No abstract

Mixed-effect modeling has been suggested as a possible tool to detect and describe drug interactions in patient populations receiving drug combinations for the treatment of disease states. The mixed-effect modeling program, NONMEM, was used to measure the effects of the well-known digoxin-quinidine and digoxin-verapamil drug interactions in 294 patients receiving oral digoxin as hospital inpatients. Fourteen percent of the population took either quinidine or verapamil concurrently with digoxin (mean quinidine dose = 857 +/- 397 mg/day, verapamil = 261 +/- 110 mg/day). Two regression models for digoxin oral clearance were used. Model 1 used the knowledge that digoxin is eliminated by both renal and nonrenal routes (TVCL = ClNR+m.CrCl, where TVCL is the population digoxin oral clearance, ClNR is the nonrenal clearance, and m is the slope of the line that relates creatinine clearance (CrCl) to digoxin clearance); model 2 used a more conventional regression approach with a simple series of multipliers. For both models, quinidine administration decreased population digoxin oral clearance by approximately 45% and verapamil therapy decreased population digoxin oral clearance by approximately 30%. These values are similar to those found by traditional drug interaction studies conducted in small patient or normal subject populations. Mixed-effect modeling can detect clinically relevant drug interactions and produce information similar to that found in traditional pharmacokinetic crossover study designs.

No abstract