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Translational Pharmacokinetic-Pharmacodynamic Modeling from Nonclinical to Clinical Development: A Case Study of Anticancer Drug, Crizotinib

  • Review Article
  • Theme: Translational Modeling and Dose Selection: From Preclinical to Humans
  • Published:
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Abstract

Attrition risk related to efficacy is still a major reason why new chemical entities fail in clinical trials despite recently increased understanding of translational pharmacology. Pharmacokinetic-pharmacodynamic (PKPD) analysis is key to translating in vivo drug potency from nonclinical models to patients by providing a quantitative assessment of in vivo drug potency with mechanistic insight of drug action. The pharmaceutical industry is clearly moving toward more mechanistic and quantitative PKPD modeling to have a deeper understanding of translational pharmacology. This paper summarizes an anticancer drug case study describing the translational PKPD modeling of crizotinib, an orally available, potent small molecule inhibitor of multiple tyrosine kinases including anaplastic lymphoma kinase (ALK) and mesenchymal-epithelial transition factor (MET), from nonclinical to clinical development. Overall, the PKPD relationships among crizotinib systemic exposure, ALK or MET inhibition, and tumor growth inhibition (TGI) in human tumor xenograft models were well characterized in a quantitative manner using mathematical modeling: the results suggest that 50% ALK inhibition is required for >50% TGI whereas >90% MET inhibition is required for >50% TGI. Furthermore, >75% ALK inhibition and >95% MET inhibition in patient tumors were projected by PKPD modeling during the clinically recommended dosing regimen, twice daily doses of crizotinib 250 mg (500 mg/day). These simulation results of crizotinib-mediated ALK and MET inhibition appeared consistent with the currently reported clinical responses. In summary, the present paper presents an anticancer drug example to demonstrate that quantitative PKPD modeling can be used for predictive translational pharmacology from nonclinical to clinical development.

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References

  1. Chien JY, Friedrich S, Heathman MA, de Alwis DP, Sinha V. Pharmacokinetics/Pharmacodynamics and the stages of drug development: role of modeling and simulation. AAPS J. 2005;7:E544–59.

    Article  PubMed  Google Scholar 

  2. Cohen A. Pharmacokinetic and pharmacodynamic data to be derived from early-phase drug development: designing informative human pharmacology studies. Clin Pharmacokinet. 2008;47:373–81.

    Article  PubMed  CAS  Google Scholar 

  3. Derendorf H, Lesko LJ, Chaikin P, Colburn WA, Lee P, Miller R, et al. Pharmacokinetic/pharmacodynamic modeling in drug research and development. J Clin Pharmacol. 2000;40:1399–418.

    PubMed  CAS  Google Scholar 

  4. Lesko LJ, Rowland M, Peck CC, Blaschke TF. Optimizing the science of drug development: opportunities for better candidate selection and accelerated evaluation in humans. Pharm Res. 2000;17:1335–44.

    Article  PubMed  CAS  Google Scholar 

  5. Senderowicz AM. Information needed to conduct first-in-human oncology trials in the United States: a view from a former FDA medical reviewer. Clin Cancer Res. 2010;16:1719–25.

    Article  PubMed  Google Scholar 

  6. Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, et al. Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today. 2012.

  7. Anger GJ, Piquette-Miller M. Translational pharmacology: harnessing increased specialization of research within the basic biological sciences. Clin Pharmacol Ther. 2008;83:797–801.

    Article  PubMed  CAS  Google Scholar 

  8. Lotsch J, Geisslinger G. Bedside-to-bench pharmacology: a complementary concept to translational pharmacology. Clin Pharmacol Ther. 2010;87:647–9.

    Article  PubMed  CAS  Google Scholar 

  9. Mager DE, Jusko WJ. Development of translational pharmacokinetic-pharmacodynamic models. Clin Pharmacol Ther. 2008;83:909–12.

    Article  PubMed  CAS  Google Scholar 

  10. Lindner MD. Clinical attrition due to biased preclinical assessments of potential efficacy. Pharmacol Ther. 2007;115:148–75.

    Article  PubMed  CAS  Google Scholar 

  11. Schuster D, Laggner C, Langer T. Why drugs fail–a study on side effects in new chemical entities. Curr Pharm Des. 2005;11:3545–59.

    Article  PubMed  CAS  Google Scholar 

  12. Kerbel RS. Human tumor xenografts as predictive preclinical models for anticancer drug activity in humans: better than commonly perceived-but they can be improved. Cancer Biol Ther. 2003;2:S134–9.

    PubMed  CAS  Google Scholar 

  13. Peterson JK, Houghton PJ. Integrating pharmacology and in vivo cancer models in preclinical and clinical drug development. Eur J Cancer. 2004;40:837–44.

    Article  PubMed  CAS  Google Scholar 

  14. Kelland LR. Of mice and men: values and liabilities of the athymic nude mouse model in anticancer drug development. Eur J Cancer. 2004;40:827–36.

    Article  PubMed  CAS  Google Scholar 

  15. Burchill SA. What do, can and should we learn from models to evaluate potential anticancer agents? Future Oncol. 2006;2:201–11.

    Article  PubMed  CAS  Google Scholar 

  16. Hollingshead MG. Antitumor efficacy testing in rodents. J Natl Cancer Inst. 2008;100:1500–10.

    Article  PubMed  CAS  Google Scholar 

  17. Liu L, Di Paolo J, Barbosa J, Rong H, Reif K, Wong H. Antiarthritis effect of a novel Bruton’s tyrosine kinase (BTK) inhibitor in rat collagen-induced arthritis and mechanism-based pharmacokinetic/pharmacodynamic modeling: relationships between inhibition of BTK phosphorylation and efficacy. J Pharmacol Exp Ther. 2011;338:154–63.

    Article  PubMed  CAS  Google Scholar 

  18. Salphati L, Wong H, Belvin M, Bradford D, Edgar KA, Prior WW, et al. Pharmacokinetic-pharmacodynamic modeling of tumor growth inhibition and biomarker modulation by the novel phosphatidylinositol 3-kinase inhibitor GDC-0941. Drug Metab Dispos. 2010;38:1436–42.

    Article  PubMed  CAS  Google Scholar 

  19. Wong H, Belvin M, Herter S, Hoeflich KP, Murray LJ, Wong L, et al. Pharmacodynamics of 2-[4-[(1E)-1-(hydroxyimino)-2,3-dihydro-1H-inden-5-yl]-3-(pyridine-4-yl)-1H-pyraz ol-1-yl]ethan-1-ol (GDC-0879), a potent and selective B-Raf kinase inhibitor: understanding relationships between systemic concentrations, phosphorylated mitogen-activated protein kinase kinase 1 inhibition, and efficacy. J Pharmacol Exp Ther. 2009;329:360–7.

    Article  PubMed  CAS  Google Scholar 

  20. Yamazaki S, Nguyen L, Vekich S, Shen Z, Yin MJ, Mehta PP, et al. Pharmacokinetic-pharmacodynamic modeling of biomarker response and tumor growth inhibition to an orally available heat shock protein 90 inhibitor in a human tumor xenograft mouse model. J Pharmacol Exp Ther. 2011;338:964–73.

    Article  PubMed  CAS  Google Scholar 

  21. Yamazaki S, Skaptason J, Romero D, Lee JH, Zou HY, Christensen JG, et al. Pharmacokinetic-pharmacodynamic modeling of biomarker response and tumor growth inhibition to an orally available cMet kinase inhibitor in human tumor xenograft mouse models. Drug Metab Dispos. 2008;36:1267–74.

    Article  PubMed  CAS  Google Scholar 

  22. Yamazaki S, Vicini P, Shen Z, Zou HY, Lee J, Li Q, et al. Pharmacokinetic-pharmacodynamic modeling of crizotinib for anaplastic lymphoma kinase inhibition and anti-tumor efficacy in human tumor xenograft mouse models. J Pharmacol Exp Ther. 2012.

  23. Cui JJ, Tran-Dube M, Shen H, Nambu M, Kung PP, Pairish M, et al. Structure based drug design of crizotinib (PF-02341066), a potent and selective dual inhibitor of mesenchymal-epithelial transition factor (c-MET) kinase and anaplastic lymphoma kinase (ALK). J Med Chem. 2011;54:6342–63.

    Article  PubMed  CAS  Google Scholar 

  24. Kwak EL, Bang YJ, Camidge DR, Shaw AT, Solomon B, Maki RG, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med. 2010;363:1693–703.

    Article  PubMed  CAS  Google Scholar 

  25. Ou SH. Crizotinib: a novel and first-in-class multitargeted tyrosine kinase inhibitor for the treatment of anaplastic lymphoma kinase rearranged non-small cell lung cancer and beyond. Drug Des Devel Ther. 2011;5:471–85.

    Article  PubMed  Google Scholar 

  26. Rikova K, Guo A, Zeng Q, Possemato A, Yu J, Haack H, et al. Global survey of phosphotyrosine signaling identifies oncogenic kinases in lung cancer. Cell. 2007;131:1190–203.

    Article  PubMed  CAS  Google Scholar 

  27. Soda M, Choi YL, Enomoto M, Takada S, Yamashita Y, Ishikawa S, et al. Identification of the transforming EML4-ALK fusion gene in non-small-cell lung cancer. Nature. 2007;448:561–6.

    Article  PubMed  CAS  Google Scholar 

  28. Camidge DR, Bang YJ, Kwak EL, Iafrate AJ, Varella-Garcia M, Fox SB, et al. Activity and safety of crizotinib in patients with ALK-positive non-small-cell lung cancer: updated results from a phase 1 study. Lancet Oncol. 2012.

  29. Shaw AT, Solomon B, Kenudson MM. Crizotinib and testing for ALK. J Natl Compr Canc Netw. 2011;9:1335–41.

    PubMed  CAS  Google Scholar 

  30. Tan W, Wilner KD, Bang Y, Kwak EL, Maki RG, Camidge DR. Pharmacokinetics (PK) of PF-02341066, a dual ALK/MET inhibitor after multiple oral doses to advanced cancer patients. J Clin Oncol. 2010;28:15s.

    Google Scholar 

  31. Sheiner LB. The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods. Drug Metab Rev. 1984;15:153–71.

    Article  PubMed  CAS  Google Scholar 

  32. Dayneka NL, Garg V, Jusko WJ. Comparison of four basic models of indirect pharmacodynamic responses. J Pharmacokinet Biopharm. 1993;21:457–78.

    Article  PubMed  CAS  Google Scholar 

  33. Mager DE, Wyska E, Jusko WJ. Diversity of mechanism-based pharmacodynamic models. Drug Metab Dispos. 2003;31:510–8.

    Article  PubMed  CAS  Google Scholar 

  34. Sheiner LB, Stanski DR, Vozeh S, Miller RD, Ham J. Simultaneous modeling of pharmacokinetics and pharmacodynamics: application to d-tubocurarine. Clin Pharmacol Ther. 1979;25:358–71.

    PubMed  CAS  Google Scholar 

  35. Bissery MC, Vrignaud P, Lavelle F, Chabot GG. Experimental antitumor activity and pharmacokinetics of the camptothecin analog irinotecan (CPT-11) in mice. Anticancer Drugs. 1996;7:437–60.

    Article  PubMed  CAS  Google Scholar 

  36. Gompertz B. On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies. Phil Trans R Soc London. 1825;115:513–85.

    Article  Google Scholar 

  37. Hart D, Shochat E, Agur Z. The growth law of primary breast cancer as inferred from mammography screening trials data. Br J Cancer. 1998;78:382–7.

    Article  PubMed  CAS  Google Scholar 

  38. Lobo ED, Balthasar JP. Pharmacodynamic modeling of chemotherapeutic effects: application of a transit compartment model to characterize methotrexate effects in vitro. AAPS PharmSci. 2002;4:E42.

    Article  PubMed  Google Scholar 

  39. Simeoni M, Magni P, Cammia C, De Nicolao G, Croci V, Pesenti E, et al. Predictive pharmacokinetic-pharmacodynamic modeling of tumor growth kinetics in xenograft models after administration of anticancer agents. Cancer Res. 2004;64:1094–101.

    Article  PubMed  CAS  Google Scholar 

  40. Sun YN, Jusko WJ. Transit compartments versus gamma distribution function to model signal transduction processes in pharmacodynamics. J Pharm Sci. 1998;87:732–7.

    Article  PubMed  CAS  Google Scholar 

  41. Butterfield LH, Potter DM, Kirkwood JM. Multiplex serum biomarker assessments: technical and biostatistical issues. J Transl Med. 2011;9:173.

    Article  PubMed  CAS  Google Scholar 

  42. Godschalk RW, Van Schooten FJ, Bartsch H. A critical evaluation of DNA adducts as biological markers for human exposure to polycyclic aromatic compounds. J Biochem Mol Biol. 2003;36:1–11.

    Article  PubMed  CAS  Google Scholar 

  43. Plummer R, Jones C, Middleton M, Wilson R, Evans J, Olsen A, et al. Phase I study of the poly(ADP-ribose) polymerase inhibitor, AG014699, in combination with temozolomide in patients with advanced solid tumors. Clin Cancer Res. 2008;14:7917–23.

    Article  PubMed  CAS  Google Scholar 

  44. FDA. Xalkori Clinical Pharmacology and Biopharmaceutical Review(s) (http://www.accessdata.fda.gov/drugsatfda_docs/nda/2011/202570Orig1s000ClinPharmR.pdf). 2011.

  45. Zou HY, Li Q, Lee J, Engstrom L, Lu MW, Young A, et al. Antitumor efficacy of crizotinib (PF-02341066), a potent and selective ALK and c-Met RTK inhibitor. In: EML4-ALK driven NSCLC tumors in vitro and in vivo. At the 102nd AACR Annual Meeting; April 2–6, 2011, Orlando, Florida Abstract LB-390. American Society for Cancer Research, Philadelphia, PA; 2011.

  46. Zou HY, Li Q, Lee JH, Arango ME, McDonnell SR, Yamazaki S, et al. An orally available small-molecule inhibitor of c-Met, PF-2341066, exhibits cytoreductive antitumor efficacy through antiproliferative and antiangiogenic mechanisms. Cancer Res. 2007;67:4408–17.

    Article  PubMed  CAS  Google Scholar 

  47. Yamazaki S, Skaptason J, Romero D, Vekich S, Jones HM, Tan W, et al. Prediction of oral pharmacokinetics of cMet kinase inhibitors in humans: physiologically based pharmacokinetic model versus traditional one-compartment model. Drug Metab Dispos. 2011;39:383–93.

    Article  PubMed  CAS  Google Scholar 

  48. Sugawara M, Okamoto K, Kadowaki T, Kusano K, Fukamizu A, Yoshimura T. Expressions of cytochrome P450, UDP-glucuronosyltranferase, and transporter genes in monolayer carcinoma cells change in subcutaneous tumors grown as xenografts in immunodeficient nude mice. Drug Metab Dispos. 2010;38:526–33.

    Article  PubMed  CAS  Google Scholar 

  49. Christensen JG, Zou HY, Arango ME, Li Q, Lee JH, McDonnell SR, et al. Cytoreductive antitumor activity of PF-2341066, a novel inhibitor of anaplastic lymphoma kinase and c-Met, in experimental models of anaplastic large-cell lymphoma. Mol Cancer Ther. 2007;6:3314–22.

    Article  PubMed  CAS  Google Scholar 

  50. Jusko WJ, Ko HC, Ebling WF. Convergence of direct and indirect pharmacodynamic response models. J Pharmacokinet Biopharm. 1995;23:5–8. discussion 9–10.

    Article  PubMed  CAS  Google Scholar 

  51. Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis. 2010;31:2–8.

    Article  PubMed  CAS  Google Scholar 

  52. Teicher BA. Tumor models for efficacy determination. Mol Cancer Ther. 2006;5:2435–43.

    Article  PubMed  CAS  Google Scholar 

  53. Takimoto CH. Pharmacokinetics and pharmacodynamic biomarkers in early oncology drug development. Eur J Cancer. 2009;45 Suppl 1:436–8.

    Article  PubMed  Google Scholar 

  54. Wong H, Choo EF, Alicke B, Ding X, La H, McNamara E, et al. Antitumor activity of targeted and cytotoxic agents in xenograft models correlates with clinical response: A pharmacokinetic-pharmacodynamic analysis. Mol Cancer Ther. 2011;10:Abstract A11.

    Google Scholar 

  55. Ou SH, Kwak EL, Siwak-Tapp C, Dy J, Bergethon K, Clark JW, et al. Activity of crizotinib (PF02341066), a dual mesenchymal-epithelial transition (MET) and anaplastic lymphoma kinase (ALK) inhibitor, in a non-small cell lung cancer patient with de novo MET amplification. J Thorac Oncol. 2011;6:942–6.

    Article  PubMed  Google Scholar 

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Acknowledgment

I greatly thank Bhasker Shetty, Bill J. Smith, Paolo Vicini (Pharmacokinetics, Dynamics and Metabolism, Pfizer Worldwide Research & Development, San Diego, CA) and Keith D. Wilner (Clinical Pharmacology, Pfizer, Worldwide Research & Development, San Diego, CA) for helpful discussion and comments on this manuscript.

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  1. Pharmacokinetics, Dynamics and Metabolism, La Jolla Laboratories, Pfizer Worldwide Research & Development, 10777 Science Center Drive, San Diego, California, 92121, USA

    Shinji Yamazaki

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Correspondence to Shinji Yamazaki.

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Yamazaki, S. Translational Pharmacokinetic-Pharmacodynamic Modeling from Nonclinical to Clinical Development: A Case Study of Anticancer Drug, Crizotinib. AAPS J 15, 354–366 (2013). https://doi.org/10.1208/s12248-012-9436-4

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  • DOI: https://doi.org/10.1208/s12248-012-9436-4

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