Detailed investigation of a gamma-cyclodextrin inclusion complex with L-thyroxine for improved pharmaceutical formulations

Thyroxine is a naturally occurring human hormone produced by the thyroid gland. Clinical applications of thyroxine to treat several chronic disorders are limited by poor water solubility and instability under physiological conditions. An inclusion complex of levo-thyroxine (l-thyroxine), the active form of the hormone with gamma cyclodextrin (gamma-CD) has been obtained and studied with the aim of improving oral delivery rather than the injection formulation of the sodium salt. In addition to greater patient acceptability, inclusion complexes often improve aqueous solubility and bioavailability, stability, and reduce toxicity of drugs, thus providing enhanced pharmaceutical formulations. Physicochemical characterization of the inclusion complex was carried out using Fourier transform infrared spectroscopy, X-ray diffractometry, differential scanning calorimetry, scanning electron microscopy and proton nuclear magnetic resonance spectroscopy. Intermolecular dipolar interactions for the inclusion complex were also studied using 2 dimensional ROESY experiments. Formation of the inclusion complex between the protons H3 and H5 of cyclodextrin with aromatic protons of thyroxine was confirmed by their dipolar interaction. Molecular modelling was used to understand the basis for the complex formation and predict the formation of other complexes. Interestingly, we found that l-thyroxine forms an inclusion complex only with the larger gamma-CD and not with other available alpha and beta forms.

Stress and strain-driven algorithmic formulations for finite strain viscoplasticity for hybrid and standard finite elements

This work deals with the formulation and implementation of finite deformation viscoplasticity within the framework of stress-based hybrid finite element methods. Hybrid elements, which are based on a two-field variational formulation, are much less susceptible to locking than conventional displacement-based elements. The conventional return-mapping scheme cannot be used in the context of hybrid stress methods since the stress is known, and the strain and the internal plastic variables have to be recovered using this known stress field.We discuss the formulation and implementation of the consistent tangent tensor, and the return-mapping algorithm within the context of the hybrid method. We demonstrate the efficacy of the algorithm on a wide range of problems.

Minimum enclosing spheres formulations for support vector ordinal regression

We present two new support vector approaches for ordinal regression. These approaches find the concentric spheres with minimum volume that contain most of the training samples. Both approaches guarantee that the radii of the spheres are properly ordered at the optimal solution. The size of the optimization problem is linear in the number of training samples. The popular SMO algorithm is adapted to solve the resulting optimization problem. Numerical experiments on some real-world data sets verify the usefulness of our approaches for data mining.

In situ surface modification of molybdenum-doped organic-inorganic hybrid TiO2 nanoparticles under hydrothermal conditions and treatment of pharmaceutical effluent

Molybdenum-doped TiO2 organic-inorganic hybrid nanoparticles were synthesized under mild hydrothermal conditions by in situ surface modification using n-butylamine. This was carried out at 150 degrees C at autogeneous pressure over 18 h. n-Butylamine was selected as a surfactant since it produced nanoparticles of the desired size and shape. The products were characterized using powder X-ray diffraction, Fourier transform infrared spectrometry, dynamic light-scattering spectroscopy, UV-Vis spectroscopy and transmission electron microscopy. Chemical oxygen demand was estimated in order to determine the photodegradation efficiency of the molybdenum-doped TiO2 hybrid nanoparticles in the treatment of pharmaceutical effluents. It was found that molybdenum-doped TiO2 hybrid nanoparticles showed higher photocatalytic efficiency than untreated TiO2 nanoparticles.

Comparative study of different formulations and solution methods for two phase flow in saturated porous media

Six models (Simulators) are formulated and developed with all possible combinations of pressure and saturation of the phases as primary variables. A comparative study between six simulators with two numerical methods, conventional simultaneous and modified sequential methods are carried out. The results of the numerical models are compared with the laboratory experimental results to study the accuracy of the model especially in heterogeneous porous media. From the study it is observed that the simulator using pressure and saturation of the wetting fluid (PW, SW formulation) is the best among the models tested. Many simulators with nonwetting phase as one of the primary variables did not converge when used along with simultaneous method. Based on simulator 1 (PW, SW formulation), a comparison of different solution methods such as simultaneous method, modified sequential and adaptive solution modified sequential method are carried out on 4 test problems including heterogeneous and randomly heterogeneous problems. It is found that the modified sequential and adaptive solution modified sequential methods could save the memory by half and as also the CPU time required by these methods is very less when compared with that using simultaneous method. It is also found that the simulator with PNW and PW as the primary variable which had problem of convergence using the simultaneous method, converged using both the modified sequential method and also using adaptive solution modified sequential method. The present study indicates that pressure and saturation formulation along with adaptive solution modified sequential method is the best among the different simulators and methods tested.

Robust Formulations for Handling Uncertainty in Kernel Matrices

We study the problem of uncertainty in the entries of the Kernel matrix, arising in SVM formulation. Using Chance Constraint Programming and a novel large deviation inequality we derive a formulation which is robust to such noise. The resulting formulation applies when the noise is Gaussian, or has finite support. The formulation in general is non-convex, but in several cases of interest it reduces to a convex program. The problem of uncertainty in kernel matrix is motivated from the real world problem of classifying proteins when the structures are provided with some uncertainty. The formulation derived here naturally incorporates such uncertainty in a principled manner leading to significant improvements over the state of the art. 1.

A Comparative Study of the Formulations for Topology Optimization of Compliant Mechanisms

The topology optimization problem for the synthesis of compliant mechanisms has been formulated in many different ways in the last 15 years, but there is not yet a definitive formulation that is universally accepted. Furthermore, there are two unresolved issues in this problem. In this paper, we present a comparative study of five distinctly different formulations that are reported in the literature. Three benchmark examples are solved with these formulations using the same input and output specifications and the same numerical optimization algorithm. A total of 35 different synthesis examples are implemented. The examples are limited to desired instantaneous output direction for prescribed input force direction. Hence, this study is limited to linear elastic modeling with small deformations. Two design parameterizations, namely, the frame element based ground structure and the density approach using continuum elements, are used. The obtained designs are evaluated with all other objective functions and are compared with each other. The checkerboard patterns, point flexures, the ability to converge from an unbiased uniform initial guess, and the computation time are analyzed. Some observations are noted based on the extensive implementation done in this study. Complete details of the benchmark problems and the results are included. The computer codes related to this study are made available on the internet for ready access.

Formulation development and evaluation of lamivudine controlled release tablets using cross-linked sago starch

Objectives: Modified starches based polymeric substances find utmost applicability in pharmaceutical formulation development. Cross-linked starches showed very promising results in drug delivery application. The present investigation concerns with the development of controlled release tablets of lamivudine using cross-linked sago starch. Methods: The cross-linked derivative was synthesized with phosphorous oxychloride and native sago starch in basic pH medium. The cross-linked sago starch was tested for acute toxicity and drug-excipient compatibility study. The formulated tablets were evaluated for various physical characteristics, in vitro dissolution release study and in vivo pharmacokinetic study in rabbit model. Results: In vitro release study showed that the optimized formulation exhibited highest correlation (R) in case of zero order kinetic model and the release mechanism followed a combination of diffusion and erosion process. There was a significant difference in the pharmacokinetic parameters (T-max, C-max, AUC, V-d, T-1/2, and MDT) of the optimized formulation as compared to the marketed conventional tablet Lamivir (R). Conclusion: The cross-linked starch showed promising results in terms of controlling the release behavior of the active drug from the matrix. The hydrophilic matrix synthesized by cross-linking could be used with a variety of active pharmaceutical ingredients for making their controlled/sustained release formulations.

Robust formulations for clustering-based large-scale classification

Chebyshev-inequality-based convex relaxations of Chance-Constrained Programs (CCPs) are shown to be useful for learning classifiers on massive datasets. In particular, an algorithm that integrates efficient clustering procedures and CCP approaches for computing classifiers on large datasets is proposed. The key idea is to identify high density regions or clusters from individual class conditional densities and then use a CCP formulation to learn a classifier on the clusters. The CCP formulation ensures that most of the data points in a cluster are correctly classified by employing a Chebyshev-inequality-based convex relaxation. This relaxation is heavily dependent on the second-order statistics. However, this formulation and in general such relaxations that depend on the second-order moments are susceptible to moment estimation errors. One of the contributions of the paper is to propose several formulations that are robust to such errors. In particular a generic way of making such formulations robust to moment estimation errors is illustrated using two novel confidence sets. An important contribution is to show that when either of the confidence sets is employed, for the special case of a spherical normal distribution of clusters, the robust variant of the formulation can be posed as a second-order cone program. Empirical results show that the robust formulations achieve accuracies comparable to that with true moments, even when moment estimates are erroneous. Results also illustrate the benefits of employing the proposed methodology for robust classification of large-scale datasets.

Tuning Mechanical Properties of Pharmaceutical Crystals with Multicomponent Crystals: Voriconazole as a Case Study

Crystals of voriconazole, an antifungal drug, are soft in nature, and this is disadvantageous during compaction studies where pressure is applied on the solid. Crystal engineering is used to make cocrystals and salts with modified mechanical properties (e.g., hardness). Cocrystals with biologically safe coformers such as fumaric acid, 4-hydroxybenzoic acid, and 4-aminobenzoic acid and salts with hydrochloric acid and oxalic acid are prepared through solvent assisted grinding. The presence (salt) or absence (cocrystal) of proton transfer in these multicomponent crystals is unambiguously confirmed with single crystal X-ray diffraction. All the cocrystals have 1:1 stoichiometry, whereas salts exhibit variable stoichiometries such as HCl salt (1:2) and oxalate salts (1:1.5 and 1:1). The nanoindentation technique was applied on single crystals of the salts and cocrystals. The salts exhibit better hardness than the drug and cocrystals in the order salts drug cocrystals. The molecular origin of this mechanical modulation is explained on the basis of slip planes in the crystal structure and relative orientations of the molecules with respect to the nanoindentation direction. The hydrochloride salt is the hardest solid in this family. This may be useful for tableting of the drug during formulation and in drug development.

Phytomedicinal importance of Saraca asoca (Ashoka): an exciting past, an emerging present and a promising future

Since the dawn of civilization, natural resources have remained the mainstay of various remedial approaches of humans vis-a-vis a large number of illnesses. Saraca asoca (Roxb.) de Wilde (Saraca indica L.) belonging to the family Caesalpiniaceae has been regarded as a universal panacea in old Indian Ayurvedic texts and has especially been used to manage gynaecological complications and infections besides treating haemmorhagic dysentery, uterine pain, bacterial infections, skin problems, tumours, worm infestations, cardiac and circulatory problems. Almost all parts of the plant are considered pharmacologically valuable. Extensive folkloric practices and ethnobotanical applications of this plant have even lead to the availability of several commercial S. asoca formulations recommended for different indications though adulteration of these remains a pressing concern. Though a wealth of knowledge on this plant is available in both the classical and modern literature, extensive research on its phytomedicinal worth using state-of-the-art tools and methodologies is lacking. Recent reports on bioprospecting of S. asoca endophytic fungi for industrial bioproducts and useful pharmacologically relevant metabolites provide a silver lining to uncover single molecular bio-effectors from its endophytes. Here, we describe socio-ethnobotanical usage, present the current pharmacological status and discuss potential bottlenecks in harnessing the proclaimed phytomedicinal worth of this prescribed Ayurvedic medicinal plant. Finally, we also look into the possible future of the drug discovery and pharmaceutical R&D efforts directed at exploring its pharma legacy.