Leveraging the impact of diversity in neurodevelopmental disability by integrating machine learning in personalized interventions.

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Neurodevelopmental disability (NDD), which is an umbrella term for autism, attention deficit, and intellectual and learning disability, affects 13% of the population. It has major economic and quality-of-life impacts on NDD individuals and families, and substantial economic burden on the healthcare system. So far, treatment is aimed only at general symptoms, which often leads to low efficacy and frequent side effects. The advent of novel genetic testing methods has provided plenty of evidence of the major impact that genes and their regulation have on clinical presentation in NDD. Nonetheless, there is a large diversity among individuals with NDD, even with the same genetic mutation. This is not unique to NDD as it is seen widely in many other medical conditions. The complexity derived from the genetic heterogeneity and the clinical (neuro) diversity has proven challenging to traditional approaches for treatment. Recent research in the UK and Canada has led to the development of large databases recording detailed information about individuals with NDD. Artificial intelligence (AI) now provides us with the tools to quickly analyze the information in those datasets. In particular, we will use machine learning (ML) to manage complex information, leading to the acceleration and better prioritization of interventions. Also, our project takes a novel view on the understanding of genomic information in NDD. Instead of directing our focus only on exploring data from a single individual or small group of individuals carrying the same gene mutation, our team will apply ML to large databases to identify features (from genes and their biology) correlated with improved clinical outcomes. In addition, we will use ML to better understand the interdependence between different symptoms to develop treatments that have a globally positive impact. In other words, we would find solutions that improve cognitive skills without impacting sleep negatively or generating more anxiety, as has been seen in previous clinical trials. We will finish by providing the entire scientific community with an open access portal, including our research findings, which will be integrated with the current Open Targets platform, a partnership between academia and industry in the UK that allows researchers to access linked data on diseases, genes and drugs in a single site. Researchers will be able to provide further information, which will improve the ML model. To ensure that we accomplish our objectives, we have assembled a team of experts in clinical and genetics of NDD: Dr. Bolduc (Canada); in computer science of genomics, molecular and pharmacological data: Dr. Dunham (UK); bioinformatics: Dr. Droit; machine learning: Dr. Greiner; social sciences, patient engagement and health economic: Dr. Zwicker. Our team has also developed strong links with NDD patient and research organizations in Canada and the UK, which will provide insight throughout the project. We are supported by collaborators involved in family and government engagement, ethics and data management in the UK and Canada. The project will also be a unique opportunity for multidisciplinary international training. Our project will show how ML can disassemble the complexity and diversity seen in NDD to develop more successful interventions. It will allow us to develop new ML approaches that will be readily applicable to other disorders where personalized interventions have been lagging behind diagnosis. More importantly, it will bring together families, society and scientists into a shared space where more and better information is exchanged. Finally, our project will embrace responsible implementation of data privacy and confidentiality while recognizing the need for data sharing to develop better interventions.