Prediction of Human Pharmacokinetics of Fomepizole from Preclinical Species Pharmacokinetics Based on Normalizing Time Course Profiles

Abstract

Fomepizole is used as an antidote to treat methanol poisoning due to its selectivity towards alcohol dehydrogenase. In the present study, the goal is to develop a method to predict the fomepizole human plasma concentration versus time profile based on the preclinical pharmacokinetics using the assumption of superimposability on simulated time course profiles of animals and humans. Standard allometric equations with/without correction factors were also assimilated in the prediction. The volume of distribution at steady state (Vss) predicted by simple allometry (57.55 L) was very close to the reported value (42.17 L). However, clearance (CL) prediction by simple allometry was at least 3-fold higher to the reported value (33.86 mL/min); hence, multiple correction factors were used to predict the clearance. Both brain weight and maximum life span potential could predict the CL with 1.22- and 1.01-fold difference. Specifically, the predicted Vss and CL values via interspecies scaling were used in the prediction of series of human intravenous pharmacokinetic parameters, while the simulation of human oral profile was done by the use of absorption rate constant (Ka) from dog following the applicability of human bioavailability value scaled from dog data. In summary, the findings indicate that the utility of diverse allometry approaches to derive the human pharmacokinetics of fomepizole after intravenous/oral dosing.

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References

  1. 1.

    Theorell H, Yonetani T, Sjöberg B. On the effects of some heterocyclic compounds on the enzymic activity of liver alcohol dehydrogenase. Acta Chem Scand. 1969;23(1):255–60.

    CAS  PubMed  Google Scholar 

  2. 2.

    Li TK, Theorell H. Human liver alcohol dehydrogenase: inhibition by pyrazole and pyrazole analogs. Acta Chem Scand. 1969;23(3):892–902.

    CAS  PubMed  Google Scholar 

  3. 3.

    Makar AB, Tephly TR. Inhibition of monkey liver alcohol dehydrogenase by 4-methylpyrazole. Biochem Med. 1975;13(4):334–42.

    CAS  PubMed  Google Scholar 

  4. 4.

    Lester D, Keokosky WZ, Felzenberg F. Effect of pyrazoles and other compounds on alcohol metabolism. Q J Stud Alcohol. 1968;29(2):449–54.

    CAS  PubMed  Google Scholar 

  5. 5.

    Blomstrand R, Ingemansson SO. Studies on the effect of 4-methylpyrazole on methanol poisoning using the monkey as an animal model: with particular reference to the ocular toxicity. Drug Alcohol Depend. 1984;13(4):343–55.

    CAS  PubMed  Google Scholar 

  6. 6.

    Zakharov S, Navratil T, Pelclova D. Fomepizole in the treatment of acute methanol poisonings: experience from the Czech mass methanol outbreak 2012-2013. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2014;158(4):641–9.

    PubMed  Google Scholar 

  7. 7.

    O'Neill B, Williams AF, Dubowski KM. Variability in blood alcohol concentrations. Implications for estimating individual results. J Stud Alcohol. 1983;44(2):222–30.

    CAS  PubMed  Google Scholar 

  8. 8.

    McMartin KE, Sebastian CS, Dies D, Jacobsen D. Kinetics and metabolism of fomepizole in healthy humans. Clin Toxicol (Phila). 2012;50(5):375–83.

    CAS  Google Scholar 

  9. 9.

    Narita I, Shimada M, Nakamura N, Murakami R, Fujita T, Fukuda W, et al. Successful resuscitation of a patient with life-threatening metabolic acidosis by hemodialysis: a case of ethylene glycol intoxication. Case Rep Nephrol. 2017;9529028.

  10. 10.

    Boxenbaum H. Interspecies scaling, physiological time and the ground plan for pharmacokinetics. J Pharmacokinet Biopharm. 1982;10(2):201–27.

    CAS  PubMed  Google Scholar 

  11. 11.

    Boxenbaum H. Interspecies pharmacokinetic scaling and evolutionary-comparative paradigm. Drug Metab Rev. 1984;15(5–6):1071–21.

    CAS  PubMed  Google Scholar 

  12. 12.

    Chandrasekhar DV, Suresh PS, Dittakavi S, Hiremath RA, Bhamidipati RK, Richter W, et al. LC-ESI-MS/MS determination of 4-methylpyrazole in dog plasma and its application to a pharmacokinetic study in dogs. Biomed Chromatogr. 2018;32(2):e4065.

    Google Scholar 

  13. 13.

    Van den Bergh A, Sinha V, Gilissen R, Straetemans R, Wuyts K, Morrison D, et al. Prediction of human oral plasma concentration-time profiles using preclinical data: comparative evaluation of prediction approaches in early pharmaceutical discovery. Clin Pharmacokinet. 2011;50(8):505–17.

    PubMed  Google Scholar 

  14. 14.

    Wajima T, Yano Y, Fukumura K, Oguma T. Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles. J Pharm Sci. 2004;93(7):1890–900.

    CAS  PubMed  Google Scholar 

  15. 15.

    Kurihara A, Naganuma H, Hisaoka M, Tokiwa H, Kawahara Y. Prediction of human pharmacokinetics of panipenem-betamipron, a new carbapenem, from animal data. Antimicrob Agents Chemother. 1992;36(9):1810–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Marraffa J, Forrest A, Grant W, Stork C, McMartin K, Howland MA. Oral administration of fomepizole produces similar blood levels as identical intravenous dose. Clin Toxicol (Phila). 2008;46(3):181–6.

    CAS  Google Scholar 

  17. 17.

    DigitizeIt - plot digitizer software. Digitize graphs, charts and math data. https://www.digitizeit.de/. Accessed 10 Sept 2018.

  18. 18.

    Mahmood I. Application of allometric principles for the prediction of pharmacokinetics in human and veterinary drug development. Adv Drug Deliv Rev. 2007;59(11):1177–92.

    CAS  PubMed  Google Scholar 

  19. 19.

    Srinivas NR. Allometry as a tool in drug development: case studies, perspectives and general considerations. In: drug development perspectives-considerations, challenges and strategies. New York: HNB Publishing; 2010. p. 47–106.

    Google Scholar 

  20. 20.

    Pavankumar VV, Vinu CA, Mullangi R, Srinivas NR. Preclinical pharmacokinetics and interspecies scaling of ragaglitazar, a novel biliary excreted PPAR dual activator. Eur J Drug Metab Pharmacokinet. 2007;32(1):29–37.

    Google Scholar 

  21. 21.

    Mullangi R, Ahlawat P, Trivedi RK, Srinivas NR. Use of bile correction factors for allometric prediction of human pharmacokinetic parameters of torcetrapib, a facile cholesteryl ester transfer protein inhibitor. Eur J Drug Metab Pharmacokinet. 2009;34(1):57–63.

    CAS  PubMed  Google Scholar 

  22. 22.

    Gilibili RR, Mullangi R, Srinivas NR. Intravenous prediction of human pharmacokinetic parameters for ketorolac, a non-steroidal anti-inflammatory agent, using allometric approach. Eur J Drug Metab Pharmacokinet. 2011;36(2):87–93.

    CAS  PubMed  Google Scholar 

  23. 23.

    Gilibili RR, Bhamidipati RK, Mullangi R, Srinivas NR. Retrospective and prospective human intravenous and oral pharmacokinetic projection of DPP-IV inhibitors using simple allometric principles – case studies of ABT-279, ABT-341, alogliptin, carmegliptin, sitagliptin and vildagliptin. J Pharm Pharm Sci. 2015;18(3):434–8.

    CAS  PubMed  Google Scholar 

  24. 24.

    Bhamidipati RK, Gurav S, Police A, Mohd Z, Rajagopal S, Mullangi R. The human pharmacokinetics of odanacatib, a novel cathepsin K inhibitor, predicted by interspecies scaling: a retrospective analysis. Int J Pharm. 2017;2(1):47–55.

    CAS  Google Scholar 

  25. 25.

    Bhamidipati RK, Mullangi R, Srinivas NR. Interspecies scaling of urinary excretory amounts of nine drugs belonging to different therapeutic areas with diverse chemical structures - accurate prediction of the human urinary excretory amounts. Xenobiotica. 2017;47(2):112–8.

    CAS  PubMed  Google Scholar 

  26. 26.

    Jairam RK, Mallurwar SR, Sulochana SP, Chandrasekar DV, Bhamidipati RK, Richter W, et al. Prediction of human pharmacokinetics of bendamustine from preclinical species pharmacokinetics based on normalizing time course profiles. Drug Res. 2019;69(1):32–9.

    CAS  Google Scholar 

  27. 27.

    Suresh PS, Jairam RK, Chandrasekhar DV, Vinod AB, Hiremath RK, Raj A, et al. Prediction of human pharmacokinetics of ulixertinib, a novel ERK1/2 inhibitor from mice, rats, and dogs pharmacokinetics. Eur J Drug Metab Pharmacokinet. 2018;43(4):453–60.

    CAS  PubMed  Google Scholar 

  28. 28.

    Beatty L, Green R, Magee K, Zed P. A systematic review of ethanol and fomepizole use in toxic alcohol ingestions. Emerg Med Int. 2013;638057.

  29. 29.

    Mahmood I, Balian JD. Interspecies scaling: predicting clearance of drugs in humans. Three different approaches. Xenobiotica. 1996;26(9):887–95.

    CAS  PubMed  Google Scholar 

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Correspondence to Ramesh Mullangi.

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Jairam, R.K., Mallurwar, S.R., Sulochana, S.P. et al. Prediction of Human Pharmacokinetics of Fomepizole from Preclinical Species Pharmacokinetics Based on Normalizing Time Course Profiles. AAPS PharmSciTech 20, 221 (2019). https://doi.org/10.1208/s12249-019-1434-8

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KEY WORDS

  • fomepizole
  • clinical pharmacokinetics
  • interspecies scaling
  • preclinical pharmacokinetics
  • simulations