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PK/PD Modelling of the QT Interval: a Step Towards Defining the Translational Relationship Between In Vitro, Awake Beagle Dogs, and Humans

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Abstract

Inhibiting the human ether-a-go-go-related gene (hERG)-encoded potassium ion channel is positively correlated with QT-interval prolongation in vivo, which is considered a risk factor for the occurrence of Torsades de Pointes (TdP). A pharmacokinetic/pharmacodynamic model was developed for four compounds that reached the clinic, to relate drug-induced QT-interval change in awake dogs and humans and to derive a translational scaling factor a 1. Overall, dogs were more sensitive than humans to QT-interval change, an a 1 of 1.5 was found, and a 10% current inhibition in vitro produced a higher percent QT-interval change in dogs as compared to humans. The QT-interval changes in dogs were predictive for humans. In vitro and in vivo information could reliably describe the effects in humans. Robust translational knowledge is likely to reduce the need for expensive thorough QT studies; therefore, expanding this work to more compounds is recommended.

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References

  1. Valentin J-P. Reducing QT liability and proarrhythmic risk in drug discovery and development. Br J Pharmacol. 2010;159:5–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pollard CE, Abi Gerges N, Bridgland-Taylor MH, Easter A, Hammond TG, Valentin J-P. An introduction to QT interval prolongation and non-clinical approaches to assessing and reducing risk. Br J Pharmacol. 2010;159:12–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Moss AJ. The QT, interval and torsade de pointes. Drug Saf. 1999;21:5–10.

    Article  CAS  PubMed  Google Scholar 

  4. Sarapa N, Britto MR. Challenges of characterizing proarrhythmic risk due to QTc prolongation induced by nonadjuvant anticancer agents. Expert Opin Drug Saf. 2008;7:305–18.

    Article  CAS  PubMed  Google Scholar 

  5. Rock EP et al. Assessing proarrhythmic potential of drugs when optimal studies are infeasible. Am Heart J. 2009;157:827–36.

    Article  CAS  PubMed  Google Scholar 

  6. ICH Expert Working Group. ICH Harmonised tripartite guideline. E14 Clinical evaluation of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. 2005.

  7. ICH Expert Working Group. ICH Harmonised tripartite guideline. S7A Safety pharmacology studies for human pharmaceuticals. 2000.

  8. ICH Expert Working Group. ICH Harmonised tripartite guideline. S7B The non-clinical evaluation of the potential for delayed ventricular repolarization (QT interval prolongation) by human pharmaceuticals. 2005.

  9. Tornoe CW, Garnett CE, Wang Y, Florian J, Li M, Gobburu V. Creation of a knowledge management system for QT analyses. J Clin Pharmacol. 2010;51:1035–42.

    Article  PubMed  Google Scholar 

  10. Darpo B et al. Results from the IQ-CSRC prospective study support replacement of the thorough QT study by QT assessment in the early clinical phase. Clin Pharmacol Ther. 2015;97:326–35.

    Article  CAS  PubMed  Google Scholar 

  11. Darpo B et al. The IQ-CSRC prospective clinical phase 1 study: can early QT assessment using exposure response analysis replace the thorough QT study? Ann Noninvasive Electrocardiol. 2014;19:70–81.

    Article  PubMed  Google Scholar 

  12. Pastino G, et al. Lemborexant does not affect the QT interval: high-precision QT analysis from early clinical studies. Annual Meeting of the American College of Clinical Pharmacology, September 27-29, 2015, San Francisco, CA. Clinical Pharm In Drug Dev. 2015;4:1-60. doi: 10.1002/cpdd.216

  13. Cavero I, Mestre M, Guillon JM, Crumb W. Drugs that prolong QT interval as an unwanted effect: assessing their likelihood of inducing hazardous cardiac dysrhythmias. Expert Opin Pharmacother. 2000;1:947–73.

    Article  CAS  PubMed  Google Scholar 

  14. Hammond TG et al. Methods of collecting and evaluating non-clinical cardiac electrophysiology data in the pharmaceutical industry: results of an international survey. Cardiovasc Res. 2001;49:741–50.

    Article  CAS  PubMed  Google Scholar 

  15. Netzer R, Ebneth A, Bischoff U, Pongs O. Screening lead compounds for QT interval prolongation. Drug Discov Today. 2001;6:78–84.

    Article  CAS  PubMed  Google Scholar 

  16. Ollerstam A et al. Comparison of the QT interval response during sinus and paced rhythm in conscious and anesthetized beagle dogs. J Pharmacol Toxicol Methods. 2007;56:131–44.

    Article  CAS  PubMed  Google Scholar 

  17. Chain ASY, Dubois VFS, Danhof M, Sturkenboom MCJM, Della PO. Identifying the translational gap in the evaluation of drug-induced QTc interval prolongation. Br J Clin Pharmacol. 2013;76:708–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dubois VFS, Yu H, Danhof M, Della PO. Model-based evaluation of drug-induced QTc prolongation for compounds in early development. Br J Clin Pharmacol. 2015;79:148–61.

    Article  CAS  PubMed  Google Scholar 

  19. Gotta V et al. Inter-study variability of preclinical in vivo safety studies and translational exposure-QTc relationship—a PKPD meta-analysis. Br J Pharmacol. 2015;172:4364–79.

    Article  CAS  PubMed  Google Scholar 

  20. Marostica E et al. Modelling of drug-induced QT-interval prolongation: estimation approaches and translational opportunities. J Pharmacokinet Pharmacodyn. 2015;42:659–79.

    Article  PubMed  Google Scholar 

  21. Avelox, Bayer Healthcare, West Haven, CT. http://www.accessdata.fda.gov/drugsatfda_docs/label/2010/021277s038lbl.pdf.

  22. Parkinson J et al. Translational pharmacokinetic-pharmacodynamic modeling of QTc effect in dog and human. J Pharmacol Toxicol. 2013;68:357–66.

    Article  CAS  Google Scholar 

  23. Holzgrefe H et al. Preclinical QT safety assessment: cross-species comparisons and human translation from an industry consortium. J Pharmacol Toxicol. 2014;69:61–101.

    Article  CAS  Google Scholar 

  24. Black JW, Leff P. Operational model of pharmacological agonism. Proc R Soc Lond. 1983;220:141–62.

    Article  CAS  PubMed  Google Scholar 

  25. Jonker DM, Kenna LA, Leishman D, Wallis R, Milligan PA, Jonsson EN. A pharmacokinetic-pharmacodynamic model for the quantitative prediction of dofetilide clinical QT prolongation from human ether-a-go-go-related gene current inhibition data. Clin Pharmacol Ther. 2005;77:572–82.

    Article  CAS  PubMed  Google Scholar 

  26. Cavero I, Holzgrefe H. Comprehensive in vitro Proarrhythmia Assay, a novel in vitro/in silico paradigm to detect ventricular proarrhythmic liability: a visionary 21st century initiative. Expert Opin Drug Saf. 2014;13:745–58.

    Article  CAS  PubMed  Google Scholar 

  27. Novak GP, Kelley M, Zannikos P, Klein B. Carisbamate (RWJ-333369). Neurotherapeutics. 2007;4:106–9.

    Article  CAS  PubMed  Google Scholar 

  28. Clinical Trial Register. https://www.clinicaltrialsregister.eu/ctr-search/search?query=2004-003781-14.

  29. Chargari C et al. Preclinical assessment of JNJ-26854165 (serdemetan), a novel tryptamine compound with radiosensitizing activity in vitro and in tumor xenografts. Cancer Lett. 2011;312:209–18.

    Article  CAS  PubMed  Google Scholar 

  30. Hamill OP, Marty A, Neher E, Sakmann B, Sigworth FJ. Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch. 1981;391:85–100.

    Article  CAS  PubMed  Google Scholar 

  31. Rowland M, Sheiner LB, Steimer JL. Variability in drug therapy: description, estimation and control. New York: Raven; 1985.

    Google Scholar 

  32. Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides. Icon development solutions, Ellicott City, MD, USA;1989-2009.

  33. Piotrovsky V. Pharmacokinetic-pharmacodynamic modeling in the data analysis and interpretation of drug-induced QT/QTc prolongation. AAPS J. 2005;7:609–24.

    Article  Google Scholar 

  34. Pramanik D. Principles of physiology. Academic Publishers; 2007.

  35. Schwarz GE. Estimating the dimension of a model. Ann Stat. 1978;6:461–4.

    Article  Google Scholar 

  36. Lunn DJ, Thomas A, Best N, Spiegelhalter DJ. WinBUGS—a Bayesian modeling framework: concepts, structure and extensibility. Stat Comput. 2000;10:325–37.

    Article  Google Scholar 

  37. Karlsson M, Holford N. A tutorial on Visual Predictive Checks. Population Approach Group in Europe (PAGE) 17th Meeting, Abstract 1434. 2008 http://www.page-meeting.org/default.asp?abstract=1434.

  38. R Development Core Team. R: a language and environment for statistical computing. (R Foundation for Statistical Computing, Vienna, 2014).

  39. Eichenbaum G et al. Role of mixed ion channel effects in the cardiovascular safety assessment of the novel anti-MRSA fluoroquinolone JNJ-Q2. Br J Pharmacol. 2012;166:1694–707.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Mirams GR et al. Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk. Cardiovasc Res. 2011;91:53–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We thank the healthy volunteers, patients and their families, investigators, study centre staff, and Janssen study personnel, in particular those who provided input into the analysis and/or who reviewed the article but who are not listed as authors. This research was sponsored by Janssen Research & Development, Beerse, Belgium.

Author information

Author notes

  1. Eleonora Marostica

    Present address: LAP&P Consultants B.V., Leiden, The Netherlands

Authors and Affiliations

  1. Laboratory of Medical Biochemistry and Clinical Analysis, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium

    Eleonora Marostica, Jan Van Bocxlaer, Koen Boussery & An Vermeulen

  2. Global Safety Pharmacology, Janssen R&D, Janssen Pharmaceutica NV, Beerse, Belgium

    Karel Van Ammel, Ard Teisman & David Gallacher

  3. Statistics & Decision Sciences, Janssen R&D, Janssen Pharmaceutica NV, Beerse, Belgium

    Filip De Ridder

  4. Clinical Pharmacology & Pharmacometrics, Janssen R&D, Janssen Pharmaceutica NV, Beerse, Belgium

    An Vermeulen

Authors
  1. Eleonora Marostica
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Corresponding author

Correspondence to Eleonora Marostica.

Ethics declarations

Preclinical in vivo studies were conducted in accordance with ‘the provision of the European Convention’ on the protection of vertebrate animals, which are used for experimental and other scientific purposes, and with ‘the Appendices A and B’, made in Strasbourg on 18 March 1986 (Belgian Act of 18 October 1991). The clinical studies were conducted in accordance with the principles set forth in the Declaration of Helsinki, the International Conference on Harmonization, and the Guidelines for Good Clinical Practice. Written informed consent was obtained from each subject prior to participation in the trials.

Conflict of Interest

K.V.A., A.T., D.G., F.D.R., and A.V. are employees of Janssen R&D, a division of Janssen Pharmaceutica NV. A.V. also works as a Guest Professor at the Faculty of Pharmaceutical Sciences at Ghent University.

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Marostica, E., Van Ammel, K., Teisman, A. et al. PK/PD Modelling of the QT Interval: a Step Towards Defining the Translational Relationship Between In Vitro, Awake Beagle Dogs, and Humans. AAPS J 18, 1000–1012 (2016). https://doi.org/10.1208/s12248-016-9920-3

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  • DOI: https://doi.org/10.1208/s12248-016-9920-3

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