Skip to main content
SpringerLink
Log in

The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases

  • Published:
Journal of Computer-Aided Molecular Design Aims and scope Submit manuscript

Abstract

Owing to their potential to cause serious adverse health effects, significant efforts have been made to develop antidotes for organophosphate (OP) anticholinesterases, such as nerve agents. To be optimally effective, antidotes must not only reactivate inhibited target enzymes, but also have the ability to cross the blood–brain barrier (BBB). Progress has been made toward brain-penetrating acetylcholinesterase reactivators through the development of a new group of substituted phenoxyalkyl pyridinium oximes. To help in the selection and prioritization of compounds for future synthesis and testing within this class of chemicals, and to identify candidate broad-spectrum molecules, an in silico framework was developed to systematically generate structures and screen them for reactivation efficacy and BBB penetration potential.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

The supporting in vitro experimental data, SMILES representations of the target compounds, Structural characteristics of the target compounds, and other information related to the models and their verification are available in the Supplementary Material.

References

  1. Tucker J (2007) War of nerves: chemical warfare from World War I to Al-Qaeda. Reprint edition. Anchor, New York

    Google Scholar 

  2. Costanzi S, Machado J-H, Mitchell M (2018) Nerve agents: what they are, how they work, how to counter them. ACS Chem Neurosci 9(5):873–885

    Article  CAS  PubMed  Google Scholar 

  3. King AM, Aaron CK (2015) Organophosphate and carbamate poisoning. Emerg Med Clin 33(1):133–151

    Article  Google Scholar 

  4. Clement J (1979) Efficacy of pro-PAM (n-methyl-1, 6-dihydropyridine-2-carbaldoxime hydrochloride) as a prophylaxis against organophosphate poisoning. Toxicol Appl Pharmacol 47(2):305–311

    Article  CAS  PubMed  Google Scholar 

  5. Kuca K, Cabal J, Jun D, Kassa J, Bartosova L, Kunesova G, Dohnal V (2005) Strategy for the development of new acetylcholinesterase reactivators—antidotes used for treatment of nerve agent poisonings. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub 149(2):429–31

    Article  CAS  PubMed  Google Scholar 

  6. Skovira JW, O’Donnell JC, Koplovitz I, Kan RK, McDonough JH, Shih T-M (2010) Reactivation of brain acetylcholinesterase by monoisonitrosoacetone increases the therapeutic efficacy against nerve agents in Guinea pigs. Chemico-biol interact 187(1–3):318–324

    Article  CAS  Google Scholar 

  7. Moshiri M, Darchini-Maragheh E, Balali-Mood M (2012) Advances in toxicology and medical treatment of chemical warfare nerve agents. DARU J Pharm Sci 20:1–24

    Article  Google Scholar 

  8. Chambers JE, Meek EC, Chambers HW (2016) Novel brain-penetrating oximes for reactivation of cholinesterase inhibited by sarin and VX surrogates. Ann NY Acad Sci 1374(1):52–58

    Article  CAS  PubMed  Google Scholar 

  9. Chambers JE, Chambers HW, Funck KE, Meek EC, Pringle RB, Ross MK (2016) Efficacy of novel phenoxyalkyl pyridinium oximes as brain-penetrating reactivators of cholinesterase inhibited by surrogates of sarin and VX. Chemico-Biol Interact 259(Pt B):154–159. https://doi.org/10.1016/j.cbi.2016.07.004

    Article  CAS  Google Scholar 

  10. Chambers JE, Chambers HW, Meek EC, Pringle RB (2013) Testing of novel brain-penetrating oxime reactivators of acetylcholinesterase inhibited by nerve agent surrogates. Chemico-Biol Interact 203(1):135–138. https://doi.org/10.1016/j.cbi.2012.10.017

    Article  CAS  Google Scholar 

  11. Chambers J, Wiygul S, Harkness J, Chambers H (1988) Effects of acute paraoxon and atropine exposures on retention of shuttle avoidance-behavior in rats. Neurosci Res Commun 3(2):85–92

    CAS  Google Scholar 

  12. Ellman GL, Courtney KD, Andres V, Feather-Stone RM (1961) A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem Pharmacol 7:88–95. https://doi.org/10.1016/0006-2952(61)90145-9

    Article  CAS  PubMed  Google Scholar 

  13. Chambers JE, Chambers HW, Meek EC, Pringle RB (2013) Testing of novel brain-penetrating oxime reactivators of acetylcholinesterase inhibited by nerve agent surrogates. Chemico-biol Interact 203(1):135–138

    Article  CAS  Google Scholar 

  14. Wilson C, Cooper NJ, Briggs ME, Cooper AI, Adams DJ (2018) Investigating the breakdown of the nerve agent simulant methyl paraoxon and chemical warfare agents GB and VX using nitrogen containing bases. Org Biomol Chem 16(47):9285–9291

    Article  CAS  PubMed  Google Scholar 

  15. Chaubey K, Alam SI, Waghmare CK, Singh L, Srivastava N, Bhattacharya BK (2019) Differential proteome analysis of rat plasma after diisopropyl fluorophosphate (DFP) intoxication, a surrogate of nerve agent sarin. Chemico-Biol Interact 298:66–71

    Article  CAS  Google Scholar 

  16. Meng F, Xi Y, Huang J, Ayers PW (2021) A curated diverse molecular database of blood–brain barrier permeability with chemical descriptors. Sci Data. https://doi.org/10.1038/s41597-021-01069-5

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wang W, Kim MT, Sedykh A, Zhu H (2015) Developing enhanced blood–brain barrier permeability models: integrating external bio-assay data in QSAR modeling. Pharm Res 32:3055–3065

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Ooms F, Weber P, Carrupt P-A, Testa B (2002) A simple model to predict blood–brain barrier permeation from 3d molecular fields. Biochim Biophys Acta Mol Basis Dis 1587(2–3):118–125

    Article  CAS  Google Scholar 

  19. Mensch J, Jaroskova L, Sanderson W, Melis A, Mackie C, Verreck G, Brewster ME, Augustijns P (2010) Application of Pampa-models to predict BBB permeability including efflux ratio, plasma protein binding and physicochemical parameters. Int J Pharm 395(1–2):182–197

    Article  CAS  PubMed  Google Scholar 

  20. Plisson F, Piggott AM (2019) Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders. Mar Drugs 17(2):81

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Zhao YH, Abraham MH, Ibrahim A, Fish PV, Cole S, Lewis ML, Groot MJ, Reynolds DP (2007) Predicting penetration across the blood–brain barrier from simple descriptors and fragmentation schemes. J Chem Inf Model 47(1):170–175

    Article  CAS  Google Scholar 

  22. Li H, Yap CW, Ung CY, Xue Y, Cao ZW, Chen YZ (2005) Effect of selection of molecular descriptors on the prediction of blood–brain barrier penetrating and nonpenetrating agents by statistical learning methods. J Chem Inf Model 45(5):1376–1384

    Article  CAS  PubMed  Google Scholar 

  23. Landrum G (2013) Rdkit documentation. Release 1(1—-79):4

    Google Scholar 

  24. Thakur A, Patwa J, Sharma A, Flora SJ (2022) Synthesis, molecular docking, BSA, and in vitro reactivation study of imidazopyridine oximes against paraoxon inhibited acetylcholinesterase. Med Chem 18(2):273–287

    Article  CAS  PubMed  Google Scholar 

  25. Bhattacharjee KA, Musilek K, Kuca K (2013) In silico pharmacophore modeling on known pyridinium oxime reactivators of cyclosarin (GF) inhibited ache to aid discovery of potential, more efficacious novel non-oxime reactivators. Curr Comput Aided Drug Des 9(3):402–411

    Article  CAS  PubMed  Google Scholar 

  26. Bhattacharjee AK, Marek E, Le HT, Gordon RK (2012) Discovery of non-oxime reactivators using an in silico pharmacophore model of oxime reactivators of op-inhibited acetylcholinesterase. Eur J Med Chem 49:229–238

    Article  CAS  PubMed  Google Scholar 

  27. Bhattacharjee KA, Kuca K, Musilek K, Gordon KR (2012) An in silico stereo-electronic comparison of conventional pyridinium oximes and k-oximes for organophosphate (OP) poisoning. Med Chem 8(2):230–245

    Article  CAS  PubMed  Google Scholar 

  28. Lo R, Chandar NB, Kesharwani MK, Jain A, Ganguly B (2013) In silico studies in probing the role of kinetic and structural effects of different drugs for the reactivation of tabun-inhibited ache. PLoS ONE 8(12):79591

    Article  Google Scholar 

  29. Petroianu G, Lorke D (2008) Pyridinium oxime reactivators of cholinesterase inhibited by diisopropyl-fluorophosphate (DFP): predictive value of in-vitro testing for in vivo efficacy. Mini Rev Med Chem 8(13):1328–1342

    Article  CAS  PubMed  Google Scholar 

  30. Lugokenski TH, Gubert P, Bueno DC, Nogara PA, Aquino Saraiva R, Barcelos RP, Carratu VS, Bresolin L, Vargas Barbosa NB, Pereira ME et al (2012) Effect of different oximes on rat and human cholinesterases inhibited by methamidophos: a comparative in vitro and in silico study. Basic Clin Pharmacol Toxicol 111(6):362–370

    Article  CAS  PubMed  Google Scholar 

  31. Ho TK (1995) Random decision forests. In: Proceedings of 3rd international conference on document analysis and recognition, vol 1, pp. 278–282. IEEE

  32. Berrar D (2018) Bayes’ theorem and naive Bayes classifier. Encyclop Bioinform Comput Biol ABC Bioinform 403:412

    Google Scholar 

  33. Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189–1232

    Article  Google Scholar 

  34. Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q, Liu T-Y (2017) LightGBM: a highly efficient gradient boosting decision tree. In: Advances in neural information processing systems, vol 30

  35. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 2016, pp 785–794

  36. Nembrini S, König IR, Wright MN (2018) The revival of the Gini importance? Bioinformatics 34(21):3711–3718

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. MacQueen I (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley symposium on mathematical statistics problems, 1967, pp 281–297

  38. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  39. Mukherjee S, Gupta RD (2020) Organophosphorus nerve agents: types, toxicity, and treatments. J Toxicol 2020:3007984. https://doi.org/10.1155/2020/3007984

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kumar R, Sharma A, Alexiou A, Bilgrami AL, Kamal MA, Ashraf GM (2022) DeePred-BBB: a blood brain barrier permeability prediction model with improved accuracy. Front Neurosci 16:858126

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yuan Y, Zheng F, Zhan C-G (2018) Improved prediction of blood–brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints. AAPS J 20:1–10

    Article  Google Scholar 

  42. Sakiyama H, Fukuda M, Okuno T (2021) Prediction of blood–brain barrier penetration (BBBP) based on molecular descriptors of the free-form and in-blood-form datasets. Molecules 26(24):7428

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Chen B (2022) Retrosynthetic planning with retro*. Original-date: 2020-06-27T03:50:40Z. https://github.com/binghong-ml/retro_star. Accessed 12 Nov 2022

  44. Chen B, Li C, Dai H, Song L (2020) Retro*: learning retrosynthetic planning with neural guided a* search. In: International conference on machine learning, 2020, pp 1608–1616. PMLR

  45. Wang Z, Yang H, Wu Z, Wang T, Li W, Tang Y, Liu G (2018) In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods. ChemMedChem 13(20):2189–2201

    Article  CAS  PubMed  Google Scholar 

  46. Shaker B, Yu M-S, Song JS, Ahn S, Ryu JY, Oh K-S, Na D (2021) LightBBB: computational prediction model of blood–brain-barrier penetration based on lightGBM. Bioinformatics 37(8):1135–1139

    Article  CAS  PubMed  Google Scholar 

  47. Liu L, Zhang L, Feng H, Li S, Liu M, Zhao J, Liu H (2021) Prediction of the blood–brain barrier (BBB) permeability of chemicals based on machine-learning and ensemble methods. Chem Res Toxicol 34(6):1456–1467

    Article  CAS  PubMed  Google Scholar 

  48. Yu T-H, Su B-H, Battalora LC, Liu S, Tseng YJ (2022) Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power. Brief Bioinform 23(1):377

    Article  Google Scholar 

  49. Banks WA (2009) Characteristics of compounds that cross the blood–brain barrier. BMC Neurol 9(Suppl 1):3. https://doi.org/10.1186/1471-2377-9-S1-S3

    Article  CAS  Google Scholar 

Download references

Funding

Research presented in this publication was supported in part by the National Institutes of Health under Award Numbers U01NS083430 (JC), U01NS107127 (JC) and U01NS123255 (JC, EM). The views expressed in this publication are those of the authors and do not reflect the official policy or position of the National Institutes of Health.

Author information

Authors and Affiliations

  1. Chemical and Biological Engineering, Colorado State University, 1370 Campus Delivery, Fort Collins, CO, 80523-1370, USA

    Sohaib Habiballah & Brad Reisfeld

  2. Center for Environmental Health Sciences, College of Veterinary Medicine, Mississippi State University, 240 Wise Center Drive, Mississippi State, MS, 39762-6100, USA

    Janice Chambers & Edward Meek

  3. Colorado School of Public Health, Colorado State University, 1612 Campus Delivery, Fort Collins, CO, 80523-1612, USA

    Brad Reisfeld

Authors
  1. Sohaib Habiballah
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Janice Chambers
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Edward Meek
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Brad Reisfeld
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

SH wrote the manuscript, developed the approach and mathematical models, conducted the simulations, and prepared the figures. JC and EM provided the experimental data and reviewed modeling results. BR wrote the manuscript, developed the methodology, reviewed all models and results, and provided supervision and resources. All authors reviewed the manuscript.

Corresponding author

Correspondence to Brad Reisfeld.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file 1 (pdf 115 KB)

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Habiballah, S., Chambers, J., Meek, E. et al. The in silico identification of novel broad-spectrum antidotes for poisoning by organophosphate anticholinesterases. J Comput Aided Mol Des 37, 755–764 (2023). https://doi.org/10.1007/s10822-023-00537-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10822-023-00537-x

Keywords

Search

Navigation