Methotrexate is considered by many practitioners to be the agent of choice in the treatment of rheumatoid arthritis. The pharmacokinetics of methotrexate have been reported to exhibit significant intersubject variability. Therefore, this study was undertaken to evaluate the population pharmacokinetics of methotrexate during long-term administration in adults with rheumatoid arthritis.
Methotrexate pharmacokinetics were evaluated in a 36 month study of 62 adults with rheumatoid arthritis. Patients received oral or intramuscular doses of methotrexate weekly with pharmacokinetic studies performed every 6 months. Data were analyzed with nonlinear mixed effects modeling.
Three thousand two hundred and sixty post oral or intramuscular dose serum methotrexate concentrations comprising 425 individual concentration vs time profiles were modeled using NONMEM. Covariates that significantly (P < 0.005) influenced the disposition of methotrexate were age (AGE, years), body weight (BW, kg), creatinine clearance (CL(CR), 1 h(-1)), gender (GEN; 0 = male, 1 = female), dose (DOSE, micromol), and fed vs fasted state (FED; 0 = fasted, 1 = fed). The final model describing the biexponential disposition of methotrexate was clearance(CL, 1 h(-1)) = (0.0810*BW + 0.257*CL(CR))*(1-(0.167*GEN); central volume (Vc, 1) = 0.311*BW; peripheral volume (Vp, 1) = 0.469*BW-0.169*AGE; intercompartmental clearance (Q, 1 h(-1)) = 4.27*(1-0.355*GEN); oral absorption rate constant (ka(p.o.), h(-1)) = 4.70-0.0439*DOSE*(1-0.507*FED); intramuscular absorption rate constant (ka(i.m.), h(-1)) = 0.122*DOSE; relative bioavailability (F) = 93.4%; and oral absorption lag time (LAG(p.o.), min) = 13.5. Pharmacokinetic parameters (%CV) for a typical fasted male subject in this study were CL, 7.341 h(-1) (27%); Vc, 23.51 (28%); Vp, 25.31 (31%); Q, 4.25 1 h(-1) (41%); ka(p.o.), 3.67 h(-1) (77%); and ka(i.m.), 3.09 h(-1) (44%).
The population pharmacokinetics of methotrexate in adults with rheumatoid arthritis were well described by this investigation. Substantial interpatient variability was explained by incorporating patient specific data into regression equations predicting pharmacokinetic parameters.