Skip to main content
SpringerLink
Log in

A limited sampling method to estimate methotrexate pharmacokinetics in patients with rheumatoid arthritis using a Bayesian approach and the population data modeling program P-PHARM

  • Pharmacokinetics And Disposition
  • Published:
European Journal of Clinical Pharmacology Aims and scope Submit manuscript

Abstract

This paper describes a methodology to calculate methotrexate (MTX) pharmacokinetic parameters after intramuscular administration using two samples and the population parameters. Total and free MTX were measured over a 36-h period in 56 rheumatoid arthritis patients; 14 patients were studied after a two-dose scheme at 15-day intervals. The Hill equation was used to relate the free MTX to the total MTX changes in plasma concentrations, and a two-compartment open model was used to fit the total MTX plasma concentrations. A non-linear mixed effect procedure was used to estimate the population parameters and to explore the interindividual variability in relation to the following covariables: age, weight, height, haemoglobin, erythrocyte sedimentation rate, platelet count, creatinine clearance, rheumatoid factor, C-reactive protein, swelling joint count, and Ritchie's articular index. Population parameters were evaluated for 40 patients using a three-step approach. The population average parameters and the interindividual variabilities expressed as coefficients of variation (CV%) were: CL, 6.94 l · h-1 (20.5%); V, 34.8 l (32.2%); k12, 0.0838 h-1 (47.7%); k21, 0.0769 h-1 (61.6%); ka, 4.31 h-1 (58%); Emax, 1.12 μmol · l-1 (19.7%); γ, 0.932 (12.3%); and EC50, 2.14 μmol · l-1 (27.3%). Thirty additional data sets (16 new patients and 14 patients of the previous population but treated on a separate occasion) were used to evaluate the predictive performance of the population parameters. Twelve blood samples were collected from each individual in order to calculate individual parameters using standard fitting procedures. These values were compared to the ones estimated using a Bayesian approach with population parameters as a priori information together with two samples, selected from the individual observations. The results show that the bias was not statistically different from zero and the precision of these parameters was excellent.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Kremer JM (1994) The mechanism of action of methotrexate in rheumatoid arthritis. The search continues. J Rheumatol 21:1–5.

    CAS  PubMed  Google Scholar 

  2. Furst, DE, Kremer JM (1988) Methotrexate in rheumatoid arthritis. Arthritis Rheum. 31:305–314.

    Article  CAS  PubMed  Google Scholar 

  3. Wilkens RF (1990) Resolve methotrexate is the drug of choice after NSAID in rheumatoid arthritis. Semin Arthritis Rheum 20:76–80.

    Article  Google Scholar 

  4. Sany J, Anaya, JM, Lussiez V, Couret M, Combe, B, Daures JP (1991) Treatment of rheumatoid arthritis with methotrexate: a prospective open long term study of 191 cases, J Rheumatol 18:1323–1327.

    CAS  PubMed  Google Scholar 

  5. Felson DT, Anderson JJ, Meenan, RF (1992) Use of short-term efficacy/toxicity tradeoffs to select second-line drugs in rheumatoid arthritis; a metaanalysis of published clinical trials. Arthritis Rheum 35:1117–1125.

    Article  CAS  PubMed  Google Scholar 

  6. Weinblatt ME, Weisman BN, Holdsworth DE, Fraser PA, Maier AL, Falchuk KR, Coblyn JS (1992) Long term prospective study of methotrexate in the treatment of rheumatoid arthritis: 84-month update. Arthritis Rheum 35:129–137.

    Article  CAS  PubMed  Google Scholar 

  7. Kremer JM, Phelps CT (1990) Long term prospective study of the use of methotrexate in the treatment of rheumatoid arthritis update after a mean of 90 months. Arthritis Rheum 35:138–145.

    Article  Google Scholar 

  8. Pincus T, Callahan LF (1993) Variability in individual responses of 532 patients with rheumatoid arthritis to first-line and second-line drugs. Agents Actions [Suppl] 44:67–75.

    CAS  PubMed  Google Scholar 

  9. Seideman P (1993) Preliminary report. Methotrexate—the relationship between dose and clinical effect. Br J Rheumatol 32:751–753.

    Article  CAS  PubMed  Google Scholar 

  10. Bannwarth B, Labat L, Moride Y, Schaeverbeke T (1994) Methotrexate in rheumatoid athritis. An update. Drugs 47:25–50.

    Article  CAS  PubMed  Google Scholar 

  11. Olsen EA, Durham MD (1991) The pharmacology of methotrexate. J Am Acad Dermatol 25:306–318.

    Article  CAS  PubMed  Google Scholar 

  12. Herman R, Ven-Pedersen P, Hoffman J, Koehnke R, Furst DE (1989) Pharmacokinetics of low-dose methrotrexate in rheumatoid arthritis patients. J Pharm Sci 78:165–171.

    Article  CAS  PubMed  Google Scholar 

  13. Stewart CF, Christensen ML, Evans RP, Cremer M, Evans WE (1987) Influence of concomitant aspirin or prednisone on methotrexate synovial fuid concentration. J Pharmacol Exp Ther 243:131–137.

    CAS  PubMed  Google Scholar 

  14. Stewart CF, Fleming RA, Germain BF, Arkin CR, Evan WE (1990) Coadministration of naproxen and low-dcose methotrexate in patients with rheumatoid arthritis. Clin Pharmacol Ther 47:540–546.

    Article  CAS  PubMed  Google Scholar 

  15. Tracy TS, Krohn K, Jones DR, Bradley JD, Hall SD, Brater DC (1992) The effects of salicylate, ibuprofen and naproxen on the disposition of methotrexate in rheumatoid arthritis. Eur J Clin Pharmacol 42:121–125.

    Article  CAS  PubMed  Google Scholar 

  16. Edelman J, Biggs DF, Jamali F, Russel AS (1984) Low-dose methotrexate kinetics in arthritis. Clin Pharmacol Ther 35:382–386.

    Article  CAS  PubMed  Google Scholar 

  17. Combe B, Edno L, Lafforgue P, Bologna C, Bernard JC, Acquaviva P, Sany J, Bressolle F (1995) Total and free methotrexate pharmacokinetics, with and without piroxicam, in rheumatoid arthritis patients. Br J Rheumatol 34:421–428.

    Article  CAS  PubMed  Google Scholar 

  18. Arnett FC, Edworthy SM, Bloch DA, McShane DJ, Fries JF, Cooper NS, Healey LA, Kaplan SR, Liang MH, Luthra HS, Medsger TA Jr, Mitchell DM, Neustadt DH, Pinals RS, Schaller JG, Sharp JT, Wilder RL, Hunder GG (1988) The American Rheumatism Association 1987 revised criteria for the classification of rheumatoid arthritis. Arthritis Rheum 31:315–324.

    Article  CAS  PubMed  Google Scholar 

  19. Kraus A, Alarcón-Segovia D (1991) Low-dose MTX and NSAID induced “mild” renmal insufficiency and severe neutropenia. J Rheumatol 8:174.

    Google Scholar 

  20. SIMED (1994) P-PHARM User's Guide, 2nd edn. SIMED, Créteil, France.

    Google Scholar 

  21. Gomeni R, Pineau G, Mentré, F. (1994) Population kinetics and conditional assessment of the optimal dosage regimen using the PHARM software package. Anticancer Res. 14:2321–2326.

    CAS  PubMed  Google Scholar 

  22. Chou TC (1976) Derivation and properties of Michaelis-Menten type and Hill type equations for reference ligands. J Theor Biol 59:253–276.

    Article  CAS  PubMed  Google Scholar 

  23. McNamara PJ, Levy G (1981) Combined effect of concentration dependent protein binding and Michaelis-Menten elimination kinetics on time course of drug concentrations in plasma. Acta Pharm Fenn 90:99–106.

    CAS  Google Scholar 

  24. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39:1–38.

    Google Scholar 

  25. Maître PO, Bührer M, Thomson D, Stanski DR (1991) A three-step approach combining Bayesian regression and NONMEM population analysis: application to midazolam. J Pharmacokinet Bipharm 19:377–384.

    Article  Google Scholar 

  26. Al-Banna, MK, Kelman AW, Whiting B (1990) Experimental design and efficient parameter estimation in population pharmacokinetics. J Pharmacokinet Biopharm 18:347–360.

    Article  CAS  PubMed  Google Scholar 

  27. Endrenyi L (1981) Design of experiments for estimating enzyme and pharmacokinetic experiments. In: Endrenyi L (ed) Kinetic data analysis, design and analysis of enzyme and pharmacokinetic experiments. Plenum Press, New York, pp 137–167.

    Google Scholar 

  28. Gomeni R (1990) Estimate of the number and of the temporal sequence of measurements in a kinetic experiment. Fundam Clin Pharmacol 4 [Suppl 2]:117s–123s.

  29. Sheiner LB, Beal SL (1981) Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm 9:503–512.

    Article  CAS  PubMed  Google Scholar 

  30. Sheiner LB, Beal SL (1981) Evaluation of methods for estimating population pharmacokinetic parameters. II. Biexponential model and experimental pharmacokinetic data J Pharmacokinet Biopharm 9:635–651.

    Article  CAS  PubMed  Google Scholar 

  31. Gabrielli A, Leoni P, Danieli G (1987) Methotrexate and NSAIDs. Br Med J 294:776.

    Article  Google Scholar 

  32. Taillan B, Chichmanian RM, Fuzibet JG, Vinti H, Pesce A, Dujardin P (1989) Pancytopenie au cours d'une polyarthrite rhumatoïde traitée par des faibles doses de méthotrexate. Rev Rhum Mal Ostéoartic 56:717.

    CAS  PubMed  Google Scholar 

  33. Singh RR, Maalviya NN, Pandey JN, Guleria JS (1986) Fatal interaction between methotrexate and naproxen. Lancet I:1390.

    Article  Google Scholar 

  34. Seideman P, Müller-Suur R, Ekman E (1993) Renal effects of low dose MTX in RA. J Rheumatol 20:1126–1128.

    CAS  PubMed  Google Scholar 

  35. Mc Kendry R, Dale P (1993) Adverse effects of low dose methotrexate therapy in rheumatoid arthritis. J Rheumatol 20:1850–1856.

    CAS  Google Scholar 

  36. Freeman-Narrod M, Gerstley BJ, Engstrom PF, Bornstein RS (1975) Comparison of serum concentrations of methotrexate after various routes of administration. Cancer 36:1619–1624.

    Article  CAS  PubMed  Google Scholar 

  37. Bologna C, Anaya JM, Bressolle F, Jorgensen C, Alric R, Sany J (1995) Correlation between methotrexate pharmacokinetic parameters, clinical status and laboratory test results in patients with rheumatoid arthritis treated intramuscularly. J Clin Exp Rheumatol 13:465–470.

    CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratoire de Pharmacocinétique, Faculté de Pharmacie, 15, Ave. Ch. Flahault, F-34060, Montpellier Cedex 01, France

    F. Bressolle

  2. Laboratoire de Pharmacocinétique, Hôpital Carémeau, F-30900, Nîmes, France

    F. Bressolle & L. Edno

  3. Service d'Immuno-Rhumatologie, Hôpital Gui de Chauliac, F-34000, Montpellier, France

    C. Bologna & J. Sany

  4. Service de Rhumatologie, Hôpital Carémeau, F-30900, Nîmes, France

    J. C. Bernard & B. Combe

  5. SIMED, 9–11 Rue Georges Enesco, F-94008, Créteil, France

    R. Gomeni

Authors
  1. F. Bressolle
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. L. Edno
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. Bologna
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. J. Sany
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. J. C. Bernard
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. B. Combe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R. Gomeni
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bressolle, F., Edno, L., Bologna, C. et al. A limited sampling method to estimate methotrexate pharmacokinetics in patients with rheumatoid arthritis using a Bayesian approach and the population data modeling program P-PHARM. Eur J Clin Pharmacol 49, 285–292 (1996). https://doi.org/10.1007/BF00226329

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00226329

Key words

Search

Navigation