MIXED-EFFECT MODELING FOR DETECTION AND EVALUATION OF DRUG-INTERACTIONS - DIGOXIN-QUINIDINE AND DIGOXIN-VERAPAMIL COMBINATIONS

Mixed-effect modeling has been suggested as a possible tool to detect and describe drug interactions in patient populations receiving drug c ombinations for the treatment of disease states. The mixed-effect mode ling program, NONMEM, was used to measure the effects of the well-know n digoxin-quinidine and digoxin-verapamil drug interactions in 294 pat ients 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 u sed. Model 1 used the knowledge that digoxin is eliminated by both ren al and nonrenal routes (TVCL = Cl-NR + m . Cr-Cl, where TVCL is the po pulation digoxin oral clearance, Cl-NR is the nonrenal clearance, and m is the slope of the line that relates creatinine clearance (Cr-Cl) t o digoxin clearance); model 2 used a more conventional regression appr oach with a simple series of multipliers. For both models, quinidine a dministration decreased population digoxin oral clearance by similar t o 45% and verapamil therapy decreased population digoxin oral clearanc e by similar to 30%. These values are similar to those found by tradit ional drug interaction studies conducted in small patient or normal su bject populations. Mixed-effect modeling can detect clinically relevan t drug interactions and produce information similar to that found in t raditional pharmacokinetic crossover study designs.