Abnormal mTOR Activation in Autism

2018 ◽  
Vol 41 (1) ◽  
pp. 1-23 ◽  
Author(s):  
Kellen D. Winden ◽  
Darius Ebrahimi-Fakhari ◽  
Mustafa Sahin
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Mtor Inhibitors ◽  
Autism Spectrum ◽  
Energy Availability ◽  
Molecular Processes ◽  
Neurodevelopmental Outcomes ◽  
Mechanistic Target Of Rapamycin ◽  
Neuronal Processes ◽  
Growth Abnormalities

The mechanistic target of rapamycin (mTOR) is an important signaling hub that integrates environmental information regarding energy availability and stimulates anabolic molecular processes and cell growth. Abnormalities in this pathway have been identified in several syndromes in which autism spectrum disorder (ASD) is highly prevalent. Several studies have investigated mTOR signaling in developmental and neuronal processes that, when dysregulated, could contribute to the development of ASD. Although many potential mechanisms still remain to be fully understood, these associations are of great interest because of the clinical availability of mTOR inhibitors. Clinical trials evaluating the efficacy of mTOR inhibitors to improve neurodevelopmental outcomes have been initiated.

New Strategies for Clinical Trials in Autism Spectrum Disorder

2020 ◽  
Vol 15 ◽  
Author(s):  
Rini Pauly ◽  
Catherine A. Ziats ◽  
Ludovico Abenavoli ◽  
Charles E. Schwartz ◽  
Luigi Boccuto
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Genetic Profiling ◽  
Omics Technologies ◽  
Molecular Abnormalities ◽  
Sequencing Technologies ◽  
Linear Relationships ◽  
Personalized Approach

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition that poses several challenges in terms of clinical diagnosis and investigation of molecular etiology. The lack of knowledge on the pathogenic mechanisms underlying ASD has hampered the clinical trials that so far have tried to target ASD behavioral symptoms. In order to improve our understanding of the molecular abnormalities associated with ASD, a deeper and more extensive genetic profiling of targeted individuals with ASD was needed. Methods: The recent availability of new and more powerful sequencing technologies (third-generation sequencing) has allowed to develop novel strategies for characterization of comprehensive genetic profiles of individuals with ASD. In particular, this review will describe integrated approaches based on the combination of various omics technologies that will lead to a better stratification of targeted cohorts for the design of clinical trials in ASD. Results: In order to analyze the big data collected by assays such as whole genome, epigenome, transcriptome, and proteome, it is critical to develop an efficient computational infrastructure. Machine learning models are instrumental to identify non-linear relationships between the omics technologies and therefore establish a functional informative network among the different data sources. Conclusion: The potential advantage provided by these new integrated omics-based strategies is to better characterize the genetic background of ASD cohorts, identify novel molecular targets for drug development, and ultimately offer a more personalized approach in the design of clinical trials for ASD.

Novel Therapeutics in Childhood Onset Psychiatric Disorders

2013 ◽  
pp. 1061-1064
Author(s):  
Dorothy E. Grice ◽  
Alexander Kolevzon ◽  
Walter E. Kaufmann ◽  
Joseph D. Buxbaum
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Down Syndrome ◽  
Intellectual Disability ◽  
Neurodevelopmental Disorders ◽  
Autism Spectrum ◽  
Model Systems ◽  
Childhood Onset ◽  
Preclinical Evaluation ◽  
Novel Therapeutics

Neurodevelopmental disorders are frequently the result of genetic and genomic abnormalities associated with high risk for disease. Creating analogous mutations in cell and animal models permits the assessment of underlying neurobiological mechanisms, generates clues about useful therapeutic targets, and provides systems for preclinical evaluation of novel therapeutics. This chapter briefly summarizes several clinical trials in neurodevelopmental disorders, all based on neurobiological findings in model systems, including trials in Down syndrome (DS) and several monogenic forms of intellectual disability (ID) and/or autism spectrum disorder (ASD).

scholarly journals Imputing the Number of Responders from the Mean and Standard Deviation of CGI-Improvement in Clinical Trials Investigating Medications for Autism Spectrum Disorder

2021 ◽  
Vol 11 (7) ◽  
pp. 908
Author(s):  
Spyridon Siafis ◽  
Alessandro Rodolico ◽  
Oğulcan Çıray ◽  
Declan G. Murphy ◽  
Mara Parellada ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Standard Deviation ◽  
Linear Regression ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Imputation Method ◽  
P Value ◽  
Subgroup Differences ◽  
The Mean

Introduction: Response to treatment, according to Clinical Global Impression-Improvement (CGI-I) scale, is an easily interpretable outcome in clinical trials of autism spectrum disorder (ASD). Yet, the CGI-I rating is sometimes reported as a continuous outcome, and converting it to dichotomous would allow meta-analysis to incorporate more evidence. Methods: Clinical trials investigating medications for ASD and presenting both dichotomous and continuous CGI-I data were included. The number of patients with at least much improvement (CGI-I ≤ 2) were imputed from the CGI-I scale, assuming an underlying normal distribution of a latent continuous score using a primary threshold θ = 2.5 instead of θ = 2, which is the original cut-off in the CGI-I scale. The original and imputed values were used to calculate responder rates and odds ratios. The performance of the imputation method was investigated with a concordance correlation coefficient (CCC), linear regression, Bland–Altman plots, and subgroup differences of summary estimates obtained from random-effects meta-analysis. Results: Data from 27 studies, 58 arms, and 1428 participants were used. The imputation method using the primary threshold (θ = 2.5) had good performance for the responder rates (CCC = 0.93 95% confidence intervals [0.86, 0.96]; β of linear regression = 1.04 [0.95, 1.13]; bias and limits of agreements = 4.32% [−8.1%, 16.74%]; no subgroup differences χ2 = 1.24, p-value = 0.266) and odds ratios (CCC = 0.91 [0.86, 0.96]; β = 0.96 [0.78, 1.14]; bias = 0.09 [−0.87, 1.04]; χ2 = 0.02, p-value = 0.894). The imputation method had poorer performance when the secondary threshold (θ = 2) was used. Discussion: Assuming a normal distribution of the CGI-I scale, the number of responders could be imputed from the mean and standard deviation and used in meta-analysis. Due to the wide limits of agreement of the imputation method, sensitivity analysis excluding studies with imputed values should be performed.

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