Utilizing machine learning techniques to predict the blood-brain barrier permeability of compounds detected using LCQTOF-MS in Malaysian Kelulut honey

References

• P. Moingeon, M. Kuenemann, and M. Guedj, Artificial intelligence-enhanced drug design and development: Toward a computational precision medicine, Drug Discov. Today 27 (2022), pp. 215–222. doi:10.1016/j.drudis.2021.09.006.
   PubMed Web of Science ®Google Scholar
• K. Singh, V.B. Yadav, U. Yadav, G. Nath, A. Srivastava, P. Zamboni, P. Kerkar, P.S. Saxena, and A.V. Singh, Evaluation of biogenic nanosilver-acticoat for wound healing: A tri-modal in silico, in vitro and in vivo study, Coll. Surf. A Physicochem. Eng. Asp. 670 (2023), pp. 131575. doi:10.1016/j.colsurfa.2023.131575.
   Google Scholar
• S.J. Maceachern and N.D. Forkert, Machine learning for precision medicine, Genome 64 (2021), pp. 416–425. doi:10.1139/gen-2020-0131.
   PubMed Web of Science ®Google Scholar
• I.H. Rizvi, C. Raychaudhury, and D. Pal, Combinatorial Drug Discovery from Activity-Related Substructure Identification, Springer, Cham, New York City, 2019.
   Google Scholar
• S. Hayat, S. Wang, and J.B. Liu, Valency-based topological descriptors of chemical networks and their applications, Appl. Math. Model. 60 (2018), pp. 164–178. doi:10.1016/j.apm.2018.03.016.
   Web of Science ®Google Scholar
• F. İslamoğlu and E. Hacıfazlıoğlu, Investigation of the usability of some triazole derivative compounds as drug active ingredients by ADME and molecular docking properties, Moroccan J. Chem. 4 (2022), pp. 861–880.
   Google Scholar
• P. Agarwal, J. Huckle, J. Newman, and D.L. Reid, Trends in small molecule drug properties: A developability molecule assessment perspective, Drug Discov. Today 27 (2022), pp. 103366. doi:10.1016/j.drudis.2022.103366.
   PubMed Web of Science ®Google Scholar
• M.Y. Ansari, V. Chandrasekar, A.V. Singh, and S.P. Dakua, Re-routing drugs to blood brain barrier: A comprehensive analysis of machine learning approaches with fingerprint amalgamation and data balancing, IEEE Access 11 (2023), pp. 9890–9906. doi:10.1109/ACCESS.2022.3233110.
   Web of Science ®Google Scholar
• A.A. Dar, SEPARATION chromatography: An important tool for drug discovery, J. Sep. Sci. 43 (2020), pp. 105–119. doi:10.1002/jssc.201900656.
   PubMed Web of Science ®Google Scholar
• C. Türkeş and Ş. Beydemir, Inhibition of human serum paraoxonase-i with antimycotic drugs: In vitro and in silico studies, Appl. Biochem. Biotechnol. 190 (2019), pp. 252–269. doi:10.1007/s12010-019-03073-3.
   PubMed Web of Science ®Google Scholar
• O.M. Darwesh, R.H. Mahmoud, S.M. Abdo, and D.A. Marrez, Isolation of Haematococcus lacustris as source of novel anti-multi-antibiotic resistant microbes agents; fractionation and identification of bioactive compounds, Biotechnol. Rep. 35 (2022), pp. e00753. doi:10.1016/j.btre.2022.e00753.
   Google Scholar
• B. Sidders, A. Karlsson, L. Kitching, R. Torella, P. Karila, and A. Phelan, Network-based drug discovery : Coupling network pharmacology with phenotypic screening for neuronal excitability, J. Mol. Biol. 430 (2018), pp. 3005–3015. doi:10.1016/j.jmb.2018.07.016.
   PubMed Web of Science ®Google Scholar
• M.R. Berthold, N. Cebron, F. Dill, T.R. Gabriel, T. Kötter, T. Meinl, P. Ohl, C. Sieb, K. Thiel, and B. Wiswedel, KNIME analytics platform [Version 4.6.0, Switzerland], 2007; software available at https://www.knime.com.
   Google Scholar
• M. Svecla, G. Garrone, F. Faré, G. Aletti, G.D. Norata, and G. Beretta, DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform, Proteomics 21 (2021), pp. 1–12. doi:10.1002/pmic.202000319.
   Web of Science ®Google Scholar
• A.V. Singh, V. Chandrasekar, N. Paudel, P. Laux, A. Luch, D. Gemmati, V. Tissato, K.S. Prabhu, S. Uddin, and S.P. Dakua, Integrative toxicogenomics: Advancing precision medicine and toxicology through artificial intelligence and OMICs technology, Biomed. Pharmacother. 163 (2023), pp. 114784. doi:10.1016/j.biopha.2023.114784.
   PubMed Web of Science ®Google Scholar
• S. Alsenan, I. Al-turaiki, and A. Hafez, A recurrent neural network model to predict blood – brain barrier permeability, Comput. Biol. Chem. 89 (2020), pp. 107377. doi:10.1016/j.compbiolchem.2020.107377.
   PubMed Web of Science ®Google Scholar
• I.F. Martins, A.L. Teixeira, L. Pinheiro, and A.O. Falcao, A Bayesian approach to in silico blood-brain barrier penetration modeling, J. Chem. Inf. Model. 52 (2012), pp. 1686–1697. doi:10.1021/ci300124c.
   PubMed Web of Science ®Google Scholar
• Z. Gao, Y. Chen, X. Cai, R. Xu, and C. Sahinalp, Predict drug permeability to blood-brain-barrier from clinical phenotypes: Drug side effects and drug indications, Bioinformatics 33 (2017), pp. 901–908. doi:10.1093/bioinformatics/btw713.
   PubMed Web of Science ®Google Scholar
• M. Adenot and R. Lahana, Blood-brain barrier permeation models: Discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates, J. Chem. Inf. Comput. Sci. 44 (2004), pp. 239–248. doi:10.1021/ci034205d.
   PubMedGoogle Scholar
• F. Plisson and A.M. Piggott, Predicting blood–brain barrier permeability of marine-derived kinase inhibitors using ensemble classifiers reveals potential hits for neurodegenerative disorders, Mar. Drugs 17 (2019), pp. 81. doi:10.3390/md17020081.
   PubMed Web of Science ®Google Scholar
• M. Singh, R. Divakaran, L.S.K. Konda, and R. Kristam, A classification model for blood brain barrier penetration, J. Mol. Graph. Model. 96 (2020), pp. 107516. doi:10.1016/j.jmgm.2019.107516.
   PubMed Web of Science ®Google Scholar
• Z. Wang, H. Yang, Z. Wu, T. Wang, W. Li, Y. Tang, and G. Liu, In silico prediction of blood–brain barrier permeability of compounds by machine learning and resampling methods, ChemMedChem 13 (2018), pp. 2189–2201. doi:10.1002/cmdc.201800533.
   PubMed Web of Science ®Google Scholar
• Z. Wu, B. Ramsundar, E.N. Feinberg, J. Gomes, C. Geniesse, A.S. Pappu, K. Leswing, and V. Pande, MoleculeNet: A benchmark for molecular machine learning, Chem. Sci. 9 (2018), pp. 513–530. doi:10.1039/C7SC02664A.
   PubMed Web of Science ®Google Scholar
• Y. Yuan, F. Zheng, and C.G. Zhan, Improved prediction of blood–brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints, AAPS J. 20 (2018), pp. 54. doi:10.1208/s12248-018-0215-8.
   PubMed Web of Science ®Google Scholar
• B. Shaker, M.S. Yu, J.S. Song, S. Ahn, J.Y. Ryu, K.S. Oh, and D. Na, LightBBB: Computational prediction model of blood-brain-barrier penetration based on LightGBM, Bioinformatics 37 (2021), pp. 1135–1139. doi:10.1093/bioinformatics/btaa918.
   PubMed Web of Science ®Google Scholar
• F. Broccatelli, C.A. Larregieu, G. Cruciani, T.I. Oprea, and L.Z. Benet, Improving the prediction of the brain disposition for orally administered drugs using BDDCS, Adv. Drug Deliv. Rev. 64 (2012), pp. 95–109. doi:10.1016/j.addr.2011.12.008.
   PubMed Web of Science ®Google Scholar
• F. Meng, Y. Xi, J. Huang, and P.W. Ayers, A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors, Blood-Brain Barrier Database (B3DB), 2021; software available at https://doi.org/10.6084/m9.figshare.15634230.v3.
   Google Scholar
• V. Rattan, R. Mittal, J. Singh, and V. Malik, Analyzing the application of SMOTE on machine learning classifiers, in 2021 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 2021, pp. 692–695.
   Google Scholar
• P. Banerjee, F.O. Dehnbostel, and R. Preer, Prediction is a balancing act: Importance of sampling methods to balance sensitivity and specificity of predictive models based on imbalanced chemical data sets, Front. Chem. 6 (2018), pp. 1–11. doi:10.3389/fchem.2018.00362.
   PubMed Web of Science ®Google Scholar
• V. Chandrasekar, M.Y. Ansari, A.V. Singh, S. Uddin, K.S. Prabhu, S. Dash, S. Al Khodor, A. Terranegra, M. Avella, and S.P. Dakua, Investigating the use of machine learning models to understand the drugs permeability across placenta, IEEE Access 11 (2023), pp. 52726–52739. doi:10.1109/ACCESS.2023.3272987.
   Web of Science ®Google Scholar
• D. Dablain, B. Krawczyk, and N.V. Chawla, DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data, in IEEE Transactions on Neural Networks and Learning Systems, Padua, Italy, 2022, pp. 1–15.
   Google Scholar
• A. Ramezankhani, O. Pournik, J. Shahrabi, F. Azizi, F. Hadaegh, and D. Khalili, The impact of oversampling with SMOTE on the performance of 3 classifiers in prediction of type 2 diabetes, Med. Decis. Mak. 36 (2016), pp. 137–144. doi:10.1177/0272989X14560647.
   PubMed Web of Science ®Google Scholar
• V. Kumar, S. Patiyal, A. Dhall, N. Sharma, and G.P.S. Raghava, B3pred: A random-forest-based method for predicting and designing blood–brain barrier penetrating peptides, Pharmaceutics 13 (2021), pp. 1237. doi:10.3390/pharmaceutics13081237.
   PubMed Web of Science ®Google Scholar
• D. Roy, V.K. Hinge, and A. Kovalenko, To pass or not to pass: Predicting the blood-brain barrier permeability with the 3D-RISM-KH molecular solvation theory, ACS Omega 4 (2019), pp. 16774–16780. doi:10.1021/acsomega.9b01512.
   PubMed Web of Science ®Google Scholar
• Z. Shi, Y. Chu, Y. Zhang, Y. Wang, and D.Q. Wei, Prediction of blood-brain barrier permeability of compounds by fusing resampling strategies and extreme gradient boosting, IEEE Access 9 (2021), pp. 9557–9566. doi:10.1109/ACCESS.2020.3047852.
   Web of Science ®Google Scholar
• V.A. Dev and M.R. Eden, Formation lithology classification using scalable gradient boosted decision trees, Comput. Chem. Eng. 128 (2019), pp. 392–404. doi:10.1016/j.compchemeng.2019.06.001.
   Web of Science ®Google Scholar
• S. Touzani, J. Granderson, and S. Fernandes, Gradient boosting machine for modeling the energy consumption of commercial buildings, Energy Build. 158 (2018), pp. 1533–1543. doi:10.1016/j.enbuild.2017.11.039.
   Web of Science ®Google Scholar
• L. Liu, L. Zhang, H. Feng, S. Li, M. Liu, J. Zhao, and H. Liu, Prediction of the blood-brain barrier (BBB) permeability of chemicals based on machine-learning and ensemble methods, Chem. Res. Toxicol. 34 (2021), pp. 1456–1467. doi:10.1021/acs.chemrestox.0c00343.
   PubMed Web of Science ®Google Scholar
• T.H. Yu, B.H. Su, L.C. Battalora, S. Liu, and Y.J. Tseng, Ensemble modeling with machine learning and deep learning to provide interpretable generalized rules for classifying CNS drugs with high prediction power, Brief. Bioinf. 23 (2022), pp. 1–13. doi:10.1093/bib/bbab377.
   Web of Science ®Google Scholar
• M. Hossin and M. Sulaiman, A review on evaluation metrics for data classification evaluations, Int. J. Data Min. Knowl. Manag. Process 5 (2015), pp. 01–11. doi:10.5121/ijdkp.2015.5201.
   Google Scholar
• J.N. Mandrekar, Receiver operating characteristic curve in diagnostic test assessment, J. Thorac. Oncol. 5 (2010), pp. 1315–1316. doi:10.1097/JTO.0b013e3181ec173d.
   PubMed Web of Science ®Google Scholar
• M.M. Aspar, R. Edros, and N.A. Hamzah, Antibacterial evaluation of Malaysian kelulut, tualang and acacia honey against wound infecting bacteria, in IOP Conference Series: Materials Science and Engineering 991, Kuala Lumpur, Malaysia, 2020, pp. 012065.
   Google Scholar
• M.A. Mohd, R. Edros, and N.A. Hamzah, Antibacterial properties of Kelulut, Tualang and Acacia honey against fourteen clinically-isolated strains of bacteria-infecting wound, in AIP Conference Proceedings, 2252, Poznan, Poland, 2020, pp. 020001.
   Google Scholar
• M.A. Prats-Sedano, G. Savulich, A. Surendranathan, P.C. Donaghy, A.J. Thomas, J.B. Rowe, L. Su, and J.T. O’Brien, The revised addenbrooke’s cognitive examination can facilitate differentiation of dementia with Lewy bodies from Alzheimer’s disease, Int. J. Geriatr. Psychiatry 36 (2021), pp. 831–838. doi:10.1002/gps.5483.
   PubMed Web of Science ®Google Scholar
• F. Sessa and M. Rahm, Electronegativity equilibration, J. Phys. Chem. A 126 (2022), pp. 5472–5482. doi:10.1021/acs.jpca.2c03814.
   PubMedGoogle Scholar
• S. Racioppi and M. Rahm, In-situ electronegativity and the bridging of chemical bonding concepts, Chem. Eur. J. 27 (2021), pp. 18156–18167. doi:10.1002/chem.202103477.
   PubMed Web of Science ®Google Scholar
• A.V. Singh, M. Varma, P. Laux, S. Choudhary, A.K. Datusalia, N. Gupta, A. Luch, A. Gandhi, P. Kulkarni, and B. Nath, Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: A comprehensive review, Arch. Toxicol. 97 (2023), pp. 963–979. doi:10.1007/s00204-023-03471-x.
   PubMedGoogle Scholar
• V. Shalgunov, S. Lopes van den Broek, I. Vang Andersen, R. García Vázquez, N.R. Raval, M. Palner, Y. Mori, G. Schäfer, B. Herrmann, H. Mikula, N. Beschorner, M. Nedergaard, S. Syvänen, M. Barz, G. Moos Knudsen, U.M. Battisti, and M.M. Herth, Pretargeted imaging beyond the blood-brain barrier, RSC Med. Chem. 14 (2022), pp. 444–453. doi:10.1039/D2MD00360K.
   PubMedGoogle Scholar
• R. Laczkó-Rigó, R. Jójárt, E. Mernyák, É. Bakos, A. Tuerkova, B. Zdrazil, and C. Özvegy-Laczka, Structural dissection of 13-epiestrones based on the interaction with human organic anion-transporting polypeptide, OATP2B1, J. Steroid Biochem. Mol. Biol. 200 (2020), pp. 105652. doi:10.1016/j.jsbmb.2020.105652.
   PubMed Web of Science ®Google Scholar
• A.Z. Summers, J.B. Gilmer, C.R. Iacovella, P.T. Cummings, and C. Mccabe, MoSDeF, a Python framework enabling large-scale computational screening of soft matter: Application to chemistry-property relationships in lubricating monolayer films, J. Chem. Theory Comput. 16 (2020), pp. 1779–1793. doi:10.1021/acs.jctc.9b01183.
   PubMed Web of Science ®Google Scholar
• R. Hamed, A.D. Eyal, E. Berman, and S. Eyal, In silico screening for clinical efficacy of antiseizure medications: Not all central nervous system drugs are alike, Epilepsia 64 (2023), pp. 311–319. doi:10.1111/epi.17479.
   PubMed Web of Science ®Google Scholar
• A. El Aissouq, H. Toufik, F. Lamchouri, M. Stitou, and A. Ouammou, QSAR study of isonicotinamides derivatives as Alzheimr’s disease inhibitors using PLS-R and ANN methods, in Proceedings - 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS), Taza, Morocco, 2019, pp. 1–7.
   Google Scholar
• T. Constantinescu, C.N. Lungu, and I. Lung, Lipophilicity as a central component of drug-like properties of chalchones and flavonoid derivatives, Molecules 24 (2019), pp. 1–11. doi:10.3390/molecules24081505.
   Web of Science ®Google Scholar
• O.A. Joseph, K. Babatomiwa, A. Niyi, O. Olaposi, I. Olumide, D. Development, A. Akoko, A. Akoko, D. Res, O.A. Joseph, D. Development, A. Akoko, and O. State, Molecular docking and 3d qsar studies of c000000956 as a potent inhibitor of bace-1 * preparation of protein structure molecules, Drug Res. (Stuttg) 69 (2019), pp. 451–457. doi:10.1055/a-0849-9377.
   PubMed Web of Science ®Google Scholar
• J. Williams, V. Siramshetty, Ð.-T. Nguyễn, E.C. Padilha, M. Kabir, K.-R. Yu, A.Q. Wang, T. Zhao, M. Itkin, P. Shinn, E.A. Mathé, X. Xu, and P. Shah, Using in vitro ADME data for lead compound selection: An emphasis on PAMPA ph 5 permeability and oral bioavailability, Bioorg. Med. Chem. 56 (2022), pp. 116588. doi:10.1016/j.bmc.2021.116588.
   PubMed Web of Science ®Google Scholar
• M. Awale, R. Van Deursen, and J.L. Reymond, MQN-mapplet: Visualization of chemical space with interactive maps of DrugBank, ChEMBL, PubChem, GDB-11, and GDB-13, J. Chem. Inf. Model 53 (2013), pp. 509–518. doi:10.1021/ci300513m.
   PubMed Web of Science ®Google Scholar
• M. Medic-Saric, S. Rendic, V. Vestemar, and S. Saric, A comparative study of some topological indices and log P in structure-property-activity analysis of phenylalkylamines, Acta Pharm. 43 (1993), pp. 15–26.
   Google Scholar
• P.L.A. Popelier, Quantum chemical topology, Struct. Bond. 170 (2016), pp. 71–118.
   Web of Science ®Google Scholar
• P. Bleiziffer, K. Schaller, and S. Riniker, Machine learning of partial charges derived from high-quality quantum-mechanical calculations, J. Chem. Inf. Model. 58 (2018), pp. 579–590. doi:10.1021/acs.jcim.7b00663.
   PubMed Web of Science ®Google Scholar
• I. Loryan, V. Sinha, C. Mackie, A. Van Peer, W.H. Drinkenburg, A. Vermeulen, D. Heald, M. Hammarlund-Udenaes, and C.M. Wassvik, Molecular properties determining unbound intracellular and extracellular brain exposure of CNS drug candidates, Mol. Pharm. 12 (2015), pp. 520–532.
   PubMed Web of Science ®Google Scholar
• C. Wang, X. Man, and L. Ou, Molecular dynamics study of the effect of temperature on the flotation behavior of sodium oleate on the surface of diaspore, Miner. Eng. 192 (2023), pp. 108004. doi:10.1016/j.mineng.2023.108004.
   Web of Science ®Google Scholar
• N. Qin, X. Lu, Y. Liu, Y. Qiao, W. Qu, F. Feng, and H. Sun, Recent research progress of Uncaria spp. based on alkaloids: Phytochemistry, pharmacology and structural chemistry, Eur. J. Med. Chem. 210 (2021), pp. 112960. doi:10.1016/j.ejmech.2020.112960.
   PubMed Web of Science ®Google Scholar
• M.E. Heitzman, C.C. Neto, E. Winiarz, A.J. Vaisberg, and G.B. Hammond, Ethnobotany, phytochemistry and pharmacology of Uncaria (Rubiaceae), Phytochemistry 66 (2005), pp. 5–29.
   PubMed Web of Science ®Google Scholar
• D. Martins and C.V. Nunez, Secondary metabolites from Rubiaceae species, Molecules 20 (2015), pp. 13422–13495. doi:10.3390/molecules200713422.
   PubMed Web of Science ®Google Scholar
• H.J. Barrales-Cureño, Pharmacological applications and in vitro biotechnological production of anticancer alkaloids of Catharanthus roseus TT - Aplicaciones farmacológicas y producción biotecnológica in vitro de los alcaloides anticancerígenos de Catharanthus roseus, Biotecnol. Apl. 32 (2015), pp. 1101–1110.
   Google Scholar
• C. Huang, X. Xia, J. He, Y. Liu, Z. Shao, T. Hu, C. Yu, X. Liu, Q. Xu, B. Liu, N. Liu, Y. Liao, and H. Huang, ERα is a target for butein-induced growth suppression in breast cancer, Am. J. Cancer Res. 10 (2020), pp. 3721–3736.
   PubMed Web of Science ®Google Scholar
• M. Taher, M.S. Amri, D. Susanti, M.B. Abdul Kudos, N.F.A. Md Nor, and Y. Syukri, Medicinal uses, phytochemistry, and pharmacological properties of Piper aduncum L, Sains Malaysiana 49 (2020), pp. 1829–1851. doi:10.17576/jsm-2020-4908-07.
   Web of Science ®Google Scholar
• X. Li, B. Chen, H. Xie, Y. He, D. Zhong, and D. Chen, Antioxidant structure–activity relationship analysis of five dihydrochalcones, Molecules 23 (2018), pp. 1–13.
   Web of Science ®Google Scholar
• M. Rudrapal, J. Khan, A.A. Bin Dukhyil, R.M.I.I. Alarousy, E.I. Attah, T. Sharma, S.J. Khairnar, and A.R. Bendale, Chalcone scaffolds, bioprecursors of flavonoids: Chemistry, bioactivities, and pharmacokinetics, Molecules 26 (2021), pp. 1–21. doi:10.3390/molecules26237177.
   Web of Science ®Google Scholar
• R.S.L. Taylor and G.H.N. Towers, Antibacterial constituents of the Nepalese medicinal herb, Centipeda minima, Phytochemistry 47 (1998), pp. 631–634. doi:10.1016/S0031-9422(97)00534-7.
   PubMed Web of Science ®Google Scholar
• Y.J. Wang, X.Y. Wang, X.Y. Hao, Y.M. Yan, M. Hong, S.F. Wei, Y.L. Zhou, Q. Wang, Y.X. Cheng, and Y.Q. Liu, Ethanol extract of Centipeda minima exerts antioxidant and neuroprotective effects via activation of the Nrf2 signaling pathway, Oxid. Med. Cell. Longev. 2019 (2019), pp. 9421037. doi:10.1155/2019/9421037.
   PubMed Web of Science ®Google Scholar
• R. Zhang, Q. Guo, E.J. Kennelly, C. Long, and X. Chai, Diverse alkaloids and biological activities of Fumaria (Papaveraceae): An ethnomedicinal group, Fitoterapia 146 (2020), pp. 104697. doi:10.1016/j.fitote.2020.104697.
   PubMed Web of Science ®Google Scholar
• C. Shepherd, P. Giacomin, S. Navarro, C. Miller, A. Loukas, and P. Wangchuk, A medicinal plant compound, capnoidine, prevents the onset of inflammation in a mouse model of colitis, J. Ethnopharmacol. 211 (2018), pp. 17–28. doi:10.1016/j.jep.2017.09.024.
   PubMed Web of Science ®Google Scholar
• R. Ahmad and F. Salim, Oxindole Alkaloids of Uncaria (Rubiaceae, Subfamily Cinchonoideae): A Review on Its Structure, Properties, and Bioactivities, Vol. 45, Elsevier, Amsterdam, The Netherlands, 2015.
   Google Scholar
• D. Yan, Z. Ma, C. Liu, C. Wang, Y. Deng, W. Liu, and B. Xu, Corynoxine B ameliorates HMGB1-dependent autophagy dysfunction during manganese exposure in SH-SY5Y human neuroblastoma cells, Food Chem. Toxicol. 124 (2019), pp. 336–348. doi:10.1016/j.fct.2018.12.027.
   PubMed Web of Science ®Google Scholar
• X. Hou, J.O. Watzlawik, F.C. Fiesel, and W. Springer, Autophagy in Parkinson’s disease, J. Mol. Biol. 432 (2020), pp. 2651–2672. doi:10.1002/bab.2194.
   PubMed Web of Science ®Google Scholar
• M.Z. Yan, Q. Chang, Y. Zhong, B.X. Xiao, L. Feng, F.R. Cao, R. Le Pan, Z.S. Zhang, Y.H. Liao, and X.M. Liu, Lotus Leaf alkaloid extract displays sedative-hypnotic and anxiolytic effects through GABAA receptor, J. Agric. Food Chem. 63 (2015), pp. 9277–9285. doi:10.1021/acs.jafc.5b04141.
   PubMed Web of Science ®Google Scholar
• R. HarishKumar and C.I. Selvaraj, Nuciferine from Nelumbo nucifera Gaertn. Attenuates isoproterenol-induced myocardial infarction in Wistar rats, Biotechnol. Appl. Biochem. 69 (2022), pp. 1176–1189.
   PubMed Web of Science ®Google Scholar
• L.H. Ye, X.X. He, C. You, X. Tao, L.S. Wang, M. Di Zhang, Y.F. Zhou, and Q. Chang, Pharmacokinetics of nuciferine and N-nornuciferine, two major alkaloids from Nelumbo nucifera leaves, in rat plasma and the brain, Front. Pharmacol. 9 (2018), pp. 1–7. doi:10.3389/fphar.2018.00902.
   PubMed Web of Science ®Google Scholar
• X. Ma, F.J. Mandausch, V. Sahoo, L. Popovic, M. Hostiuc, J.P. Wintgens, J. Qiu, N. Kannaiyan, M.J. Rossner, M.C. Wehr, B. Ndikubwimana, and F. Ngendahimana, Hepatic metabolomics combined with network pharmacology approach to reveal the correlation of anti-depression effect and nourishing blood effect of Angelicae sinensis Radix, J. Emerg. Technol. Innov. Res. 24 (2021), pp. 5–7.
   Google Scholar
• Q. Xie, L. Zhang, L. Xie, Y. Zheng, K. Liu, H. Tang, Y. Liao, and X. Li, Z-ligustilide: A review of its pharmacokinetics and pharmacology, Phyther. Res. 34 (2020), pp. 1966–1991. doi:10.1002/ptr.6662.
   PubMed Web of Science ®Google Scholar
• V. Bunel, M.-H. Antoine, J. Nortier, P. Duez, and C. Stévigny, Nephroprotective effects of ferulic acid, Z-ligustilide and E-ligustilide isolated from Angelica sinensis against cisplatin toxicity in vitro, Toxicol. Vitro 29 (2015), pp. 458–467. doi:10.1016/j.tiv.2014.12.017.
   PubMed Web of Science ®Google Scholar
• C. Wang, J. Wan, Z. Mei, and X. Yang, Acridone alkaloids with cytotoxic and antimalarial activities from Zanthoxylum simullans Hance, Pharmacogn. Mag. 10 (2014), pp. 73–76. doi:10.4103/0973-1296.126669.
   PubMed Web of Science ®Google Scholar
• M. Mirghani, W. J Osman, E.A.E. Garelnabi, M.S. Mohammed, and O.H.A. Ali, Pulicaria crispa (Forssk) Oliv: A review of ethnopharmacology, phytochemistry and biological activities, Khartoum J. Pharm. Sci. 1 (2020), pp. 13–25.
   Google Scholar
• S. Kebbi, M.L. Ciavatta, A.M. Mahmoud, M. Carbone, A. Ligresti, R. Seghiri, and M. Gavagnin, Sesquiterpene lactones with the 12,8-guaianolide skeleton from Algerian Centaurea omphalotricha, Biomolecules 11 (2021), pp. 1–10. doi:10.3390/biom11071053.
   Web of Science ®Google Scholar
• U.K. Karmakar, N. Ishikawa, K. Toume, M.A. Arai, S.K. Sadhu, F. Ahmed, and M. Ishibashi, Sesquiterpenes with TRAIL-resistance overcoming activity from Xanthium strumarium, Bioorg. Med. Chem. 23 (2015), pp. 4746–4754. doi:10.1016/j.bmc.2015.05.044.
   PubMed Web of Science ®Google Scholar
• K. Chester, S. Zahiruddin, A. Ahmad, W. Khan, S. Paliwal, and S. Ahmad, Bioautography-based identification of antioxidant metabolites of Solanum nigrum l. and exploration its hepatoprotective potential agchester, Pharmacogn. Mag. 13 (Suppl 2017), pp. 179–188.
   Google Scholar
• P. Zeng, X.M. Wang, C.Y. Ye, H.F. Su, and Q. Tian, The main alkaloids in Uncaria rhynchophylla and their anti-Alzheimer’s disease mechanism determined by a network pharmacology approach, Int. J. Mol. Sci. 22 (2021), pp. 3612. doi:10.3390/ijms22073612.
   PubMed Web of Science ®Google Scholar
• L. Chen, Y. Huang, X. Yu, J. Lu, W. Jia, J. Song, L. Liu, Y. Wang, Y. Huang, J. Xie, and M. Li, Corynoxine protects dopaminergic neurons through inducing autophagy and diminishing neuroinflammation in rotenone-induced animal models of Parkinson’s disease, Front. Pharmacol. 12 (2021), pp. 1–11.
   Web of Science ®Google Scholar
• V. Coca-Ruíz, I. Suárez, J. Aleu, and I.G. Collado, Structures, occurrences and biosynthesis of 11,12,13-tri-nor-sesquiterpenes, an intriguing class of bioactive metabolites, Plants 11 (2022), pp. 769. doi:10.3390/plants11060769.
   Google Scholar
• T. Mihajilov-Krstev, B. Jovanović, B. Zlatković, J. Matejić, J. Vitorović, V. Cvetković, B. Ilić, L. Đorđević, N. Joković, D. Miladinović, T. Jakšić, N. Stanković, V.S. Jovanović, and N. Bernstein, Phytochemistry, toxicology and therapeutic value of Petasites hybridus subsp. Ochroleucus (common butterbur) from the Balkans, Plants 9 (2020), pp. 700. doi:10.3390/plants9060700.
   Web of Science ®Google Scholar
• L. Zhang, Z. Hong, R. Rong Zhang, X. Zhen Sun, Y. Fang Yuan, J. Hu, and X. Wang, Bakkenolide A inhibits leukemia by regulation of HDAC3 and PI3K/Akt-related signaling pathways, Biomed. Pharmacother. 83 (2016), pp. 958–966. doi:10.1016/j.biopha.2016.07.049.
   PubMed Web of Science ®Google Scholar
• M. Kerboua, M.A. Ahmed, N. Samba, R. Aitfella-Lahlou, L. Silva, J.F. Boyero, C. Raposo, and J.M.L. Rodilla, Phytochemical investigation of new Algerian lichen species: Physcia mediterranea nimis, Molecules 26 (2021), pp. 1–20. doi:10.3390/molecules26041121.
   Web of Science ®Google Scholar
• I. Rahayu and K.H. Timotius, Phytochemical analysis, antimutagenic and antiviral activity of Moringa oleifera L. leaf infusion: In vitro and in silico studies, Molecules 27 (2022), pp. 4017. doi:10.3390/molecules27134017.
   PubMed Web of Science ®Google Scholar
• M.D. Firdaus, N. Artanti, M. Hanafi, and Rosmalena, Phytochemical constituents, and in vitro antidiabetic and antioxidant properties of various extracts of kenikir (Cosmos caudatus) leaves, Pharmacogn. J. 13 (2021), pp. 890–895. doi:10.5530/pj.2021.13.114.
   Google Scholar
• M. Westermann, A.G. Adomako-Bonsu, S. Thiele, S.S. Çiçek, H.J. Martin, and E. Maser, Inhibition of human carbonyl reducing enzymes by plant anthrone and anthraquinone derivatives, Chem. Biol. Interact. 354 (2022), pp. 109823. doi:10.1016/j.cbi.2022.109823.
   PubMed Web of Science ®Google Scholar
• X. Zhang, X. Yang, Z. Cao, Z. Zhao, and Y. Zhang, Isoxanthohumol exerts anticancer activity against drug-resistant thyroid cancer cells by inhibiting cell migration and invasion, apoptosis induction and targeting PI3K/AKT/m-TOR signaling pathway, Trop. J. Pharm. Res. 20 (2021), pp. 1151–1157. doi:10.4314/tjpr.v20i6.8.
   Web of Science ®Google Scholar
• T. Krajnović, N. N. Pantelić, K. Wolf, T. Eichhorn, D. Maksimović-Ivanić, S. Mijatović, L.A. Wessjohann, and G.N. Kaluđerović, Anticancer potential of xanthohumol and isoxanthohumol loaded into SBA- mesoporous silica particles against B16F10 melanoma cells, Materials (Basel) 15 (2022), pp. 5028.
   PubMed Web of Science ®Google Scholar
• M.C. Cheng, T.H. Lee, Y.T. Chu, L.L. Syu, S.J. Hsu, C.H. Cheng, J. Wu, and C.K. Lee, Melanogenesis inhibitors from the rhizoma of Ligusticum sinense in B16-f10 melanoma cells in vitro and zebrafish in vivo, Int. J. Mol. Sci. 19 (2018), pp. 1–17. doi:10.3390/ijms19123994.
   Web of Science ®Google Scholar
• R.N. De Almeida, M. De Fátima Agra, F.N.S. Maior, and D.P. De Sousa, Essential oils and their constituents: Anticonvulsant activity, Molecules 16 (2011), pp. 2726–2742. doi:10.3390/molecules16032726.
   PubMed Web of Science ®Google Scholar
• I.A.P. Jourjine, L. Zeisel, J. Krauß, and F. Bracher, Synthesis of highly substituted fluorenones via metal-free TBHP-promoted oxidative cyclization of 2-(aminomethyl)biphenyls. Application to the total synthesis of nobilone, Beilstein J. Org. Chem. 17 (2021), pp. 2668–2679. doi:10.3762/bjoc.17.181.
   PubMed Web of Science ®Google Scholar
• X. Zhang, J.K. Xu, J. Wang, N.L. Wang, H. Kurihara, S. Kitanaka, and X.S. Yao, Bioactive bibenzyl derivatives and fluorenones from Dendrobium nobile, J. Nat. Prod. 70 (2007), pp. 24–28. doi:10.1021/np060449r.
   PubMed Web of Science ®Google Scholar
• W.J. Geldenhuys, A.S. Mohammad, C.E. Adkins, and P.R. Lockman, Molecular determinants of blood–brain barrier permeation, Ther. Deliv. 6 (2015), pp. 961–971. doi:10.4155/tde.15.32.
   PubMed Web of Science ®Google Scholar
• Z. Wu, Z. Xian, W. Ma, Q. Liu, X. Huang, B. Xiong, S. He, and W. Zhang, Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method, Comput. Meth. Prog. Biomed. 200 (2021), pp. 105943. doi:10.1016/j.cmpb.2021.105943.
   PubMed Web of Science ®Google Scholar
• A.S. Guntner, T. Bögl, F. Mlynek, and W. Buchberger, Large-scale evaluation of collision cross sections to investigate blood-brain barrier permeation of drugs, Pharmaceutics 13 (2021), pp. 2141. doi:10.3390/pharmaceutics13122141.
   PubMed Web of Science ®Google Scholar
• D.J. Begley, ABC transporters and the blood-brain barrier, Curr. Pharm. Des. 10 (2004), pp. 1295–1312. doi:10.2174/1381612043384844.
   PubMed Web of Science ®Google Scholar