The current study was aimed to develop stable and reproducible liposomal formulations of Diclofenac sodium (DFS) and Diclofenac potassium (DFP) using purified soya lecithin (PSL) and purified egg lecithin (PEL) for oral delivery. For the accomplishment of analysis task of DFS and DFP in In-vitro/In-vivo evaluation as well as for entrapment studies, two simultaneous methods were developed and validated. In one study for analysis of DFS, an isocratic system was employed for the flow of mobile phase consisting of 10 mM sodium dihydrogen phosphate buffer and acetonitrile in molar ratio of 67: 33 with adjusted pH of 3.2. The stationary phase was hypersil ODS column (C18, 250×4.6 mm i.d., 5 μm) with controlled temperature of 30 ̊C. DFS in liposomes, microcapsules and marketed drug products was determined in range of 99.76-99.84%. FLP and TMD in microcapsules and brands formulation were 99.78 - 99.94 % and 99.80 - 99.82 %, respectively. Single step liquid-liquid extraction procedure using combination of acetonitrile and trichloroacetic acid (TCA) as protein precipitating agent was employed. The detection limits (at S/N ratio 3) of quality control solutions and plasma samples were 10, 20, and 20 ng.mL-1 for DFS, FLP and TMD, respectively. The Assay was acceptable in linear dynamic range. All other validation parameters were found in limits of FDA and ICH method validation guidelines. The proposed method for DFS analysis was found as sensitive, accurate and precise and applied for dissolution studies as well as in human plasma samples for bioequivalence and pharmacokinetics studies. For analysis of DFP, a new, easy and consistent reversed-phase high- performance liquid chromatographic method with diode array detection was developed and validated for DFP and MLX (Meloxicam. The optimized mobile phase was used in the molar ratio of 20:20:60 (v/v/v) mixture of acetonitrile, methanol and 20 x 10-3 M potassium dihydrogen phosphate buffer (pH 3.7), pumped at an optimized flow rate of 1.0 mL.min-1. The linearity was performed in the concentration range of 15 ng.mL−1 to 10μg.mL-1 with r2 values of 0.9989 ± 0.13 and 0.9979 ± 0.11 (n = 6) for DP and MLX, respectively. The assay was repeatable at concentration levels of 10 ng.mL-1, 1 μg.mL-1 and 10 μg.mL-1 with coefficient of variation of 0.168 - 0.603% for 10 ng.mL-1 (DP), 15 ng.mL-1 (MLX) and 1 μg.mL-1 &10 μg.mL-1 for DP and MLX. The LOD values were 0.3 and 0.5 ng.mL−1, while values of LOQ were 10 ng.mL-1 and 15 ng.mL-1, for DP and MLX. The present method was applied in advanced drug delivery formulations (Liposomes), In-vitro and In-vivo studies. An important part of study was development of an optimized liposomal formulation of diclofenac sodium (DFS) of most suitable concentration of formulating ingredients, soya lecithin (SL) and Cholesterol (CH) with maximum entrapment efficiency. For this purpose, response surface methodology (RSM) was used to optimize formulation variable. Cholesterol was selected as independent variable 1 and designated as X1 while soya lecithin was independent variable 2 designated as X2. The response was the entrapment of drug and designated as dependent variable Y. The two formulation ingredients were ranged with central composite rotatable design (CCD) and quadratic model at five levels (α=1.267) was followed for blending the liposomal formulation. It was observed that cholesterol (variable 1) may decrease the entrapment of DFS in the order of increasing concentration while soya lecithin (variable 2) was found to increase entrapment (dependant variable, Y) with increasing concentration. The central composite design has resulted in an optimized formulation (Formulation No. 9) with an optimum concentration of cholesterol and soya lecithin (ratio of 25:75) with maximum of entrapment of 82.56%. The study was also extended to compare different methods employed for the preparation of liposomes using optimized formulation by RSM. It was concluded that formulation prepared by micro- emulsification evaporation (MEE) followed by freeze drying method showed maximum entrapment of DFS. A comprehensive study was conducted for development of liposomal formulations of DFS and DFP with variable concentrations of purified soya lecithin (PSL) and purified egg lecithin (PEL) employing micro-emulsification evaporation method (MEE) followed by freeze drying. The prepared liposomes were free flowing and of uniform particle size distribution in the rage of 1.01 ± 0.011 to 1.80 ± 0.008 μm for DFS liposomal formulations while the mean size of (mean ± SEM) 1.94 ± 0.008 μm for diclofenac potassium (DFP). The selected liposomal formulations of DFS and DFP were also characterized by using scanning electron microscopic studies (SEM), differential scanning calorimetry (DSC), x-ray diffractometry (XRD) and fourier transform infra-red spectroscopy (FT-IR). Drug entrapment efficiency was above 82%. The entrapping efficiency and in-vitro release of DFS and DFP of all liposomal formulations were determined by reversed phase high-performance liquid chromatography (RP-HPLC). Different kinetics models of in-vitro were applied and release of DFS and DFP from liposomal formulations of DFS and DFP and it was concluded that release followed higuchi model and relatively zero order release, calculated on the basis of r2 value of straight line fit equation. A sustained release was observed for 16-24 hours from all range of liposomal formulations. The selected formulations after similarity factor (f-2) were subjected to in-vivo evaluation in eighteen healthy human subjects. Present study results in new formulation of DFS and DFP using PSL and PSL for oral delivery, which was found stable, reproducible and sustained release by using modified micro-emulsification evaporation method (MEE) followed by freeze drying which was found a probable and better to produce liposomes for oral drug delivery system (ODDS). Keywords: Liposomes; Phospholipids; Diclofenac sodium; Diclofenac potassium; Validation; Response surface Methodology (RSM), micro- encapsulation vesicle method (MEE); In-vitro Release; Kinetics Models; Higuchi Model; In-vivo studies.
مولانا محمد سلمان خاں بھوپالی ندوی مرحوم ندوۃ العلما کے ایک اور لایق فرزند اور مولانا محمد عمران خاں بھوپالی ندوی مرحوم کے برادر خورد جناب مولانا محمد سلمان خاں ندوی مرحوم کی وفات حسرت آیات کی خبر بہت تاخیر سے ان کے صاحبزادے مولوی حافظ کلیم خاں ندوی کے ایک خط سے ملی، اناﷲ وانا الیہ راجعون۔ وہ دارالعلوم تاج المساجد کے سابق مہتمم تھے، لیکن ان کا سب سے روشن کارنامہ تاج المساجد کی تعمیر نو ہے۔ مولانا محمد عمران خاں ندوی مرحوم نے جس شاہانہ اولوالعزمی سے اس عظیم الشان مسجد کی تعمیر و تکمیل کا خاکہ بنایا تھا اس کو مولانا سلمان خاں صاحب نے حقیقتاً رنگ و روغن عطا کیا، جن لوگوں نے مسجد کی تعمیر و تکمیل میں ان کی شبانہ روز کی محنت، جانفشانی اور لگن دیکھی ہے وہ جانتے ہیں کہ اس تعمیر میں ان کا لہو تک شامل ہوگیا ایک عالم کی اس معمارانہ اور مہند سانہ مہارت پر بڑے بڑے انجینئر اور آرکی ٹیکٹ بھی حیران رہ گئے، طبعاً وہ نہایت شریف، متین، کم سخن اور مہمان نواز انسان تھے، اپنے تمام بیٹوں کو انھوں نے حفظ قرآن مجید کی دولت سے بہرہ یاب کیا اور سب کوند وہ ہیں تعلیم دلائی، وہ خود بڑے دیندار تھے متعدد بار حج بیت اﷲ کی سعادت حاصل کی۔ اﷲ تعالیٰ ان کی نیکیوں اور دینی خدمات کو قبول کرے اور جنت نعیم میں جگہ عطا فرمائے، آمین۔ ( عمیر الصدیق دریابادی ندوی ، اگست ۱۹۹۱ء)
Prophet Hood consists of guidance from Allah to humankind. It is a Allah given blessing and a favor that is bestowed on an individual chosen be Him to convey His message, which cannot be acquired or earned otherwise. There has never been a human being so well-respected, loved and followed as Muhammad (SAW), the final messenger of Allah. There has never been a person who has changed world history so dramatically as Muhammad (SAW) and his message. The Prophet (SAW) was the single most important person in the history of the world. Knowledge of the Prophetic Biography is necessary for every Muslim and sharing it with everyone is a responsibility. The importance of a complete biography of the Messenger as available to us cannot be under estimated in this troubled time since both Muslims as well as Non-Muslims have serious knowledge gap when it comes to even approaching the nature of the Final Prophet and the Ultimate Messenger of God sent to all of humanity, who came to restore the primordial religion of Man, the submission to Allah and His Commands. Muhammad (SAW) serves as: - Allah’s messenger and prophet to all mankind as an example of human behavior and noble character Therefore, in studying his life-story we should derive lessons and morals that can help us in our lives today.
Financial distress is an active research area particularly for business community of Pakistan due to economic conditions, electricity shortage and political situation. Banks are also taking keen interest in this area after the global financial crisis of year 2008. Therefore, the question that how financial distress can be predicted accurately has been widely debated by many scholars by using traditional statistical models. However, earlier research has not adequately addressed the issue of predicting financial distress. Adding to that the rate of financial distress is also getting harder to estimate by using traditional statistical models, because firms are becoming more complex and creating refined plans to hide their real financial situation. To prevent this condition latest prediction models are adopted by many countries which can give early indication of firm?s financial distress with highly accurate results. In this regard, prediction of financial distress by Neural Network Model is not much explored in Pakistan for foreseeing the financial health of firms. This paper addresses this issue and uses Neural Network Model to predict financial distress of firms in Pakistan by selecting suitable independent variables.
The sample of 22 private sector conventional banks listed at Pakistan Stock Exchange is selected. The time series financial statements of these banks are selected for 15 years (2001 to 2015).Selected sample time frame is (pre-crisis 2001-2007), (crisis 2008) and (post-crisis 2009-2015). To test first hypothesis,4 Altman''s ratios from revised Altman''s Z-Score Model are calculated from these financial statements of selected banks. This study used three layered Neural Network Model consisting of input layer, hidden layer and output layer. The 4 independent explanatory variables/ input are 4 Altman''s ratios and 1 dependent variable/output is probable financial distress. After determining the Neural Network architecture, cross-validation re-sampling procedure is used to train, validate, and test a Neural Network by using commerciallyavailable MATLAB software. The best and most appropriate Neural Networks model, constructed by combining input variables of 4 Altman''s ratios, resulted in the R value of 0.99 that shows a relatively high accuracy given the error ratio in the input variables. These results confirmed the second hypothesis. By testing third hypothesis, distressed and non distressed banks are correctly classified with reference to Altman?s ratio