- 中国精品科技期刊
- 中国高校百佳科技期刊
- 中国中文核心期刊
- 中国科学引文数据库核心期刊
| Citation: |
XU Qing, LYU Min, DENG Hongxiao, et al. Advances in the application of machine learning in the identification and authentication of synthetic cannabinoids[J]. J China Pharm Univ, 2024, 55(3): 316 − 325. DOI: 10.11665/j.issn.1000-5048.2023113003
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Synthetic cannabinoids (SCs) are synthetic psychoactive substances that can pose a public health risk. The SCs are structurally variable and susceptible to structural modification. The rapid emergence of structurally unknown synthetic cannabinoids has led to new challenges in their identification. In recent years, machine learning has made great progress and has been widely applied to other fields, providing new strategies for the identification of unknown synthetic cannabinoids and the inference of possible sources. This paper describes the principles of commonly used machine learning methods and the application of machine learning techniques to mass spectrometry, Raman spectroscopy, metabolomics and quantitative conformational relationships of synthetic cannabinoids, aiming to provide new ideas for the identification of unknown synthetic cannabinoids.
| [1] |
Wiley JL, Marusich JA, Huffman JW. Moving around the molecule: relationship between chemical structure and in vivo activity of synthetic cannabinoids[J]. Life Sci, 2014, 97(1): 55-63. doi: 10.1016/j.lfs.2013.09.011
|
| [2] |
Schurman LD, Lu D, Kendall DA, et al. Molecular mechanism and cannabinoid pharmacology[J]. Handb Exp Pharmacol, 2020, 258: 323-353.
|
| [3] |
Alves VL, Gonçalves JL, Aguiar J, et al. The synthetic cannabinoids phenomenon: from structure to toxicological properties. A review[J]. Crit Rev Toxicol, 2020, 50(5): 359-382. doi: 10.1080/10408444.2020.1762539
|
| [4] |
Alzu’bi A, Almahasneh F, Khasawneh R, et al. The synthetic cannabinoids menace: a review of health risks and toxicity[J]. Eur J Med Res, 2024, 29(1): 49. doi: 10.1186/s40001-023-01443-6
|
| [5] |
Banister SD, Connor M. The chemistry and pharmacology of synthetic cannabinoid receptor agonist new psychoactive substances: evolution[J]. Handb Exp Pharmacol, 2018, 252: 191-226.
|
| [6] |
Tai S, Fantegrossi WE. Pharmacological and toxicological effects of synthetic cannabinoids and their metabolites[J]. Curr Top Behav Neurosci, 2017, 32: 249-262.
|
| [7] |
Fantegrossi WE, Moran JH, Radominska-Pandya A, et al. Distinct pharmacology and metabolism of K2 synthetic cannabinoids compared to Δ(9)-THC: mechanism underlying greater toxicity[J]. [J]? Life Sci, 2014, 97(1): 45-54.
|
| [8] |
Yan FR. Application and advance of artificial intelligence in biomedical field[J]. J China Pharm Univ (中国药科大学学报), 2023, 54(3): 263-268.
|
| [9] |
Wang C, Xiao F, Li M, et al. Application progress of artificial intelligence in the screening and identification of drug targets[J]. J China Pharm Univ (中国药科大学学报), 2023, 54(3): 269-281.
|
| [10] |
Yu ZH, Zhang LM, Zhang MN, et al. Artificial intelligence-based drug development: current progress and future challenges[J]. J China Pharm Univ (中国药科大学学报), 2023, 54(3): 282-293.
|
| [11] |
Jiang T, Gradus JL, Rosellini AJ. Supervised machine learning: a brief primer[J]. Behav Ther, 2020, 51(5): 675-687. doi: 10.1016/j.beth.2020.05.002
|
| [12] |
Ringnér M. What is principal component analysis[J]? Nat Biotechnol, 2008, 26(3): 303-304. doi: 10.1038/nbt0308-303
|
| [13] |
Gilbert N, Mewis RE, Sutcliffe OB. Classification of fentanyl analogues through principal component analysis (PCA) and hierarchical clustering of GC–MS data[J]. Forensic Chem, 2020, 21: 100287. doi: 10.1016/j.forc.2020.100287
|
| [14] |
Jiménez-Carvelo AM, González-Casado A, Bagur-González MG, et al. Alternative data mining/machine learning methods for the analytical evaluation of food quality and authenticity - A review[J]. Food Res Int, 2019, 122: 25-39. doi: 10.1016/j.foodres.2019.03.063
|
| [15] |
Amendolia SR, Cossu G, Ganadu ML, et al. A comparative study of K-nearest neighbour, support vector machine and multi-layer perceptron for thalassemia screening[J]. Chemom Intell Lab Syst, 2003, 69(1/2): 13-20.
|
| [16] |
Broséus J, Anglada F, Esseiva P. The differentiation of fibre- and drug type Cannabis seedlings by gas chromatography/mass spectrometry and chemometric tools[J]. Forensic Sci Int, 2010, 200(1/2/3): 87-92.
|
| [17] |
Thijs B, AxelJan R, Melvin G, et al. Decision trees and random forests[J]. Am J Orthod Dentofac Orthop Off Publ Am Assoc Orthod Const Soc Am Board Orthod, 2023, 164(6): 894-897.
|
| [18] |
Winkler DA, Le TC. Performance of deep and shallow neural networks, the universal approximation theorem, activity cliffs, and QSAR[J]. Mol Inform, 2017, 36(1/2): 10.1002/minf. 201600118.
|
| [19] |
Yang YQ, Liu DP, Hua ZD, et al. Machine learning-assisted rapid screening of four types of new psychoactive substances in drug seizures[J]. J Chem Inf Model, 2023, 63(3): 815-825. doi: 10.1021/acs.jcim.2c01342
|
| [20] |
Wong SL, Ng LT, Tan J, et al. Screening unknown novel psychoactive substances using GC-MS based machine learning[J]. Forensic Chem, 2023, 34: 100499. doi: 10.1016/j.forc.2023.100499
|
| [21] |
Lee SY, Lee ST, Suh S, et al. Revealing unknown controlled substances and new psychoactive substances using high-resolution LC-MS-MS machine learning models and the hybrid similarity search algorithm[J]. J Anal Toxicol, 2022, 46(7): 732-742. doi: 10.1093/jat/bkab098
|
| [22] |
Koshute P, Hagan N, Jameson NJ. Machine learning model for detecting fentanyl analogs from mass spectra[J]. Forensic Chem, 2022, 27: 100379. doi: 10.1016/j.forc.2021.100379
|
| [23] |
Moorthy AS, Kearsley AJ, Mallard WG, et al. Mass spectral similarity mapping applied to fentanyl analogs[J]. Forensic Chem, 2020, 19. doi: 10.1016/j.forc.2020.100237.
|
| [24] |
Setser AL, Waddell Smith R. Comparison of variable selection methods prior to linear discriminant analysis classification of synthetic phenethylamines and tryptamines[J]. Forensic Chem, 2018, 11: 77-86. doi: 10.1016/j.forc.2018.10.002
|
| [25] |
Bonetti JL, Samanipour S, van Asten AC. Utilization of machine learning for the differentiation of positional NPS isomers with direct analysis in real time mass spectrometry[J]. Anal Chem, 2022, 94(12): 5029-5040. doi: 10.1021/acs.analchem.1c04985
|
| [26] |
Münster-Müller S, Matzenbach I, Knepper T, et al. Profiling of synthesis-related impurities of the synthetic cannabinoid Cumyl-5F-PINACA in seized samples of e-liquids via multivariate analysis of UHPLC-MSn data[J]. Drug Test Anal, 2020, 12(1): 119-126. doi: 10.1002/dta.2673
|
| [27] |
Lee J, Jiang H. Analysis of indole and indazole amides synthetic cannabinoids by differential Raman spectroscopy based on ANN[J]. J Forensic Sci, 2022, 67(6): 2242-2252. doi: 10.1111/1556-4029.15133
|
| [28] |
Tian LC, Jiang H, Chen TZ. A rapid and nondestructive approach for forensic identification of novel psychoactive substances using shifted-excitation Raman difference spectroscopyand machine learning[J]. J Raman Spectrosc, 2023, 54(5): 540-550. doi: 10.1002/jrs.6508
|
| [29] |
Streun GL, Steuer AE, Poetzsch SN, et al. Towards a new qualitative screening assay for synthetic cannabinoids using metabolomics and machine learning[J]. Clin Chem, 2022, 68(6): 848-855. doi: 10.1093/clinchem/hvac045
|
| [30] |
Olesti E, De Toma I, Ramaekers JG, et al. Metabolomics predicts the pharmacological profile of new psychoactive substances[J]. J Psychopharmacol, 2019, 33(3): 347-354. doi: 10.1177/0269881118812103
|
| [31] |
Khan K, Benfenati E, Roy K. Consensus QSAR modeling of toxicity of pharmaceuticals to different aquatic organisms: ranking and prioritization of the DrugBank database compounds[J]. Ecotoxicol Environ Saf, 2019, 168: 287-297. doi: 10.1016/j.ecoenv.2018.10.060
|
| [32] |
Lee W, Park SJ, Hwang JY, et al. QSAR model for predicting the cannabinoid receptor 1 binding affinity and dependence potential of synthetic cannabinoids[J]. Molecules, 2020, 25(24): 6057. doi: 10.3390/molecules25246057
|
| [33] |
Paulke A, Proschak E, Sommer K, et al. Synthetic cannabinoids: in silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model[J]. Toxicol Lett, 2016, 245: 1-6. doi: 10.1016/j.toxlet.2016.01.001
|
| [34] |
Risoluti R, Materazzi S, Gregori A, et al. Early detection of emerging street drugs by near infrared spectroscopy and chemometrics[J]. Talanta, 2016, 153: 407-413. doi: 10.1016/j.talanta.2016.02.044
|
| [35] |
de Castro JS, Rodrigues CHP, Bruni AT. In silico infrared characterization of synthetic cannabinoids by quantum chemistry and chemometrics[J]. J Chem Inf Model, 2020, 60(4): 2100-2114. doi: 10.1021/acs.jcim.9b00871
|
| [36] |
Liu CM, Song CH, Jia W, et al. The application of 19F NMR spectroscopy for the analysis of fluorinated new psychoactive substances (NPS)[J]. Forensic Sci Int, 2022, 340: 111450. doi: 10.1016/j.forsciint.2022.111450
|
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