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Comprehensive Integration of Precision Medicine Strategies in Neurology and Psychiatry: A P4 Approach

NextEdge Admin

20 Apr 2024

3 min 30 sec

molecular,Neurology,Psychiatry,artificial intelligence,machine learning

The full deployment of Precision Medicine in clinical practice is envisioned to adopt the ‘P4’ medicine paradigm, encompassing predictive, preventive, personalized, and participatory strategies. In this framework, individuals are stratified based on their disease risk (predictive), large-scale screening is employed for early detection, enabling timely therapeutic interventions (preventive), treatments are tailored to individual social, clinical, and biological characteristics (personalized), and actionable plans are optimized through patient-centered data collection, including self-monitoring and assessments (participatory).

Oncology has been at the forefront of PM, with tissue-agnostic molecular pathway-targeting drugs gaining approval from the US FDA. Genetic studies have identified highly penetrant causal variants and risk genes in various neurological and psychiatric conditions as well, revolutionizing clinical care toward genetically informed diagnostic and therapeutic decision-making.

Polygenic risk scores (PRS), combining genetic variants based on their effect sizes, offer opportunities for translating genomic findings into clinical care, identifying individuals with increased susceptibility. Blood-based biomarkers, being cost-effective and minimally invasive, hold promise for large-scale screening, streamlining diagnostic workups in neurological and psychiatric diseases.

Neuroimaging, encompassing molecular and structural/functional techniques, provides noninvasive visualization of the central nervous system. Molecular imaging methods, like positron emission tomography (PET) and single-photon emission computed tomography (SPECT), directly detect disease-associated molecular and cellular processes, contributing to the identification of diagnostic biomarkers for neurodegenerative diseases.

Advancements in systems biology, driven by high-throughput omics science and data mining, have been instrumental in understanding the pathophysiology of neurological and psychiatric diseases, offering insights into potential biomarkers.

Digital health technologies enable comprehensive and portable data collection, capturing diverse disease-related phenotypes beyond clinical visits. These technologies provide longitudinal, unobtrusive monitoring of central and peripheral autonomic functions, offering quantitative data on parameters like heart rate, motion, and sleep.

The evolving landscape of big data, incorporating various health measures, presents analytical challenges addressed by artificial intelligence (AI) and machine learning (ML) algorithms. AI, particularly deep learning, revolutionizes clinical research by predicting disease trajectories, identifying subgroups, and uncovering hidden biological signatures.

In essence, PM in neurology and psychiatry integrates genetic information, fluid biomarkers, neuroimaging, and systems biology to usher in personalized, preventive, and participatory approaches, reshaping diagnostic, and therapeutic landscapes.