The aim of the current study was to design an oral sustained release matrix tablet of metformin HCl and to optimize the drug release proˆle 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 proˆle. 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 diŠusion 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 signiˆcant eŠect 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 signiˆcant ( p＜0.05). Validation of optimization study, performed using 8 conˆrmatory 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 eŠect of the 2 factors on the in vitro drug release, the study helped inˆnding the optimum formulation with sustained drug release.

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.Key words artificial neural network; multilayer perceptron; metformin HCl; sustained release; matrix tablet Chem. Pharm. Bull. 56(2) 150-155 (2008)

Aim: The aim of this study was to develop an in-vitro-in-vivo correlation (IVIVC) for two ibuprofen (IF) and extended release (ER) formulations and to compare their plasma concentrations. Materials and Methods: In vitro release rate data were obtained for each formulation using the USP apparatus 2, paddle stirrer at 100 rpm in pH 6.8 phosphate buffer. In vitro samples were analyzed using high performance liquid chromatography (HPLC) with ultraviolet detection, and in vivo samples were analyzed using a HPLC assay an additional model dependent approaches for different types of release rate of IF was prepared for evaluating the external predictability. Results and Discussion: A Higuchi model optimally fits the in vitro data. The similarity factor (f 2) was used to analyze the dissolution data. In vivo plasma concentrations and pharmacokinetic parameters in New Zealand rabbits were obtained after administering oral, Poly-β-hydroxybutyrate, and hydroxypropyl methylcellulose base ER formulations. A linear correlation model was developed using percent absorbed data and percent dissolved data from the two formulations. Conclusion: Linear regression analyses of the mean percentage of dose absorbed versus the mean in vitro release resulted in a significant correlation (r 2 >0.95) for the two formulations.

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.

The development and subsequent validation of an in vitro-in vivo correlation (IVIVC) is an increasingly important component of extended release dosage form optimization. An IVIVC is a relationship (preferable linear) between a biological parameters (C max , T max , or AUC) produced by a dosage form and an in vitro characteristics (e.g., in vitro dissolution).1) The in vitro dissolution curve is usually determined by a suitable dissolution test and in vivo absorption curve is frequently determined by deconvolution using model dependent (e.g., Wagner-Nelson or Loo-Regleman) or model independent (e.g., DeMons) methods.2,3) Level A, B, C and multiple Level C correlation has been described by the Food and Drug Administration (FDA) for IVIVC. The highest level correlation, Level A, is usually linear and is a direct relationship between the amounts of drug dissolved and the amount of drug absorbed. 1,4,5) The recent in vitro-in vivo correlation guidance developed by the FDA, states that the main objective of developing and evaluating an IVIVC is to enable the dissolution test to serve as a surrogate for in vivo bioavailability studies. This may reduce the numbers of bioequivalence studies required for approval as well as during scale-up and post approval change.5) There are numerous examples of Level A correlations in the literature, however many fall short in assessing the predictability of the correlation. The process for the development and validation of an IVIVC has been outlined in the FDA IVIVC guidance.5) The development of the correlation usually involves the following three steps: (1) develop formulation with different release rates, e.g.slow, moderate and fast, (2) obtain in vitro dissolution profiles and in vivo plasma concentration profiles for these formulations, and (3) estimate the in vivo absorption or in vitro dissolution time course using an appropriate deconvolution technique for each formulation. The internal validation 5) of the correlation focuses on using prediction error metrics to determine how well the IVIVC model predict the plasma concentration profile of those formulations used to develop the correlation.Establishing a correlation between the in vivo plasma concentration profile and in vitro dissolution profile of an extended release formulation has been great interest for a number of years. Extended release of drugs in the gastrointestinal tract following oral administration is the intended rate-limiting factor in the absorption process. It is therefore desirable to use in vitro data to predict in vivo bioavailability parameters for the rational development and evaluation process for extended-release dosage forms. 6,7) Glipizide (N-[2-[4-(cyclohexylcarbamoylsulfamoyl)phenyl]-ethyl]-5-methyl-pyridine-2-carboxamide) is a hypoglycemic agent of the sulfonylurea group. 8) Numerous IVIVC studies of extended release formulation have been previously reported, [9][10][11][12][13][14] although there are none involving extended release glipizide formulations. Therefore, the purpose of this study was ...