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image of Quantitative Structure-Property Relationship (QSPR) Modeling of Central Nervous System (CNS) Drug Activity using Molecular Descriptors

Abstract

Background

L-type amino acid transporter-1 is a drug that stimulates the functions of the brain’s central nervous system. Membrane transporters have evolved, leading to a distinct approach in L-type amino acid transporter-1 drug delivery. One of the transporters used for transporting drugs across biological membranes is the L-type amino acid transporter-1. It is widely discussed in the medicinal field.

Objectives

Numerous investigations indicate a close connection between the properties of alkanes and the diversity of central nervous system drugs in the brain, specifically log P and molecular weight. One important study that analyzes structural properties is focused on topological descriptors. Recently, topological indices have found application in the development of quantitative structure-activity relationships. These indices are correlated with the physicochemical properties of BCNS-acting drugs and their biological activity.

Methods

The study employs significant methods of calculating topological indices: the edge set partition method and the Djokovi´c-Winkler relation (cut method) are utilized to calculate the values of these descriptors.

Results

The results of distance and degree-based topological descriptors have been derived. The strong correlation between topological descriptors and the physicochemical properties of BCNS-acting drugs has been studied.

Conclusions

This article identifies important topological features for various CNS medications, aiming to support researchers in understanding the properties of molecules and their biological activity. Furthermore, we demonstrate how strongly these behaviors correspond to the physicochemical properties of central nervous system drugs.

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2024-11-08
2024-12-24
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References

  1. Kim S.M. Faix P.H. Schnitzer J.E. Overcoming key biological barriers to cancer drug delivery and efficacy. J. Control. Release 2017 267 15 30 10.1016/j.jconrel.2017.09.016 28917530
    [Google Scholar]
  2. Miller G. Drug targeting. Breaking down barriers. Science 2002 297 5584 1116 1118 10.1126/science.297.5584.1116 12183610
    [Google Scholar]
  3. Lee J.T. Rochell S.J. Kriseldi R. Kim W.K. Mitchell R.D. Functional properties of amino acids: Improve health status and sustainability. Poult. Sci. 2023 102 1 102288 10.1016/j.psj.2022.102288 36436367
    [Google Scholar]
  4. Sutera F.M. De Caro V. Giannola L.I. Small endogenous molecules as moiety to improve targeting of CNS drugs. Expert Opin. Drug Deliv. 2017 14 1 93 107 10.1080/17425247.2016.1208651 27367188
    [Google Scholar]
  5. Zhang Y. Cheng Q. Xue Y. Yao K. Syeda M.Z. Xu J. Wu J. Wang Z. Tang L. Mu Q. LAT1 targeted brain delivery of temozolomide and sorafenib for effective glioma therapy. Nano Res. 2023 16 7 9743 9751 10.1007/s12274‑023‑5568‑3
    [Google Scholar]
  6. Kageyama T. Nakamura M. Matsuo A. Yamasaki Y. Takakura Y. Hashida M. Kanai Y. Naito M. Tsuruo T. Minato N. Shimohama S. The 4F2hc/LAT1 complex transports l-DOPA across the blood–brain barrier. Brain Res. 2000 879 1-2 115 121 10.1016/S0006‑8993(00)02758‑X 11011012
    [Google Scholar]
  7. Dickens D. Webb S.D. Antonyuk S. Giannoudis A. Owen A. Rädisch S. Hasnain S.S. Pirmohamed M. Transport of gabapentin by LAT1 (SLC7A5). Biochem. Pharmacol. 2013 85 11 1672 1683 10.1016/j.bcp.2013.03.022 23567998
    [Google Scholar]
  8. van Bree J.B.M.M. Audus K.L. Borchardt R.T. Carrier-mediated transport of baclofen across monolayers of bovine brain endothelial cells in primary culture. Pharm. Res. 1988 5 6 369 371 10.1023/A:1015959628008 3244648
    [Google Scholar]
  9. Gynther M. Jalkanen A. Lehtonen M. Forsberg M. Laine K. Ropponen J. Leppänen J. Knuuti J. Rautio J. Brain uptake of ketoprofen–lysine prodrug in rats. Int. J. Pharm. 2010 399 1-2 121 128 10.1016/j.ijpharm.2010.08.019 20727958
    [Google Scholar]
  10. Gynther M. Laine K. Ropponen J. Leppänen J. Mannila A. Nevalainen T. Savolainen J. Järvinen T. Rautio J. Large neutral amino acid transporter enables brain drug delivery via prodrugs. J. Med. Chem. 2008 51 4 932 936 10.1021/jm701175d 18217702
    [Google Scholar]
  11. Killian D.M. Hermeling S. Chikhale P.J. Targeting the cerebrovascular large neutral amino acid transporter (LAT1) isoform using a novel disulfide-based brain drug delivery system. Drug Deliv. 2007 14 1 25 31 10.1080/10717540600559510 17107928
    [Google Scholar]
  12. Peura L. Malmioja K. Huttunen K. Leppänen J. Hämäläinen M. Forsberg M.M. Rautio J. Laine K. Laine K. Design, synthesis and brain uptake of LAT1-targeted amino acid prodrugs of dopamine. Pharm. Res. 2013 30 10 2523 2537 10.1007/s11095‑012‑0966‑3 24137801
    [Google Scholar]
  13. Peura L. Malmioja K. Laine K. Leppänen J. Gynther M. Isotalo A. Rautio J. Large amino acid transporter 1 (LAT1) prodrugs of valproic acid: new prodrug design ideas for central nervous system delivery. Mol. Pharm. 2011 8 5 1857 1866 10.1021/mp2001878 21770378
    [Google Scholar]
  14. Rauf A. Naeem M. Hanif A. Quantitative structure–properties relationship analysis of Eigen‐value ‐based indices using COVID ‐19 drugs structure. Int. J. Quantum Chem. 2023 123 4 e27030 10.1002/qua.27030 36718482
    [Google Scholar]
  15. Aslam A. Bashir Y. Ahmad S. Gao W. On topological indices of certain dendrimer structures. Zeitschrift fur Naturforsch. Sect A J Phys Sci. 2017 72 6 559 66
    [Google Scholar]
  16. Hosamani S. Perigidad D. Jamagoud S. Maled Y. Gavade S. QSPR anlysis of certain degree based topological indices. J. Stat. Appl. Probab. 2017 6 2 361 371 10.18576/jsap/060211
    [Google Scholar]
  17. Randi’c M. Comparative structure-property studies: Regressions using a single descriptor. Croat. Chem. Acta 1933 66 289 312
    [Google Scholar]
  18. Randi’c M. Quantitative Structure-Propert Relationship: boiling points and planar benzenoids. New J. Chem. 1996 20 1001 1009
    [Google Scholar]
  19. Shanmukha M.C. Basavarajappa N.S. Anilkumar K.N. Predicting physico-chemical properties of octane isomers using QSPR Approach, Malaya. J. Math. 2020 8 1 104 116
    [Google Scholar]
  20. Hayat S. Imran M. Liu J.B. Correlation between the Estrada index and Q-electronic energies for benzenoid hydrocarbons with applica-tions to boron nanotubes. Int. J. Quant. Chem 2019
    [Google Scholar]
  21. Aslam A. Ahmad S. Gao W. On topological indices of boron triangular nanotubes. Z. Naturforsch. A 2017 72 8 711 716 10.1515/zna‑2017‑0135
    [Google Scholar]
  22. Hayat S. Wang S. Liu J.B. Valency-based topological descriptors of chemical networks and their applications. Appl. Math. Model. 2018 60 164 178 10.1016/j.apm.2018.03.016
    [Google Scholar]
  23. Togan M. Yurttas I.N. Cangul. Some formulae and inequalities on several Zagreb indices of r-subdivision graphs. Enlightments of Pure and Applied Mathematics 2015 1 1 29 45
    [Google Scholar]
  24. Hosamani S.M. Lokesha V. Cangul I.N. Devendraiah K.M. On certain topological indices of the derived graphs of subdivision graphs. TWMS J. Appl. & Eng. Math. 2016 6 2 324 332
    [Google Scholar]
  25. Alwardi A. Alqesmah A. Rangarajan R. Cangul I.N. Entire Zagreb indices of graphs. Discrete Math. Algorithms Appl. 2018 10 3 1850037 10.1142/S1793830918500374
    [Google Scholar]
  26. Hayat S. Imran M. Liu J.B. An efficient computational technique for degree and distance based topological descriptors with applications. IEEE Access 2019 7 32276 32296
    [Google Scholar]
  27. Hayat S. Distance-based graphical indices for predicting thermodynamic properties of benzenoid hydrocarbons with applications. Comput. Mater. Sci. 2023 230 112492 10.1016/j.commatsci.2023.112492
    [Google Scholar]
  28. Gutman I. Degree based topological indices. Croat. Chem. Acta 2013 86 4 351 361 10.5562/cca2294
    [Google Scholar]
  29. Furtula B. Graovac A. Vukičević D. Augmented Zagreb index. J. Math. Chem. 2010 48 2 370 380 10.1007/s10910‑010‑9677‑3
    [Google Scholar]
  30. Shirdel G.H. RezaPour, H.; Sayadi, A.M; The hyper-zagreb index of graph operations. Iran. J. Math. Chem. 2013 4 2 213 220
    [Google Scholar]
  31. Hasni R. Md Husin N.H. Du Z. Unicyclic graphs with maximum Randić indices. Commun. Comb. Optim. 2023 8 1 161 172
    [Google Scholar]
  32. Timmanaikar S. Polynomial Regression Analysis for Some Anticancer Drugs with Degree Based Topological Indices. Tuijin Jishu/Journal of Propulsion Technology 2024 45 1
    [Google Scholar]
  33. Zhou H. Ahmad M. Siddiqui M.K. On some bounds of first Gourava index for Ψ-sum graphs. Discrete Math. Algorithms Appl. 2024 2024 2450006 10.1142/S179383092450006X
    [Google Scholar]
  34. Wiener H. Structural determination of paraffin boiling points. J. Am. Chem. Soc. 1947 69 1 17 20 10.1021/ja01193a005 20291038
    [Google Scholar]
  35. Sahaya Vijay J. Roy S. Computation of Wiener Descriptor for Melamine Cyanuric Acid Structure. Polycycl. Aromat. Compd. 2024 44 2 1057 1071 10.1080/10406638.2023.2186441
    [Google Scholar]
  36. Gutman I. A formula for the Wiener number of trees and its extension to graphs containing cycles. Graph Theory Notes NY. 1994 27 9 15
    [Google Scholar]
  37. Gutman I. Ashrafi A.R. The edge version of the Szeged index. Croat. Chem. Acta 2008 81 263 266
    [Google Scholar]
  38. Khalifeh M.H. Yousefi-Azari H. Ashrafi A.R. The first and second Zagreb indices of some graph operations. Discrete Appl. Math. 2009 157 4 804 811 10.1016/j.dam.2008.06.015
    [Google Scholar]
  39. Khadikar P.V. Karmarkar S. Agrawal V.K. A novel PI index and its applications to QSPR/QSAR studies. J. Chem. Inf. Comput. Sci. 2001 41 4 934 949 10.1021/ci0003092 11500110
    [Google Scholar]
  40. Tratnik N. Computing the Mostar index in networks with applications to molecular graphs. arXiv:1904.04131 2019 2019
    [Google Scholar]
  41. Puris E. Gynther M. Auriola S. Huttunen K.M. L-Type amino acid transporter 1 as a target for drug delivery. Pharm. Res. 2020 37 5 88 10.1007/s11095‑020‑02826‑8 32377929
    [Google Scholar]
  42. Hayat S. Alanazi S.J.F. Liu J-B. Two novel temperature-based topological indices with strong potential to predict physicochemical properties of polycyclic aromatic hydrocarbons with applications to silicon carbide nanotubes. Phys. Scr. 2024 99 5 055027 10.1088/1402‑4896/ad3ada
    [Google Scholar]
  43. Hayat S. Mahadi H. Alanazi S.J.F. Wang S. Predictive potential of eigenvalues-based graphical indices for determining thermodynamic properties of polycyclic aromatic hydrocarbons with applications to polyacenes. Comput. Mater. Sci. 2024 238 112944 10.1016/j.commatsci.2024.112944
    [Google Scholar]
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