Prediction of Tumor-to-Plasma Ratios of Basic Compounds in Subcutaneous Xenograft Mouse Models

  • Prashant B. Nigade
  • Jayasagar Gundu
  • K. Sreedhara Pai
  • Kumar V. S. Nemmani
Original Research Article
  • 49 Downloads

Abstract

Background

Predicting target site drug concentrations is of key importance for rank ordering compounds before proceeding to chronic pharmacodynamic models. We propose generic tumor-specific correlation-based regression equations to predict tumor-to-plasma ratios (tumor-Kps) in slow- and fast-growing xenograft mouse models.

Methods

Disposition of 14 basic small molecules was investigated extensively in mouse plasma, tissues and tumors after a single oral dose administration. Linear correlation was assessed and compared between tumor-Kp and normal tissue-to-plasma ratio (tissue-Kps) separately for each tumor xenograft. The developed regression equations were validated by leave-one-out cross-validation (LOOCV) method.

Result

Both slow- and fast-growing tumor-Kps showed good correlation (r 2 ≥ 0.7) with majority of the normal tissue-Kps. Substantial difference was observed in the slopes of developed equations between two xenografts, which was in line with observed difference in tumor distribution. The linear correlations between tumor-Kp and skin- or spleen-Kp were within the acceptable statistical criteria (LOOCV) across xenografts and the class of compounds evaluated. Since > 70% of tumor-Kps from the test data sets were predicted within a factor of twofold for both slow- and fast-growing xenograft mouse models, the results validate the applicability of the developed equations across xenografts.

Conclusion

Tumor-specific correlation-based regression equations were developed and their applicability was adequately validated across xenografts. These equations could be successfully translated to predict tumor concentrations in order to preclude experimental tumor-Kp determination.

Notes

Acknowledgements

We are grateful for the support of senior management of Lupin Limited (Research Park), Pune, India, and technical assistance of Mr. Tariq Bhat and Dr. Praveen Kumar during development of human xenograft mouse models.

Funding

The present work was supported by and conducted at Lupin Ltd (Research Park), Pune.

Compliance with Ethical Standards

Conflict of interest

Prashant B. Nigade, Jayasagar Gundu, K. Sreedhara Pai and Kumar V.S. Nemmani have no conflict of interest.

Ethical approval

In vivo studies were performed at the AAALAC accredited facility (Lupin Limited, Pune, India) in accordance with the CPCSEA (Committee for the Purpose of Control and Supervision of Experiments on Animals) guidelines and as per Institutional Animal Ethics Committee (IAEC) approved experimental protocols numbers: IAEC/PK/534, IAEC/PK/619 and IAEC/PK/620.

Supplementary material

13318_2017_454_MOESM1_ESM.docx (203 kb)
Supplementary material 1 (DOCX 202 kb)

References

  1. 1.
    Abdel-Rahman SM, Kauffman RE. The integration of pharmacokinetics and pharmacodynamics: understanding dose-response. Annu Rev Pharmacol Toxicol. 2004;44:111–36.CrossRefPubMedGoogle Scholar
  2. 2.
    de Lange EC, Danhof M. Considerations in the use of CSF pharmacokinetics to predict brain target concentrations in the clinical setting: implications of the barriers between blood and brain. Clin Pharmacokinet. 2002;41:691–703.CrossRefPubMedGoogle Scholar
  3. 3.
    Read KD, Braggio S. Assessing brain free fraction in early drug discovery. Expert Opin Drug Metab Toxicol. 2010;6(3):337–44.CrossRefPubMedGoogle Scholar
  4. 4.
    Poulin P, Theil FP. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J Pharm Sci. 2000;89(1):16–35.CrossRefPubMedGoogle Scholar
  5. 5.
    Poulin P, Theil FP. Prediction of pharmacokinetics prior to in vivo studies. 1. mechanism-based prediction of volume of distribution. J Pharm Sci. 2002;91(1):129–56.CrossRefPubMedGoogle Scholar
  6. 6.
    Bj¨orkman S. Prediction of the volume of distribution of a drug: which tissue–plasma partition coefficients are needed? J Pharm Pharmacol. 2002;54(9):1237–45.CrossRefGoogle Scholar
  7. 7.
    Rodgers T, Leahy D, Rowland M. Physiologically based pharmacokinetic modeling 1: predicting the tissue distribution of moderate-to-strong bases. J Pharm Sci. 2005;94(6):1259–76.CrossRefPubMedGoogle Scholar
  8. 8.
    Rodgers T, Leahy D, Rowland M. Tissue distribution of basic drugs: accounting for enantiomer, compound and regional difference amongst beta-blocking drugs in rat. J Pharm Sci. 2005;94(6):1237–48.CrossRefPubMedGoogle Scholar
  9. 9.
    Rodgers T, Rowland M. Physiologically based pharmacokinetic modelling 2: predicting the tissue distribution of acids, very weak bases, neutrals and zwitterions. J Pharm Sci. 2006;95(6):1238–57.CrossRefPubMedGoogle Scholar
  10. 10.
    Richter WF, Starke V, Whitby B. The distribution pattern of radioactivity across different tissues in quantitative whole-body autoradiography (QWBA) studies. Eur J Pharm Sci. 2006;28(1–2):155–65.CrossRefPubMedGoogle Scholar
  11. 11.
    Jansson R, Bredberg U, Ashton M. Prediction of drug tissue to plasma concentration ratios using a measured volume of distribution in combination with lipophilicity. J Pharm Sci. 2008;97(6):2324–39.CrossRefPubMedGoogle Scholar
  12. 12.
    Poulin P, Theil FP. Development of a novel method for predicting human volume of distribution at steady-state of basic drugs and comparative assessment with existing methods. J Pharm Sci. 2009;98(12):4941–61.CrossRefPubMedGoogle Scholar
  13. 13.
    Poulin P, Ekin S, Theil FP. A hybrid approach to advancing quantitative prediction of tissue distribution of basic drugs in human. Toxicol Appl Pharmacol. 2011;250(2):194–212.CrossRefPubMedGoogle Scholar
  14. 14.
    Graham H, Walker M, Jones O, Yates J, Galetin A, Aarons L. Comparison of in vivo and in silico methods used for prediction of tissue: plasma partition coefficients in rat. J Pharm Pharmacol. 2012;64(3):383–96.CrossRefPubMedGoogle Scholar
  15. 15.
    Poulin P, Dambach DM, Hartley DH, Ford K, Theil FP, Harstad E, Halladay J, Choo E, Boggs J, Liederer BM, Dean B, Diaz D. An algorithm for evaluating potential tissue drug distribution in toxicology studies from readily available pharmacokinetic parameters. J Phar Sci. 2013;102(10):3816–29.CrossRefGoogle Scholar
  16. 16.
    Yun YE, Edginton AN. Correlation-based prediction of tissue-to plasma partition coefficients using readily available input parameters. Xenobiotica. 2013;43(10):839–52.CrossRefPubMedGoogle Scholar
  17. 17.
    Poulin P, Hop CE, Salphati L, Liederer BM. Correlation of tissue-to-plasma partition coefficient between normal tissues and subcutaneous xenografts of human tumor cell lines in mouse as a prediction tool of drug penetration in tumors. J Pharm Sci. 2013;102(4):1355–69.CrossRefPubMedGoogle Scholar
  18. 18.
    Patrick P, Chen YH, Ding X, Gould SE, Hop CE, Messick K, Oeh J, Liederer BM. Prediction of drug distribution in subcutaneous xenografts of human tumor cell lines and healthy tissues in mouse: application of the tissue composition-based model to antineoplastic drugs. J Pharm Sci. 2015;104(4):1508–21.CrossRefGoogle Scholar
  19. 19.
    Williamson MJ, Silva MD, Terkelsen J, Robertson R, Yu L, Xia C, Hatsis P, Bannerman B, Babcock T, Cao Y, Kupperman E. The relationship among tumor architecture, pharmacokinetics, pharmacodynamics, and efficacy of bortezomib in mouse xenograft models. Mol Cancer Ther. 2009;8(12):3234–43.CrossRefPubMedGoogle Scholar
  20. 20.
    Nigade PB, Gundu J, Pai KS, Nemmani KV. Prediction of tissue-to-plasma ratios of basic compounds in mice. Eur J Drug Metab Pharmacokinet. 2017;42(5):835–47.CrossRefPubMedGoogle Scholar
  21. 21.
    Moore DS, Notz WI, Flinger MA. The basic practice of statistics. 6th ed. New York: W. H. Freeman and Company; 2013. p. 138.Google Scholar
  22. 22.
    Kiralj R, Ferreira MC. Basic validation procedures models in QSAR and QSPR studies: theory and application. J Braz Chem Soc. 2009;20(4):770–87.CrossRefGoogle Scholar
  23. 23.
    The Report From the Expert Group on (Quantitative) Structure-Activity Relationships [(Q)Sars] on the Principles for the Validation Of (Q)Sars. OECD Environment Health and Safety Publications Series on Testing and Assessment No. 49. OECD: Paris, 2004.Google Scholar
  24. 24.
    Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models. OECD Environment Health and Safety Publications Series on Testing and Assessment No. 69. OECD: Paris, 2007.Google Scholar
  25. 25.
    Downey CM, Singla AK, Villemaire ML, Buie HR, Boyd SK, Jirik FR. Quantitative ex-vivo micro-computed tomographic imaging of blood vessels and necrotic regions within tumors. PLoS One. 2012.  https://doi.org/10.1371/journal.pone.0041685.Google Scholar
  26. 26.
    Kallinowski F, Schlenger KH, Runkel S, Kloes M, Stohrer M, Okunieff P, Vaupel P. Blood flow, metabolism, cellular microenvironment, and growth rate of human tumor xenografts. Cancer Res. 1989;49(14):3759–64.PubMedGoogle Scholar
  27. 27.
    Saleem A, Price PM. Early tumor drug pharmacokinetics is influenced by tumor perfusion but not plasma drug exposure. Clin Cancer Res. 2008;14(24):8184–90.CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Raghunand N, Mahoney BP, Gillies RJ. Tumor acidity, ion trapping and chemotherapeutics. II. pH-dependent partition coefficients predict importance of ion trapping on pharmacokinetics of weakly basic chemotherapeutic agents. Biochem Pharmacol. 2003;66(7):1219–29.CrossRefPubMedGoogle Scholar
  29. 29.
    Brown RP, Delp MD, Lindstedt SL, Rhomberg LR, Beliles RP. Physiological parameter values for physiologically based pharmacokinetic models. Toxicol Ind Health. 1997;13(4):407–84.CrossRefPubMedGoogle Scholar
  30. 30.
    Kazmi F, Hensley T, Pope C, Funk RS, Loewen GJ, Buckley DB, Parkinson A. Lysosomal sequestration (trapping) of lipophilic amine (cationic amphiphilic) drugs in immortalized human hepatocytes (Fa2N-4 cells). Drug Metab Dispos. 2013;41(4):897–905.CrossRefPubMedPubMedCentralGoogle Scholar
  31. 31.
    Bradshaw-Pierce EL, Pitts TM, Tan AC, McPhillips K, West M, Gustafson DL, Halsey C, Nguyen L, Lee NV, Kan JL, Murray BW, Eckhardt SG. Tumor p-glycoprotein correlates with efficacy of PF-3758309 in in vitro and in vivo models of colorectal cancer. Front Pharmacol. 2013;4:22.  https://doi.org/10.3389/fphar.2013.00022.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Heffron TP. Small molecule kinase inhibitors for the treatment of brain cancer. J Med Chem. 2016;59(22):10030–66.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2017

Authors and Affiliations

  • Prashant B. Nigade
    • 1
  • Jayasagar Gundu
    • 1
  • K. Sreedhara Pai
    • 2
  • Kumar V. S. Nemmani
    • 3
  1. 1.Department of Drug Metabolism and PharmacokineticsLupin Limited (Research Park)PuneIndia
  2. 2.Department of Pharmacology, Manipal College of Pharmaceutical SciencesManipal UniversityManipalIndia
  3. 3.Department of PharmacologyLupin Limited (Research Park)PuneIndia

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