Optimization of hydrogel composition for effective release of drug
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R. K. Pavan Kumar Pannala
Abstract
Hydrogels are possible materials that could be useful in medication delivery systems. Diverse release mechanisms are used when drug molecules embedded in the hydrogel structure need to be released. Both case I and case II of transport refer to the release of the medication during the intermolecular arrangement because of swelling. Numerous mathematical models have been proposed that only include one form of transport; nevertheless, both transport pathways are required for the entire release of a drug from a gel matrix. The case I transport during swelling and the case II transport during the fully swollen condition are both displayed by crosslinked hyaluronic acid hydrogel systems. The methodology put out in this paper enables for the selection of suitable gel compositions while attempting to account for both transit instances. In the Data Envelopment Analysis coupled with principal component analysis approaches are enable the optimization and selection of gel compositions that account for both transport situations.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Kodavaty, J. Poly (vinyl alcohol) and hyaluronic acid hydrogels as potential biomaterial systems-a comprehensive review. J Drug Deliv Sci Technol 2022;71:103298. https://doi.org/10.1016/j.jddst.2022.103298.Search in Google Scholar
2. Kodavaty, J, Deshpande, AP. Self-assembly and drying assisted microstructural domain formation in poly (vinyl alcohol) and hyaluronic acid gels. Polym Bull 2017;74:3605–17. https://doi.org/10.1007/s00289-017-1913-6.Search in Google Scholar
3. Kodavaty, J, Deshpande, AP. Evaluation of solute diffusion and polymer relaxation in cross-linked hyaluronic acid hydrogels: experimental measurement and relaxation modeling. Polym Bull 2021;78:2605–26. https://doi.org/10.1007/s00289-020-03224-1.Search in Google Scholar
4. Ray, SC. The transformation function, technical efficiency, and the ccr ratio. Eur J Oper Res 2019;278:422–9. https://doi.org/10.1016/j.ejor.2019.02.014.Search in Google Scholar
5. Banker, RD, Abraham, C, Cooper, WW. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Manag Sci 1984;30:1078–92. https://doi.org/10.1287/mnsc.30.9.1078.Search in Google Scholar
6. Agarwal, S, Yadav, SP, Singh, SP. A new slack dea model to estimate the impact of slacks on the efficiencies. Int J Oper Res 2011;12:241–56. https://doi.org/10.1504/ijor.2011.042915.Search in Google Scholar
7. Rolf, F, Grosskopf, S, Whittaker, G. Network dea. In: Modeling data irregularities and structural complexities in data envelopment analysis. Springer; 2007:209–40 pp.Search in Google Scholar
8. Meeusen, W, van Den Broeck, J. Efficiency estimation from cobb-douglas production functions with composed error. Int Econ Rev 1977;18:435–44. https://doi.org/10.2307/2525757.Search in Google Scholar
9. Tone, K. A slacks-based measure of efficiency in data envelopment analysis. Eur J Oper Res 2001;130:498–509. https://doi.org/10.1016/s0377-2217(99)00407-5.Search in Google Scholar
10. Golany, B, Roll, Y. An application procedure for dea. Omega 1989;17:237–50. https://doi.org/10.1016/0305-0483(89)90029-7.Search in Google Scholar
11. Hosseinzadeh Lotfi, FRF, Navidi, N. Ranking efficient units in dea by using topsis method. Appl Math Sci 2011;5:805–15.Search in Google Scholar
12. Qi, X-G, Guo, B. Determining common weights in data envelopment analysis with shannon’s entropy. Entropy 2014;16:6394–414. https://doi.org/10.3390/e16126394.Search in Google Scholar
13. Simar, L, Wilson, PW. A general methodology for bootstrapping in non-parametric Frontier models. J Appl Stat 2000;27:779–802. https://doi.org/10.1080/02664760050081951.Search in Google Scholar
14. Fernandez-Palacin, F, Auxiliadora Lopez-Sanchez, M, Munõz-Márquez, M. Stepwise selection of variables in dea using contribution loads. Pesqui Oper 2018;38:31–52. https://doi.org/10.1590/0101-7438.2018.038.01.0031.Search in Google Scholar
15. Li, Y, Shi, X, Yang, M, Liang, L. Variable selection in data envelopment analysis via akaike’s information criteria. Ann Oper Res 2017;253:453–76. https://doi.org/10.1007/s10479-016-2382-2.Search in Google Scholar
16. Morita, H, NecmiAvkiran, K. Selecting inputs and outputs in data envelopment analysis by designing statistical experiments (¡ special issue¿ operations research for performance evaluation). J Oper Res Soc Jpn 2009;52:163–73. https://doi.org/10.15807/jorsj.52.163.Search in Google Scholar
17. Wang, CN, Lin, HS, Hsu, HP, Van-Tinh Le, Lin, TF. Applying data envelopment analysis and grey model for the productivity evaluation of Vietnamese agroforestry industry. Sustainability 2016;8:1139. https://doi.org/10.3390/su8111139.Search in Google Scholar
18. Kodavaty, J, Kumar Pannala, RKP, Singh, M, Bharti, A, Ahmad, QF. Optimizing composition of a drug gel using release kinetics–a new way of approach. Mater Today Proc 2022;66:1611–6. https://doi.org/10.1016/j.matpr.2022.05.249.Search in Google Scholar
19. Zhu, J. Data envelopment analysis vs. principal component analysis: an illustrative study of economic performance of Chinese cities. Eur J Oper Res 1998;111:50–61. https://doi.org/10.1016/s0377-2217(97)00321-4.Search in Google Scholar
20. Premachandra, IM. A note on dea vs principal component analysis: an improvement to joe zhu’s approach. Eur J Oper Res 2001;132:553–60. https://doi.org/10.1016/s0377-2217(00)00145-4.Search in Google Scholar
21. Adler, N, Golany, B. Evaluation of deregulated airline networks using data envelopment analysis combined with principal component analysis with an application to western europe. Eur J Oper Res 2001;132:260–73. https://doi.org/10.1016/s0377-2217(00)00150-8.Search in Google Scholar
22. Adler, N, Joseph, B. Measuring airport quality from the airlines’ viewpoint: an application of data envelopment analysis. Transport Pol 2001;8:171–81. https://doi.org/10.1016/s0967-070x(01)00011-7.Search in Google Scholar
23. Chen, J, Zheng, W, Zhang, F, Park, NK, He, X, Yin, W. Operational efficiency evaluation of iron ore logistics at the ports of bohai bay in China: based on the pca-dea model. Math Probl Eng 2016;2016:1–13. https://doi.org/10.1155/2016/9604819.Search in Google Scholar
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Articles in the same Issue
- Frontmatter
- Research Articles
- Tuning of PID controllers for unstable first-order plus dead time systems
- Oxygen excess ratio control of PEM fuel cell: fractional order modeling and fractional filter IMC-PID control
- Proposal and energy/exergy/economic analyses of a smart heat recovery for distillation tower of the Naphtha Hydrotreating Unit of the Petrochemical Plant; designing a low-carbon plant
- Three-dimensional CFD study on thermo-hydraulic behaviour of finned tubes in a heat exchange system for heat transfer enhancement
- A simulation and thermodynamic improvement of the methanol production process with economic analysis: natural gas vapor reforming and utilization of carbon capture
- Optimization of hydrogel composition for effective release of drug
- Mathematical modelling of water-based biogas scrubber operating at digester pressure
- COCO, a process simulator: methane oxidation simulation & its agreement with commercial simulator’s predictions
- Hydrodynamics of shear thinning fluid in a square microchannel: a numerical approach
- Parameter estimation in non-linear chemical processes: an opposite point-based differential evolution (OPDE) approach
Articles in the same Issue
- Frontmatter
- Research Articles
- Tuning of PID controllers for unstable first-order plus dead time systems
- Oxygen excess ratio control of PEM fuel cell: fractional order modeling and fractional filter IMC-PID control
- Proposal and energy/exergy/economic analyses of a smart heat recovery for distillation tower of the Naphtha Hydrotreating Unit of the Petrochemical Plant; designing a low-carbon plant
- Three-dimensional CFD study on thermo-hydraulic behaviour of finned tubes in a heat exchange system for heat transfer enhancement
- A simulation and thermodynamic improvement of the methanol production process with economic analysis: natural gas vapor reforming and utilization of carbon capture
- Optimization of hydrogel composition for effective release of drug
- Mathematical modelling of water-based biogas scrubber operating at digester pressure
- COCO, a process simulator: methane oxidation simulation & its agreement with commercial simulator’s predictions
- Hydrodynamics of shear thinning fluid in a square microchannel: a numerical approach
- Parameter estimation in non-linear chemical processes: an opposite point-based differential evolution (OPDE) approach
