Methodologic aspects of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with tacrine.

Abstract

Tacrine is a cholinesterase inhibitor with activity in the central nervous system originafly marketed for the reversal ofcompetitive neuromuscular blockade. Because a marked reduction in cholinergic neurons is a halr of brain changes in Alzhelmer disease, tacrine has been studied in two placebo-controlled clinical trials of patients with probable Alzhelmer disease. (6) and protocol 970-04 (7). Analysis ofthe double-blind phase of the 970-01 trial led to the conclusion that there was a significant difference between tacrine and placebo with a mean difference of 2.5 ADASC units after 6 weeks of active treatment at the best tolerated dose (40 or 80 mg/day) (8). This difference is =10(% ofthe mean ADASC score at the start ofthe trial and the disease progresses at -6 units/year.The efficacy of tacrine in these studies was assessed by standard analysis of variance (ANOVA) and covariance (ANCOVA) techniques. Due to the complexity of the trial protocol, only a subset of all treated patients were evaluated for efficacy by these techniques-i.e., patients who participated in the placebo-controlled double-blind phase-and then only over that phase. Of a total of 909 patients in both trials with an average of 5.8 assessments of ADASC per patient, only 400 patients were included in these efficacy analyses. Although ANCOVA and ANOVA techniques are based on linear fixed effects models, they are directed toward detecting randomized group differences-with and without adjustment for covariates. Questions that could not be answered by these techniques include (i) was the benefit proportional to dose, (ii) what is the effect of tacrine in the entire population studied for as long as they were studied, and (iii) how long did the effect of tacrine take to appear and how long did it last.We describe a regression model for the data from these trials that is nonlinear and contains both fixed and random (mixed) effects. Parameters of the model are estimated by using the NONMEM software package of Beal and Sheiner (9). A feature of NONMEM is that information from each patient contributes to the estimation of model parameters even though all parameters may not be identifiable in each patient. This approach has allowed us to pose the above questions, as well as others, in quantitative form and address them to the Abbreviations: ADASC, Alzheimer disease assessment scale; MLE, maximum-likelihood estimation.tTo whom reprint requests should be addressed. 11466The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. §1734 solely to indicate this fact.

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