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Tacrine has been studied in two clinical trials of identical design in patients with probable Alzhelmer disease. One trial enrolled patients in the United States, while the other enrolled patients in France. A population phr dynamic model has been used to describe the cognitive component of the Alzheimer disease assessment scale (ADASC) using mixed effects nonlinear ession. The model parameters and their population variability and covariance were eimated by using NONMEM. During an observation period of up to 5 months, the rate of disease progrsion was 6.17 ADASC units/year. The effect of tacrine was described best by a shift in the disease progress curve (-2.99 ADASC units or 177.6 days at a dose of 80 mg/day). The placebo effects asited with tacrine and placebo treatment were similar in ma de and time course. There was no evidence of tolerance to tacrine but tolerance to the placebo treatment developed during the study. tTo whom reprint requests should be addressed. 11471The 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.

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.

No abstract

Since millions suffer from major depressive and bipolar disorders, the modeling, characterization, classification, and diagnostic analysis of such mental disorders bear great significance in medical and pharmaceutical research. YinYang bipolar sets are introduced for neurobiological modeling and diagnostic analysis of such disorders. It is shown that bipolar sets and a bipolar dynamic modus ponens (BDMP) build a technological bridge from a linear, static, and closed world to a nonlinear, dynamic, and open world of equilibria, quasi-equilibria, or nonequilibria and provide a novel model for bipolar neurobiological diagnostic analysis with added rigor to the current standard. It is shown that bipolar inference can help in understanding both the classic manifestations and the counterintuitive symptoms of bipolar disorders with applications in clinical psychopharmacology. Mathematical and visual characterizations of core features of such disorders are presented. A unified diagnosis and outcome model of different treatments are presented for different types of bipolar disorders. The significance and novelty of this work is twofold: (1) it introduces YinYang into biomedicine for the understanding of certain neurobiological disorders and fosters a new standard model for clinical, therapeutic, and pharmacological research and applications; (2) it presents a mathematical basis for the characterization of mood regulation in individuals and/or a cohort of patients with applications in biomedical engineering and potential applications in nanotechnologies for integrated care of major depressive and bipolar disorders.