Preparation, characterization and application of organic-inorganic hybrid ractopamine multi-template molecularly imprinted capillary monolithic column

The classification and regression trees (CART) possess the advantage of being able to handle large data sets and yield readily interpretable models. A conventional method of building a regression tree is recursive partitioning, which results in a good but not optimal tree. Ant colony system (ACS), which is a meta-heuristic algorithm and derived from the observation of real ants, can be used to overcome this problem. The purpose of this study was to explore the use of CART and its combination with ACS for modeling of melting points of a large variety of chemical compounds. Genetic algorithm (GA) operators (e.g., cross averring and mutation operators) were combined with ACS algorithm to select the best solution model. In addition, at each terminal node of the resulted tree, variable selection was done by ACS-GA algorithm to build an appropriate partial least squares (PLS) model. To test the ability of the resulted tree, a set of approximately 4173 structures and their melting points were used (3000 compounds as training set and 1173 as validation set). Further, an external test set containing of 277 drugs was used to validate the prediction ability of the tree. Comparison of the results obtained from both trees showed that the tree constructed by ACS-GA algorithm performs better than that produced by recursive partitioning procedure.     

Development and evaluation of a monolithic floating drug delivery system for acyclovir

Acyclovir (ACV), a model drug for this study, is one of the most effective drugs against viruses of the herpes group. Absorption of orally administered ACV is variable and incomplete, with a bioavailability of ca. 15-30%. The drug is absorbed in the duodenum after oral administration and hence, preparation of a floating drug delivery system (FDDS) for ACV may increase oral absorption of the drug. ACV matrix tablets (200?mg) containing an effervescent base (sodium bicarbonate and citric acid) and a binary combination of hydroxypropyl methylcellulose (HPMC) K4M with carbopol or sodium carboxymethyl cellulose (Na CMC) or polyvinylpyrrolidone (PVP) and/or sodium alginate were prepared by the direct compression method. The tablets were evaluated for physicochemical properties and in vitro floating ability (floating lag-time and duration), bioadhesiveness and drug release. The drug release studies were carried out in 0.1?N HCl (pH 1.2) at 37±0.5°C. At appropriate time intervals, samples were withdrawn and assayed spectrophotometrically at λ(max)=259?nm. The floating test showed tablets containing 15% effervescent base had a floating lag time of 10-30?s and a duration of floating time of 24?h. The formulations containing HPMC-PVP, HPMC-Na CMC, HPMC-carbopol, and HPMC-sodium alginate released about 60-90% of their drug content during a 12-h period. Increasing carbopol caused slower drug release. We concluded that the proposed tablets with 15% effervescent base, 20-30% HPMC, 30% Na CMC (and/or 20% PVP or 20% sodium alginate) showed good floating and drug release properties in vitro, and should be considered as FDDS for ACV.     

基于支持向量学习机方法的人体小肠吸收药物活性的预测

为了预测分子在人体小肠中的吸收,本文计算了表征分子的电子、拓扑、几何结构、分子形状等特征的102个分子描述符,用遗传算法变量选择方法使描述符减少到47个。体系共包含了230个化合物分子,69个不能被吸收(mA-),161个可以被吸收(HIA )。对建立的SVM模型,用5重交叉验证和独立测试集进行验证,预测正确率分别达到79．1％和77．1％,结果具有较好的一致性。在模型验证中,通过聚类分析方法组合训练集和测试集,保证了模型的稳定性,提高了建模效率。     

SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique

The main oral drug absorption barriers are fluid cell membranes, and generally drugs are absorbed by a passive diffusion mechanism. On the other hand, the blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug partitioning process into biological systems. In this study, relationships between the BMC retention data of a heterogeneous set of 12 drugs and their pharmacokinetics parameters (human oral drug absorption and BBB penetration ability) are studied and the predictive ability of the models is evaluated. Modeling of log k _BMC of these compounds was established by multiple linear regression in two different concentrations (0.07 and 0.09 M) of sodium dodecyl sulfate (SDS). The results showed a fair correlation between human oral drug absorption and BMC retention data in 0.09 M SDS (R ² = 0.864) and a good correlation between the blood–brain distribution coefficient and BMC retention data in 0.07 M of SDS (R ² = 0.887). Application of the developed models to a prediction set demonstrated that the model is also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values are in good agreement with the experimental value.     

Quantum mechanical polar surface area

A correlation has been established between the absorbed fraction of training-set molecules after oral administration in humans and the Quantum Mechanical Polar Surface Area (QMPSA). This correlation holds for the QMPSA calculated with structures where carboxyl groups are deprotonated. The correlation of the absorbed fraction and the QMPSA calculated on the neutral gas phase optimized structures is much less pronounced. This suggests that the absorption process is mainly determined by polar interactions of the drug molecules in water solution. Rules are given to derive the optimal polar/apolar ranges of the electrostatic potential.     

基于分子参数的药物小肠吸收预测模型

选择100个化合物作为数据集,随机选取其中80个为训练集,其他分子为验证集,并为每个化合物分子计算了30个参数.通过采用五种不同多元线性回归分析方法对其训练模拟,建立了数学模型,并用验证集检验了所建模型的预测能力.结果发现向后筛选法为最优小肠吸收建模方法.由该法所建模型的统计结果良好(R2>0.80),应用于验证集时也表现出较强预测能力.该模型确定了对小肠吸收影响较大的分子参数,有助于指导进一步的新药筛选和开发.     

Predicting Caco-2 cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis

A methodology termed membrane-interaction QSAR (MI-QSAR) analysis has been developed in order to predict the behavior of organic compounds interacting with the phospholipid-rich regions of biological membranes. One important application of MI-QSAR analysis is to estimate ADME properties including the transport of organic solutes through biological membranes as a computational approach to forecasting drug intestinal absorption. A training set of 30 structurally diverse drugs, whose permeability coefficients across the cellular membranes of Caco-2 cells were measured, was used to construct significant MI-QSAR models of Caco-2 cell permeation. Cellular permeation is found to depend primarily upon aqueous solvation free energy (solubility) of the drug, the extent of drug interaction with a model phospholipid (DMPC) monolayer, and the conformational flexibility of the solute within the model membrane. A test set of eight drugs was used to evaluate the predictivity of the MI-QSAR models. The permeation coefficients of the test set compounds were predicted with the same accuracy as the compounds of the training set.     

SDS-Based Biomembrane Mimetic Chromatography for Prediction of Human Drug Transport as an in Vitro Technique

The main oral drug absorption barriers are fluid cell membranes, and generally drugs are absorbed by a passive diffusion mechanism. On the other hand, the blood–brain barrier (BBB) is considered to be the main barrier to drug transport into the central nervous system (CNS). The BBB restricts the passive diffusion of many drugs from blood to brain. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases in adequate experimental conditions, can be useful as an in vitro system in mimicking the drug partitioning process into biological systems. In this study, relationships between the BMC retention data of a heterogeneous set of 12 drugs and their pharmacokinetics parameters (human oral drug absorption and BBB penetration ability) are studied and the predictive ability of the models is evaluated. Modeling of log k _BMC of these compounds was established by multiple linear regression in two different concentrations (0.07 and 0.09 M) of sodium dodecyl sulfate (SDS). The results showed a fair correlation between human oral drug absorption and BMC retention data in 0.09 M SDS (R ² = 0.864) and a good correlation between the blood–brain distribution coefficient and BMC retention data in 0.07 M of SDS (R ² = 0.887). Application of the developed models to a prediction set demonstrated that the model is also reliable with good predictive accuracy. The external and internal validation results showed that the predicted values are in good agreement with the experimental value.

     

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.     

Significance analysis and multiple pharmacophore models for differentiating P-glycoprotein substrates

P-glycoprotein (Pgp) mediated drug efflux affects the absorption, distribution, and clearance of a broad structural variety of drugs. Early assessment of the potential of compounds to interact with Pgp can aid in the selection and optimization of drug candidates. To differentiate nonsubstrates from substrates of Pgp, a robust predictive pharmacophore model was targeted in a supervised analysis of three-dimensional (3D) pharmacophores from 163 published compounds. A comprehensive set of pharmacophores has been generated from conformers of whole molecules of both substrates and nonsubstrates of P-glycoprotein. Four-point 3D pharmacophores were employed to increase the amount of shape information and resolution, including the ability to distinguish chirality. A novel algorithm of the pharmacophore-specific t-statistic was applied to the actual structure-activity data and 400 sets of artificial data (sampled by decorrelating the structure and Pgp efflux activity). The optimal size of the significant pharmacophore set was determined through this analysis. A simple classification tree using nine distinct pharmacophores was constructed to distinguish nonsubstrates from substrates of Pgp. An overall accuracy of 87.7% was achieved for the training set and 87.6% for the external independent test set. Furthermore, each of nine pharmacophores can be independently utilized as an accurate marker for potential Pgp substrates.     

Use of surface charges from DFT calculations to predict intestinal absorption

A model for prediction of percent intestinal absorption (%Abs) of neutral molecules was developed based upon surface charges of the molecule calculated by density functional theory (DFT). The surface charges are decomposed into sigma moments which are correlated to a partition coefficient representing transfer of the molecule between water and the epithelial membrane. The model was built and tested using a data set of 241 drugs. It achieved an RMS deviation of 13% on a training set of 38 compounds as well as on a test set of 107 drugs for which the experimental data were classified as high quality. Property maps of the molecule, depicting which atoms contribute to or hinder absorption, are produced to aid drug design.     

Formulation variation and in vitro-in vivo correlation for a rapidly swellable three-layered tablet of tamsulosin HCl

In order to develop a preferable once-a-day oral tablet formulation, various formulations of three-layered tablets containing tamsulosin HCl as a hydrophilic model drug were evaluated and compared with a commercial reference, tamsulosin OCAS?. When the test tablet was exposed to a release medium, the medium quickly permeated to the mid-layer and the two barrier layers swelled surrounding the mid-layer rapidly. Volume expansion showed faster and enough swelling of the three-layered tablet up to 2 h. Larger amount of barrier layers caused reduced release kinetics and a high molecular weight polymer showed more resistance against agitation force. A formulation with water-soluble mid-layer showed fast erosion decreasing its volume significantly. On the pharmacokinetic study, the mean ratio of area under the curve (AUC) and C(max) for the test formulation to the reference was 0.69 and 0.84, respectively, showing that the absorption of the drug was less complete than the reference. Plasma concentration at 24 h of the test formulation was higher than the reference. The Wagner-Nelson method showed that decreased initial dissolution rate might be the cause of the less complete absorption. On considering in vitro-in vivo correlation (IVIVC), level A, the reference (R2=0.981) showed more linear relationship than the test (R2=0.918) due to the decreased dissolution and absorption rate of the formulation. This result suggests that the in vitro dissolution profiles and release kinetics might be useful in correlating absorption kinetics as well as overall plasma drug concentration-time profiles for formulation studies.     

Drug carrying hydrogel base wound dressing

A special drug carrying hydrogel base wound dressing by radiation preparation is developed for hospital uses. The dressing possesses high water absorption property. Radiation preparation is carried out using a Van de Graaff Accelerator as an electron radiation source. The effect of absorbed dose and cmount of crosslinking agent on the gel fraction and swelling ratio of the hydrogel were determined respectively. As a bio-medical material, standard tests were made. Results showed properties conforming with requirements for clinical applications. Results obtained from clinical tests were good.     

Ab initio computations on small copper compounds-CuO

Ab initio computations on CuO were performed with the Hartree-Fock method and a proper dissociation MC SCF procedure. In particular the influence of the GTO basis set for Cu in molecular computations has been studied. A potential energy curve and a Mulliken population analysis are presented. The equilibrium bond length is predicted reasonably well but the dissociation energy is only a fraction of the observed value. The bond appears to be a slightly ionic δ-type bond, in which metal 3d orbitals participate only to a small extent.