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Characterization of critical parameters using an air–liquid interface model with RPMI 2650 cells for permeability studies of small molecules

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Abstract

The field of nasal drug delivery gained enormously on interest over the past decade. Performing nasal in vivo studies is expensive and time-consuming, but also unfeasible for an initial high-throughput compound and formulation screening. Therefore, the development of fast and high-throughput in vitro models to screen compounds for their permeability through the nasal epithelium and mucosa is constantly expanding. Yet, the protocols used for nasal in vitro permeability studies are varying, which limits the comparability and reproducibility of generated data. This project aimed to elucidate the influence of different culture and assay parameters of RPMI 2650 cells grown under air–liquid interface (ALI) conditions on the transepithelial electrical resistance (TEER) and apparent permeability (Papp) values of five selected reference compounds, covering the range of low to moderate to high permeability. The influence of the passage number, seeding density, and timepoint of airlift was minimal in our approach, while the substrate pore density had a significant influence on the Papp values of carbamazepine, propranolol, and metoprolol, classified as highly permeable compounds, but not on atenolol and aciclovir. Elevation of the experimental concentration of carbamazepine, propranolol, and metoprolol in the donor compartment had an increasing effect on the Papp values, while prolonging the assay time did not have a significant influence. Based on the results reported here, RPMI 2650 cells cultured under ALI conditions offer the possibility of a standardized high-throughput screening model for small molecules and their formulations for in vitro drug permeation studies to predict and select optimal conditions for their nasal delivery.

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The raw/processed data required to reproduce these findings cannot be shared at this time due to legal or ethical reasons.

References

  1. Chamanza R, Wright JA. A review of the comparative anatomy, histology, physiology and pathology of the nasal cavity of rats, mice, dogs and non-human primates. Relevance to inhalation toxicology and human health risk assessment. J Comp Pathol. 2015;153:287–314.

  2. Keller L-A, Merkel O, Popp A. Intranasal drug delivery: opportunities and toxicologic challenges during drug development. Drug Deliv Transl Res. 2022;12:735–57.

  3. Bai S, Yang T, Abbruscato TJ, Ahsan F. Evaluation of human nasal RPMI 2650 cells grown at an air-liquid interface as a model for nasal drug transport studies. J Pharm Sci. 2008;97:1165–78.

    Article  CAS  PubMed  Google Scholar 

  4. The principles of humane experimental technique. Med J Aust. 1960;1.

  5. Haasbroek-Pheiffer A, Viljoen A, Steenekamp J, Chen W, Hamman J. An ex vivo investigation on drug permeability of sheep nasal epithelial tissue membranes from the respiratory and olfactory regions. Curr Drug Deliv. 2022.

  6. Karasulu E, Yavaşoğlu A, Evrenşanal Z, Uyanıkgil Y, Karasulu HY. Permeation studies and histological examination of sheep nasal mucosa following administration of different nasal formulations with or without absorption enhancers. Drug Deliv. 2008;15:219–25.

    Article  CAS  PubMed  Google Scholar 

  7. Ladel S, Maigler F, Flamm J, Schlossbauer P, Handl A, Hermann R, et al. Impact of glycosylation and species origin on the uptake and permeation of IgGs through the nasal airway mucosa. Pharmaceutics. 2020;12.

  8. Wadell C, Björk E, Camber O. Permeability of porcine nasal mucosa correlated with human nasal absorption. Eur J Pharm Sci Off J Eur Fed Pharm Sci. 2003;18:47–53.

    CAS  Google Scholar 

  9. Sibinovska N, Žakelj S, Kristan K. Suitability of RPMI 2650 cell models for nasal drug permeability prediction. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV. 2019;145:85–95.

    Article  CAS  Google Scholar 

  10. Mercier C, Perek N, Delavenne X. Is RPMI 2650 a suitable in vitro nasal model for drug transport studies? Eur J Drug Metab Pharmacokinet. 2018;43:13–24.

    Article  CAS  PubMed  Google Scholar 

  11. Moore GE, Sandberg AA. Studies of a human tumor cell line with a diploid karyotype. Cancer. 1964;17:170–5.

    Article  CAS  PubMed  Google Scholar 

  12. De Fraissinette A, Brun R, Felix H, Vonderscher J, Rummelt A. Evaluation of the human cell line RPMI 2650 as an in vitro nasal model. Rhinology. 1995;33:194–8.

    PubMed  Google Scholar 

  13. Werner U, Kissel T. In-vitro cell culture models of the nasal epithelium: a comparative histochemical investigation of their suitability for drug transport studies. Pharm Res. 1996;13:978–88.

    Article  CAS  PubMed  Google Scholar 

  14. Gerber W, Svitina H, Steyn D, Peterson B, Kotzé A, Weldon C, et al. Comparison of RPMI 2650 cell layers and excised sheep nasal epithelial tissues in terms of nasal drug delivery and immunocytochemistry properties. J Pharmacol Toxicol Methods. 2022;113:107131.

    Article  CAS  PubMed  Google Scholar 

  15. Kreft ME, Jerman UD, Lasič E, Lanišnik Rižner T, Hevir-Kene N, Peternel L, et al. The characterization of the human nasal epithelial cell line RPMI 2650 under different culture conditions and their optimization for an appropriate in vitro nasal model. Pharm Res. 2015;32:665–79.

    Article  CAS  PubMed  Google Scholar 

  16. Ladel S, Schlossbauer P, Flamm J, Luksch H, Mizaikoff B, Schindowski K. Improved in vitro model for intranasal mucosal drug delivery: primary olfactory and respiratory epithelial cells compared with the permanent nasal cell line RPMI 2650. Pharmaceutics. 2019;11.

  17. Anderson JM, Van Itallie CM. Physiology and function of the tight junction. Cold Spring Harb Perspect Biol. 2009;1:a002584.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Srinivasan B, Kolli AR, Esch MB, Abaci HE, Shuler ML, Hickman JJ. TEER measurement techniques for in vitro barrier model systems. J Lab Autom. 2015;20:107–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kürti L, Veszelka S, Bocsik A, Ozsvári B, Puskás LG, Kittel A, et al. Retinoic acid and hydrocortisone strengthen the barrier function of human RPMI 2650 cells, a model for nasal epithelial permeability. Cytotechnology. 2013;65:395–406.

    Article  PubMed  Google Scholar 

  20. Pozzoli M, Ong HX, Morgan L, Sukkar M, Traini D, Young PM, et al. Application of RPMI 2650 nasal cell model to a 3D printed apparatus for the testing of drug deposition and permeation of nasal products. Eur J Pharm Biopharm Off J Arbeitsgemeinschaft Pharm Verfahrenstechnik EV. 2016;107:223–33.

    Article  CAS  Google Scholar 

  21. Wengst A, Reichl S. RPMI 2650 epithelial model and three-dimensional reconstructed human nasal mucosa as in vitro models for nasal permeation studies. Eur J Pharm Biopharm. 2010;74:290–7.

    Article  CAS  PubMed  Google Scholar 

  22. Ye D, López Mármol Á, Lenz V, Muschong P, Wilhelm-Alkubaisi A, Weinheimer M, et al. Mucin-protected Caco-2 assay to study drug permeation in the presence of complex biorelevant media. Pharmaceutics. 2022;14:699.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dolberg AM, Reichl S. Expression of P-glycoprotein in excised human nasal mucosa and optimized models of RPMI 2650 cells. Int J Pharm. 2016;508:22–33.

    Article  CAS  PubMed  Google Scholar 

  24. Mercier C, Hodin S, He Z, Perek N, Delavenne X. Pharmacological characterization of the RPMI 2650 model as a relevant tool for assessing the permeability of intranasal drugs. Mol Pharm. 2018;15:2246–56.

    Article  CAS  PubMed  Google Scholar 

  25. EMA. ICH M9 guideline on biopharmaceutics classification system-based biowaivers. [Internet]. 2020. Available from: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-m9-biopharmaceutics-classification-system-based-biowaivers-step-5_en.pdf.

  26. Mallants R, Vlaeminck V, Jorissen M, Augustijns P. An improved primary human nasal cell culture for the simultaneous determination of transepithelial transport and ciliary beat frequency. J Pharm Pharmacol. 2009;61:883–90.

    Article  CAS  PubMed  Google Scholar 

  27. Lee M-K, Yoo J-W, Lin H, Kim Y-S, Kim D-D, Choi Y-M, et al. Air-liquid interface culture of serially passaged human nasal epithelial cell monolayer for in vitro drug transport studies. Drug Deliv. 2005;12:305–11.

    Article  CAS  PubMed  Google Scholar 

  28. Gonçalves VSS, Matias AA, Poejo J, Serra AT, Duarte CMM. Application of RPMI 2650 as a cell model to evaluate solid formulations for intranasal delivery of drugs. Int J Pharm. 2016;515:1–10.

    Article  PubMed  Google Scholar 

  29. Hughes P, Marshall D, Reid Y, Parkes H, Gelber C. The costs of using unauthenticated, over-passaged cell lines: how much more data do we need? Biotechniques. 2007;43:575–86.

    Article  CAS  PubMed  Google Scholar 

  30. Illum L. Nasal drug delivery—possibilities, problems and solutions. J Controlled Release. 2003;87:187–98.

    Article  CAS  Google Scholar 

  31. Illum L. Nasal drug delivery: new developments and strategies. Drug Discov Today. 2002;7:1184–9.

    Article  CAS  PubMed  Google Scholar 

  32. Zur M, Gasparini M, Wolk O, Amidon GL, Dahan A. The low/high BCS permeability class boundary: physicochemical comparison of metoprolol and labetalol. Mol Pharm. 2014;11:1707–14.

    Article  CAS  PubMed  Google Scholar 

  33. Chamberlain CA, Rubio VY, Garrett TJ. Impact of matrix effects and ionization efficiency in non-quantitative untargeted metabolomics. Metabolomics Off J Metabolomic Soc. 2019;15:135.

    Google Scholar 

  34. Dams R, Huestis MA, Lambert WE, Murphy CM. Matrix effect in bio-analysis of illicit drugs with LC-MS/MS: influence of ionization type, sample preparation, and biofluid. J Am Soc Mass Spectrom. 2003;14:1290–4.

    Article  CAS  PubMed  Google Scholar 

  35. Marttin E, Schipper NG, Verhoef JC, Merkus FWH. Nasal mucociliary clearance as a factor in nasal drug delivery. Adv Drug Deliv Rev. 1998;29:13–38.

    Article  CAS  PubMed  Google Scholar 

  36. Pandya VK, Tiwari RS. Nasal mucociliary clearance in health and disease. Indian J Otolaryngol Head Neck Surg. 2006;58:332–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Sigurdsson HH, Kirch J, Lehr C-M. Mucus as a barrier to lipophilic drugs. Int J Pharm. 2013;453:56–64.

    Article  CAS  PubMed  Google Scholar 

  38. Pohl EE, Krylov AV, Block M, Pohl P. Changes of the membrane potential profile induced by verapamil and propranolol. Biochim Biophys Acta BBA - Biomembr. 1998;1373:170–8.

    Article  CAS  Google Scholar 

  39. Berger JT, Voynow JA, Peters KW, Rose MC. Respiratory carcinoma cell lines. Am J Respir Cell Mol Biol. 1999;20:500–10.

    Article  CAS  PubMed  Google Scholar 

  40. Collett A, Tanianis-Hughes J, Warhurst G. Rapid induction of P-glycoprotein expression by high permeability compounds in colonic cells in vitro: a possible source of transporter mediated drug interactions? Biochem Pharmacol. 2004;68:783–90.

    Article  CAS  PubMed  Google Scholar 

  41. Bachmakov I, Werner U, Endress B, Auge D, Fromm MF. Characterization of beta-adrenoceptor antagonists as substrates and inhibitors of the drug transporter P-glycoprotein. Fundam Clin Pharmacol. 2006;20:273–82.

    Article  CAS  PubMed  Google Scholar 

  42. Yang JJ, Kim KJ, Lee VH. Role of P-glycoprotein in restricting propranolol transport in cultured rabbit conjunctival epithelial cell layers. Pharm Res. 2000;17:533–8.

    Article  CAS  PubMed  Google Scholar 

  43. Bruewer M, Nusrat A. Regulation of paracellular transport across tight junctions by the actin cytoskeleton [internet]. Landes Bioscience; 2013 [cited 2023 Jan 2]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK6487/.

  44. Cho H-J, Balakrishnan P, Lin H, Choi M-K, Kim D-D. Application of biopharmaceutics classification system (BCS) in drug transport studies across human respiratory epithelial cell monolayers. J Pharm Investig. 2012;42:147–53.

    Article  CAS  Google Scholar 

  45. Larregieu CA, Benet LZ. Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery. Mol Pharm. 2014;11:1335–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Naderkhani E, Erber A, Škalko-Basnet N, Flaten GE. Improved permeability of acyclovir: optimization of mucoadhesive liposomes using the phospholipid vesicle-based permeation assay. J Pharm Sci. 2014;103:661–8.

    Article  CAS  PubMed  Google Scholar 

  47. Ates M, Kaynak MS, Sahin S. Effect of permeability enhancers on paracellular permeability of acyclovir. J Pharm Pharmacol. 2016;68:781–90.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We want to thank Patricia Muschong and Manuel Weinheimer for their support, the fruitful discussions, helping us to shape our experiments and reviewing the manuscript. Further, we want to thank Peter Reinhardt and his team for offering us the possibility to use the cell culture facilities, initial cell culture training, the initial organization and logistics support to purchase and cultivate the cell line and proofreading. We also want to thank Anita Wilhelm-Alkubaisi and Yurani Caicedo Zea for the HPLC analysis and compound detection. All are AbbVie employees.

Funding

AbbVie sponsored and funded the study; contributed to the design; participated in the collection, analysis, and interpretation of data and in writing, reviewing and approval of the final publication. L.A.B., K.W., and A.P. are employees or former employees of AbbVie and may own AbbVie stocks. O.M.M. is a Professor at the Ludwig-Maximilians-University and L.A.B.’s doctoral adviser and is an external adviser for AbbVie on unrelated projects.

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Author notes

  1. Lea-Adriana Barlang and Kristina Weinbender contributed equally.

Authors and Affiliations

  1. Preclinical Safety, AbbVie Deutschland GmbH & Co. KG, Knollstraße, 67061, Ludwigshafen, Germany

    Lea-Adriana Barlang, Kristina Weinbender & Andreas Popp

  2. Department of Pharmacy, Pharmaceutical Technology and Biopharmacy, Ludwig-Maximilians-University, Butenandtstraße 5-13, 81337, Munich, Germany

    Lea-Adriana Barlang & Olivia M. Merkel

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  1. Lea-Adriana Barlang
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Contributions

The authors of this article include L.A.B., K.W., O.M.M., and A.P. L.A.B. and K.W. are joined first authors of the article, performed the investigations, the validation, discussed the data, did the formal analysis, performed the literature research, and wrote the original draft. L.A.B. conceptualized the project, established the methodology, and implemented all review versions. O.M.M. and A.P. supervised the overall project and reviewed the manuscript.

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Correspondence to Lea-Adriana Barlang.

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Olivia M. Merkel is a Scientific Board Member for Coriolis Pharma GmbH, AMW GmbH, and Carver Biosciences and an Advisor for PARI Pharma GmbH, Boehringer-Ingelheim International GmbH, and AbbVie on unrelated projects. All other authors have no relevant financial or non-financial interests to disclose.

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Barlang, LA., Weinbender, K., Merkel, O.M. et al. Characterization of critical parameters using an air–liquid interface model with RPMI 2650 cells for permeability studies of small molecules. Drug Deliv. and Transl. Res. 14, 1601–1615 (2024). https://doi.org/10.1007/s13346-023-01474-w

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