.971 for the training set and 0.667 for the test set. The contribution of the charge-based descriptor was high in both models. CONCLUSIONS: We built a M/PAUC prediction model using QSAR/QSPR. These predictive models can play an auxiliary role in evaluating the milk transferability of drugs.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids.

Objectives: We performed a virtual screening of olive secoiridoids of the OliveNetTM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out. Methods: Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction. Results and discussions: Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding. Conclusion: AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

ROC Curves for the Statistical Analysis of Microarray Data.

A receiver operating characteristic (ROC) curve is a graphical plot that illustrates the diagnostic ability of a binary classifier as a function of its discrimination threshold. This chapter is an overview on the use of ROC curves for microarray data. The notion of ROC curve and its motivation is introduced in Subheading 1. Relevant scientific contributions concerning the use of ROC curves for microarray data are briefly reviewed in Subheading 2. The special case with covariates is considered in Subheading 3. Two relevant aspects are reviewed in this section: the use of LASSO techniques for selecting and combining relevant markers and how to correct for multiple testing when a large number of markers are available. Finally, some conclusions are included.

On the use of min-max combination of biomarkers to maximize the partial area under the ROC curve.

BACKGROUND: Evaluation of diagnostic assays and predictive performance of biomarkers based on the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) are vital in diagnostic and targeted medicine. The partial area under the curve (pAUC) is an alternative metric focusing on a range of practical and clinical relevance of the diagnostic assay. In this article, we adopt and extend the min-max method to the estimation of the pAUC when multiple continuous scaled biomarkers are available and compare the performances of our proposed approach with existing approaches via simulations. METHODS: We conducted extensive simulation studies to investigate the performance of different methods for the combination of biomarkers based on their abilities to produce the largest pAUC estimates. Data were generated from different multivariate distributions with equal and unequal variance-covariance matrices. Different shapes of the ROC curves, false positive fraction ranges, and sample size configurations were considered. We obtained the mean and standard deviation of the pAUC estimates through re-substitution and leave-one-pair-out cross validation. RESULTS: Our results demonstrate that the proposed method provides the largest pAUC estimates under the following three important practical scenarios: (1) multivariate normally distributed data for non-diseased and diseased participants have unequal variance-covariance matrices; or (2) the ROC curves generated from individual biomarker are relative close regardless of the latent normality distributional assumption; or (3) the ROC curves generated from individual biomarker have straight-line shapes. CONCLUSIONS: The proposed method is robust and investigators are encouraged to use this approach in the estimation of the pAUC for many practical scenarios.

Combining multiple biomarkers linearly to maximize the partial area under the ROC curve.

It is now common in clinical practice to make clinical decisions based on combinations of multiple biomarkers. In this paper, we propose new approaches for combining multiple biomarkers linearly to maximize the partial area under the receiver operating characteristic curve (pAUC). The parametric and nonparametric methods that have been developed for this purpose have limitations. When the biomarker values for populations with and without a given disease follow a multivariate normal distribution, it is easy to implement our proposed parametric approach, which adopts an alternative analytic expression of the pAUC. When normality assumptions are violated, a kernel-based approach is presented, which handles multiple biomarkers simultaneously. We evaluated the proposed as well as existing methods through simulations and discovered that when the covariance matrices for the disease and nondisease samples are disproportional, traditional methods (such as the logistic regression) are more likely to fail to maximize the pAUC while the proposed methods are more robust. The proposed approaches are illustrated through application to a prostate cancer data set, and a rank-based leave-one-out cross-validation procedure is proposed to obtain a realistic estimate of the pAUC when there is no independent validation set available.

Performance of variable selection methods using stability-based selection.

BACKGROUND: Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well. RESULTS: Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important. CONCLUSIONS: Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.

Easy and accurate variance estimation of the nonparametric estimator of the partial area under the ROC curve and its application.

The receiver operating characteristic (ROC) curve is a popular technique with applications, for example, investigating an accuracy of a biomarker to delineate between disease and non-disease groups. A common measure of accuracy of a given diagnostic marker is the area under the ROC curve (AUC). In contrast with the AUC, the partial area under the ROC curve (pAUC) looks into the area with certain specificities (i.e., true negative rate) only, and it can be often clinically more relevant than examining the entire ROC curve. The pAUC is commonly estimated based on a U-statistic with the plug-in sample quantile, making the estimator a non-traditional U-statistic. In this article, we propose an accurate and easy method to obtain the variance of the nonparametric pAUC estimator. The proposed method is easy to implement for both one biomarker test and the comparison of two correlated biomarkers because it simply adapts the existing variance estimator of U-statistics. In this article, we show accuracy and other advantages of the proposed variance estimation method by broadly comparing it with previously existing methods. Further, we develop an empirical likelihood inference method based on the proposed variance estimator through a simple implementation. In an application, we demonstrate that, depending on the inferences by either the AUC or pAUC, we can make a different decision on a prognostic ability of a same set of biomarkers. Copyright © 2016 John Wiley & Sons, Ltd.

On use of partial area under the ROC curve for evaluation of diagnostic performance.

Evaluation of diagnostic performance is a necessary component of new developments in many fields including medical diagnostics and decision making. The methodology for statistical analysis of diagnostic performance continues to develop, offering new analytical tools for conventional inferences and solutions for novel and increasingly more practically relevant questions. In this paper, we focus on the partial area under the Receiver Operating Characteristic (ROC) curve or pAUC. This summary index is considered to be more practically relevant than the area under the entire ROC curve (AUC), but because of several perceived limitations, it is not used as often. To improve interpretation, results for pAUC analysis are frequently reported using a rescaled index such as the standardized partial AUC proposed by McClish (1989). We derive two important properties of the relationship between the 'standardized' pAUC and the defined range of interest, which could facilitate a wider and more appropriate use of this important summary index. First, we mathematically prove that the 'standardized' pAUC increases with increasing range of interest for practically common ROC curves. Second, using comprehensive numerical investigations, we demonstrate that, contrary to common belief, the uncertainty about the estimated standardized pAUC can either decrease or increase with an increasing range of interest. Our results indicate that the partial AUC could frequently offer advantages in terms of statistical uncertainty of the estimation. In addition, selection of a wider range of interest will likely lead to an increased estimate even for standardized pAUC.