Genetics of Schizophrenia and Smoking: An Approach to Studying their Comorbidity Based on Epidemiological Findings

The association between schizophrenia and tobacco smoking has been described in more than 1,000 articles, many with inadequate methodology. The studies on this association can focus on: (1) current smoking, ever smoking or smoking cessation; (2) non-psychiatric controls or controls with severe mental illness (e.g., bipolar disorder); and (3) higher smoking frequency or greater usage in smokers. The association with the most potential for genetic studies is that between ever daily smoking and schizophrenia; it may reflect a shared genetic vulnerability. To reduce the number of false-positive genes, we propose a three-stage approach derived from epidemiological knowledge. In the first stage, only genetic variations associated with ever daily smoking that are simultaneously significant within the non-psychiatric controls, the bipolar disorder controls and the schizophrenia cases will be selected. Only those genetic variations that are simultaneously significant in the three hypothesis tests will be tested in the second stage, where the prevalence of the genes must be significantly higher in schizophrenia than in bipolar disorder, and significantly higher in bipolar disorder than in controls. The genes simultaneously significant in the second stage will be included in a third stage where the gene variations must be significantly more frequent in schizophrenia patients who did not start smoking daily until their 20s (late start) versus those who had an early start. Any genetic approach to psychiatric disorders may fail if attention is not given to comorbidity and epidemiological studies that suggest which comorbidities are likely to be explained by genetics and which are not. Our approach, which examines the results of epidemiological studies on comorbidities and then looks for genes that simultaneously satisfy epidemiologically suggested sets of hypotheses, may also apply to the study of other major illnesses

Clobazam Therapeutic Drug Monitoring: A Comprehensive Review of the Literature With Proposals to Improve Future Studies

Background: Clobazam was recently approved for Lennox–Gastaut syndrome in the United States. There is no published review article focused on clobazam therapeutic drug monitoring (TDM) in English. Methods: More than 200 clobazam articles identified by a PubMed search were carefully reviewed for information on clobazam pharmacokinetics. Clobazam is mainly metabolized by a cytochrome P450 (CYP) isoenzyme, CYP3A4, to its active metabolite, N-desmethylclobazam. Then, N-desmethylclobazam is mainly metabolized by CYP2C19 unless the individual has no CYP2C19 activity [poor metabolizer (PM)]. Results: Using a mechanistic approach to reinterpret the published findings of steady-state TDM and single-dosing pharmacokinetic studies, 4 different serum clobazam concentration ratios were studied. The available limited steady-state TDM data suggest that the serum N-desmethylclobazam/clobazam ratio can be useful for clinicians, including identifying CYP2C19 PMs (ratio \u3e25 in the absence of inhibitors). There are 3 possible concentration/dose (C/D) ratios. The clobazam C/D ratio has the potential to measure the contribution of CYP3A4 activity to the clearance of clobazam from the body. The N-desmethylclobazam C/D ratio does not seem to be a good measure of clobazam clearance and should be substituted with the total (clobazam + N-desmethylclobazam) C/D ratio. Conclusions: Future clobazam TDM studies need to use trough concentrations after steady state has been reached (\u3e3 weeks in normal individuals and several months in CYP2C19 PMs). These future studies need to explore the potential of clobazam and total C/D ratios. Better studies on the relative potency of N-desmethylclobazam compared with the parent compound are needed to provide weighted total serum concentrations that correct for the possible lower N-desmethylclobazam pharmacodynamic activity. Standardization and more studies of C/D ratios from clobazam and other drugs can be helpful to move TDM forward

Construction of the Design Matrix for Generalized Linear Mixed-Effects Models in the Context of Clinical Trials of Treatment Sequences

The problem of constructing a design matrix of full rank for generalized linear mixed-effects models (GLMMs) has not been addressed in statistical literature in the context of clinical trials of treatment sequences. Solving this problem is important because the most popular estimation methods for GLMMs assume a design matrix of full rank, and GLMMs are useful tools in statistical practice. We propose new developments in GLMMs that address this problem. We present a new model for the design and analysis of clinical trials of treatment sequences, which utilizes some special sequences called skip sequences. We present a theorem showing that estimators computed through quasi-likelihood, maximum likelihood or generalized least squares, or through robust approaches, exist only if appropriate skip sequences are used. We prove theorems that establish methods for implementing skip sequences in practice. In particular, one of these theorems computes the necessary skip sequences explicitly. Our new approach allows building design matrices of full rank and facilitates the implementation of regression models in the experimental design and data analysis of clinical trials of treatment sequences. We also explain why the standard approach to constructing dummy variables is inappropriate in studies of treatment sequences. The methods are illustrated with a data analysis of the STAR*D study of sequences of treatments for depression.La estimación de los efectos de arrastre es un problema difícil en el diseño y análisis de ensayos clínicos de secuencias de tratamientos, incluyendo ensayos cruzados. Excepto por diseños simples, estos efectos son usualmente no identificables y, por lo tanto, no estimables. La imposición de restricciones a los parámetros es a menudo no justificada y produce diferentes estimativos de los efectos de arrastre dependiendo de la restricción impuesta. Las inversas generalizadas o el balance de tratamientos a menudo permiten estimar losefectos principales de tratamiento, pero no resuelven el problema de estimar la contribución de los efectos de arrastre de una secuencia de tratamiento. Además, los períodos de lavado no siempre son factibles o éticos. Los diseños con parámetros no identificables comúnmente tienen matrices de diseño que no son de rango completo. Por lo tanto, proponemos métodos para la construcción de matrices de rango completo, sin imponer restricciones artificiales en los efectos de arrastre. Nuestros métodos son aplicables en un contextode modelos lineales mixtos generalizados. Presentamos un nuevo modelo para el diseño y análisis de ensayos clínicos de secuencias de tratamientos, llamado Sistema Anticrónico, e introducimos secuencias de tratamiento especiales llamadas Secuencias de Salto. Demostramos que los efectos de arrastre son identificables sólo si se usan Secuencias de Salto apropiadas. Explicamos cómo implementar en la práctica estas secuencias, y presentamos un método para calcular las secuencias apropiadas. Presentamos aplicaciones al diseño de un estudio cruzado con 3 tratamientos y 3 períodos, y al análisis del estudio STAR*D de secuencias de tratamientos para la depresión