Refine your search
Co-Authors
Journals
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
Swami, Hemant
- RP-HPLC Method for Simultaneous Estimation of Simvastatin and Ezetimibe in Bulk Drug and its Combined Dosage Form
Abstract Views :166 |
PDF Views:0
Authors
Affiliations
1 Shri Ravi Shankar College of Pharmacy, Bhanpura Road, Bhopal, (M.P), IN
2 IPS College of Pharmacy, Gwalior, (M.P), IN
3 Truba Institute of Pharmacy, Karond Bypass, Bhopal, (M.P), IN
1 Shri Ravi Shankar College of Pharmacy, Bhanpura Road, Bhopal, (M.P), IN
2 IPS College of Pharmacy, Gwalior, (M.P), IN
3 Truba Institute of Pharmacy, Karond Bypass, Bhopal, (M.P), IN
Source
Asian Journal of Research in Chemistry, Vol 1, No 1 (2008), Pagination: 29-31Abstract
This work is concerned with application of simple, accurate, precise and highly selective reverse phase high performance liquid chromatographic (RP-HPLC) method for simultaneous estimation of simvastatin and ezetimibe in combined dosage form. Chromatographic separation was achieved isocratically at 25°C±0.5°C on Luna C18 column (250×4.6 mm i.d.) with a mobile phase composed of methanol: water: acetonitrile in the ratio of 75: 18.75: 6.25 % v/v/v at flow rate of 1.8 ml/min. Detection is carried out using a UV-PDA detector at 231 nm. The retention time of simvastatin and ezetimibe was found to be 13.5±0.5 min and 4.02±0.3 min. respectively. The method was found to be linear in the range of 1-50 μg/ml with mean recovery of 99.21% for simvastatin and 99.50% for ezetimibe. The correlation coefficients for all components are close to 1. The developed method was validated according to ICH guidelines and values of accuracy, precision and other statistical analysis were found to be in good accordance with the prescribed values. Thus the proposed method was successfully applied for simultaneous determination of simvastatin and ezetimibe in routine analysis.Keywords
Simvastatin, Ezetimibe, RP-HPLC.
- Bioefficacy of Different Biopesticides against Major Foliage Feeders on Soybean [Glycine max (L.) Merrill]
Abstract Views :226 |
PDF Views:202
Authors
Affiliations
1 Department of Entomology, Maharana Pratap University of Agriculture and Technology, Udaipur - 313001, Rajasthan, IN
2 Krishi Vigyan Kendra, Badgaon, Udaipur - 313001, Rajasthan, IN
1 Department of Entomology, Maharana Pratap University of Agriculture and Technology, Udaipur - 313001, Rajasthan, IN
2 Krishi Vigyan Kendra, Badgaon, Udaipur - 313001, Rajasthan, IN
Source
Journal of Biological Control, Vol 33, No 4 (2019), Pagination: 378-381Abstract
Field experiments were conducted to evaluate the efficacy of different biopesticides, viz., Nomuraea rileyi @ 1 × 108 conidia/lit, Beauveria bassiana @ 1 × 109 CFU/ml minimum @ 5 ml/lit, Metarhizium anisopliae @ 1 × 109 CFU/ml minimum @ 5 ml/lit, dipel @ 1 kg/ha, spinosad 45 SC @ 0.5 ml/lit, neem seed kernel extract (NSKE) @ 5% and neem oil @ 2% against foliage feeders of soybean namely, semi looper (Chrysodeixis acuta) and tobacco caterpillar (Spodoptera litura) during Kharif, 2016. The result revealed that all treatments were significantly superior over control. The mean larval population of C. acuta and S. litura ranged from 1.29 to 9.37 and 0.92 to 6.98 larvae per meter row length (mrl) at 3, 7 and 10 days respectively after the application of treatments. The treatments comprising Spinosad 45 SC @ 0.5 ml/lit proved highly effective in reducing the population of C. acuta and S. litura with lowest overall mean larval population of 4.71 and 3.02 larvae per mrl, respectively. Against C. acuta NSKE @ 5% was least effective and against S. litura neem oil was observed as least effective with maximum over all mean larval populations of 7.75 and 4.97, respectively.Keywords
Biopesticides, Chrysodeixis acuta, Foliage Feeders, Soybean, Spodoptera lituraFull Text
References
- Asokan R, Mohandas S, Anand L. 2000. Biofertilizers and biopesticides for horticultural crops. Indian Hortic.
- : 44–53.
- Gadhiya HA, Borad PK, Bhut JB. 2014. Effectivness of synthetic insecticides against Helicoverpa armigera (Hubner) hardwick and Spodoptera litura (Fabricius) infesting groundnut. Bioscan 1: 23-26.
- Hall TS, Leonard BR, Boethel DJ, Gore J, Dugger P, Richter D. 2000. Toxicity of indoxacarb, thiodicarb and spinosad to the soybean looper (Lepidoptera: Noctuidae) on Bt cotton (cv. Nucton 33B) and conventional cotton (cv. PP 5415). In: Proceedings of Beltwide Cotton Conferences, San Antonio, USA. 2: 956-958.
- Harish G. 2008. Studies on incidence and management of defoliator pests of soybean. M. Sc (Agri) thesis submitted to University of Agricultural Sciences Dharwad, Karnataka, India, 65 pp.
- Jat SK, Ameta OP. 2013. Relative efficacy of biopesticides and newer insecticides against Helicoverpa armigera (Hub.) in tomato. Bioscan 2: 579-582.
- Knight K., Brier H, Desborough P. 2000. The efficacy of new insecticides and dipel for soybean looper control in soybean and effects on beneficial insects and arthropods, pp. 62-71. In: Proceedings of 11th Australian Soybean Conference, Ballina, Australia.
- Padiwal NK, Rana BS, Ameta OP. 2008. Assessment of losses caused by insect pests of soybean. Indian J Pl Prot. 1: 121-122.
- Ranga Rao GV, Rupela OP, Rao VR, Reddy YVR. 2007. Role of biopesticides in crop protection: present status and future prospects. Indian J Pl Prot. 1: 1-9.
- Sharma AN, Ansari MM. 2007. Potential of naturally occurring bio-control agents of Lepidopteran defoliators infesting soybean. Soybean Res. 5: 84-86.
- Selvaraj K and Kaushik HD 2013. Entomopathogenicity of Beauveria bassiana (Balsamo) Vuillemin against Hyadaphis coriandri (Das). Ann Pl Prot Sci. 1: 24-26.
- The Era of Artificial Intelligence in Pharmaceutical Industries - A Review
Abstract Views :64 |
PDF Views:0
Authors
Affiliations
1 Sagar Institute of Research and Technology - Pharmacy, Bhopal, MP., IN
2 School of Pharmaceutical Science, SAGE University, Indore, M.P., IN
1 Sagar Institute of Research and Technology - Pharmacy, Bhopal, MP., IN
2 School of Pharmaceutical Science, SAGE University, Indore, M.P., IN
Source
Research Journal of Science and Technology, Vol 14, No 3 (2022), Pagination: 183-187Abstract
As a growing sector, the Era of Artificial Intelligence, Machine Learning and Data Science in the Pharmaceutical Industry contributes in the drug discovery process, giving emphasis on how new technologies have improved effectiveness. As in the current scenario artificial intelligence including machine learning may be considered the future for a wide range of disciplines and industries specially the pharmaceutical industry. As we know today pharmaceutical industries producing a single approved drug cost the company millions with many years of rigorous testing prior to its approval, reducing costs and time is of high interest. The involvement of Artificial Intelligence will be useful to the pharmaceutical industry and also be of interest to anyone doing research in chemical biology, computational chemistry, medicinal chemistry and bioinformatics.Keywords
Artificial Intelligence, Pharmaceutical, Machine learning, Research, Chemistry.References
- Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24(3):773-80.
- Hassanzadeh P, Atyabi F, Dinarvand R. The significance of artificial intelligence in drug delivery system design. Adv Drug Deliv Rev. 2019;151:169-90.
- Russel S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. AI Mag. 2015;36(4):105-14.
- Duch W, Setiono R, Zurada JM. Computational intelligence methods for rulebased data understanding. Proc IEEE. 2004;92(5):771-805.
- Dasta JF. Application of artificial intelligence to pharmacy and medicine. Hosp Pharm. 1992;27(4):319-22.
- Jiang F, Jiang Y, Zhi H. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017;2(4):230-43.
- Gobburu JV, Chen EP. Artificial neural networks as a novel approach to integrated pharmacokinetic-pharmacodynamic analysis. J Pharm Sci. 1996;85(5):505-10.
- Sakiyama Y. The use of machine learning and nonlinear statistical tools for ADME prediction. Expert Opin Drug Metab Toxicol. 2009;5(2):149-69.
- Ramesh A. Artificial intelligence in medicine. Ann. R. Coll. Surg. Engl. 2004; 86:334–338.
- Miles J., Walker A. The potential application of artificial intelligence in transport. IEE Proc.-Intell. Transport Syst. 2006;153:183–198
- Yang Y., Siau K. MWAIS; 2018. A Qualitative Research on Marketing and Sales in the Artificial Intelligence Age.
- Wirtz B.W. Artificial intelligence and the public sector—applications and challenges. Int. J. Public Adm. 2019;42:596–615.
- Smith R.G., Farquhar A. The road ahead for knowledge management: an AI perspective. AI Mag. 2000;21 17–17.
- Lamberti M.J. A study on the application and use of artificial intelligence to support drug development. Clin. Ther. 2019;41:1414–1426.
- Beneke F., Mackenrodt M.-O. Artificial intelligence and collusion. IIC Int. Rev. Intellectual Property Competition Law. 2019;50:109–134.
- Steels L., Brooks R. Routledge; 2018. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents.
- Bielecki A., Bielecki A. Foundations of artificial neural networks. In: Kacprzyk Janusz., editor. Models of Neurons and Perceptrons: Selected Problems and Challenges. Springer International Publishing; 2019. pp. 15–28. Polish academy of sciences, Warsaw, Poland.
- Kalyane D. Artificial intelligence in the pharmaceutical sector: current scene and future prospect. In: Tekade Rakesh K., editor. The Future of Pharmaceutical Product Development and Research. Elsevier; 2020. pp. 73–107.
- Russell S, Dewey D, Tegmark M. Research priorities for robust and beneficial artificial intelligence. Ai Magazine. 2015 Dec 31;36(4):105-14.
- Lakshmi Teja T, Keerthi P, Debarshi Datta NB. Recent trends in the usage of robotics in pharmacy.
- Yussupova N, Kovács G, Boyko M, Bogdanova D. Models and methods for quality management based on artificial intelligence applications. Acta Polytechnica Hungarica. 2016 Mar; 13(3):45-60 22.
- Brady M. Artificial intelligence and robotics. In Robotics and Artificial Intelligence 1984 (pp. 47-63). Springer, Berlin, Heidelberg.
- Guo M. A prototype intelligent hybrid system for hard gelatin capsule formulation development. Pharm. Technol. 2002;6:44–52. 24. Mehta C.H. Computational modeling for formulation design. Drug Discovery Today. 2019;24:781–788.
- Zhao C. Toward intelligent decision support for pharmaceutical product development. J. Pharm. Innovation. 2006;1:23–35.
- Rantanen J., Khinast J. The future of pharmaceutical manufacturing sciences. J. Pharm. Sci. 2015;104:3612–3638.
- Ketterhagen W.R. Process modeling in the pharmaceutical industry using the discrete element method. J. Pharm. Sci. 2009;98:442–470.
- Chen W. Mathematical model-based accelerated development of extended-release metformin hydrochloride tablet formulation. AAPS PharmSciTech. 2016;17:1007–1013.
- Meziane F. Intelligent systems in manufacturing: current developments and future prospects. Integr. Manuf. Syst. 2000;11:218–238.
- Steiner S. Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 2019;363:eaav2211.
- Faure A. Process control and scale-up of pharmaceutical wet granulation processes: a review. Eur. J. Pharm. Biopharm. 2001;52:269–277.
- Landin M. Artificial intelligence tools for scaling up of high shear wet granulation process. J. Pharm. Sci. 2017;106:273–277.
- Das M.K., Chakraborty T. ANN in pharmaceutical product and process development. In: Puri Munish., editor. Artificial Neural Network for Drug Design, Delivery and Disposition. Elsevier; 2016. pp. 277–293.
- Gams M. Integrating artificial and human intelligence into tablet production process. AAPS PharmSciTech. 2014;15:1447–1453.
- Kraft, D.L. System and methods for the production of personalized drug products. US20120041778A1.
- Aksu B. A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation. Pharm. Dev. Technol. 2013;18:236–245.
- 96. Goh W.Y. Application of a recurrent neural network to prediction of drug dissolution profiles. Neural Comput. Appl. 2002;10:311–317.
- Drăgoi E.N. On the use of artificial neural networks to monitor a pharmaceutical freeze-drying process. Drying Technol. 2013;31:72–81. 38. Reklaitis R. PharmaHub; 2008. Towards Intelligent Decision Support for Pharmaceutical Product Development.
- Wang X. 2009 International Conference on Computational Intelligence and Software Engineering. IEEE; 2009. Intelligent quality management using knowledge discovery in databases; pp. 1–4.
- Hay M. Clinical development success rates for investigational drugs. Nat. Biotechnol. 2014;32:40–51.