scholarly journals The Autism Biomarkers Consortium for Clinical Trials (ABC-CT): Scientific Context, Study Design, and Progress towards Biomarker Qualification

2019 ◽  
Author(s):  
James C. McPartland ◽  
Raphael A. Bernier ◽  
Shafali S. Jeste ◽  
Geraldine Dawson ◽  
Charles A. Nelson ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Trials ◽  
Clinical Research ◽  
Biomarker Discovery ◽  
Longitudinal Design ◽  
Governance Structure ◽  
Open Science ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Biomarker Research

AbstractClinical research in neurodevelopmental disorders remains reliant upon clinician and caregiver measures. Limitations of these approaches indicate a need for objective, quantitative, and reliable biomarkers to advance clinical research. Extant research suggests the potential utility of multiple candidate biomarkers; however, effective application of these markers in trials requires additional understanding of replicability, individual differences, and intra-individual stability over time. The Autism Biomarkers Consortium for Clinical Trials (ABC-CT) is a multi-site study designed to investigate a battery of electrophysiological (EEG) and eye-tracking (ET) indices as candidate biomarkers for autism spectrum disorder (ASD). The study complements published biomarker research through: inclusion of large, deeply phenotyped cohorts of children with autism spectrum disorder (ASD) and typical development; a longitudinal design; a focus on well-evidenced candidate biomarkers harmonized with an independent sample; high levels of clinical, regulatory, technical, and statistical rigor; adoption of a governance structure incorporating diverse expertise in the ASD biomarker discovery and qualification process; prioritization of open science, including creation of a repository containing biomarker, clinical, and genetic data; and use of economical and scalable technologies that are applicable in developmental populations and those with special needs. The ABC-CT approach has yielded encouraging results, with one measure accepted into the FDA’s Biomarker Qualification Program to date. Through these advances, the ABC-CT and other biomarker studies in progress hold promise to deliver novel tools to improve clinical trials research in ASD.

scholarly journals Paving the Way toward Personalized Medicine: Current Advances and Challenges in Multi-OMICS Approach in Autism Spectrum Disorder for Biomarkers Discovery and Patient Stratification

2021 ◽  
Vol 11 (1) ◽  
pp. 41
Author(s):  
Areej G. Mesleh ◽  
Sara A. Abdulla ◽  
Omar El-Agnaf
Keyword(s):  
Autism Spectrum Disorder ◽  
Biomarker Discovery ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Early Screening ◽  
Success Stories ◽  
Social Communication Skills ◽  
Actual Increase ◽  
Biomarker Research

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder characterized by impairments in two main areas: social/communication skills and repetitive behavioral patterns. The prevalence of ASD has increased in the past two decades, however, it is not known whether the evident rise in ASD prevalence is due to changes in diagnostic criteria or an actual increase in ASD cases. Due to the complexity and heterogeneity of ASD, symptoms vary in severity and may be accompanied by comorbidities such as epilepsy, attention deficit hyperactivity disorder (ADHD), and gastrointestinal (GI) disorders. Identifying biomarkers of ASD is not only crucial to understanding the biological characteristics of the disorder, but also as a detection tool for its early screening. Hence, this review gives an insight into the main areas of ASD biomarker research that show promising findings. Finally, it covers success stories that highlight the importance of precision medicine and the current challenges in ASD biomarker discovery studies.

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.

scholarly journals An Analysis of Canadian Institute for Health Research Funding for Research on Autism Spectrum Disorder

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
R. Deonandan ◽  
E. Y. Liu ◽  
B. Kolisnyk ◽  
A. T. M. Konkle
Keyword(s):  
Autism Spectrum Disorder ◽  
Clinical Research ◽  
Biomedical Research ◽  
Health Research ◽  
Research Funding ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Institutional Response ◽  
Behavioural Interventions ◽  
Canadian Institute

We examined patterns of Canadian Institute for Health Research (CIHR) funding on autism spectrum disorder (ASD) research. From 1999 to 2013, CIHR funded 190 ASD grants worth $48 million. Biomedical research received 43% of grants (46% of dollars), clinical research 27% (41%), health services 10% (7%), and population health research 8% (3%). The greatest number of grants was given in 2009, but 2003 saw the greatest amount. Funding is clustered in a handful of provinces and institutions, favouring biomedical research and disfavouring behavioural interventions, adaptation, and institutional response. Preference for biomedical research may be due to the detriment of clinical research.

scholarly journals Longitudinal prediction of language emergence in infants at high and low risk for autism spectrum disorder

2016 ◽  
Vol 29 (1) ◽  
pp. 319-329 ◽  
Author(s):  
Sarah R. Edmunds ◽  
Lisa V. Ibañez ◽  
Zachary Warren ◽  
Daniel S. Messinger ◽  
Wendy L. Stone
Keyword(s):  
Autism Spectrum Disorder ◽  
Longitudinal Design ◽  
Autism Spectrum ◽  
Low Risk ◽  
Spectrum Disorder ◽  
Expressive Vocabulary ◽  
Motor Imitation ◽  
New Information ◽  
Language Growth ◽  
Prospective Longitudinal

AbstractThis study used a prospective longitudinal design to examine the early developmental pathways that underlie language growth in infants at high risk (n = 50) and low risk (n = 34) for autism spectrum disorder in the first 18 months of life. While motor imitation and responding to joint attention (RJA) have both been found to predict expressive language in children with autism spectrum disorder and those with typical development, the longitudinal relation between these capacities has not yet been identified. As hypothesized, results revealed that 15-month RJA mediated the association between 12-month motor imitation and 18-month expressive vocabulary, even after controlling for earlier levels of RJA and vocabulary. These results provide new information about the developmental sequencing of skills relevant to language growth that may inform future intervention efforts for children at risk for language delay or other developmental challenges.

scholarly journals Blood biomarker discovery for autism spectrum disorder: A proteomic analysis

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246581
Author(s):  
Laura Hewitson ◽  
Jeremy A. Mathews ◽  
Morgan Devlin ◽  
Claire Schutte ◽  
Jeon Lee ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Biological Markers ◽  
Proteomic Analysis ◽  
Biomarker Discovery ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Blood Biomarker ◽  
Specificity And Sensitivity ◽  
Wide Range

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and social interaction and restricted, repetitive patterns of behavior, interests, or activities. Given the lack of specific pharmacological therapy for ASD and the clinical heterogeneity of the disorder, current biomarker research efforts are geared mainly toward identifying markers for determining ASD risk or for assisting with a diagnosis. A wide range of putative biological markers for ASD is currently being investigated. Proteomic analyses indicate that the levels of many proteins in plasma/serum are altered in ASD, suggesting that a panel of proteins may provide a blood biomarker for ASD. Serum samples from 76 boys with ASD and 78 typically developing (TD) boys, 18 months-8 years of age, were analyzed to identify possible early biological markers for ASD. Proteomic analysis of serum was performed using SomaLogic’s SOMAScanTM assay 1.3K platform. A total of 1,125 proteins were analyzed. There were 86 downregulated proteins and 52 upregulated proteins in ASD (FDR < 0.05). Combining three different algorithms, we found a panel of 9 proteins that identified ASD with an area under the curve (AUC) = 0.8599±0.0640, with specificity and sensitivity of 0.8217±0.1178 and 0.835±0.1176, respectively. All 9 proteins were significantly different in ASD compared with TD boys, and were significantly correlated with ASD severity as measured by ADOS total scores. Using machine learning methods, a panel of serum proteins was identified that may be useful as a blood biomarker for ASD in boys. Further verification of the protein biomarker panel with independent test sets is warranted.

Sign in / Sign up

Export Citation Format

Share Document