Influence of Prosolv and Prosolv:Mannitol 200 direct compression fillers on the physicomechanical properties of atorvastatin oral dispersible tablets.

The objective of the present study was to evaluate the influence of Prosolv® and Prosolv®: Mannitol 200 direct compression (DC) fillers on the physicomechanical characteristics of oral dispersible tablets (ODTs) of crystalline atorvastatin calcium. ODTs were formulated by DC and were analyzed for weight uniformity, hardness, friability, drug content, disintegration and dissolution. Three disintegration time (DT) test methods; European Pharmacopoeia (EP) method for conventional tablets (Method 1), a modification of this method (Method 2) and the EP method for oral lyophilisates (Method 3) were compared as part of this study. All ODTs showed low weight variation of <2.5%. Prosolv® only ODTs showed the highest tablet hardness of ∼ 73 N, hardness decreased with increasing mannitol content. Friability of all formulations was <1% although friability of Prosolv®:Mannitol ODTs was higher than for pure Prosolv®. DT of all ODTs was <30 s. Method 2 showed the fastest DT. Method 3 was non-discriminatory giving a DT of 13-15 s for all formulations. Atorvastatin dissolution from all ODTs was >60% within 5 min despite the drug being crystalline. Prosolv® and Prosolv®:Mannitol-based ODTs are suitable for ODT formulations by DC to give ODTs with high mechanical strength, rapid disintegration and dissolution.

Extended release dosage form of glipizide: development and validation of a level A in vitro-in vivo correlation.

Defining a quantitative and reliable relationship between in vitro drug release and in vivo absorption is highly desired for rational development, optimization, and evaluation of controlled-release dosage forms and manufacturing process. During the development of once daily extended-release (ER) tablet of glipizide, a predictive in vitro drug release method was designed and statistically evaluated using three formulations with varying release rates. In order to establish internally and externally validated level A in vitro-in vivo correlation (IVIVC), a total of three different ER formulations of glipizide were used to evaluate a linear IVIVC model based on the in vitro test method. For internal validation, a single-dose four-way cross over study (n=6) was performed using fast-, moderate-, and slow-releasing ER formulations and an immediate-release (IR) of glipizide as reference. In vitro release rate data were obtained for each formulation using the United States Pharmacopeia (USP) apparatus II, paddle stirrer at 50 and 100 rev. min(-1) in 0.1 M hydrochloric acid (HCl) and pH 6.8 phosphate buffer. The f(2) metric (similarity factor) was used to analyze the dissolution data. The formulations were compared using area under the plasma concentration-time curve, AUC(0-infinity), time to reach peak plasma concentration, T(max), and peak plasma concentration, C(max), while correlation was determined between in vitro release and in vivo absorption. A linear correlation model was developed using percent absorbed data versus percent dissolved from the three formulations. Predicted glipizide concentrations were obtained by convolution of the in vivo absorption rates. Prediction errors were estimated for C(max) and AUC(0-infinity) to determine the validity of the correlation. Apparatus II, pH 6.8 at 100 rev. min(-1) was found to be the most discriminating dissolution method. Linear regression analysis of the mean percentage of dose absorbed versus the mean percentage of in vitro release resulted in a significant correlation (r(2)>or=0.9) for the three formulations.

Optimization of metformin HCl 500 mg sustained release matrix tablets using Artificial Neural Network (ANN) based on Multilayer Perceptrons (MLP) model.

The aim of the present study was to apply the simultaneous optimization method incorporating Artificial Neural Network (ANN) using Multi-layer Perceptron (MLP) model to the development of a metformin HCl 500 mg sustained release matrix tablets with an optimized in vitro release profile. The amounts of HPMC K15M and PVP K30 at three levels (-1, 0, +1) for each were selected as casual factors. In vitro dissolution time profiles at four different sampling times (1 h, 2 h, 4 h and 8 h) were chosen as output variables. 13 kinds of metformin matrix tablets were prepared according to a 2(3) factorial design (central composite) with five extra center points, and their dissolution tests were performed. Commercially available STATISTICA Neural Network software (Stat Soft, Inc., Tulsa, OK, U.S.A.) was used throughout the study. The training process of MLP was completed until a satisfactory value of root square mean (RSM) for the test data was obtained using feed forward back propagation method. The root mean square value for the trained network was 0.000097, which indicated that the optimal MLP model was reached. The optimal tablet formulation based on some predetermined release criteria predicted by MLP was 336 mg of HPMC K15M and 130 mg of PVP K30. Calculated difference (f(1) 2.19) and similarity (f(2) 89.79) factors indicated that there was no difference between predicted and experimentally observed drug release profiles for the optimal formulation. This work illustrates the potential for an artificial neural network with MLP, to assist in development of sustained release dosage forms.

Formulation and optimization of sustained release matrix tablet of metformin HCl 500 mg using response surface methodology.

The aim of the current study was to design an oral sustained release matrix tablet of metformin HCl and to optimize the drug release profile using response surface methodology. Tablets were prepared by non-aqueous wet granulation method using HPMC K 15M as matrix forming polymer. A central composite design for 2 factors at 3 levels each was employed to systematically optimize drug release profile. HPMC K 15M (X(1)) and PVP K 30 (X(2)) were taken as the independent variables. The dependent variables selected were % of drug released in 1 hr (rel(1 hr)), % of drug released in 8 hrs (rel(8 hrs)) and time to 50% drug release (t(50%)). Contour plots were drawn, and optimum formulations were selected by feasibility and grid searches. The formulated tablets followed Higuchi drug release kinetics and diffusion was the dominant mechanism of drug release, resulting in regulated and complete release within 8 hrs. The polymer (HPMC K 15M) and binder (PVP K 30) had significant effect on the drug release from the tablets (p<0.05). Polynomial mathematical models, generated for various response variables using multiple linear regression analysis, were found to be statistically significant (p<0.05). Validation of optimization study, performed using 8 confirmatory runs, indicated very high degree of prognostic ability of response surface methodology, with mean percentage error (+/-S.D.) 0.0437+/-0.3285. Besides unraveling the effect of the 2 factors on the in vitro drug release, the study helped in finding the optimum formulation with sustained drug release.

In-vitro and in-vivo correlation for two gliclazide extended-release tablets.

The aim of this study was to perform an in-vitro-in-vivo correlation (IVIVC) for two 60-mg gliclazide extended-release formulations (Fast and Slow release) given once a day and to compare their plasma concentrations over time. In-vitro release rate data were obtained for each formulation using the USP apparatus II, paddle stirrer at 50 and 100 rev min(-1) in 0.1 M HCl and pH 7.4 phosphate buffer. The similarity factor (f2) was used to analyse the dissolution data. Eighteen healthy subjects participated in the study, conducted according to a completely randomized, two-way crossover design. The formulations were compared using area under the plasma concentration-time curve, AUC(0-infinity), time to reach peak plasma concentration, Tmax, and peak plasma concentration Cmax, while correlation was determined between in-vitro release and in-vivo absorption. A linear correlation model was developed using percent absorbed data versus percent dissolved data from the two formulations. Predicted gliclazide concentrations were obtained by use of a curve fitting equation. Prediction errors were estimated for Cmax and area under the curve AUC(0-infinity) to determine the validity of the correlation. 0.1 M HCl at 50 rev min(-1) was found to be the most discriminating dissolution method. Linear regression analysis of the mean percentage of dose absorbed versus the mean percentage of in-vitro release resulted in a significant correlation (r2 > 0.98) for the two formulations. An average percent prediction error for Cmax was 4.15% for Fast release and 3.99% for Slow release formulation whereas for AUC(0-infinity) it was 6.36% and 4.66% for Fast release and Slow release formulation, respectively.