In silico-in vitro estimation of lipophilicity and permeability association for succinimide derivatives using chromatographic anisotropic systems and parallel artificial membrane permeability assay.

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    • Source:
      Publisher: Wiley Country of Publication: England NLM ID: 8610241 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1099-0801 (Electronic) Linking ISSN: 02693879 NLM ISO Abbreviation: Biomed Chromatogr Subsets: MEDLINE
    • Publication Information:
      Publication: 1990- : Chichester : Wiley
      Original Publication: London : Heyden & Son, c1986-1990
    • Subject Terms:
    • Abstract:
      Passive permeability is one of the key features that determine absorbability and one of the most studied properties in the early phases of drug development. Newly synthesized succinimide derivatives from two different series (1-aryl-3-methylsuccinimides and 1-aryl-3-ethyl-3-methylsuccinimides) with high biological potential have been subjected to estimation of their passive permeability and their association with (a) experimentally obtained anisotropic lipophilicity, (b) in silico-calculated lipophilicity and (c) in silico-predicted permeability and absorbability. Non-cellular-based parallel artificial membrane permeability assay was applied for quantifying their passive permeation, expressed as logP app . Passive permeation was governed by the lipophilicity of the analysed compounds, and anisotropic lipophilicity was related with statistically significant passive transcellular diffusion (r 2  = 0.614, P < 0.001). Moreover, experimentally determined passive permeability, logP app , was statistically significantly associated with both in silico-predicted absorption constant, k a (r 2  = 0.7886, P < 0.001), and human intestinal absorption (HIA) in percentage (r 2  = 0.484, P < 0.001), respectively. However, there was no statistically significant relationship between experimentally obtained permeability on non-cellular-based model and in silico-predicted Caco-2 permeability based on the predictions conducted on two different software. Based on the obtained results, anisotropic systems are promising surrogates for determining lipophilicity, except for compounds with acidic functional groups that are completely ionized under (pH = 7.4).
      (© 2022 John Wiley & Sons Ltd.)
    • References:
      Abuhelwa, A. Y., Foster, D. J., & Upton, R. N. (2016a). A quantitative review and meta-models of the variability and factors affecting oral drug absorption - part I: Gastrointestinal pH. The AAPS Journal, 18(5), 1309-1321. https://doi.org/10.1208/s12248-016-9952-8.
      Abuhelwa, A. Y., Foster, D. J., & Upton, R. N. (2016b). A quantitative review and meta-models of the variability and factors affecting oral drug absorption - part II: Gastrointestinal transit time. The AAPS Journal, 18(5), 1322-1333. https://doi.org/10.1208/s12248-016-9953-7.
      Banjac, N., Trišović, N., Valentić, N., Ušćumlić, G., & Petrović, S. (2011). Succinimides: Synthesis, properties and anticonvulsant activity. Chemical Industry, 65(4), 439-453. https://doi.org/10.2298/HEMIND110224030B.
      Banjac, N. R., Božić, B. Đ., Mirković, J. M., Vitnik, V. D., Vitnik, Ž. J., Valentić, N. V., & Ušćumlić, G. S. (2017). Experimental and theoretical study on the structure-property relationship of novel 1-aryl-3-methylsuccinimides. Journal of Molecular Structure, 1129, 271-282. https://doi.org/10.1016/j.molstruc.2016.09.086.
      Berben, P., Bauer-Brandl, A., Brandl, M., Faller, B., Flaten, G. E., Jacobsen, A. C., Brouwers, J., & Augustijns, P. (2018). Drug permeability profiling using cell-free permeation tools: Overview and applications. European Journal of Pharmaceutical Sciences: Official Journal of the European Federation for Pharmaceutical Sciences, 119, 219-233. https://doi.org/10.1016/j.ejps.2018.04.016.
      Bermejo, M., Avdeef, A., Ruiz, A., Nalda, R., Ruell, J. A., Tsinman, O., González, I., Fernández, C., Sánchez, G., Garrigues, T. M., & Merino, V. (2004). PAMPA- a drug absorption in vitro model: 7. Comparing rat in situ, Caco-2, and PAMPA permeability of fluoroquinolones. European Journal of Pharmaceutical Sciences, 21(4), 429-441. https://doi.org/10.1016/j.ejps.2003.10.009.
      Biancolillo, A., Mennitti, L., Foschi, M., & Marini, F. (2022). Advanced analytical tools for the estimation of gut permeability of compounds of pharmaceutical interest. Applied Sciences, 12(3), 1326. https://doi.org/10.3390/app12031326.
      Bober, K., Bębenek, E., & Boryczka, S. (2019). Application of TLC for evaluation of the lipophilicity of newly synthetized esters: Betulin derivatives. Journal of Analytical Methods in Chemistry, 2019, 1297659. https://doi.org/10.1155/2019/1297659.
      Caron, G., & Ermondi, G. (2008). Lipophilicity: Chemical Nature and Biological Relevance. In R. Mannhold, H. Kubinyi, & G. Folkers (Eds.), Molecular drug properties. Molecular Drug Properties: Measurement and Prediction. (pp. 315-329). Wiley-VCH. https://doi.org/10.1002/9783527621286.ch12.
      Charalabidis, A., Sfouni, M., Bergström, C., & Macheras, P. (2019). The biopharmaceutics classification system (BCS) and the biopharmaceutics drug disposition classification system (BDDCS): Beyond guidelines. International Journal of Pharmaceutics, 566, 264-281. https://doi.org/10.1016/j.ijpharm.2019.05.041.
      Chillistone, S., & Hardman, J. G. (2014). Factors affecting drug absorption and distribution. Anaesthesia & Intensive Care Medicine, 15, 309-313. https://doi.org/10.1016/J.MPAIC.2008.02.005.
      Corrêa, R., Filho, V. C., Rosa, P. W., Pereira, C. I., Schlemper, V., & Nunes, R. J. (1997). Synthesis of new succinimides and sulphonated derivatives with analgesic action in mice. Pharmacy and Pharmacology Communications, 3(2), 67-71. https://doi.org/10.1111/j.2042-7158.1997.tb00224.x.
      Cuadros-Rodrı́guez, L., Gámiz-Gracia, L., Almansa-López, E. M., & Bosque-Sendra, J. M. (2001). Calibration in chemical measurement processes. II. A methodological approach. TrAC Trends in Analytical Chemistry, 20(11), 620-636. https://doi.org/10.1016/S0165-9936(01)00111-X.
      Daina, A., Michielin, O., & Zoete, V. (2014). iLOGP: A simple, robust and efficient description of n-octanol/water partition coefficient for drug design using the GB/SA approach. Journal of Chemical Information and Modeling, 54(12), 3284-3301. https://doi.org/10.1021/ci500467k.
      Di, L., Artursson, P., Avdeef, A., Benet, L. Z., Houston, J. B., Kansy, M., Kerns, E. H., Lennernäs, H., Smith, D. A., & Sugano, K. (2020). The critical role of passive permeability in designing successful drugs. ChemMedChem, 15, 1862-1874. https://doi.org/10.1002/cmdc.202000419.
      Diukendjieva, A., Tsakovska, I., Alova, P., Pencheva, T., Pajeva, I., Worth, A. P., Madden, J. C., & Cronin, M. T. D. (2019). Advances in the prediction of gastrointestinal absorption: Quantitative structure-activity relationship (QSAR) modelling of PAMPA permeability. Computational Toxicology, 10, 51-59. https://doi.org/10.1016/j.comtox.2018.12.008.
      Egan, W. J., Merz, K. M., & Baldwin, J. J. (2000). Prediction of drug absorption using multivariate statistics. Journal of Medicinal Chemistry, 43(21), 3867-3877. https://doi.org/10.1021/jm000292e.
      Ertl, P., Rohde, B., & Selzer, P. (2000). Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. Journal of Medicinal Chemistry, 43(20), 3714-3717. https://doi.org/10.1021/jm000942e.
      Fairstein, M., Swissa, R., & Dahan, A. (2013). Regional-dependent intestinal permeability and BCS classification: Elucidation of pH-related complexity in rats using pseudoephedrine. The AAPS Journal, 15(2), 589-597. https://doi.org/10.1208/s12248-013-9462-x.
      Fortuna, A., Alves, G., Soares-Da-Silva, P., & Falcão, A. (2012). Optimization of a parallel artificial membrane permeability assay for the fast and simultaneous prediction of human intestinal absorption and plasma protein binding of drug candidates: Application to dibenz [b, f] azepine-5-carboxamide derivatives. Journal of Pharmaceutical Sciences, 101(2), 530-540. https://doi.org/10.1002/jps.22796.
      Ghose, A. K., Viswanadhan, V. N., & Wendoloski, J. J. (1999). A knowledge-based approach in designing combinatorial or medicinal chemistry libraries for drug discovery. 1. A qualitative and quantitative characterization of known drug databases. Journal of Combinatorial Chemistry, 1(1), 55-68. https://doi.org/10.1021/cc9800071.
      Goehring, R. R., Greenwood, T. D., Pisipati, J. S., & Wolfe, J. F. (1991). Synthesis and anticonvulsant evaluation of some new 2-benzylsuccinimides. Journal of Pharmaceutical Sciences, 80(8), 790-792. https://doi.org/10.1002/jps.2600800818.
      Kansy, M., Senner, F., & Gubernator, K. (1998). Physicochemical high throughput screening: Parallel artificial membrane permeation assay in the description of passive absorption processes. Journal of Medicinal Chemistry, 41(7), 1007-1010. https://doi.org/10.1021/jm970530e.
      Kovačević, S., Banjac, M. K., Milošević, N., Ćurčić, J., Marjanović, D., Todorović, N., Krmar, J., Podunavac-Kuzmanović, S., Banjac, N., & Ušćumlić, G. (2020). Comparative chemometric and quantitative structure-retention relationship analysis of anisotropic lipophilicity of 1-arylsuccinimide derivatives determined in high-performance thin-layer chromatography system with aprotic solvents. Journal of Chromatography a, 1628, 461439. https://doi.org/10.1016/j.chroma.2020.461439.
      Kovačević, S., Banjac, M. K., Podunavac-Kuzmanović, S., Milošević, N., Ćurčić, J., Vulić, J., Šeregelj, V., Banjac, N., & Ušćumlić, G. (2020). Chromatographic and computational screening of anisotropic lipophilicity and pharmacokinetics of newly synthesized 1-aryl-3-ethyl-3-methylsuccinimides. Computational Biology and Chemistry, 84, 107161. https://doi.org/10.1016/j.compbiolchem.2019.107161.
      Leung, S. S., Sindhikara, D., & Jacobson, M. P. (2016). Simple predictive models of passive membrane permeability incorporating size-dependent membrane-water partition. Journal of Chemical Information and Modeling, 56(5), 924-929. https://doi.org/10.1021/acs.jcim.6b00005.
      Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Advanced Drug Delivery Reviews, 46, 3-26. https://doi.org/10.1016/s0169-409x(00)00129-0.
      Liu, X., Testa, B., & Fahr, A. (2011). Lipophilicity and its relationship with passive drug permeation. Pharmaceutical Research, 28(5), 962-977. https://doi.org/10.1007/s11095-010-0303-7.
      Lobo, S. (2020). Is there enough focus on lipophilicity in drug discovery? Expert Opinion on Drug Discovery, 15(3), 261-263. https://doi.org/10.1080/17460441.2020.1691995.
      Milosevic, N. P., Kojic, V., Curcic, J., Jakimov, D., Milic, N., Banjac, N., Uscumlic, G., & Kaliszan, R. (2017). Evaluation of in silico pharmacokinetic properties and in vitro cytotoxic activity of selected newly synthesized N-succinimide derivatives. Journal of Pharmaceutical and Biomedical Analysis, 137, 252-257. https://doi.org/10.1016/j.jpba.2017.01.042.
      Muegge, I., Heald, S. L., & Brittelli, D. (2001). Simple selection criteria for drug-like chemical matter. Journal of Medicinal Chemistry, 44(12), 1841-1846. https://doi.org/10.1021/jm015507e.
      Nasal, A., Siluk, D., & Kaliszan, R. (2003). Chromatographic retention parameters in medicinal chemistry and molecular pharmacology. Current Medicinal Chemistry, 10(5), 381-426. https://doi.org/10.2174/0929867033368268.
      Oja, M., & Maran, U. (2018). pH-permeability profiles for drug substances: Experimental detection, comparison with human intestinal absorption and modelling. European Journal of Pharmaceutical Sciences, 123, 429-440. https://doi.org/10.1016/j.ejps.2018.07.014.
      Oja, M., Sild, S., & Maran, U. (2019). Logistic classification models for pH-permeability profile: Predicting permeability classes for the biopharmaceutical classification system. Journal of Chemical Information and Modeling, 59(5), 2442-2455. https://doi.org/10.1021/acs.jcim.8b00833.
      Perisic-Janjic, N., Kaliszan, R., Milosevic, N., Uscumlic, G., & Banjac, N. (2013). Chromatographic retention parameters in correlation analysis with in silico biological descriptors of a novel series of N-phenyl-3-methyl succinimide derivatives. Journal of Pharmaceutical and Biomedical Analysis, 72, 65-73. https://doi.org/10.1016/j.jpba.2012.09.006.
      Petković Cvetković, J., Božić, B. Đ., Banjac, N. R., Petrović, J., Soković, M., Vitnik, V. D., Vitnik, Ž. J., Ušćumlić, G. S., & Valentić, N. V. (2019). Synthesis, antimicrobial activity and quantum chemical investigation of novel succinimide derivatives. Journal of Molecular Structure, 1181, 148-156. https://doi.org/10.1016/j.molstruc.2018.12.083.
      Raevsky, O. A. (2004). Physicochemical descriptors in property-based drug design. Mini Reviews in Medicinal Chemistry, 4(10), 1041-1052. https://doi.org/10.2174/1389557043402964.
      Sadiq, A., Mahmood, F., Ullah, F., Ayaz, M., Ahmad, S., Haq, F. U., Khan, G., & Jan, M. S. (2015). Synthesis, anticholinesterase and antioxidant potentials of ketoesters derivatives of succinimides: A possible role in the management of Alzheimer's. Chemistry Central Journal, 9(1), 1-9. https://doi.org/10.1186/s13065-015-0107-2.
      Samiei, N., Shafaati, A., Zarghi, A., Moghimi, H. R., & Foroutan, S. M. (2014). Enhancement and in vitro evaluation of amifostine permeation through artificial membrane (PAMPA) via ion pairing approach and mechanistic selection of its optimal counter ion. European Journal of Pharmaceutical Sciences, 51, 218-223. https://doi.org/10.1016/j.ejps.2013.10.002.
      Sugano, K., Kansy, M., Artursson, P., Avdeef, A., Bendels, S., Di, L., Ecker, G. F., Faller, B., Fischer, H., Gerbtzoff, G., Lennernaes, H., & Senner, F. (2010). Coexistence of passive and carrier-mediated processes in drug transport. Nature Reviews Drug Discovery, 9(8), 597-614. https://doi.org/10.1038/nrd3187.
      Sun, H., Nguyen, K., Kerns, E., Yan, Z., Yu, K. R., Shah, P., Jadhav, A., & Xu, X. (2017). Highly predictive and interpretable models for PAMPA permeability. Bioorganic & Medicinal Chemistry, 25(3), 1266-1276. https://doi.org/10.1016/j.bmc.2016.12.049.
      Takagi, T., Ramachandran, C., Bermejo, M., Yamashita, S., Yu, L. X., & Amidon, G. L. (2006). A provisional biopharmaceutical classification of the top 200 oral drug products in the United States, Great Britain, Spain, and Japan. Molecular Pharmaceutics, 3(6), 631-643. https://doi.org/10.1021/mp0600182.
      Thomas, G. (2000). Medicinal chemistry: An introduction. John Wiley & Sons.
      Ulrich, N., Goss, K. U., & Ebert, A. (2021). Exploring the octanol-water partition coefficient dataset using deep learning techniques and data augmentation. Communications Chemistry, 4(1), 1-10. https://doi.org/10.1038/s42004-021-00528-9.
      Varma, M. V., Gardner, I., Steyn, S. J., Nkansah, P., Rotter, C. J., Whitney-Pickett, C., Zhang, H., Di, L., Cram, M., Fenner, K. S., & El-Kattan, A. F. (2012). pH-dependent solubility and permeability criteria for provisional biopharmaceutics classification (BCS and BDDCS) in early drug discovery. Molecular Pharmaceutics, 9(5), 1199-1212. https://doi.org/10.1021/mp2004912.
      Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W., & Kopple, K. D. (2002). Molecular properties that influence the oral bioavailability of drug candidates. Journal of Medicinal Chemistry, 45(12), 2615-2623. https://doi.org/10.1021/jm020017n.
      Wang, N. N., Dong, J., Deng, Y. H., Zhu, M. F., Wen, M., Yao, Z. J., Lu, A. P., Wang, J. B., & Cao, D. S. (2016). ADME properties evaluation in drug discovery: Prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting. Journal of Chemical Information and Modeling, 56(4), 763-773. https://doi.org/10.1021/acs.jcim.5b00642.
      Wildman, S. A., & Crippen, G. M. (1999). Prediction of physicochemical parameters by atomic contributions. Journal of Chemical Information and Computer Sciences, 39(5), 868-873. https://doi.org/10.1021/ci990307l.
      Wu, F., Zhou, Y., Li, L., Shen, X., Chen, G., Wang, X., Liang, X., Tan, X. M., & Huang, Z. (2020). Computational approaches in preclinical studies on drug discovery and development. Frontiers in Chemistry, 8, 726. https://doi.org/10.3389/fchem.2020.00726.
      Xiang, T. X., & Anderson, B. D. (1998). Influence of chain ordering on the selectivity of dipalmitoylphosphatidylcholine bilayer membranes for permeant size and shape. Biophysical Journal, 75(6), 2658-2671. https://doi.org/10.1016/S0006-3495(98)77711-2.
      Yang, N. J., & Hinner, M. J. (2015). Getting Across the Cell Membrane: An Overview for Small Molecules, Peptides, and Proteins. In A. Gautier & M. Hinner (Eds.), Site-specific protein labeling. Methods in Molecular Biology. (pp. 29-53). Humana Press. https://doi.org/10.1007/978-1-4939-2272-7_3.
      Zur, M., Hanson, A. S., & Dahan, A. (2014). The complexity of intestinal permeability: Assigning the correct BCS classification through careful data interpretation. European Journal of Pharmaceutical Sciences, 61, 11-17. https://doi.org/10.1016/j.ejps.2013.11.007.
    • Grant Information:
      451-03-68/2022-14/200114 Ministry of Education, Science and Technological Development of the Republic of Serbia
    • Contributed Indexing:
      Keywords: chromatography; in silico; lipophilicity; parallel artificial membrane permeability assay
    • Accession Number:
      0 (Anticonvulsants)
      0 (Membranes, Artificial)
      0 (Succinimides)
    • Publication Date:
      Date Created: 20220520 Date Completed: 20220816 Latest Revision: 20220816
    • Publication Date:
      20240829
    • Accession Number:
      10.1002/bmc.5413
    • Accession Number:
      35595284