Unveiling the Anticancer Potential of Formylbenzyl-N,N-Dimethylmethanaminium-Based Ionic Liquids via Cheminformatics Approaches
This article is part of Special Issue:
Sonaxi,
Anshul Singh,
Mozhgan Afshari,
Ravi Tomar,
Sonaxi
Department of Chemistry , Baba Mastnath University , Rohtak , 124021 , India , babamastnathuniversity.com
Search for more papers by this authorCorresponding Author
Anshul Singh
Department of Chemistry , Baba Mastnath University , Rohtak , 124021 , India , babamastnathuniversity.com
Search for more papers by this authorCorresponding Author
Mozhgan Afshari
Department of Chemistry, Sho.C. , Islamic Azad University , Shoushtar , Iran , azad.ac.ir
Search for more papers by this authorRavi Tomar
Department of Chemistry , SRM Institute of Science and Technology-Delhi-NCR Campus , Modinagar, Ghaziabad , Uttar Pradesh, 201204 , India
Search for more papers by this author Sonaxi,
Anshul Singh,
Mozhgan Afshari,
Ravi Tomar,
Sonaxi
Department of Chemistry , Baba Mastnath University , Rohtak , 124021 , India , babamastnathuniversity.com
Search for more papers by this authorCorresponding Author
Anshul Singh
Department of Chemistry , Baba Mastnath University , Rohtak , 124021 , India , babamastnathuniversity.com
Search for more papers by this authorCorresponding Author
Mozhgan Afshari
Department of Chemistry, Sho.C. , Islamic Azad University , Shoushtar , Iran , azad.ac.ir
Search for more papers by this authorRavi Tomar
Department of Chemistry , SRM Institute of Science and Technology-Delhi-NCR Campus , Modinagar, Ghaziabad , Uttar Pradesh, 201204 , India
Search for more papers by this authorFirst published: 25 March 2025
Academic Editor: Davut Avci
Abstract
Inhibiting tubulin polymerisation, an essential step for cell proliferation, can control cell division. Our in silico study aims to screen out the derivatives of 1,3-benzodioxole-tagged formylbenzyl-N, N-dimethylmethanaminium ionic liquid for their potential as anticancer agents. All parameters requisite for a compound to act as a pertinent drug candidate are sequentially measured. A library of fifteen 1,3-benzodioxole-based ionic liquids with different anions has been designed for molecular docking. Based on binding affinity obtained from protein–ligand molecular docking, ionic liquid comprising of anion bistrifluoromethylamide, octyl sulphate, trifluoromethanesulphonate, dichloroacetate and trifluoroacetate, with common cation—1-(benzo[d][1,3] dioxol-5-yl)-N-(4-formylbenzyl)-N,N-dimethylmethanaminium, was found potent among all. The hydrogen bonding and nonbond interactions among ligands and proteins were analysed to determine the efficacy of ligands. The docking results revealed that Compounds 7 and 10 (binding energy −361.33 and −345.4 kcal/mol) might pave the way for potential pharmaceutical candidates. The physicochemical and absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of selected compounds were studied to know their potential as drug candidates. The docked complexes of screened compounds were further commenced for dynamic simulation to fathom the compatibility of compounds. These potent compounds can be synthesised and biologically evaluated for their inhibition effect on microtubule formation.
Conflicts of Interest
The authors declare no conflicts of interest.
Open Research
Data Availability Statement
The data that support the findings of this study are available on request from the corresponding author.
Supporting Information
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Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
References
- 1 Siegel R. L., Miller K. D., Fuchs H. E., and Jemal A., Cancer Statistics, 2022, CA: A Cancer Journal for Clinicians. (2022) 72, no. 1, 7–33, https://doi.org/10.3322/caac.21708.
- 2 DeSantis C. E., Lin C. C., Mariotto A. B. et al., Cancer Treatment and Survivorship Statistics, CA: A Cancer Journal for Clinicians. (2014) 64, no. 4, 252–271, https://doi.org/10.3322/caac.21235, 2-s2.0-84904155440.
- 3 Falzone L., Salomone S., and Libra M., Evolution of Cancer Pharmacological Treatments at the Turn of the Third Millennium, Frontiers in Pharmacology. (2018) 9, https://doi.org/10.3389/fphar.2018.01300, 2-s2.0-85056833259.
- 4 Al-blewi F., Rezki N., Naqvi A. et al., A Profile of the In Vitro Anti-tumor Activity and In Silico ADME Predictions of Novel Benzothiazole Amide-Functionalized Imidazolium Ionic Liquids, International Journal of Molecular Sciences. (2019) 20, no. 12, https://doi.org/10.3390/ijms20122865, 2-s2.0-85068471522.
- 5 Celik S., Albayrak A. T., Akyuz S., and E Ozel A., Molecular Modelling and Vibrational Investigations of Ammonium-Based Ionic Liquid (CLTOAB), Journal of Biomolecular Structure and Dynamics. (2019) 37, no. 10, 2515–2526, https://doi.org/10.1080/07391102.2018.1495578, 2-s2.0-85057557713.
- 6 Haryadi W. and Pranowo H. D., Molecular Docking and Dynamics Analysis of Halogenated Imidazole Chalcone as Anticancer Compounds, Pharmacia. (2023) 70, no. 2, 323–329, https://doi.org/10.3897/pharmacia.70.e101989.
- 7 Azmal M., Paul J. K., Prima F. S., Talukder O. F., and Ghosh A., An In Silico Molecular Docking and Simulation Study to Identify Potential Anticancer Phytochemicals Targeting the RAS Signaling Pathway, PLoS One. (2024) 19, no. 9, https://doi.org/10.1371/journal.pone.0310637.
- 8 Saldanha L., Langel Ü., and Vale N., In Silico Studies to Support Vaccine Development, Pharmaceutics. (2023) 15, no. 2, https://doi.org/10.3390/pharmaceutics15020654.
- 9 Kharb S., Yadav S., Singh A., Sarkar A., and Tomar R., Molecular Docking and Physicochemical Studies of 1,3-benzodioxole Tagged Dacarbazine Derivatives as an Anticancer Agent, Artificial Cells, Nanomedicine, and Biotechnology. (2023) 51, no. 1, 520–530, https://doi.org/10.1080/21691401.2023.2253470.
- 10 Azad I., Nasibullah M., Khan T., Hassan F., and Akhter Y., Exploring the Novel Heterocyclic Derivatives as Lead Molecules for Design and Development of Potent Anticancer Agents, Journal of Molecular Graphics and Modelling. (2018) 81, 211–228, https://doi.org/10.1016/j.jmgm.2018.02.013, 2-s2.0-85044586590.
- 11 Ononamadu C. and Ibrahim A., Molecular Docking and Prediction of ADME/drug-likeness Properties of Potentially Active Antidiabetic Compounds Isolated from Aqueous-Methanol Extracts of Gymnema Sylvestre and Combretum Micranthum, BioTechnologia. (2021) 102, no. 1, 85–99, https://doi.org/10.5114/bta.2021.103765.
- 12 Balavignesh V., Srinivasan E., Babu N. G. R., and Saravanan N., INTERNATIONAL JOURNAL OF PHARMACY & LIFE SCIENCES, Life Sciences. (2013) 4, no. 4.
- 13 Kumar D., Kumari K., Vishvakarma V. K. et al., Promising Inhibitors of Main Protease of Novel Corona Virus to Prevent the Spread of COVID-19 Using Docking and Molecular Dynamics Simulation, Journal of Biomolecular Structure and Dynamics. (2021) 39, no. 13, 4671–4685, https://doi.org/10.1080/07391102.2020.1779131.
- 14 Kumar D., Kumari K., Jayaraj A. et al., Understanding the Binding Affinity of Noscapines with Protease of SARS-CoV-2 for COVID-19 Using MD Simulations at Different Temperatures, Journal of Biomolecular Structure and Dynamics. (2021) 39, no. 7, 2659–2672, https://doi.org/10.1080/07391102.2020.1752310.
- 15 Macindoe G., Mavridis L., Venkatraman V., Devignes M.-D., and Ritchie D. W., HexServer: an FFT-Based Protein Docking Server Powered by Graphics Processors, Nucleic Acids Research. (2010) 38, no. Web Server, W445–W449, https://doi.org/10.1093/nar/gkq311, 2-s2.0-77954275738.
- 16 Torres P. H. M., Sodero A. C. R., Jofily P., and Silva F. P., Key Topics in Molecular Docking for Drug Design, International Journal of Molecular Sciences. (2019) 20, no. 18, https://doi.org/10.3390/ijms20184574, 2-s2.0-85072522963.
- 17 Da Fonseca A. M., Caluaco B. J., Madureira J. M. C. et al., Screening of Potential Inhibitors Targeting the Main Protease Structure of SARS-CoV-2 via Molecular Docking, and Approach with Molecular Dynamics, RMSD, RMSF, H-Bond, SASA and MMGBSA, Molecular Biotechnology. (2024) 66, no. 8, 1919–1933, https://doi.org/10.1007/s12033-023-00831-x.
- 18 Kavallaris M., Microtubules and Resistance to Tubulin-Binding Agents, Nature Reviews Cancer. (2010) 10, no. 3, 194–204, https://doi.org/10.1038/nrc2803, 2-s2.0-77649191871.
- 19
Wang F.,
Yang W., and
Hu X., Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics, International Journal of Molecular Sciences. (2018) 20, no. 1, https://doi.org/10.3390/ijms20010115, 2-s2.0-85059265147.
10.3390/ijms20010115 Google Scholar
- 20 Rahman M. B. A., Jumbri K., Basri M., Abdulmalek E., Sirat K., and Salleh A. B., Synthesis and Physico-Chemical Properties of New Tetraethylammonium-Based Amino Acid Chiral Ionic Liquids, Molecules. (2010) 15, no. 4, 2388–2397, https://doi.org/10.3390/molecules15042388, 2-s2.0-77951809879.
- 21 Kanakkanthara A. and Miller J. H., Βiii-tubulin Overexpression in Cancer: Causes, Consequences, and Potential Therapies, Biochimica et Biophysica Acta, Reviews on Cancer. (2021) 1876, no. 2, https://doi.org/10.1016/j.bbcan.2021.188607.
- 22 Binarová P. and Tuszynski J., Tubulin: Structure, Functions and Roles in Disease, Cells. (2019) 8, no. 10, https://doi.org/10.3390/cells8101294.
- 23 Mariz Gomes Da Silva L. M., De Oliveira J. F., Silva W. L. et al., New 1,3-benzodioxole Derivatives: Synthesis, Evaluation of In Vitro Schistosomicidal Activity and Ultrastructural Analysis, Chemico-Biological Interactions. (2018) 283, 20–29, https://doi.org/10.1016/j.cbi.2018.01.016, 2-s2.0-85041398000.
- 24 Etsè K. S., Demonceau A., Zaragoza G., Serteyn D., and Mouithys-Mickalad A., Design, Synthesis and Biochemical Evaluation of Novel 2-Amino-3-(7-Methoxybenzo[d] [1,3]dioxol-5-Yl)propanoic Acid Using Horseradish Peroxidase (HRP) Activity, Cellular ROS Inhibition and Molecular Docking Study, Journal of Molecular Structure. (2022) 1250, https://doi.org/10.1016/j.molstruc.2021.131668.
- 25 Altıntop M. D., Sever B., Akalın Çiftçi G. et al., Synthesis and Evaluation of New Benzodioxole-Based Dithiocarbamate Derivatives as Potential Anticancer Agents and hCA-I and hCA-II Inhibitors, European Journal of Medicinal Chemistry. (2017) 125, 190–196, https://doi.org/10.1016/j.ejmech.2016.09.035, 2-s2.0-84997112201.
- 26
Micale N.,
Zappala M., and
Grasso S., Synthesis and Antitumor Activity of 1,3-Benzodioxole Derivatives, ChemInform. (2003) 34, no. 8, https://doi.org/10.1002/chin.200308111.
10.1002/chin.200308111 Google Scholar
- 27 Sehrawat H., Kumar N., Tomar R. et al., Synthesis and Characterization of Novel 1,3-benzodioxole Tagged Noscapine Based Ionic Liquids with In Silico and In Vitro Cytotoxicity Analysis on HeLa Cells, Journal of Molecular Liquids. (2020) 302, https://doi.org/10.1016/j.molliq.2020.112525.
- 28 Hassan R. M., Abd-Allah W. H., Salman A. M., El-Azzouny A. A.-S., and Aboul-Enein M. N., Design, Synthesis and Anticancer Evaluation of Novel 1,3-benzodioxoles and 1,4-benzodioxines, European Journal of Pharmaceutical Sciences. (2019) 139, https://doi.org/10.1016/j.ejps.2019.105045, 2-s2.0-85070900896.
- 29 Kumar N., Tomar R., Pandey A., Tomar V., Singh V. K., and Chandra R., Preclinical Evaluation and Molecular Docking of 1,3-benzodioxole Propargyl Ether Derivatives as Novel Inhibitor for Combating the Histone Deacetylase Enzyme in Cancer, Artificial Cells, Nanomedicine and Biotechnology. (2018) 46, no. 6, 1288–1299, https://doi.org/10.1080/21691401.2017.1369423, 2-s2.0-85028549086.
- 30 Sangeeta S., Tomar R., Tomar R., Agrawal S., and Sarkar A., 1,3-Benzodioxole Tagged Lidocaine Based Ionic Liquids as Anticancer Drug: Synthesis, Characterization and In Silico Study, ChemistrySelect. (2023) 8, no. 9, https://doi.org/10.1002/slct.202204535.
- 31 Sangeeta S., Kumari N., Kumari N., Agrawal S., Maruthi M., Sarkar A., and Tomar R., Synthesis of 1,3-Benzodioxole-Based Procaine-Tagged Ionic Liquids and Investigation of its Physiochemical and Anti-cancer Properties against A549 Cells, Journal of Molecular Liquids. (2023) 391, https://doi.org/10.1016/j.molliq.2023.123325.
- 32
Cava C. and
Castiglioni I., Integration of Molecular Docking and In Vitro Studies: A Powerful Approach for Drug Discovery in Breast Cancer, Applied Sciences. (2020) 10, no. 19, https://doi.org/10.3390/app10196981.
10.3390/app10196981 Google Scholar
- 33 Celik S., Albayrak A. T., Akyuz S., and Ozel A. E., Synthesis, Molecular Docking and ADMET Study of Ionic Liquid as Anticancer Inhibitors of DNA and COX-2, TOPII Enzymes, Journal of Biomolecular Structure and Dynamics. (2020) 38, no. 5, 1354–1364, https://doi.org/10.1080/07391102.2019.1604263, 2-s2.0-85065126233.
- 34
Kaushik M., A Review of Innovative Chemical Drawing and Spectra Prediction Computer Software, Mediterranean Journal of Chemistry. (2014) 3, no. 1, 759–766, https://doi.org/10.13171/mjc.3.1.2014.04.04.16.
10.13171/mjc.3.1.2014.04.04.16 Google Scholar
- 35 He G.-J., Sun S.-F., Zuo Q.-L. et al., In Silico Application in the Prediction of Herb-Drug Interaction for Cerebrovascular Diseases Herbs, Latin American Journal of Pharmacy. (2015) 35, no. 4.
- 36 Lewis-Atwell T., Townsend P. A., and Grayson M. N., Comparisons of Different Force Fields in Conformational Analysis and Searching of Organic Molecules: A Review, Tetrahedron. (2021) 79, https://doi.org/10.1016/j.tet.2020.131865.
- 37 Biasini M., Bienert S., Waterhouse A. et al., SWISS-MODEL: Modelling Protein Tertiary and Quaternary Structure Using Evolutionary Information, Nucleic Acids Research. (2014) 42, no. W1, W252–W258, https://doi.org/10.1093/nar/gku340, 2-s2.0-84904815625.
- 38 Bienert S., Waterhouse A., de Beer T. et al., The SWISS-MODEL Repository—New Features and Functionality, Nucleic Acids Research. (2017) 45, no. D1, D313–D319, https://doi.org/10.1093/nar/gkw1132, 2-s2.0-85016164322.
- 39 Chen X., Li H., Tian L., Li Q., Luo J., and Zhang Y., Analysis of the Physicochemical Properties of Acaricides Based on Lipinski’s Rule of Five, Journal of Computational Biology. (2020) 27, no. 9, 1397–1406, https://doi.org/10.1089/cmb.2019.0323.
- 40 Williams C. J., Headd J. J., Moriarty N. W. et al., MolProbity: More and Better Reference Data for Improved All-atom Structure Validation, Protein Science. (2018) 27, no. 1, 293–315, https://doi.org/10.1002/pro.3330, 2-s2.0-85035199313.
- 41 Studer G., Rempfer C., Waterhouse A. M., Gumienny R., Haas J., and Schwede T., QMEANDisCo—Distance Constraints Applied on Model Quality Estimation, Bioinformatics. (2020) 36, no. 6, 1765–1771, https://doi.org/10.1093/bioinformatics/btz828.
- 42 Abreu P. A., Carvalho K. D. L., Rabelo V. W., and Castro H. C., Computational Strategy for Visualizing Structures and Teaching Biochemistry, Biochemistry and Molecular Biology Education. (2019) 47, no. 1, 76–84, https://doi.org/10.1002/bmb.21199, 2-s2.0-85058976579.
- 43 Barber R. D., Software to Visualize Proteins and Perform Structural Alignments, Current. Protocols. (2021) 1, no. 11, https://doi.org/10.1002/cpz1.292.
- 44 Yadav J. K., Yadav P., Singh V. K., and Agarwal A., Molecular Docking and Density Functional Theory Studies of Potent 1,3-Disubstituted-9h-Pyrido[3,4-B] Indoles Antifilarial Compounds, Structural Chemistry. (2021) 32, no. 5, 1925–1947, https://doi.org/10.1007/s11224-021-01772-4.
- 45 Vishnuvarthan V. J., Lakshmi K. S., and Srividya A. R., In-Silico Screening of Flavonoids Targeted for Death Receptors in Cancer by Using Hex Molecular Docking, Journal of Young Pharmacists. (2017) 9, no. 2, 168–171, https://doi.org/10.5530/jyp.2017.9.33, 2-s2.0-85017497773.
- 46 Adasme M. F., Linnemann K. L., Bolz S. N. et al., PLIP 2021: Expanding the Scope of the Protein–Ligand Interaction Profiler to DNA and RNA, Nucleic Acids Research. (2021) 49, no. W1, W530–W534, https://doi.org/10.1093/nar/gkab294.
- 47 Pettersen E. F., Goddard T. D., Huang C. C. et al., UCSF Chimera—A Visualization System for Exploratory Research and Analysis, Journal of Computational Chemistry. (2004) 25, no. 13, 1605–1612, https://doi.org/10.1002/jcc.20084, 2-s2.0-4444221565.
- 48 Mirzadeh A., Kobakhidze G., Vuillemot R., Jonic S., and Rouiller I., In Silico Prediction, Characterization, Docking Studies and Molecular Dynamics Simulation of Human P97 in Complex with P37 Cofactor, BMC Molecular and Cell Biology. (2022) 23, no. 1, https://doi.org/10.1186/s12860-022-00437-2.
- 49 Seeliger D. and De Groot B. L., Ligand Docking and Binding Site Analysis with PyMOL and Autodock/Vina, Journal of Computer-Aided Molecular Design. (2010) 24, no. 5, 417–422, https://doi.org/10.1007/s10822-010-9352-6, 2-s2.0-77953325845.
- 50 Zhang S., Krumberger M., Morris M. A., Parrocha C. M. T., Kreutzer A. G., and Nowick J. S., Structure-based Drug Design of an Inhibitor of the SARS-CoV-2 (COVID-19) Main Protease Using Free Software: A Tutorial for Students and Scientists, European Journal of Medicinal Chemistry. (2021) 218, https://doi.org/10.1016/j.ejmech.2021.113390.
- 51 Ahmad I., Kuznetsov A. E., Pirzada A. S., Alsharif K. F., Daglia M., and Khan H., Computational Pharmacology and Computational Chemistry of 4-hydroxyisoleucine: Physicochemical, Pharmacokinetic, and DFT-Based Approaches, Frontiers in Chemistry. (2023) 11, https://doi.org/10.3389/fchem.2023.1145974.
- 52 Aziz M., Ejaz S. A., Tamam N. et al., Identification of Potent Inhibitors of NEK7 Protein Using a Comprehensive Computational Approach, Scientific Reports. (2022) 12, no. 1, https://doi.org/10.1038/s41598-022-10253-5.
- 53 Al-Madhagi H., Al-Adhreai A., Alsaeedy M. et al., Dual Docking of Some Synthesized Isoxazolidine Derivatives against Cathepsin L and Main Protease as a Novel Treatment Strategy for COVID-19, Journal of Molecular Structure. (2024) 1300, https://doi.org/10.1016/j.molstruc.2023.137253.
- 54 Xiong G., Wu Z., Yi J. et al., ADMETlab 2.0: an Integrated Online Platform for Accurate and Comprehensive Predictions of ADMET Properties, Nucleic Acids Research. (2021) 49, no. W1, W5–W14, https://doi.org/10.1093/nar/gkab255.
- 55
Hafeez S.,
Rasool Z.,
Hafeez S. et al., Imidazolium, Pyridinium and Pyrazinium Based Ionic Liquids with Octyl Side Chains as Potential Antibacterial Agents against Multidrug Resistant Uropathogenic E. coli, Heliyon. (2024) 10, no. 22, https://doi.org/10.1016/j.heliyon.2024.e39829.
10.1016/j.heliyon.2024.e39829 Google Scholar
- 56 Parakkal S. C., Datta R., Muthu S., and Al-Saadi A. A., Structure of Molecule, Density Gradient, Orbital Locator and Reactivity of 5,6-Dichloro-1-Cyclopentyl-2-(methylsulfinyl)-1h-Benzimidazole- Potent Inhibitor of Map Kinase, Journal of Molecular Structure. (2023) 1289, https://doi.org/10.1016/j.molstruc.2023.135794.
- 57
Abraham M. J.,
Murtola T.,
Schulz R. et al., GROMACS: High Performance Molecular Simulations through Multi-Level Parallelism from Laptops to Supercomputers, SoftwareX. (2015) 1–2, 19–25, https://doi.org/10.1016/j.softx.2015.06.001, 2-s2.0-84946416234.
10.1016/j.softx.2015.06.001 Google Scholar
- 58 Kohnke B., Kutzner C., and Grubmüller H., A GPU-Accelerated Fast Multipole Method for GROMACS: Performance and Accuracy, Journal of Chemical Theory and Computation. (2020) 16, no. 11, 6938–6949, https://doi.org/10.1021/acs.jctc.0c00744.
- 59 Vieira I. H. P., Botelho E. B., De Souza Gomes T. J., Kist R., Caceres R. A., and Zanchi F. B., Visual Dynamics: a WEB Application for Molecular Dynamics Simulation Using GROMACS, BMC Bioinformatics. (2023) 24, no. 1, https://doi.org/10.1186/s12859-023-05234-y.
- 60 Zoete V., Cuendet M. A., Grosdidier A., and Michielin O., SwissParam: A Fast Force Field Generation Tool for Small Organic Molecules, Journal of Computational Chemistry. (2011) 32, no. 11, 2359–2368, https://doi.org/10.1002/jcc.21816, 2-s2.0-79958841703.
- 61 Daina A., Michielin O., and Zoete V., SwissADME: a Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules, Scientific Reports. (2017) 7, no. 1, https://doi.org/10.1038/srep42717, 2-s2.0-85014610206.
- 62
Yadav P. K. and
Lamichhane T. R., A Comparative Analysis of Force Fields of Drug-like Molecules: Omeprazole, Favipiravir and Amoxicillin by Using Bonded and Non-bonded Potential Distributions, Journal of Nepal Physical Society. (2023) 9, no. 1, 58–72, https://doi.org/10.3126/jnphyssoc.v9i1.57599.
10.3126/jnphyssoc.v9i1.57599 Google Scholar
- 63 Prajapat M., Sarma P., Shekhar N. et al., Virtual Screening and Molecular Dynamics Simulation Study of Approved Drugs as a Binder to the Linoleic Acid Binding Site on Spike Protein of SARS-CoV-2 and Double Mutant (E484Q and L452R), Indian Journal of Pharmacology. (2022) 54, no. 6, 431–442, https://doi.org/10.4103/ijp.ijp_111_22.
- 64 Sehrawat H., Kumar N., Sood D. et al., Mechanistic Interaction of Triflate Based Noscapine Ionic Liquid with BSA: Spectroscopic and Chemoinformatics Approaches, Journal of Molecular Liquids. (2020) 315, https://doi.org/10.1016/j.molliq.2020.113695.
- 65 Ribeiro R., Botelho F. D., Pinto A. M. V. et al., Molecular Modeling Study of Natural Products as Potential Bioactive Compounds against SARS-CoV-2, Journal of Molecular Modeling. (2023) 29, no. 6, https://doi.org/10.1007/s00894-023-05586-5.
- 66 Al Johani A. R., Almutairi S. M., El-Sayed W. S., Sahu P. K., Sahu P. K., and Messali M., Design, Sustainable Synthesis, Characterization, Antimicrobial Evaluation and In Silico ADMET Prediction of New Functionalized Imidazolium Based Ionic Liquids, Asian Journal of Chemistry. (2020) 32, no. 8, 1972–1980, https://doi.org/10.14233/ajchem.2020.22747.
- 67 Bickerton G. R., Paolini G. V., Besnard J., Muresan S., and Hopkins A. L., Quantifying the Chemical Beauty of Drugs, Nature Chemistry. (2012) 4, no. 2, 90–98, https://doi.org/10.1038/nchem.1243, 2-s2.0-84856201851.
- 68 Pollastri M. P., Overview on the Rule of Five, Current Protocols in Pharmacology. (2010) no. 1, https://doi.org/10.1002/0471141755.ph0912s49, 2-s2.0-77953844274.
- 69 Edache E., UzaiRu A., Mamza P. A., and Shallangwa G. A., Investigation of Salicylidene Acylhydrazides Derivatives: Molecular Docking, ADMET, and Molecular Dynamic Simulations Were Used in Conjunction towards the Design of New Yersinia Pseudotuberculosis Inhibitors, Turkish Computational and Theoretical Chemistry. (2022) 6, no. 1, 9–30, https://doi.org/10.33435/tcandtc.1003157.
- 70 Sun H., Chow E. C., Liu S., Du Y., and Pang K. S., The Caco-2 Cell Monolayer: Usefulness and Limitations, Expert Opinion on. Drug Metabolism & Toxicology. (2008) 4, no. 4, 395–411, https://doi.org/10.1517/17425255.4.4.395, 2-s2.0-44349116738.
- 71 Van Breemen R. B. and Li Y., Caco-2 Cell Permeability Assays to Measure Drug Absorption, Expert Opinion on. Drug Metabolism & Toxicology. (2005) 1, no. 2, 175–185, https://doi.org/10.1517/17425255.1.2.175, 2-s2.0-33748887871.
- 72 Press B. and Di Grandi D., Permeability for Intestinal Absorption: Caco-2 Assay and Related Issues, Current Drug Metabolism. (2008) 9, no. 9, 893–900, https://doi.org/10.2174/138920008786485119, 2-s2.0-60749091589.
- 73 Crivori P., Reinach B., Pezzetta D., and Poggesi I., Computational Models for Identifying Potential P-Glycoprotein Substrates and Inhibitors, Molecular Pharmaceutics. (2006) 3, no. 1, 33–44, https://doi.org/10.1021/mp050071a, 2-s2.0-33644674142.
- 74 Leopoldo M., Nardulli P., Contino M., Leonetti F., Luurtsema G., and Colabufo N. A., An Updated Patent Review on P-Glycoprotein Inhibitors (2011-2018), Expert Opinion on Therapeutic Patents. (2019) 29, no. 6, 455–461, https://doi.org/10.1080/13543776.2019.1618273, 2-s2.0-85066101409.
- 75 Kumar Vishvakarma V., Nand B., Kumar V., Kumari K., Bahadur I., and Singh P., Xanthene Based Hybrid Analogues to Inhibit Protease of Novel Corona Virus: Molecular Docking and ADMET Studies, Computational Toxicology. (2020) 16, https://doi.org/10.1016/j.comtox.2020.100140.
- 76 Mulpuru V. and Mishra N., In Silico Prediction of Fraction Unbound in Human Plasma from Chemical Fingerprint Using Automated Machine Learning, ACS Omega. (2021) 6, no. 10, 6791–6797, https://doi.org/10.1021/acsomega.0c05846.
- 77 Smith D. A., Beaumont K., Maurer T. S., and Di L., Volume of Distribution in Drug Design: Miniperspective, Journal of Medicinal Chemistry. (2015) 58, no. 15, 5691–5698, https://doi.org/10.1021/acs.jmedchem.5b00201, 2-s2.0-84938919255.
- 78 Zhang X., Liu T., Fan X., and Ai N., In Silico Modeling on ADME Properties of Natural Products: Classification Models for Blood-Brain Barrier Permeability, its Application to Traditional Chinese Medicine and In Vitro Experimental Validation, Journal of Molecular Graphics and Modelling. (2017) 75, 347–354, https://doi.org/10.1016/j.jmgm.2017.05.021, 2-s2.0-85020498611.
- 79 Hassan S. F., Rashid U., Ansari F. L., and Ul-Haq Z., Bioisosteric Approach in Designing New Monastrol Derivatives: An Investigation on Their ADMET Prediction Using In Silico Derived Parameters, Journal of Molecular Graphics and Modelling. (2013) 45, 202–210, https://doi.org/10.1016/j.jmgm.2013.09.002, 2-s2.0-84884653332.
- 80 Smith D. A., Beaumont K., Maurer T. S., and Di L., Clearance in Drug Design: Miniperspective, Journal of Medicinal Chemistry. (2019) 62, no. 5, 2245–2255, https://doi.org/10.1021/acs.jmedchem.8b01263, 2-s2.0-85055350631.
- 81 Garrido A., Lepailleur A., Mignani S. M., Dallemagne P., and Rochais C., hERG Toxicity Assessment: Useful Guidelines for Drug Design, European Journal of Medicinal Chemistry. (2020) 195, https://doi.org/10.1016/j.ejmech.2020.112290.
