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P-gp Inhibition Potential in Cell-Based Models: Which “Calculation” Method is the Most Accurate?

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Abstract

The objective was to directly compare the four different “calculation” methods of assessing P-gp inhibition potential using experimental data obtained from ~60 structurally diverse internal research and marketed compounds. Bidirectional studies for digoxin (probe for P-gp substrate) were performed with and without test compounds (at 10 μM). Four different calculation methods were applied to the same dataset (raw bidirectional permeability values) to obtain the “percent inhibition of P-gp” for these compounds using the different methods. Significantly different inhibition potential was obtained with the “exact” same experimental dataset depending on the calculation method used. Subsequently, entirely different conclusions regarding the “inhibition potential” of test compound was reached due to the different calculation methods. Based on the direct comparison of these methods, method no. 3 (i.e., inhibition of B to A permeability of digoxin) is recommended as the calculation method ideal during screening stages due to its high throughput amenability. The methodology is capable of rapidly screening compounds with adequate reliability for early stage drug discovery. Method no. 3 provides an abridged version of a bidirectional study that is fully capable of identifying all non-inhibitors (0–20%), moderate inhibitors (20–60%), and potent inhibitors (>60%) and demonstrates high correlation with method no. 1 (inhibition based on both A to B and B to A permeability of digoxin). Nevertheless, method no. 1 might be appropriate for more detailed mechanistic studies required in late stage discovery and development.

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Abbreviations

A to B:

Apical to basolateral

ADME:

Absorption, distribution, metabolism, elimination

B to A:

Basolateral to apical

BCRP:

Breast cancer resistance protein

CNS:

Central nervous system

FDA:

Food and drug administration

HBSS:

Hank’s balanced salt solution

HEPES:

N-2-hydroxyethylpiperazine-N′-2-ethanesulfonic acid

LSC:

Liquid scintillation counting

MDCK:

Mardin Darby canine kidney

MRP:

Multi-drug resistance protein

PAMPA:

Parallel artificial membrane permeability assay

P-gp:

P-glycoprotein

Pc:

Permeability coefficient

TEER:

Transepithelial electrical resistance

References

  1. E. Leahey, et al. Interaction between quinidine and digoxin. JAMA. 240:533–534 (1978).

    Article  PubMed  Google Scholar 

  2. J. Lin. Drug–drug interaction mediated by inhibition and induction of P-glycoprotein. Adv. Drug Deliv. Rev. 55:53–81 (2003).

    Article  PubMed  CAS  Google Scholar 

  3. J. Lin, and M. Yamazaki. Role of P-glycoprotein in pharmacokinetics. Clin Pharmacokinet. 42:59–98 (2003).

    Article  PubMed  CAS  Google Scholar 

  4. C. Matheny, M. Lamb, K. Brouwer, and G. Pollak. Pharmacokinetic and pharmacodynamic implications of P-gp modulation. Pharmacotherapy. 21:778–796 (2001).

    Article  PubMed  CAS  Google Scholar 

  5. J. Polli, et al. Role of P-gp on CNS disposition of amprenavir, an HIV protease inhibitor. Pharm. Res. 16:1206–1212 (1999).

    Article  PubMed  CAS  Google Scholar 

  6. J. W. Polli, et al. P-glycoprotein influences the brain concentrations of cetirizine (Zyrtec), a second-generation non-sedating antihistamine. J. Pharm. Sci. 92:2082–2089 (2003).

    Article  PubMed  CAS  Google Scholar 

  7. A. Sparreboom, et al. Limited oral bioavailability and active epithelial excretion of paclitaxel (Taxol) caused by P-gp in the intestine. Proc. Natl. Acad. Sci. 94:2031–2035 (1997).

    Article  PubMed  CAS  Google Scholar 

  8. T. Watanabe, et al. Kinetic analysis of hepatobiliary transport of vincristine in perfused rat liver: Possible roles of P-gp in biliary excretion of vincristine. J. Hepatol. 16:77–88 (1992).

    Article  PubMed  CAS  Google Scholar 

  9. FDA Draft Guidance. Drug interaction studies—Study design, data analysis, and implications for dosing and labeling. 2006.

  10. P. V. Balimane, Y. H. Han, and S. Chong. Current industrial practices of assessing permeability and P-glycoprotein interaction. AAPS J. 8:E1–13 (2006).

    Article  PubMed  CAS  Google Scholar 

  11. L. Cutler, C. Howes, N. J. Deeks, T. L. Buck, and P. Jeffrey. Development of a P-glycoprotein knockout model in rodents to define species differences in its functional effect at the blood-brain barrier. J. Pharm. Sci. 95:1944–1953 (2006).

    Article  PubMed  CAS  Google Scholar 

  12. B. Feng, et al. In vitro p-glycoprotein assays to predict the in vivo interactions of p-glycoprotein with drugs in the central nervous system. Drug. Metab. Dispos. 36:268–275 (2008).

    Article  PubMed  CAS  Google Scholar 

  13. I. Hidalgo. Assessing the absorption of new pharmaceuticals. Current Topics in Medicinal Chemistry. 1:385–401 (2001).

    Article  PubMed  CAS  Google Scholar 

  14. J. P. Keogh, and J. R. Kunta. Development, validation and utility of an in vitro technique for assessment of potential clinical drug–drug interactions involving P-glycoprotein. Eur. J. Pharm. Sci. 27:543–554 (2006).

    Article  PubMed  CAS  Google Scholar 

  15. E. Kerns, et al. Combined application of parallel artificial membrane permeability assay and Caco-2 permeability assays in drug discovery. J. Pharm. Sci. 93:1440–1453 (2004).

    Article  PubMed  CAS  Google Scholar 

  16. J. Polli, et al. Rational use of in vitro P-gp assays in drug discovery. J. Pharmacol. Exp. Ther. 299:620–628 (2001).

    PubMed  CAS  Google Scholar 

  17. J. Rautio, et al. In vitro p-glycoprotein inhibition assays for assessment of clinical drug interaction potential of new drug candidates: a recommendation for probe substrates. Drug Metab. Dispos. 34:786–792 (2006).

    Article  PubMed  CAS  Google Scholar 

  18. F. Tang, K. Horie, and R. Borchardt. Are MDCK cells transfected with the human MDR1 gene a good model of the human intestinal mucosa. Pharm. Res. 19:765–772 (2002).

    Article  PubMed  CAS  Google Scholar 

  19. A.-L. Ungell. Caco-2 replace or refine. Drug Discov. Today. 1:423–430 (2004).

    Article  CAS  Google Scholar 

  20. P. Hsiao, T. Bui, R. J. Ho, and J. D. Unadkat. In vitro-to-in vivo prediction of P-glycoprotein-based drug interactions at the human and rodent blood–brain barrier. Drug. Metab. Dispos. 36:481–484 (2008).

    Article  PubMed  CAS  Google Scholar 

  21. R. Kim, et al. Interrelationship between substrates and inhibitors of human CYP3A and P-gp. Pharm. Res. 16:408–414 (1999).

    Article  PubMed  CAS  Google Scholar 

  22. M. Perloff, E. Stromer, L. von Moltke, and D. Greenblatt. Rapid assessment of P-gp inhibition and induction in vitro. Pharm. Res. 20:1177–1183 (2003).

    Article  PubMed  CAS  Google Scholar 

  23. M. D. Troutman, and D. R. Thakker. Efflux ratio cannot assess P-glycoprotein-mediated attenuation of absorptive transport: Asymmetric effect of P-glycoprotein on absorptive and secretory transport across Caco-2 cell monolayers. Pharm. Res. 20:1200–1209 (2003).

    Article  PubMed  CAS  Google Scholar 

  24. R. Stephens, et al. Kinetic profiling of P-gp mediated drug efflux in rat and human intestinal epithelia. J. Pharmacol. Exp. Ther. 296:584–591 (2001).

    PubMed  CAS  Google Scholar 

  25. P. V. Balimane, and S. Chong. Cell culture-based models for intestinal permeability: A critique. Drug Discov. Today. 10:335–343 (2005).

    Article  PubMed  CAS  Google Scholar 

  26. P. V. Balimane, K. Patel, A. Marino, and S. Chong. Utility of 96 well Caco-2 cell system for increased throughput of P-gp screening in drug discovery. Eur. J. Pharm. Biopharm. 58:99–105 (2004).

    Article  PubMed  CAS  Google Scholar 

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Correspondence to Praveen V. Balimane.

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Balimane, P.V., Marino, A. & Chong, S. P-gp Inhibition Potential in Cell-Based Models: Which “Calculation” Method is the Most Accurate?. AAPS J 10, 577–586 (2008). https://doi.org/10.1208/s12248-008-9068-x

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  • DOI: https://doi.org/10.1208/s12248-008-9068-x

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