A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor
Open accessAbstract
. Our computational analysis provides new insights into the mechanism of μOR-opioid recognition, paving the way for investigations of the structure-activity relationships of nitazenes.
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Nitazenes may carry cardiotoxic potential; computational analyses predict a high probability of hERG potassium channel inhibition, raising concerns about proarrhythmic effects.
“In addition, nitazenes may carry cardiotoxic potential; computational analyses predict a high probability of hERG potassium channel inhibition, raising concerns about proarrhythmic effects [38,39,40].”
From Nitazenes: The Emergence of a Potent Synthetic Opioid Threat (2025) · cites this paper as [39]
The Introduction reports that metonitazene overdose was associated with cardiac arrest at a higher rate than overdoses involving other substances, lending limited support to concern about cardiac harm. But the paper’s computational analyses concern μ-opioid-receptor binding; our copy does not report a prediction of hERG inhibition or proarrhythmic effects. (LLM, conf=0.78)
Experimental — accuracy not yet measuredIt is possible to predict emerging nitazene analogues using advances in forensic intelligence and artificial intelligence tools.
“Nowadays, with advances in forensic intelligence and artificial intelligence tools, it is possible to predict emerging nitazene analogues, as demonstrated in recent studies [30,39]”
From Nitazenes: The Emergence of a Potent Synthetic Opioid Threat (2025) · cites this paper as [39]
The Abstract and Results and Discussion describe computational predictions of receptor-binding poses for eight specified nitazenes, not predictions of which analogues will emerge. The Introduction notes nitazenes already found on the illicit market, but the passages do not address using forensic intelligence or artificial intelligence to anticipate emerging analogues (searched those terms and predictive analogues). (LLM, conf=0.77)
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Cites
Trott, O., & Olson, A. J. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21334 · Cited by 38,412 · see citing works
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,335 · see citing works
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21256 · Cited by 25,745 · see citing works
Ryckaert, J., Ciccotti, G., & Berendsen, H. (1977) · Journal of Computational Physics · doi:10.1016/0021-9991(77)90098-5 · Cited by 19,525 · see citing works
All-Atom Empirical Potential for Molecular Modeling and Dynamics Studies of Proteins
MacKerell, A. D., Bashford, D., Bellott, M., Dunbrack, R. L., Evanseck, J. D., Field, M. J., Fischer, S., Gao, J., Guo, H., Ha, S., et al. (1998) · The Journal of Physical Chemistry B · doi:10.1021/jp973084f · Cited by 14,270 · see citing works
Friesner, R. A., Banks, J. L., Murphy, R. B., Halgren, T. A., Klicic, J. J., Mainz, D. T., Repasky, M. P., Knoll, E. H., Shelley, M., Perry, J. K., et al. (2004) · Journal of Medicinal Chemistry · doi:10.1021/jm0306430 · Cited by 10,057 · see citing works
CHARMM‐GUI: A web‐based graphical user interface for CHARMM
Jo, S., Kim, T., Iyer, V. G., & Im, W. (2008) · Journal of Computational Chemistry · doi:10.1002/jcc.20945 · Cited by 9,284 · see citing works
Settle: An analytical version of the SHAKE and RATTLE algorithm for rigid water models
Miyamoto, S., & Kollman, P. A. (1992) · Journal of Computational Chemistry · doi:10.1002/jcc.540130805 · Cited by 7,268 · see citing works
AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings
Eberhardt, J., Santos-Martins, D., Tillack, A., & Forli, S. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00203 · Cited by 7,111 · see citing works
PTRAJ and CPPTRAJ: Software for Processing and Analysis of Molecular Dynamics Trajectory Data.
Roe, D., & Cheatham, T. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400341p · Cited by 6,969 · see citing works
CHARMM36m: An Improved Force Field for Folded and Intrinsically Disordered Proteins
Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. (2016) · Nature Methods · doi:10.1038/nmeth.4067 · Cited by 6,708 · see citing works
Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-013-9644-8 · Cited by 6,130 · see citing works
MDAnalysis: A toolkit for the analysis of molecular dynamics simulations
Michaud‐Agrawal, N., Denning, E. J., Woolf, T. B., & Beckstein, O. (2011) · Journal of Computational Chemistry · doi:10.1002/jcc.21787 · Cited by 4,549 · see citing works
Update of the CHARMM all-atom additive force field for lipids: Validation on six lipid types
Klauda, J. B., Venable, R., Freites, J. A., O'Connor, J. W., Tobias, D., Mondragon-Ramirez, C., Vorobyov, I., MacKerell, A. D., & Pastor, R. (2010) · Journal of Physical Chemistry B · doi:10.1021/jp101759q · Cited by 4,369 · see citing works
PLUMED 2: New feathers for an old bird
Tribello, G. A., Bonomi, M., Branduardi, D., Camilloni, C., & Bussi, G. (2013) · Computer Physics Communications · doi:10.1016/j.cpc.2013.09.018 · Cited by 3,427 · see citing works
Well-Tempered Metadynamics: A Smoothly Converging and Tunable Free-Energy Method
Barducci, A., Bussi, G., & Parrinello, M. (2008) · Physical Review Letters · doi:10.1103/physrevlett.100.020603 · Cited by 3,184 · see citing works
Ballesteros, J. A., & Weinstein, H. (1995) · Methods in neurosciences · doi:10.1016/s1043-9471(05)80049-7 · Cited by 3,001 · see citing works
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Eastman, P., Swails, J., Chodera, J. D., McGibbon, R. T., Zhao, Y., Beauchamp, K. A., Wang, L.-P., Simmonett, A. C., Harrigan, M. P., Stern, C. D., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005659 · Cited by 2,748 · see citing works
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J., Hernández, C. X., Schwantes, C. R., Wang, L., Lane, T. J., & Pande, V. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.08.015 · Cited by 2,358 · see citing works
OPM database and PPM web server: resources for positioning of proteins in membranes
Lomize, M. A., Pogozheva, I. D., Joo, H., Mosberg, H. I., & Lomize, A. L. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr703 · Cited by 2,126 · see citing works
Automation of the CHARMM General Force Field (CGenFF) I: bond perception and atom typing
Vanommeslaeghe, K., & MacKerell, A. D. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300363c · Cited by 2,028 · see citing works
Vanommeslaeghe, K., Raman, E. P., & MacKerell, A. D. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3003649 · Cited by 1,651 · see citing works
Long-Time-Step Molecular Dynamics through Hydrogen Mass Repartitioning.
Hopkins, C. W., Grand, S. M. L., Walker, R. C., & Roitberg, A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct5010406 · Cited by 1,317 · see citing works
Loncharich, R. J., Brooks, B. R., & Pastor, R. W. (1992) · Biopolymers · doi:10.1002/bip.360320508 · Cited by 1,197 · see citing works
Beglov, D., & Roux, B. (1994) · The Journal of Chemical Physics · doi:10.1063/1.466711 · Cited by 990 · see citing works
Structural insights into µ-opioid receptor activation
Huang, W., Manglik, A., Venkatakrishnan, A., Laeremans, T., Feinberg, E. N., Sanborn, A. L., KATO, H. E., Livingston, K. E., Thorsen, T. S., Kling, R. C., et al. (2015) · Nature · doi:10.1038/nature14886 · Cited by 895 · see citing works
Åqvist, J., Wennerström, P., Nervall, M., Bjelic, S., & Brandsdal, B. (2004) · Chemical Physics Letters · doi:10.1016/j.cplett.2003.12.039 · Cited by 516 · see citing works
Fentanyl: Receptor pharmacology, abuse potential, and implications for treatment
Comer, S. D., & Cahill, C. M. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.12.005 · Cited by 311 · see citing works
Molecular recognition of morphine and fentanyl by the human μ-opioid receptor
Zhuang, Y., Wang, Y., He, B., He, X., Zhou, X. E., Guo, S., Rao, Q., Yang, J., Liu, J., Zhou, Q., et al. (2022) · Cell · doi:10.1016/j.cell.2022.09.041 · Cited by 284 · see citing works
Self-citationClark, A. J., Tiwary, P., Borrelli, K. W., Feng, S., Miller, E. B., Abel, R., Friesner, R. A., & Berne, B. J. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00201 · Cited by 243 · see citing works
Chemical Genomics Profiling of Environmental Chemical Modulation of Human Nuclear Receptors
Huang, R., Xia, M., Cho, M.-H., Sakamuru, S., Shinn, P., Houck, K. A., Dix, D. J., Judson, R., Witt, K. L., Kavlock, R. J., et al. (2011) · Environmental Health Perspectives · doi:10.1289/ehp.1002952 · Cited by 196 · see citing works
Vandeputte, M. M., Van Uytfanghe, K., Layle, N. K., St. Germaine, D. M., Iula, D. M., & Stove, C. P. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00064 · Cited by 161 · see citing works
DARK Classics in Chemical Neuroscience: Etonitazene and Related Benzimidazoles
Ujváry, I., Christie, R., Evans-Brown, M., Gallegos, A., Jorge, R., de Morais, J., & Sedefov, R. (2021) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00037 · Cited by 141 · see citing works
How μ-opioid receptor recognizes fentanyl
Vo, Q. N., Mahinthichaichan, P., Shen, J., & Ellis, C. R. (2021) · Nature Communications · doi:10.1038/s41467-021-21262-9 · Cited by 127 · see citing works
Self-citationInsights into distinct signaling profiles of the µOR activated by diverse agonists
Qu, Q., Huang, W., Aydın, D., Paggi, J. M., Seven, A. B., Wang, H., Chakraborty, S., Che, T., DiBerto, J. F., Robertson, M. J., et al. (2022) · Nature Chemical Biology · doi:10.1038/s41589-022-01208-y · Cited by 125 · see citing works
Self-citationDARK Classics in Chemical Neuroscience: Fentanyl
Burns, M., Cunningham, C. W., & Mercer, S. L. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00174 · Cited by 104 · see citing works
Naloxone Use in Novel Potent Opioid and Fentanyl Overdoses in Emergency Department Patients
Amaducci, A., Aldy, K., Campleman, S. L., Li, S., Meyn, A., Abston, S., Culbreth, R. E., Krotulski, A., Logan, B., Wax, P., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.31264 · Cited by 84 · see citing works
Ellis, C. R., Kruhlak, N. L., Kim, M. T., Hawkins, E. G., & Stavitskaya, L. (2018) · PLoS ONE · doi:10.1371/journal.pone.0197734 · Cited by 82 · see citing works
Self-citationMolecular dynamics of fentanyl bound to μ-opioid receptor
Lipiński, P. F. J., Jarończyk, M., Dobrowolski, J. C., & Sadlej, J. (2019) · Journal of Molecular Modeling · doi:10.1007/s00894-019-3999-2 · Cited by 79 · see citing works
π‐Hole Interactions Involving Nitro Aromatic Ligands in Protein Structures
Bauzá, A., Frontera, A., & Mooibroek, T. J. (2019) · Chemistry - A European Journal · doi:10.1002/chem.201903404 · Cited by 69 · see citing works
Glatfelter, G. C., Vandeputte, M. M., Chen, L., Walther, D., Tsai, M.-H. M., Shi, L., Stove, C. P., & Baumann, M. H. (2023) · Psychopharmacology · doi:10.1007/s00213-023-06451-2 · Cited by 52 · see citing works
Self-citationDe Luca, M. A., Tocco, G., Mostallino, R., Laus, A., Caria, F., Musa, A., Pintori, N., Ucha, M., Poza, C., Ambrosio, E., et al. (2022) · Neuropharmacology · doi:10.1016/j.neuropharm.2022.109263 · Cited by 48 · see citing works
Kozell, L. B., Eshleman, A. J., Wolfrum, K. M., Swanson, T. L., Bloom, S. H., Benware, S., Schmachtenberg, J. L., Schutzer, K. A., Schutzer, W. E., Janowsky, A., et al. (2024) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.123.002052 · Cited by 42 · see citing works
Kinetics and Mechanism of Fentanyl Dissociation from the μ-Opioid Receptor
Mahinthichaichan, P., Vo, Q. N., Ellis, C. R., & Shen, J. (2021) · JACS Au · doi:10.1021/jacsau.1c00341 · Cited by 40 · see citing works
Self-citationLukauskis, D., Samways, M. L., Aureli, S., Cossins, B. P., Taylor, R. D., & Gervasio, F. L. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01142 · Cited by 38 · see citing works
Tsai, M.-H. M., Chen, L., Baumann, M. H., Canals, M., Javitch, J. A., Lane, J. R., & Shi, L. (2024) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.3c00750 · Cited by 27 · see citing works
Self-citationAnalysis of the Results of Metadynamics Simulations by metadynminer and metadynminer3d
Trapl, D., & Spiwok, V. (2022) · The R Journal · doi:10.32614/rj-2022-057 · Cited by 27 · see citing works
Structure–Kinetics Relationships of Opioids from Metadynamics and Machine Learning Analysis
Mahinthichaichan, P., Liu, R., Vo, Q. N., Ellis, C. R., Stavitskaya, L., & Shen, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00069 · Cited by 17 · see citing works
Self-citationCHARMM Force Field Parameters for Nitroalkanes and Nitroarenes
Klauda, J. B., & Brooks, B. R. (2007) · Journal of Chemical Theory and Computation · doi:10.1021/ct700191v · Cited by 14 · see citing works
Reanalysis of a μ opioid receptor crystal structure reveals a covalent adduct with BU72
Munro, T. A. (2023) · BMC Biology · doi:10.1186/s12915-023-01689-w · Cited by 12 · see citing works
Discriminative stimulus and antinociceptive effects of sixteen nitazene analogs in rodents
Gatch, M. B., Hill, R. D., Sundar, M., & Shetty, R. A. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110416 · Cited by 10 · see citing works
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Evren, A. E., Kaya, A. Z., Karakaya, A., Tutuş, B., Güngör, E. M., Nuha, D., Osmani̇ye, D., Kaya, B., Çevik, U. A., Yurttaş, L., et al. (2025) · Future Medicinal Chemistry · doi:10.1080/17568919.2025.2546783 · Cited by 10 · see citing works
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- Authors
- Clayton, J., Shi, L., Robertson, M. J., Skiniotis, G., Michaelides, M., Stavitskaya, L., & Shen, J.
- Affiliations
- University of Maryland, Baltimore (US)
- United States Department of Health and Human Services (US)
- Stanford Medicine (US)
- National Institute on Drug Abuse (US)
- United States Food and Drug Administration (US)
- Corresponding author
- Stavitskaya, Lidiya
- Shen, Jana
- Venue
- Neuropharmacology
- Publisher
- Elsevier BV
- Published
- 2025-08
- Volume
- 273
- Pages
- 110437
- ISSN
- 0028-3908 (ISSN-L)
- PMC
- PMC12279083
- OpenAlex
- W4409176997
- Cited by
- 12
- References
- 51
- Citations by year
- 2026: 8 · 2025: 4
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Neuropharmacology (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Institute on Drug Abuse
- U.S. Food and Drug Administration
- NIH National Institute of General Medical Sciences (R35GM148261)
- Oak Ridge Institute for Science and Education
- National Institutes of Health
- U.S. Department of Energy
- NIH National Cancer Institute (R01CA256557)
- Intramural Research Program (Z1A-DA000606 \u2217, Z1A-DA000069)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Receptor Mechanisms and Signaling
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuropeptides and Animal Physiology
- Keywords
- mu-opioid receptor, structure-activity relationship, opioid analgesics
- MeSH terms
- Analgesics, Opioid, Binding Sites, Humans, Protein Binding, Receptors, Opioid, mu, Molecular Dynamics Simulation, Molecular Docking Simulation
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