Drug Res (Stuttg) 2015; 65(11): 587-591
DOI: 10.1055/s-0034-1395564
Original Article
© Georg Thieme Verlag KG Stuttgart · New York

Insilico Approaches in Anesthetic Drug Development: Computer Aided Drug Designing

Q.-X. Peng
1   Department of Anesthesiology, The First Hospital of Changsha, Changsha, China
,
X.-H. Guan
1   Department of Anesthesiology, The First Hospital of Changsha, Changsha, China
,
Z.-G. Yi
1   Department of Anesthesiology, The First Hospital of Changsha, Changsha, China
,
Y.-P. Su
1   Department of Anesthesiology, The First Hospital of Changsha, Changsha, China
› Author Affiliations
Further Information

Publication History

received 21 August 2014

accepted 30 October 2014

Publication Date:
02 December 2014 (online)

Abstract

Objective: Computer Aided Drug Designing is fast becoming an important tool in Drug discovery, and in the field of anesthetic drug development we are the first to use in silico approaches to look for novel anesthetic compounds.

Design: The approach of molecular modeling, Virtual screening, Drug-likeness, molecular docking and molecular dynamics simulations (MDS) was employed for this study.

Result: Our approach of virtual screening Drug-likeness, adsorption, distribution, metabolism, excretion and toxicity analysis of around 50 000 compounds from Inter Bio Screen (IBS) Database have given us top 5 Lead compounds against ASN289 of γ-aminobutyric acid (GABAA) receptor, a common target of known anesthetic compounds. Out of the top 5 Lead compounds one (Lead 5) was selected for further MDS analysis based on its Binding free energy and number of physical interactions with GABAA.

Conclusion: The MDS analysis of Lead 5 reveals the complex to be stable and thus suitable for further in vitro and in vivo analysis.

 
  • References

  • 1 Alvarez JC. High-throughput docking as a source of novel drug leads. Curr Opin Chem Biol 2004; 8: 365-370
  • 2 Chikan NA, Bhavaniprasad V, Anbarasu K et al. From natural products to drugs for epimutation computer-aided drug design. Appl Biochem Biotechnol 2013; 170: 164-175
  • 3 Eckenhoff RG, Zheng W, Kelz MB. From anesthetic mechanisms research to drug discovery. Clinical Pharmacology & Therapeutics 2008; 84: 144-148
  • 4 Keith MW. The nature of the site of general anesthesia. International review of neurobiology 1985; 27: 1-61
  • 5 Urban BW. The site of anesthetic action. Modern Anesthetics. Springer; Berlin Heidelberg: 2008. 182. 3-29
  • 6 Stoelting RK, Hillier SC. Pharmacology and physiology in asthetic practice. Lippincott Williams & Wilkins; 2012
  • 7 Franks NP, Lieb WR. Do general anaesthetics act by competitive binding to specific receptors. Nature 1984; 310: 599-601
  • 8 Krasowski MD, Jenkins A, Flood P et al. General anesthetic potencies of a series of propofol analogs correlate with potency for potentiation of gammaaminobutyric acid (GABA) current atthe GABA(A) receptor but not with lipid solubility. J Pharmacol Exp Ther 2001; 297: 338-351
  • 9 Krasowski MD, Hong X, Hopfinger AJ et al. 4D-QSAR analysis of a set of propofol analogues: mapping binding sites for an anesthetic phenol on the GABA(A) receptor. J Med Chem 2002; 45: 3210-3221
  • 10 Atucha E, Hammerschmidt F, Zolle I et al. Structure– activity relationship of etomidate derivatives at the GABA(A) receptor: comparison with binding to 11betahydroxylase. Bioorg Med Chem Lett 2009; 19: 4284-4287
  • 11 Veleiro AS, Burton G. Structure– activity relationships of neuroactive steroids acting on the GABAA receptor. Curr Med Chem 2009; 16: 455-472
  • 12 Phillips GH. Structure–activity relationships in steroidal aensthetics. J Steroid Biochem 1975; 6: 607-613
  • 13 Chisari M, Eisenmann LN, Krishnan K et al. The influence of neuroactive steroid lipophilicity on GABAA receptor modulation: evidence for a low-affinity interaction. J Neurophysiol 2009; 102: 1254-1264
  • 14 Belelli D, Lambert JJ, Peters JA et al. The interaction of the general anesthetic etomidate with the γ-aminobutyric acid type A receptor is influenced by a single amino acid. Proceedings of the National Academy of Sciences 1997; 94: 11031-11036
  • 15 Hughes JP, Rees S, Kalindjian SB et al. Principles of early drug discovery. British journal of pharmacology 2011; 162: 1239-1249
  • 16 Davis AM, Riley RJ. Predictive ADMET studies, the challenges and the opportunities. Curr Opin Chem Biol 2004; 8: 378-386
  • 17 Van de Waterbeemd H, Gifford E. ADMET in silico modelling: towards prediction paradise?. Nat Rev Drug Discov 2003; 2: 192-204
  • 18 Biasini M, Bienert S, Waterhouse A et al. SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information. Nucleic Acids Research 2014; 42: W252-W258
  • 19 Arnold K, Bordoli L, Kopp J et al. The SWISS-MODEL Workspace: A web-based environment for protein structure homology modelling. Bioinformatics 2006; 22: 195-201
  • 20 Bordoli L, Kiefer F, Arnold K et al. Protein structure homology modelling using SWISS-MODEL Workspace. Nature Protocols 2009; 4: 1-13
  • 21 Thompson MA. ArgusLab 4.0. 1. Planaria Software LLC; Seattle, WA: 2004
  • 22 Lagorce D, Sperandio O, Galons H et al. FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects. BMC bioinformatics 2008; 9: 396
  • 23 Morris GMI, Huey R, Lindstrom W et al. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 2009; 30: 2785-2791
  • 24 Accelrys Software Inc. . Discovery Studio Modeling Environment, Release 3.5. San Diego: Accelrys Software Inc.; 2012
  • 25 Hess B, Kutzner C, Van Der Spoel D et al. GROMACS 4: Algorithms for highly efficient, load-balanced, and scalable molecular simulation. Journal of chemical theory and computation 2008; 4: 435-447
  • 26 Lipinski CA, Lombardo F, Dominy BW et al. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev 2001; 46: 3-26
  • 27 Zhoa YH, Le J, Abraham MH et al. Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure–activity relationship (QSAR) with the Abraham descriptors. J Pharm Sci 2001; 90: 749-784
  • 28 Van de Waterbeemed H, Camenisch G, Folkers G et al. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J Drug Target 1998; 6: 151-165
  • 29 Lipinski CA, Lipinski CA. Lead- and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies 2004; 1: 337-341