A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Patil, M. P.
- Evaluation of Ginkgo biloba in Diabetic Nephrotoxicity
Authors
1 Sharad Pawar College of Pharmacy, Wanadongri, Nagpur 441110, IN
2 Tapi Valley’s, College of Pharmacy, Faizpur, Ta. Yawal , Dist. Jalgaon (MS), IN
3 Sharad Pawar College of Pharmacy, Wanadongri, Nagpur-441110, IN
Source
Research Journal of Pharmacognosy and Phytochemistry, Vol 3, No 6 (2011), Pagination: 286-288Abstract
Nephrotoxicity is the major cause of morbidity and mortality in diabetes and it is the leading cause of end stage renal disease. Hyperglycemia induced oxygen free radicals cause oxidative stress and subsequent oxidative damages, leading cell and tissue injury. Sprague-Dawley rats of both sexes were divided into 4 groups, Control, Diabetic control , Diabetic + Ginkgo biloba and Diabetic + Vit E group . Blood urea, serum creatinine and serum uric acid as well as plasma malondialdehyde, superoxide dismutase, catalase, reduced glutathione were estimated and histopathological studies of kidneys were performed. Alloxan at the dose of 120mg/kg i.p, for 2 months at the interval of 14 days, induced diabetes. This prolonged diabetes increased oxidative stress and caused an increase in the levels of serum creatinine, urea, uric acid and plasma malondialdehyde while there were decrease in the levels of superoxide dismutase, catalase and reduced glutathione in diabetic group as compared to normal control group. Histopathological examination revealed hemorrhage, necrosis, and infiltration of leukocytes around the glomerulus and interstitial spaces. Co-administration of Ginkgo biloba 300mg/kg orally, daily for 2 months in diabetic-induced rats caused decreased in the levels of serum creatinine, urea, uric acid and plasma malondialdehyde. Increased levels of superoxide dismutase, catalase and reduced glutathione were also found. The study revealed protective antioxidant activity of Ginkgo biloba in diabetic nephrotoxicity.
Keywords
Diabetes, Oxidative Stress, Nephrotoxicity, Ginkgo biloba.- A Review on:Quality by Design (QbD)
Authors
1 Department of Pharmaceutics, MET’s Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik , ,423301, Savitribai Phule Pune University, Maharashtra, IN
2 Department of Pharmaceutical Chemistry, MET’s Institute of Pharmacy, Bhujbal Knowledge City, Adgaon, Nashik 423301, Savitribai Phule Pune University, Maharashtra, IN
3 MET’s Institute of Pharmacy, Adgaon, Nashik, 423301, Savitribai Phule Pune University, Maharashtra, IN
Source
Asian Journal of Research in Pharmaceutical Sciences, Vol 7, No 4 (2017), Pagination: 197-204Abstract
Quality by Design is the modern approach for quality of pharmaceuticals. Recent pharmaceutical regulatory documents have stressed the critical importance of applying quality by design (QbD) principles for in-depth process understanding to ensure that product quality is built in by design. The purpose of this paper is to discuss the pharmaceutical Quality by Design and describe how it can be used to ensure pharmaceutical quality. Quality cannot be tested into products but quality should be built in by design. Under this concept of QbD throughout designing and development of a product, it is essential to define desire product performance profile [Target product profile (TPP), Target product Quality profile (TPQP) and identify Critical quality attributed (CQA).On the basis of this we can design the product formulation and the process to meet the product attributes. These leads to recognize the impact of raw material Critical material attributes (CMA), Critical process parameter (CPP), on the CQA’s and identification and source of variability. QbD is necessary in regulatory requirement, and to implement new concepts such as design space, ICH guidelines i.e. Q8 pharmaceutical development, Q9 quality risk management, and FDAs process analytical technology (PAT)Keywords
Quality by Design (QbD), Target Product Profile (TPP), Target Product Quality Profile (TPQP), Critical Quality Attributes (CQA), Process Analytical Technology (PAT).References
- Sangshetti JN, Zaheer Z, Mahaparale PR and Chitlange SS. Quality by design (QbD) in pharmaceuticals. Unique Publication, Aurangabad. 1st edition; 2015: 20
- Gawade A, Chemate S and Kuchekar A. Pharmaceutical Quality by Design: A New Approach in Product Development. Research and Reviews: Journal of Pharmacy and Pharmaceutical Sciences. 2013; 2:5-11.
- Juran JM. On quality by design the new steps for planning quality into goods and services New York free press. 1992; 1-2.
- Food and Drug Administration. Final Report on Pharmaceutical cGMPs for the 21st Century - A Risk Based Approach. [Online] Available at: http://www.fda.gov/ cder/ gmp/ gmp 2004/ GMP_ final report 2004.htm. Accessed 10 March 2016.
- Jain S. Quality by Design (QbD): A Comprensive understanding of implementation and challenges in pharmaceutical development. Int J of Pharm Pharma Sci. 2014; 6:29-35.
- US Food and Drug Administration. Guidance for industry: Q8 (R2) pharmaceutical development. US Department of Health and Human Service, FDA, Rockville, MD; 2009.
- Looby M, Ibarra N, Pierce JJ, Buckley K, O'Donovan E, Heenan M, et al. Application of quality by design principles to the development and technology transfer of a major process improvement for the manufacture of a recombinant protein. Biotechnology Progress. 2011; 27(6): 1718-29.
- Nadpara NP, Thumar RV, Kalola VN, Patel PB. Quality by Design: A complete review. Int. J. Pharm. Sci, Rev Res. 2012; 2: 20-8.
- Roy S, Quality by design: A holistic concept of building quality in pharmaceuticals. International Journal of Pharmaceutical and Biomedical Research .2012; 3 (2): 100-108.
- Chang RK, Raw A, Lionberger R, Yu L. Generic development of topical dermatologic products, part II: quality by design for topical semisolid products. AAPS J. 2013; 15(3): 674.
- McConnell J, McGarvey B, Nunnally B. Quality risk management and variability reduction. Journal of Validation Technology. 2011:12-6.
- Mollah H, Baseman H, Long M. Risk Management Applications in Pharmaceutical and Biopharmaceutical Manufacturing: Wiley; 2013.
- White E. Risk Management for Aseptic Processing. Journal of Validation Technology. 2009; 15(2): 25-33.
- Gad SC. Handbook of Pharmaceutical Biotechnology. Gad SC, editor: Wiley; 2007.
- Keizer JA, Vos J-P, Halman JIM. Risks in new product development: devising a reference tool. R&D Management. 2005; 35(3):297-309.
- Ranga S, Jaimini M, Sharma SK, Chauhan BS, Kumar A. A review on design of experiment. International Journal of Research and Reviews in Pharmacy and Applied science. 2013; 3(6): 867-82.
- Monica RP, Rao P. Preparation and evaluation of immediate release tablets of meroclopramide HCL using Simplex Centruoid Mixture Design. International Journals of Pharma Tech Research. 2010; 2:1105.
- Q8 Pharmaceutical Development – FDA guidance.
- Kovalycsik M. Design Space and PAT - Q8 ICH Draft Guidance on Pharmaceutical Development. AVP, Wyeth Research Vaccines R&D, Quality Operations.
- Khare R. Three Romeos And A Juliet An Early Brush With Design Of Experiments. www.isixsigma.com. Accessed 10 March 2016.
- Burnham R. How to Select Design of Experiments Software. www.sixsigma.com. Accessed 10 March 2016.
- Yu LX, Amidon G, Khan MA, Hoag SW, Polli J, Raju GK, et al. Understanding pharmaceutical Quality by design. AAPS Journal. 2014; 16:771-83.
- ICH Quality Implementation working group. Point to consider, ICH endorsed guide for ICH Q8/Q9/Q10 implementation. 2011
- Altan S, Bergum J, Pfahler L, Senderak E, Sethuraman S, Vukovinsky KE. Statistical Considerations in Design Space Development Part I of III. Pharmaceutical Technology. 2010; 34(7): 66-70
- Jadhav J, Girawale N, Chaudhari R, Quality by Design (QBD) Approach used in Development of Pharmaceuticals International Journals of Pure and Applied Bioscience. 2014; 2 (5): 21
- Bubble Size Prediction in Gas–Solid Fluidized Beds using Genetic Programming
Authors
1 Solid and Hazardous Waste Management Division, CSIR-National Environmental Engineering Research Institute, Nehru Marg, Nagpur 440 020, IN
2 Department of Chemical Engineering, Laxminarayan Institute of Technology, Amravati Road, Nagpur 440 033, IN
3 Chemical Engineering and Process Development (CEPD) Division, CSIR-National Chemical Laboratory, Dr Homi Bhabha Road, Pune 411 008,, IN
Source
Current Science, Vol 115, No 10 (2018), Pagination: 1904-1912Abstract
The hydrodynamics of a gas–solid fluidized bed (FB) is affected by the bubble diameter, which in turn strongly influences the performance of a fluidized bed reactor (FBR). Thus, determining the bubble diameter accurately is of crucial importance in the design and operation of an FBR. Various equations are available for calculating the bubble diameter in an FBR. It has been found in this study that these models show a large variation while predicting the experimentally measured bubble diameters. Accordingly, the present study proposes a new equation for computing the bubble diameter in a fluidized bed. This equation has been developed using an efficient, yet infrequently employed computational intelligence (CI)-based datadriven modelling method termed genetic programming (GP). The prediction and generalization performance of the GP-based equation has been compared with that of a number of currently available equations for computing the bubble diameter in a fluidized bed and the results obtained show a good performance by the newly developed equation.Keywords
Bubble Diameter, Bubble Motion, Fluidized Bed, Genetic Programming.References
- Kwauk, M. and Li, H. Z., Handbook of Fluidization, Chemical Industry Press, Beijing (in Chinese), 2007.
- Harrison, D. and Leung, L. S., Bubble formation at an orifice in a fluidized bed. Trans. Inst. Chem. Eng., 1961, 34, 409–414.
- Zenz, F. A., Bubble formation and grid design. Inst. Chem. Eng. Symp. Ser., 1968, 30, 136–139.
- Nieuwland, J. J., Hydrodynamic modeling of gas–solid two phase flows. Ph D thesis, Twenty University, 1995.
- Yang, Y. M. and Maa, J. R., Bubble coalescence in dilute surfactant solutions. J. Colloid Interf. Sci., 1984, 98, 120–125.
- Caram, H. S. and Hsu, K. K., Bubble formation and gas leakage in fluidized beds. Chem. Eng. Sci., 1986, 41, 1445–1453.
- Hilligardt, K. and Werther, J., Local bubble gas hold up and expansion of gas–solid fluidized beds. German Chem. Eng., 1986, 9, 215–221.
- Darton, R. C., La Nauze, R. D., Davidson, J. F. and Harrison, D., Bubble growth due to coalescence in fluidized beds. Trans. Inst. Chem. Eng., 1977, 55, 274–280.
- Werther, J., The influence of the bed diameter on the hydrodynamics of gas fluidized beds. AIChE Meeting Detroit, 1973.
- Lim. K. S., Gururaja, V. S. and Agrawal, P. K., Mixing and homogenous solids in bubbling fluidized beds: theoretical modeling and experimental investigation using digital image analysis. Chem. Eng. Sci., 1993, 48, 2251–2265.
- Yasui, G. and Johanson, L. N., Characteristics of gas pockets in fluidized beds. AIChE J., 1958, 4, 445.
- Whitehead, A. B. and Young, A. D., Fluidization performance in large scale equipment Part-I. In Proceedings of International Symposium on Fluidization, Eindhoven, Netherlands, 1967, p. 84.
- Park, W. H., Kang, W. K., Copes, C. E. and Osberg, G. L., The properties of bubbles in fluidized beds of conducting particles as measured by an electro resistivity probe. Chem. Eng. Sci., 1969, 24, 851.
- Geldart, D., the size and frequency if bubbles in two and three dimensional fluidized beds. Powder Technol., 1970, 4, 41.
- Rowe, P. N., Prediction of bubble size in gas fluidized bed. Chem. Eng. Sci., 1976, 31, 285–288.
- Mori, S. and Wen, C. Y., Estimation of bubble diameter in gaseous fluidized beds. AIChE J., 1975, 21, 109–115.
- Kato, K. and Wen, C. Y., Bubble assemblage, model for fluidized bed catalytic reactors. Chem. Eng. Sci., 1969, 24, 1351–1369.
- Shen, L., Johnsson, F. and Leckner, B., Digital image analysis of hydrodynamics two dimensional bubbling fluidized beds. Chem. Eng. Sci., 2004, 59, 2601–2617.
- Horio, M. and Nonaka, A., A generalized bubble diameter correlation for gas solid fluidized beds. AIChE J., 1987, 33, 1865– 1872.
- Toor, F. D. and Calderbank, P. H., Reaction kinetics in gasfluidized catalyst beds; Part II: Mathematical models. In Proceedings International Symposium on Fluidization, Netherlands University Press, Amsterdam, 1967, pp. 373–392.
- Peters, M. H., Fan, L. S. and Sweeney, T. L., Reactant dynamics in catalytic fluidized bed reactors with flow reversal of gas in emulsion phase. Chem. Eng. Sci., 1982, 37, 553–565.
- Patil, D. J., van Sint Annaland, M. and Kuipers, J. A. M., Critical comparison of hydrodynamic models for gas–solid fluidized beds – Part II: freely bubbling gas–solid fluidized beds. Chem. Eng. Sci., 2005, 60, 73–84.
- Farshi, A., Javaherizadeh, H. and Hamzavi-Abedi, M. A., An investigation of the effect of bubble diameter on the performance of gas solid fluidized bed reactor and two phase modeling of bubbling fluidized bed reactor in melamine production. Pet. Coal, 2008, 50(1), 11–22.
- Koza, J. R., Genetically breeding populations of computer programs to solve problems in artificial intelligence. In Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence, 6–9 November 1990, pp. 819–827; http://dx.doi.org/ 10.1109/TAI.1990.130444.
- Poli, R., Langdon, W. and Mcphee, N., A field guide to genetic programming, 2008; http://lulu.com and freely available at http://www.gp-field-guide.org.uk
- Goel, P., Bapat, S., Vyas, R., Tambe, A. and Tambe, S. S., Genetic programming based quantitative structure – retention relationships for the prediction of kovats retention indices. J. Chromatogr. A., 2015, 1420, 98–109; doi:10.1016/j.chroma.2015.09.086.
- Shrinivas, K., Kulkarni, R., Saif Shaikh, Ghorpade, R., Renu Vyas, Tambe, S. S., Ponrathnam, S. and Kulkarni, B. D., Prediction of reactivity ratios in free radical copolymerization from monomer resonance–polarity (Q–e) parameters: genetic programmingbased models. Int. J. Chem. React. Eng., Published online on 4 March 2015, doi 10.1515/ijcre-2014-0039.
- Schmidt, M. and Lipson, H., Distilling free-form natural laws from experimental data. Science, 2009, 324, 81–85.