Fabrication and in vitro evaluation of bidirectional release and stability studies of mucoadhesive donut-shaped captopril tablets.

OBJECTIVE: To obtain controlled release of captopril in the stomach, coated, mucoadhesive donut-shaped tablets were designed. MATERIALS AND METHODS: Donut-shaped tablet were made of different ratios of diluents to polymer or combination of polymers by direct compression method. Top and bottom portions of the tablet were coated with water-insoluble polymer followed by mucoadhesive coating. Time of water penetration, measurement of tensile strength, mucoadhesion studies (static ex vivo and ex vivo wash-off) were taken into account for characterization of respective films. In vitro study has been performed at different dissolution mediums. Optimized batches were also prepared by wet granulation. Stability studies of optimized batches have been performed. RESULTS: The results of time of water penetration and tensile strength indicated positive response against water impermeation. Mucoadhesive studies showed that film thickness of 0.12 mm was good for retention of tablet at stomach. At pH 1.2, optimized batch of tablet made with hydroxypropyl methyl cellulose (HPMC) E15 as binder showed 80% w/w drug release within 4­5 h with maximum average release of 97.49% w/w. Similarly, maximum average releases of 96.36% w/w and 95.47% w/w were obtained with nearly same dissolution patterns using combination of HPMC E5 and HPMC E50 and sodium salt of carboxy methyl cellulose (NaCMC) 500­600 cPs instead of HPMC E15. The release profiles in the distilled water and pH 4.5 followed the above pattern except deviation at pH 6.8. Stability studies were not positive for all combinations. CONCLUSION: Coated, mucoadhesive donut-shaped tablet is good for controlled release of drug in the stomach.

In vitro-in vivo correlation and bioavailability studies of captopril from novel controlled release donut shaped tablet.

A controlled release formulation of captopril which was coated and fabricated into a donut shaped tablet formulation, was investigated in rabbit for pharmacokinetic and in vitro-in vivo correlation studies. Coated donut shaped tablets were prepared and in vitro release was studied in simulated gastric fluid at three different RPMs. New Zealand albino male rabbits have been used as animal model for in vivo study. A sensitive and simple HPLC method was developed for the determination of captopril content in rabbit plasma. In vitro release studies showed that release patterns followed zero order for around 4h. Single oral administration of coated donut shaped tablets in rabbit illustrated retained availability of captopril to the injected drug. Captopril content could pursue the same release pattern over the same time course in in vivo study. The in vivo-in vitro correlation coefficients obtained from point-to-point analysis were greater than 99% between concentrations at certain time points obtained from release study in simulated gastric fluid at different RPMs and HPLC analysis of rabbit's plasma. From the in vitro-in vivo correlation prediction it was evident that the coated donut shaped tablet is a good device for controlled delivery of captopril.

Drug delivery system based on chronobiology--A review.

With the advancement in the field of chronobiology, modern drug delivery approaches have been elevated to a new concept of chronopharmacology i.e. the ability to deliver the therapeutic agent to a patient in a staggered profile. However the major drawback in the development of such delivery system that matches the circadian rhythm requires the availability of precise technology (pulsatile drug delivery). The increasing research interest surrounding this delivery system has widened the areas of pharmaceutics in particular with many more sub-disciplines expected to coexist in the near future. This review on chronopharmaceutics gives a comprehensive emphasis on potential disease targets, revisits the existing technologies in hand and also addresses the theoretical approaches to emerging discipline such as genetic engineering and target based specific molecules. With the biological prospective approaches in delivering drugs it is well understood that safer and more realistic approaches in the therapy of diseases will be achieved in the days to come.

Niosome: A future of targeted drug delivery systems.

Over the past several years, treatment of infectious diseases and immunisation has undergone a revolutionary shift. With the advancement of biotechnology and genetic engineering, not only a large number of disease-specific biological have been developed, but also emphasis has been made to effectively deliver these biologicals. Niosomes are vesicles composed of non-ionic surfactants, which are biodegradable, relatively nontoxic, more stable and inexpensive, an alternative to liposomes. This article reviews the current deepening and widening of interest of niosomes in many scientific disciplines and, particularly its application in medicine. This article also presents an overview of the techniques of preparation of niosome, types of niosomes, characterisation and their applications.

Predictive QSAR modeling of HIV reverse transcriptase inhibitor TIBO derivatives.

Comparative quantitative structure-activity relationship (QSAR) studies have been carried out on tetrahydroimidazo[4,5,1-jk][1,4]benzodiazepine (TIBO) derivatives as reverse transcriptase inhibitors (n=70) using topological, structural, physicochemical, electronic and spatial descriptors. The data set was divided into training and test sets using a cluster-based method. Linear models were developed using multiple regression (with stepwise regression, factor analysis and genetic function approximation (GFA) as variable selection tools) and partial least squares (PLS) and combination of factor analysis and partial least squares (FA-PLS). Genetic function approximation (spline) and artificial neural networks (ANN) were used for the development of non-linear models. Using topological and structural descriptors, the best equation was obtained from GFA (spline) based on internal validation (Q(2)=0.737), but the model with the best external validation characteristics was obtained with FA-PLS (R(pred)(2)=0.707). When structural, physicochemical, electronic and spatial descriptors were used, the best Q(2) (0.740) value was obtained from GFA (spline) whereas PLS provided the best R(pred)(2) (0.784) value. When all descriptors were used in combination, the best R(pred)(2) (0.760) value and the best Q(2) (0.800) value were obtained from ANN and GFA (spline), respectively. The majority of the models satisfied the criteria of external validation recommended by Golbraikh and Tropsha (2002) and the criteria of modified r(2) (r(m)(2)) values of the test set for external validation as suggested by Roy and Roy (2008). In order to further validate selected models, an external set of 10 TIBO derivatives, which fall within the applicability domain of the models and are not shared with the compounds of the present data set, was taken from a different source, and reverse transcriptase inhibitory activity of these compounds was predicted. Acceptable values of squared correlation coefficients between the observed and predicted values of the external set compounds were obtained from the selected models suggesting true predictive potential of the models.

Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools.

Quantitative structure-activity relationship (QSAR) studies have been performed on piperidine derivatives (n = 119) as CCR5 antagonists. The whole data set was divided into a training set (75% of the dataset) and a test set (remaining 25%) on the basis of K-means clustering technique. Models developed from the training set were used to assess the predictive potential of the models using test set compounds. Initially classical type QSAR models were developed using structural, spatial, electronic, physicochemical and/or topological parameters using statistical methods like stepwise regression, partial least squares (PLS) and factor analysis followed by multiple linear regression (FA-MLR). Using topological and structural parameters, FA-MLR provided the best equation based on internal validation (Q(2) = 0.514) but the best externally validated model was obtained with PLS ([image omitted] = 0.565). When structural, physicochemical, spatial and electronic descriptors were used, the best Q(2) value (0.562) was obtained from the stepwise regression derived model whereas the best [image omitted] value (0.571) came from the PLS model. When topological descriptors were used in combination with the structural, physicochemical, spatial and electronic descriptors, the best Q(2) and [image omitted] values obtained were 0.530 (stepwise regression) and 0.580 (PLS) respectively. Attempt was made to develop 3D-QSAR models using molecular shape analysis descriptors in combination with structural, physicochemical, spatial and electronic parameters. Linear models were developed using genetic function algorithm coupled with multiple linear regression. However, the results from the 3D-QSAR study were not superior to those of the classical QSAR models. Finally, artificial neural network was employed for development of nonlinear models. The ANN models showed acceptable values of squared correlation coefficient for the observed and predicted values of the test set compounds. From the view point of external predictability, selected ANN models were superior to the linear QSAR models. All reported models satisfy the criteria of external validation as recommended by Golbraikh and Tropsha (J Mol Graphics Mod 2002; 20: 269-276), whereas the majority of the models have modified r(2) ([image omitted] ) value of the test set for external validation more than 0.5 as suggested by Roy and Roy (QSAR Comb Sci 2008; 27: 302-313).

Development of linear and nonlinear predictive QSAR models and their external validation using molecular similarity principle for anti-HIV indolyl aryl sulfones.

Quantitative structure-activity relationship (QSAR) studies have been carried out on indolyl aryl sulfones, a class of novel HIV-1 non-nucleoside reverse transcriptase inhibitors, using physicochemical, topological and structural parameters along with appropriate indicator variables. The statistical tools used were linear methods (e.g., stepwise regression analysis, partial least squares (PLS), factor analysis followed by multiple regression (FA-MLR), genetic function approximation combined with multiple linear regression (GFA-MLR) and GFA followed by PLS or G/PLS and nonlinear method (artificial neural network or ANN). In case of physicochemical parameters, GFA-MLR generated the best Equation (n = 97, R(2) = 0.862, Q(2) = 0.821). Using topological parameters, the best Equation (based on leave-one-out Q(2)) was obtained with stepwise regression technique (n = 97, R(2) = 0.867, Q(2) = 0.811). When topological and physicochemical parameters were used in combination, statistical quality increased to a great extent (n = 97, R(2) = 0.891, Q(2) = 0.849 from stepwise regression). Furthermore, the whole dataset had been divided into test (25% of whole dataset) and training (remaining 75%) sets. Models were developed based on the training set and predictive potential of such models was checked from the test set. The selection of the training set was based on K-means clustering of the standardized descriptors (topological and physicochemical). In this case also the best results were obtained with stepwise regression (n = 72, R(2) = 0.906, Q(2) = 0.853) but external predictive capacity of this model ([image omitted] ) was inferior to the model developed from GFA-MLR technique (R(2) = 0.883, Q(2) = 0.823, [image omitted] ). However, the squared regression coefficient between observed activity and predicted activity values of the test set compounds for the best linear model, i.e., GFA-MLR (r(2) = 0.736) was lower in comparison to the best nonlinear model developed using artificial neural network (r(2) = 0.781). Thus, based on external validation, the ANN models were superior to the linear models. The predictive potential of the best linear Equation (stepwise regression model) was superior to that of the previously published CoMFA (Q(2) = 0.81, SDEP(Test) = 0.89) on the same data set (Ragno R. et al., J Med Chem 2006, 49, 3172-3184). Furthermore, the physicochemical parameter based models also supported the previous observations based on docking (Ragno R. et al., J Med Chem 2005, 48, 213-223).