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Defining precision medicine approaches to autism spectrum disorders: Concepts and challenges

Research output: Contribution to journalArticle

Eva Loth, Declan G. Murphy, Will Spooren

Original languageEnglish
Article number188
JournalFrontiers in psychiatry / Frontiers Research Foundation
Volume7
Issue numberNOV
DOIs
Publication statusPublished - 29 Nov 2016

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Abstract

The tremendous clinical and etiological variability between individuals with autism spectrum disorder (ASD) has made precision medicine the most promising treatment approach. It aims to combine new pathophysiologically based treatments with objective tests (stratification biomarkers) to predict which treatment may be beneficial for a particular person. Here we discuss significant advances and current challenges for this approach: rare monogenic forms of ASD have provided a major breakthrough for the identification of treatment targets by providing a means to trace causal links from a gene to specific molecular alterations and biological pathways. To estimate whether treatment targets thus identified may be useful for larger patient groups we need a better understanding of whether different etiologies (i.e., genetic and environmental risk factors acting at different critical time points) lead to convergent or divergent molecular mechanisms, and how they map onto differences in circuit-level brain and cognitive development, and behavioral symptom profiles. Several recently failed clinical trials with syndromic forms of ASD provide valuable insights into conceptual and methodological issues linked to limitations in the translatability from animal models to humans, placebo effects, and a need for mechanistically plausible, objective outcome measures. To identify stratification biomarkers that enrich participant selection in clinical trials, large-scale multi-modal longitudinal observational studies are underway. Addressing these different factors in the next generation of research studies requires a translatable developmental perspective and multidisciplinary, collaborative efforts, with a commitment to sharing protocols and data, to increase transparency and reproducibility.

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