A Review on Anesthetic Drug Discovery with Computer Aided Drug Design

Year : 2024 | Volume : | : | Page : –
By

Satish kumar Yadav

Mohd. Wasiullah

Piyush Yadav

Ankit Maurya

Mr. Sushil Yadav

  1. Associate professor, Department of Pharmacy Prasad Institute of Technology, Jaunpur Uttar Pradesh India
  2. Principal ,Department of Pharmacy Prasad Institute of Technology, Jaunpur Uttar Pradesh India
  3. Academic Head, Department Of Pharmacy Prasad Institute of Technology, Jaunpur Uttar Pradesh India
  4. Research Scholar, Department of Pharmacy Prasad Institute of Technology, Jaunpur Uttar Pradesh India
  5. Lecturer, Department of Pharmacy Prasad Institute of Technology, Jaunpur Uttar Pradesh India

Abstract

In contemporary medicine, anaesthesia is essential for enabling surgical procedures, pain control, and patient comfort. Finding and creating safe and efficient anaesthetics is crucial to enhancing patient outcomes and developing the medical field. In this review, we provide a comprehensive overview of anesthetic drug discovery with a focus on the integration of computer-aided drug design (CADD) methodologies. Beginning with an exploration of anesthesia mechanisms and targets, we delve into the principles of CADD, including molecular modeling, pharmacophore modeling, virtual screening, molecular docking, and quantitative structure-activity relationship (QSAR) modeling. Through case studies, we highlight successful applications of these computational approaches in lead identification, optimization, and rational drug design in the field of anesthesia. Additionally, we discuss the challenges and future directions in anesthetic drug discovery, including personalized medicine, artificial intelligence, multiomics approaches, drug delivery advancements, interdisciplinary collaboration, and ethical considerations. By leveraging the power of computational methodologies and interdisciplinary collaboration, we aim to accelerate the development of novel anesthetic agents with improved efficacy, safety, and patient outcomes.

Keywords: Anesthesia, drug discovery, computer-aided drug design, molecular modeling, pharmacophore modeling, virtual screening, molecular docking, quantitative structure-activity relationship (QSAR) modeling, personalized medicine, artificial intelligence.

How to cite this article: Satish kumar Yadav, Mohd. Wasiullah, Piyush Yadav, Ankit Maurya, Mr. Sushil Yadav. A Review on Anesthetic Drug Discovery with Computer Aided Drug Design. International Journal of Antibiotics. 2024; ():-.
How to cite this URL: Satish kumar Yadav, Mohd. Wasiullah, Piyush Yadav, Ankit Maurya, Mr. Sushil Yadav. A Review on Anesthetic Drug Discovery with Computer Aided Drug Design. International Journal of Antibiotics. 2024; ():-. Available from: https://journals.stmjournals.com/ijab/article=2024/view=0


References

  1. Campagna JA, Miller KW, Forman SA. Mechanisms of actions of inhaled anesthetics. New England Journal of Medicine. 2003 May 22;348(21):2110-24.
  2. Lechner HA, Lechner ME. Anesthesia: The gift of oblivion and the mystery of consciousness. Humana Press; 2008.
  3. Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nature reviews Drug discovery. 2004 Nov;3(11):935-49.
  4. Schneider G, Fechner U. Computer-based de novo design of drug-like molecules. Nature Reviews Drug Discovery. 2005 Aug 1;4(8):649-63.
  5. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic acids research. 2016 Jan 4;44(D1):D1045-53.
  6. Koes DR, Camacho CJ. Pharmer: efficient and exact pharmacophore search. Journal of chemical information and modeling. 2011 Jun 27;51(6):1307-14.
  7. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of computational chemistry. 2010 Jan 30;31(2):455-61.
  8. Huang SY, Zou X. Advances and challenges in protein-ligand docking. International journal of molecular sciences. 2010 Aug 18;11(8):3016-34.
  9. Roy K, Kar S, Ambure P. On a simple approach for determining applicability domain of QSAR models. Chemometrics and Intelligent Laboratory Systems. 2015 Jul 15;145:22-9.
  10. Fourches D, Muratov E, Tropsha A. Trust, but verify: on the importance of chemical structure curation in cheminformatics and QSAR modeling research. Journal of chemical information and modeling. 2010 Jul 7;50(7):1189.
  11. Brown N. Computational chemogenomics: mining for drug leads in chemical space. John Wiley & Sons; 2009.
  12. Drwal MN, Banerjee P, Dunkel M, Wettig MR, Preissner R. ProTox: a web server for the in silico prediction of rodent oral toxicity. Nucleic acids research. 2014 Jul 1;42(W1):W53-8.
  13. Yang H, Lou C, Sun L, Li J, Cai Y, Wang Z, Li W, Liu G, Tang Y. admetSAR 2.0: web-service for prediction and optimization of chemical ADMET properties. Bioinformatics. 2019 Mar 15;35(6):1067-9.
  14. Hughes TB, Miller GP, Swamidass SJ. Modeling epoxidation of drug-like molecules with a deep machine learning network. ACS central science. 2015 Jul 22;1(4):168-80.
  15. Gramatica P, Cassani S, Roy PP, Kovarich S, Yap CW, Papa E. QSAR modeling is not “push a button and find a correlation”: a case study of toxicity of (benzo‐) triazoles on algae. Molecular Informatics. 2012 Dec;31(11‐12):817-35.
  16. Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin M, Dearden J, Gramatica P, Martin YC, Todeschini R, Consonni V. QSAR modeling: where have you been? Where are you going to?. Journal of medicinal chemistry. 2014 Jun 26;57(12):4977-5010.
  17. Bajusz D, Rácz A, Héberger K. Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?. Journal of cheminformatics. 2015 Dec;7:1-3
  18. Ekins S, Freundlich JS. Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets. Pharmaceutical research. 2011 Aug;28:1859-69.
  19. Schneider P, Schneider G. De novo design at the edge of chaos: Miniperspective. Journal of medicinal chemistry. 2016 May 12;59(9):4077-86.
  20. Rognan D. Structure‐based approaches to target fishing and ligand profiling. Molecular Informatics. 2010 Mar 15;29(3):176-87.
  21. McGovern SL, Helfand BT, Feng B, Shoichet BK. A specific mechanism of nonspecific inhibition. Journal of medicinal chemistry. 2003 Sep 25;46(20):4265-72.
  22. Cheng T, Zhao Y, Li X, Lin F, Xu Y, Zhang X, Li Y, Wang R, Lai L. Computation of octanol− water partition coefficients by guiding an additive model with knowledge. Journal of chemical information and modeling. 2007 Nov 26;47(6):2140-8.
  23. Rudolph U, Antkowiak B. Molecular and neuronal substrates for general anaesthetics. Nature Reviews Neuroscience. 2004 Sep 1;5(9):709-20.

Ahead of Print Subscription Review Article
Volume
Received May 10, 2024
Accepted May 14, 2024
Published July 9, 2024

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