A cochleate formulation optimized by D-optimal mixture design enhances oral bioavailability of Revaprazan.

A cochleate formulation was developed to enhance the oral bioavailability of revaprazan (RVP). Dimyristoyl phosphatidylcholine (DMPC) liposome containing dicetyl phosphate (DCP) successfully formed a cochleate after treatment with CaCl2, whereas that containing sodium deoxycholate did not. Cochleate was optimised using a D-optimal mixture design with three independent variables-DMPC (X1, 70.58 mol%), cholesterol (X2, 22.54 mol%), and DCP (X3, 6.88 mol%)-and three response variables: encapsulation efficiency (Y1, 76.92%), released amount of free fatty acid at 2 h (Y2, 39.82%), and released amount of RVP at 6 h (Y3, 73.72%). The desirability function was 0.616, showing an excellent agreement between the predicted and experimental values. The cylindrical morphology of the optimised cochleate was visualised, and laurdan spectroscopy confirmed the dehydrated membrane interface, showing an increased generalised polarisation value (approximately 0.5) over small unilamellar vesicle of RVP (RVP-SUV; approximately 0.1). The optimised cochleate showed greater resistance to pancreatic enzyme than RVP-SUV. RVP was released in a controlled manner, achieving approximately 94% release in 12 h. Following oral administration in rats, the optimised cochleate improved the relative bioavailability of RVP by approximately 274%, 255%, and 172% compared to RVP suspension, a physical mixture of RVP and the cochleate, and RVP-SUV, respectively. Thus, the optimised cochleate formulation might be a good candidate for the practical development of RVP.

Optimization of a solidified micelle formulation for enhanced oral bioavailability of atorvastatin calcium using statistical experimental design.

To enhance the oral bioavailability of atorvastatin calcium (ATV), a novel solidified micelle (S-micelle) was developed. Two surfactants, Gelucire 48/16 (G48) and Tween 20 (T20), were employed for micelle formation, and two solid carriers (SC), Florite PS-10 (FLO) and Vivapur 105 (VP105), were selected as solid carriers. The S-micelle was optimized using a Box-Behnken design with three independent variables, including G48:T20 (X1, 1.8:1), SC:G48 + T20 (X2, 0.65:1), and FLO:VP105 (X3, 1.4:0.6), resulting in a droplet size (Y1) of 198.4 nm, dissolution efficiency at 15 min in the pH 1.2 medium (Y2) of 47.6%, Carr's index (Y3) of 16.9, and total quantity (Y4) of 562.5 mg. The optimized S-micelle resulted in good correlation showing percentage prediction values less than 10%. The optimized S-micelle formed a nanosized dispersion in the aqueous phase, with a higher dissolution rate than raw ATV and crushed Lipitor®. The optimized S-micelle improved the relative bioavailability of oral ATV (25 mg equivalent/kg) in rats by approximately 509 and 271% compared to raw ATV and crushed Lipitor®, respectively. In conclusion, the optimized S-micelle possesses great potential for the development of solidified formulations for improved oral absorption of poorly water-soluble drugs.

Functionalized Lipid Nanocarriers for Simultaneous Delivery of Docetaxel and Tariquidar to Chemoresistant Cancer Cells.

The simultaneous drug delivery efficiency of a co-loaded single-carrier system of docetaxel (DTX)- and tariquidar (TRQ)-loaded nanostructured lipid carrier (NLC) functionalized with PEG and RIPL peptide (PRN) (D^T-PRN) was compared with that of a physically mixed dual-carrier system of DTX-loaded PRN (D-PRN) and TRQ-loaded PRN (T-PRN) to overcome DTX mono-administration-induced multidrug resistance. NLC samples were prepared using the solvent emulsification evaporation technique and showed homogeneous spherical morphology, with nano-sized dispersion (<220 nm) and zeta potential values of -15 to -7 mV. DTX and/or TRQ was successfully encapsulated in NLC samples (>95% encapsulation efficiency and 73-78 µg/mg drug loading). In vitro cytotoxicity was concentration-dependent; D^T-PRN exhibited the highest MDR reversal efficiency, with the lowest combination index value, and increased the cytotoxicity and apoptosis in MCF7/ADR cells by inducing cell-cycle arrest in the G2/M phase. A competitive cellular uptake assay using fluorescent probes showed that, compared to the dual nanocarrier system, the single nanocarrier system exhibited better intracellular delivery efficiency of multiple probes to target cells. In the MCF7/ADR-xenografted mouse models, simultaneous DTX and TRQ delivery using D^T-PRN significantly suppressed tumor growth as compared to other treatments. A single co-loaded system for PRN-based co-delivery of DTX/TRQ (1:1, w/w) constitutes a promising therapeutic strategy for drug-resistant breast cancer cells.

Enhanced oral absorption of insulin: hydrophobic ion pairing and a self-microemulsifying drug delivery system using a D-optimal mixture design.

The lipophilicity of a peptide drug can be considerably increased by hydrophobic ion pairing with amphiphilic counterions for successful incorporation into lipid-based formulations. Herein, to enhance the oral absorption of insulin (INS), a self-microemulsifying drug delivery system (SMEDDS) formulation was developed. Prior to optimization, INS was complexed with sodium n-octadecyl sulfate (SOS) to increase the loading into the SMEDDS. The INS-SOS complex was characterized via scanning electron microscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and its dissociation behavior. The SMEDDS was optimized using a D-optimal mixture design with three independent variables including Capmul MCM (X1, 9.31%), Labrasol (X2, 49.77%), and Tetraglycol (X3, 40.92%) and three response variables including droplet size (Y1, 115.2 nm), INS stability (Y2, 46.75%), and INS leakage (Y3, 17.67%). The desirability function was 0.766, indicating excellent agreement between the predicted and experimental values. The stability of INS-SOS against gastrointestinal enzymes was noticeably improved in the SMEDDS, and the majority of INS remained in oil droplets during release. Following oral administration in diabetic rats, the optimized SMEDDS resulted in pharmacological availabilities of 3.23% (50 IU/kg) and 2.13% (100 IU/kg). Thus, the optimized SMEDDS is a good candidate for the practical development of oral delivery of peptide drugs such as INS.

A Unified Neural Network Framework for Extended Redundancy Analysis.

Component-based approaches have been regarded as a tool for dimension reduction to predict outcomes from observed variables in regression applications. Extended redundancy analysis (ERA) is one such component-based approach which reduces predictors to components explaining maximum variance in the outcome variables. In many instances, ERA can be extended to capture nonlinearity and interactions between observed and components, but only by specifying a priori functional form. Meanwhile, machine learning methods like neural networks are typically used in a data-driven manner to capture nonlinearity without specifying the exact functional form. In this paper, we introduce a new method that integrates neural networks algorithms into the framework of ERA, called NN-ERA, to capture any non-specified nonlinear relationships among multiple sets of observed variables for constructing components. Simulations and empirical datasets are used to demonstrate the usefulness of NN-ERA. The conclusion is that in social science datasets with unstructured data, where we expect nonlinear relationships that cannot be specified a priori, NN-ERA with its neural network algorithmic structure can serve as a useful tool to specify and test models otherwise not captured by the conventional component-based models.

Bayesian Approach to Multivariate Component-Based Logistic Regression: Analyzing Correlated Multivariate Ordinal Data.

Applications of component-based models have gained much attention as a means of accompanying dimension reduction in the regression setting and have been successfully implemented to model a univariate outcome in the behavioral and social sciences. Despite the prevalence of correlated ordinal outcome data in the fields, however, most of the extant component-based models have been extended to address the multivariate ordinal issue with a simplified but unrealistic assumption of independence, which may lead to biased statistical inferences. Thus, we propose a Bayesian methodology for a component-based model that accounts for unstructured residual covariances, while regressing multivariate ordinal outcomes on pre-defined sets of predictors. The proposed Bayesian multivariate ordinal logistic model re-expresses ordinal outcomes of interest with a set of latent continuous variables based on an approximate multivariate t-distribution. This contributes not only to developing an efficient Gibbs sampler, a Markov Chain Monte Carlo algorithm, but also to facilitating the interpretation of regression coefficients as log-transformed odds ratio. The empirical utility of the proposed method is demonstrated through analyzing a subset of data, extracted from the 2009 to 2010 Health Behavior in School-Aged Children study that investigates risk factors of four different forms of bullying perpetration and victimization: physical, social, racial, and cyber.

Bayesian Mixture Model of Extended Redundancy Analysis.

Extended redundancy analysis (ERA), a generalized version of redundancy analysis (RA), has been proposed as a useful method for examining interrelationships among multiple sets of variables in multivariate linear regression models. As a limitation of the extant RA or ERA analyses, however, parameters are estimated by aggregating data across all observations even in a case where the study population could consist of several heterogeneous subpopulations. In this paper, we propose a Bayesian mixture extension of ERA to obtain both probabilistic classification of observations into a number of subpopulations and estimation of ERA models within each subpopulation. It specifically estimates the posterior probabilities of observations belonging to different subpopulations, subpopulation-specific residual covariance structures, component weights and regression coefficients in a unified manner. We conduct a simulation study to demonstrate the performance of the proposed method in terms of recovering parameters correctly. We also apply the approach to real data to demonstrate its empirical usefulness.

Copula-Based Redundancy Analysis.

Extended Redundancy Analysis (ERA) has recently been developed and widely applied to investigate component regression models. In this paper, we propose Copula-based Redundancy Analysis (CRA) to improve the performance of regression-based ERA. Our simulation results indicate that CRA is significantly superior to the regression-based ERA. We also discuss how to modify CRA to accommodate models with discrete, censored, truncated outcome variables, or a combination thereof, where ERA cannot be employed. For applications, we provide two empirical analyses: one on academic achievement and one on drug use and health.

Development of a Solid Supersaturable Micelle of Revaprazan for Improved Dissolution and Oral Bioavailability Using Box-Behnken Design.

PURPOSE: To enhance the oral bioavailability of revaprazan (RVP), a novel solid, supersaturable micelle (SSuM) was developed. METHODS: Surfactants and solid carriers were screened based on a solubility and a flowability test, respectively. Supersaturating agents, including Poloxamer 407 (P407), were screened. The SSuM was optimized using a Box-Behnken design with three independent variables, including Gelucire 44/14:Brij L4 (G44/BL4; X1) and the amounts of Florite PS-10 (FLO; X2) and Vivapur 105 (VP105; X3), and three response variables, ie, dissolution efficiency at 30 min (Y1), dissolution enhancing capacity (Y2), and Carr's index (Y3). The solid state property was evaluated, and a dissolution test was conducted. RVP, Revanex®, solid micelle (P407-free from the composition of SSuM), and SSuM were orally administrated to rats (RVP 20 mg equivalent/kg) for in vivo pharmacokinetic study. RESULTS: G44 and BL4 showed great solubility, with a critical micelle concentration range of 119.2-333.0 µg/mL. P407 had an excellent supersaturating effect. FLO and VP105 were selected as solid carriers, with a critical solidifying ratio (g/mL) of 0.30 and 0.91, respectively. With optimized values of X1 (-0.41), X2 (0.31), and X3 (-0.78), RVP (200 mg)-containing SSuM consisting of G44 (253.8 mg), BL4 (106.2 mg), FLO (99.3 mg), VP105 (199.8 mg), and P407 (40 mg) was developed, resulting in Y1 (40.3%), Y2 (0.008), and Y3 (12.3%). RVP existed in an amorphous state in the optimized SSuM, and the SSuM formed a nanosized dispersion in the aqueous phase, with approximately 71.7% dissolution at 2 h. The optimized SSuM improved the relative bioavailability of RVP in rats by approximately 478%, 276%, and 161% compared to raw RVP, Revanex®, and solid micelle, respectively. CONCLUSION: The optimized SSuM has great potential for the development of solidified formulations of poorly water-soluble drugs with improved oral absorption.

Sparse Extended Redundancy Analysis: Variable Selection via the Exclusive LASSO.

Extended Redundancy Analysis is a statistical tool for exploring the directional relationships of multiple sets of exogenous variables on a set of endogenous variables. This approach posits that the endogenous and exogenous variables are related via latent components, each of which is extracted from a set of exogenous variables, that account for the maximum variation of the endogenous variables. However, it is often difficult to distinguish between the true variables that form the latent components and the false variables that do not, especially when the association between the true variables and the exogenous set is weak. To overcome this limitation, we propose a Sparse Extended Redundancy Analysis via the Exclusive LASSO that performs variable selection while maintaining model specification. We validate the performance of the proposed approach in a simulation study. Finally, the empirical utility of this approach is demonstrated through two examples-one on a study of youth academic achievement and the other on a text analysis of newspaper data.

Optimization of solid self-dispersing micelle for enhancing dissolution and oral bioavailability of valsartan using Box-Behnken design.

A novel solid self-dispersing micelle (S-SDM) was developed to enhance the oral bioavailability of valsartan (VST) and to reduce the total mass of solidified supersaturable self-microemulsifying drug delivery system (S-SuSMEDDS), composed of Capmul MCM, Tween 80 (T80), Gelucire 44/14 (G44), Poloxamer 407, Florite PS-10 (FLO), and low-substituted hydroxypropyl cellulose B1 (HPC). Excluding oil component from S-SuSMEDDS, S-SDM was optimized using a Box-Behnken design with three independent variables: X1 (T80/G44, 0.63), X2 (FLO/HPC, 0.41), and X3 (solid carrier, 177.6 mg); and three response factors: Y1 (droplet size, 191.9 nm), Y2 (dissolution efficiency at 15 min, 55.0%), and Y3 (angle of repose, 32.4°). The desirability function was 0.636, showing an excellent agreement between the predicted and experimental values. With approximately 75% weight of S-SuSMEDDS, no distinct crystallinity of VST was observed in S-SDM, resulting in critical micelle concentration value of 32 µg/mL. Optimized S-SDM showed an approximate 4-fold improved dissolution (pH 1.2, 500 mL) compared with raw VST. Following oral administration in rats, optimized S-SDM improved relative bioavailability by approximately 235%, 216%, and 127% versus raw VST, Diovan® (commercial reference), and S-SuSMEDDS, respectively. Thus, optimized S-SDM could be a selectable candidate for developing water-insoluble drugs in reduced quantity.

Bayesian generalized structured component analysis.

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modelling, which adopts components of observed variables as proxies for latent variables and examines directional relationships among latent and observed variables. GSCA has been extended to deal with a wider range of data types, including discrete, multilevel or intensive longitudinal data, as well as to accommodate a greater variety of complex analyses such as latent moderation analysis, the capturing of cluster-level heterogeneity, and regularized analysis. To date, however, there has been no attempt to generalize the scope of GSCA into the Bayesian framework. In this paper, a novel extension of GSCA, called BGSCA, is proposed that estimates parameters within the Bayesian framework. BGSCA can be more attractive than the original GSCA for various reasons. For example, it can infer the probability distributions of random parameters, account for error variances in the measurement model, provide additional fit measures for model assessment and comparison from the Bayesian perspectives, and incorporate external information on parameters, which may be obtainable from past research, expert opinions, subjective beliefs or knowledge on the parameters. We utilize a Markov chain Monte Carlo method, the Gibbs sampler, to update the posterior distributions for the parameters of BGSCA. We conduct a simulation study to evaluate the performance of BGSCA. We also apply BGSCA to real data to demonstrate its empirical usefulness.

Bayesian Extended Redundancy Analysis: A Bayesian Approach to Component-based Regression with Dimension Reduction.

Extended redundancy analysis (ERA) combines linear regression with dimension reduction to explore the directional relationships between multiple sets of predictors and outcome variables in a parsimonious manner. It aims to extract a component from each set of predictors in such a way that it accounts for the maximum variance of outcome variables. In this article, we extend ERA into the Bayesian framework, called Bayesian ERA (BERA). The advantages of BERA are threefold. First, BERA enables to make statistical inferences based on samples drawn from the joint posterior distribution of parameters obtained from a Markov chain Monte Carlo algorithm. As such, it does not necessitate any resampling method, which is on the other hand required for (frequentist's) ordinary ERA to test the statistical significance of parameter estimates. Second, it formally incorporates relevant information obtained from previous research into analyses by specifying informative power prior distributions. Third, BERA handles missing data by implementing multiple imputation using a Markov Chain Monte Carlo algorithm, avoiding the potential bias of parameter estimates due to missing data. We assess the performance of BERA through simulation studies and apply BERA to real data regarding academic achievement.

Optimization of self-microemulsifying drug delivery system for phospholipid complex of telmisartan using D-optimal mixture design.

To improve the dissolution behavior of telmisartan (TMS), a poorly water-soluble angiotensin II receptor blocker, TMS-phospholipid complex (TPC) was prepared by solvent evaporation method and characterized by differential scanning calorimetry and powder X-ray diffractometry. The crystalline structure of TMS was transited into an amorphous state by TPC formation. The equilibrium solubility of TPC (1.3-6.1 mg/mL) in various vehicles was about 100 times higher than that of TMS (0.009-0.058 mg/mL). TPC-loaded self-microemulsifying drug delivery system (SMEDDS) formulation was optimized using the D-optimal mixture design with the composition of 14% Capryol 90 (oil; X1), 59.9% tween 80 (surfactant; X2), and 26.1% tetraglycol (cosurfactant; X3) as independent variables, which resulted in a droplet size of 22.17 nm (Y1), TMS solubilization of 4.06 mg/mL (Y2), and 99.4% drug release in 15 min (Y3) as response factors. The desirability function value was 0.854, indicating the reliability and accuracy of optimization; in addition, good agreement was found between the model prediction and experimental values of Y1, Y2, and Y3. Dissolution of raw TMS was poor and pH-dependent, where it had extremely low dissolution (< 1% for 2 h) in water, pH 4, and pH 6.8 media; however, it showed fast and high dissolution (> 90% in 5 min) in pH 1.2 medium. In contrast, the dissolution of the optimized TPC-loaded SMEDDS was pH-independent and reached over 90% within 5 min in all the media tested. Thus, we suggested that phospholipid complex formation and SMEDDS formulation using the experimental design method might be a promising approach to enhance the dissolution of poorly soluble drugs.

Functional Parallel Factor Analysis for Functions of One- and Two-dimensional Arguments.

Parallel factor analysis (PARAFAC) is a useful multivariate method for decomposing three-way data that consist of three different types of entities simultaneously. This method estimates trilinear components, each of which is a low-dimensional representation of a set of entities, often called a mode, to explain the maximum variance of the data. Functional PARAFAC permits the entities in different modes to be smooth functions or curves, varying over a continuum, rather than a collection of unconnected responses. The existing functional PARAFAC methods handle functions of a one-dimensional argument (e.g., time) only. In this paper, we propose a new extension of functional PARAFAC for handling three-way data whose responses are sequenced along both a two-dimensional domain (e.g., a plane with x- and y-axis coordinates) and a one-dimensional argument. Technically, the proposed method combines PARAFAC with basis function expansion approximations, using a set of piecewise quadratic finite element basis functions for estimating two-dimensional smooth functions and a set of one-dimensional basis functions for estimating one-dimensional smooth functions. In a simulation study, the proposed method appeared to outperform the conventional PARAFAC. We apply the method to EEG data to demonstrate its empirical usefulness.

A Unified Approach to Functional Principal Component Analysis and Functional Multiple-Set Canonical Correlation.

Functional principal component analysis (FPCA) and functional multiple-set canonical correlation analysis (FMCCA) are data reduction techniques for functional data that are collected in the form of smooth curves or functions over a continuum such as time or space. In FPCA, low-dimensional components are extracted from a single functional dataset such that they explain the most variance of the dataset, whereas in FMCCA, low-dimensional components are obtained from each of multiple functional datasets in such a way that the associations among the components are maximized across the different sets. In this paper, we propose a unified approach to FPCA and FMCCA. The proposed approach subsumes both techniques as special cases. Furthermore, it permits a compromise between the techniques, such that components are obtained from each set of functional data to maximize their associations across different datasets, while accounting for the variance of the data well. We propose a single optimization criterion for the proposed approach, and develop an alternating regularized least squares algorithm to minimize the criterion in combination with basis function approximations to functions. We conduct a simulation study to investigate the performance of the proposed approach based on synthetic data. We also apply the approach for the analysis of multiple-subject functional magnetic resonance imaging data to obtain low-dimensional components of blood-oxygen level-dependent signal changes of the brain over time, which are highly correlated across the subjects as well as representative of the data. The extracted components are used to identify networks of neural activity that are commonly activated across the subjects while carrying out a working memory task.

Two-Way Regularized Fuzzy Clustering of Multiple Correspondence Analysis.

Multiple correspondence analysis (MCA) is a useful tool for investigating the interrelationships among dummy-coded categorical variables. MCA has been combined with clustering methods to examine whether there exist heterogeneous subclusters of a population, which exhibit cluster-level heterogeneity. These combined approaches aim to classify either observations only (one-way clustering of MCA) or both observations and variable categories (two-way clustering of MCA). The latter approach is favored because its solutions are easier to interpret by providing explicitly which subgroup of observations is associated with which subset of variable categories. Nonetheless, the two-way approach has been built on hard classification that assumes observations and/or variable categories to belong to only one cluster. To relax this assumption, we propose two-way fuzzy clustering of MCA. Specifically, we combine MCA with fuzzy k-means simultaneously to classify a subgroup of observations and a subset of variable categories into a common cluster, while allowing both observations and variable categories to belong partially to multiple clusters. Importantly, we adopt regularized fuzzy k-means, thereby enabling us to decide the degree of fuzziness in cluster memberships automatically. We evaluate the performance of the proposed approach through the analysis of simulated and real data, in comparison with existing two-way clustering approaches.