Research Article
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Year 2025, , 41 - 52, 05.01.2025
https://doi.org/10.33435/tcandtc.1499322

Abstract

References

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  • [28] W. Tang, C. Cai, S. Zhao, H. Liu, Development of Reaction Density Functional Theory and Its Application to Glycine Tautomerization Reaction in Aqueous Solution, J Phys Chem C 122 (2018) 20745–20754.
  • [29] W. Tang, J. Xuan, H. Wang, S. Zhao, H. Liu, First-principles investigation of aluminum intercalation and diffusion in TiO2 materials: Anatase versus rutile, J Power Sources 384 (2018) 249–255.
  • [30] W. Tang, J. Zhao, P. Jiang, X. Xu, S. Zhao, Z. Tong, Solvent Effects on the Symmetric and Asymmetric S N 2 Reactions in the Acetonitrile Solution: A Reaction Density Functional Theory Study, J Phys Chem B 124 (2020) 3114–3122.
  • [31] W. Tang, H. Yu, T. Zhao, L. Qing, X. Xu, S. Zhao, A dynamic reaction density functional theory for interfacial reaction-diffusion coupling at nanoscale, Chem Eng Sci 236 (2021) 116513.
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  • [41] S. Yenduri, H.N. Varalakshmi, N.P. Koppuravuri, Assessment and Comparison of Greenness of UV-Spectroscopy Methods for Simultaneous Determination of Anti-Hypertensive Combination, Ind J Pharm Edu Res 58 (2024) s543–s551.
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Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies

Year 2025, , 41 - 52, 05.01.2025
https://doi.org/10.33435/tcandtc.1499322

Abstract

HPLC is one of the most widely used analytical method for determination of pharmaceuticals in pharmaceutical industry. Because of wide range availability of columns, it is difficult to choose the column while optimization and it consume lot of time. To reduce the time and solvent consumption for optimizing the column in HPLC method the best alternative is computational approach. Computational chemistry is a subfield of chemistry that employs computer modelling as a means of assisting in the resolution of difficult chemical issues. The computation of molecular structures, interactions, and properties is accomplished by the utilization of theoretical chemistry techniques that are integrated into efficient computer programs. In the current investigation, the objective was to implement a computational strategy with the purpose of optimizing the chromatographic column for the detection of certain pharmaceuticals. For the purpose of this experiment, the Avogadro with orca software was utilized to calculate the Gibbs free energy between the stationary phase and the pharmaceutical of choice for different columns, including C8 and C18. Relative binding free energies between the analyte and column were calculated and applied for selection of column. The tool was utilized for the purpose of optimizing the column in order to minimize the amount of solvent that was utilized and time to lessen the complexity of the procedure. This strategy also contributes to sustainable development goals by minimizing solvent usage for environmental friendliness.

Supporting Institution

Sri Adichunchanagiri College of Pharmacy

Thanks

All the authors are thankful to the staff of Sri Adichunchanagiri college of pharmacy for their continuous support throughout the research work.

References

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  • [2] O. Coskun, Separation Techniques: Chromatography, North Clin Istanb 3 (2016) 156-160.
  • [3] J.J. Kirkland, Development of some stationary phases for reversed-phase HPLC, J Chromatogr A 1060 (2004) 9–21.
  • [4] M.W.F. Nielen, R.W. Frei, U.A.Th. Brinkman, Chapter 1 On-Line Sample Handling and Trace Enrichment in Liquid Chromatography. The Determination of Organic Compounds in Water Samples, in: Selective Sample Handing and Detection in High Performance Liquid Chromatography, 1988: pp. 5–80.
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  • [6] R. Majors, Historical developments in HPLC and UHPLC column technology: The past 25 years, LC GC N Am 33 (2015) 818–840.
  • [7] A. Astefanei, I. Dapic, M. Camenzuli, Different Stationary Phase Selectivities and Morphologies for Intact Protein Separations, Chromatographia 80 (2017) 665–687.
  • [8] A.U. Kulikov, M.N. Galat, Comparison of C18 silica bonded phases selectivity in micellar liquid chromatography, J Sep Sci 32 (2009) 1340–1350.
  • [9] P. Jandera, Stationary phases for hydrophilic interaction chromatography, their characterization and implementation into multidimensional chromatography concepts, J Sep Sci 31 (2008) 1421–1437.
  • [10] S. Bocian, B. Buszewski, Phenyl‐bonded stationary phases—The influence of polar functional groups on retention and selectivity in reversed‐phase liquid chromatography, J Sep Sci 37 (2014) 3435–3442.
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  • [27] H. Zhang, W. Tang, P. Xie, S. Zhao, Demystifying Solvent Effects on Diels–Alder Reactions in Pure and Mixed Solvents: A Combined Electronic DFT and QM/MM Study, Ind Eng Chem Res 62 (2023) 7721–7730.
  • [28] W. Tang, C. Cai, S. Zhao, H. Liu, Development of Reaction Density Functional Theory and Its Application to Glycine Tautomerization Reaction in Aqueous Solution, J Phys Chem C 122 (2018) 20745–20754.
  • [29] W. Tang, J. Xuan, H. Wang, S. Zhao, H. Liu, First-principles investigation of aluminum intercalation and diffusion in TiO2 materials: Anatase versus rutile, J Power Sources 384 (2018) 249–255.
  • [30] W. Tang, J. Zhao, P. Jiang, X. Xu, S. Zhao, Z. Tong, Solvent Effects on the Symmetric and Asymmetric S N 2 Reactions in the Acetonitrile Solution: A Reaction Density Functional Theory Study, J Phys Chem B 124 (2020) 3114–3122.
  • [31] W. Tang, H. Yu, T. Zhao, L. Qing, X. Xu, S. Zhao, A dynamic reaction density functional theory for interfacial reaction-diffusion coupling at nanoscale, Chem Eng Sci 236 (2021) 116513.
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  • [40] S. Yenduri, H. Sulthana, N.P. Koppuravuri, Sustainablity evaluation of existed HPLC based analytical methods for quantification of amlodipine besylate and telmisartan using RGB model: A whiteness approach, Green Anal Chem 6 (2023) 100074.
  • [41] S. Yenduri, H.N. Varalakshmi, N.P. Koppuravuri, Assessment and Comparison of Greenness of UV-Spectroscopy Methods for Simultaneous Determination of Anti-Hypertensive Combination, Ind J Pharm Edu Res 58 (2024) s543–s551.
  • [42] S. Yenduri, N.P. koppuravuri, V. H N, Assessment and comparison of sustainability aspects of UV-spectroscopy methods for simultaneous determination of anti-hypertensive combination, Green Anal Chem 9 (2024) 100108.
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  • [46] M.D. Hanwell, D.E. Curtis, D.C. Lonie, T. Vandermeersch, E. Zurek, G.R. Hutchison, Avogadro: an advanced semantic chemical editor, visualization, and analysis platform, J Cheminform 4 (2012) 17.
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  • [50] S. Genders, Sustainable Development Goals (UN Global Goals), in: Encyclopedia of Sustainable Management, Springer International Publishing, Cham, 2023: pp. 3473–3476.
There are 50 citations in total.

Details

Primary Language English
Subjects Chemical Thermodynamics and Energetics
Journal Section Research Article
Authors

Suvarna Yendurı 0000-0002-2278-0525

Shifa K M 0009-0005-8850-5875

Naga Prashant Koppuravuri 0000-0003-3269-3094

Early Pub Date August 14, 2024
Publication Date January 5, 2025
Submission Date June 11, 2024
Acceptance Date July 24, 2024
Published in Issue Year 2025

Cite

APA Yendurı, S., K M, S., & Koppuravuri, N. P. (2025). Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies. Turkish Computational and Theoretical Chemistry, 9(1), 41-52. https://doi.org/10.33435/tcandtc.1499322
AMA Yendurı S, K M S, Koppuravuri NP. Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies. Turkish Comp Theo Chem (TC&TC). January 2025;9(1):41-52. doi:10.33435/tcandtc.1499322
Chicago Yendurı, Suvarna, Shifa K M, and Naga Prashant Koppuravuri. “Determining Binding Free Energy by Computational Modelling: A Theoretical Approach for Selection of Stationary Phase in Chromatographic Studies”. Turkish Computational and Theoretical Chemistry 9, no. 1 (January 2025): 41-52. https://doi.org/10.33435/tcandtc.1499322.
EndNote Yendurı S, K M S, Koppuravuri NP (January 1, 2025) Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies. Turkish Computational and Theoretical Chemistry 9 1 41–52.
IEEE S. Yendurı, S. K M, and N. P. Koppuravuri, “Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies”, Turkish Comp Theo Chem (TC&TC), vol. 9, no. 1, pp. 41–52, 2025, doi: 10.33435/tcandtc.1499322.
ISNAD Yendurı, Suvarna et al. “Determining Binding Free Energy by Computational Modelling: A Theoretical Approach for Selection of Stationary Phase in Chromatographic Studies”. Turkish Computational and Theoretical Chemistry 9/1 (January 2025), 41-52. https://doi.org/10.33435/tcandtc.1499322.
JAMA Yendurı S, K M S, Koppuravuri NP. Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies. Turkish Comp Theo Chem (TC&TC). 2025;9:41–52.
MLA Yendurı, Suvarna et al. “Determining Binding Free Energy by Computational Modelling: A Theoretical Approach for Selection of Stationary Phase in Chromatographic Studies”. Turkish Computational and Theoretical Chemistry, vol. 9, no. 1, 2025, pp. 41-52, doi:10.33435/tcandtc.1499322.
Vancouver Yendurı S, K M S, Koppuravuri NP. Determining binding free energy by computational modelling: A theoretical approach for selection of stationary phase in chromatographic studies. Turkish Comp Theo Chem (TC&TC). 2025;9(1):41-52.

Journal Full Title: Turkish Computational and Theoretical Chemistry


Journal Abbreviated Title: Turkish Comp Theo Chem (TC&TC)