The foundation and architecture of precision medicine in neurology and psychiatry

Abstract

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Keywords: Alzheimer’s disease; biomarkers; biomedical research; data science; digital health; systems medicine.

Figure 1.. The road to precision medicine (PM) in neurology and psychiatry: towards predictive, participatory, preventive, and personalized (P4) medicine and optimized patient journey.

The P4 paradigm envisions a healthcare landscape based on the elements of predictive, participatory, preventive, and personalized medicine [21,90]. The framework outlined in the current article aims to present a path for deploying the P4 paradigm in the fields of neurology and psychiatry. As summarized in the figure, the proposal is grounded on four converging pillars: systems medicine, digital technologies, biomarkers, and big data. Information is gathered from large populations to provide personalized medicine for individuals with neurological and psychiatric diseases. Digital and clinical data generated through systems medicine are gathered and integrated to create big and deep data. A structured data science approach is used to integrate complex data and provide meaningful outputs. This is the necessary substrate to support the P4 framework. This framework integrates the four Ps with the ultimate goal of prolonging health span through early interventions.

Figure 2.. Systems biology and systems neurophysiology data provide information across different spatial and temporal scales.

Multiple types of data can be obtained from systems biology, including quantification of neurobiological systems at the molecular biology level, and systems neurophysiology, which encompasses multimodal integrative imaging or recording techniques to capture data at different spatial and temporal scales. These data can be integrated for the purpose of systems modeling across spatial and temporal ranging from the atomic and molecular scale to whole brains, and from millisecond-range phenomena to processes progressing over years. Abbreviations: DTI, diffusion tensor imaging; ECOG, electrocorticogram; EEG, electroencephalography; EM, electron microscopy; fMRI, functional magnetic resonance imaging; fNIRS, functional near infrared spectroscopy; MEG, magnetoencephalography; PET, positron emission tomography; sMRI, structural magnetic resonance imaging; TMS, transcranial magnetic stimulation.

Figure 3.. Large neuroimaging studies of several major neurological, psychiatric, and developmental conditions reveal both overlap and characteristic differences in the profiles of brain alterations.

Findings depicted in the figure are by the ENIGMA Consortium [19]. Cortical grey matter thinning is prevalent in a range of conditions examined in the study, except for autism spectrum disorder and 22q11 deletion syndrome, where excess brain tissue is found. Recent work has related some of these patterns to cell-specific gene expression patterns and to neuroreceptor distributions [114], implicating specific cell types and molecular pathways in psychiatric conditions. Reproduced from [19]. Abbreviations: 22q11DS, 22q deletion syndrome; ADHD, attention deficit hyperactivity disorder; ASD, autism spectrum disorder; BD, bipolar disorder; MDD, major depressive disorder; OCD, obsessive-compulsive disorder.

Figure 4.. The evolving spectrum of neurodegenerative diseases: from late-stage syndromic phenotypes to extensive genetic–biological–clinical profiling.

This schematic describes the evolving, evidence-based concept of neurodegenerative diseases spectrum. Biomarker-guided clinical research showed that conditions with divergent clinical phenotypes exhibit genetic and pathophysiological overlap. By contrast, a traditionally defined clinical phenotype (e.g., behavioral variant of frontotemporal dementia) may have different underlying genetic mutations and pathological alterations, including brain proteinopathies. With comprehensive profiling by the integration of genetic, molecular, and multimodal imaging endophenotypes, current understanding of neurodegenerative diseases continues to evolve and future clinical advances are hoped to overcome the limitations of traditional syndrome-oriented approaches. Figure adapted from the concepts in references [5,31,40]. Abbreviation: Aβ, amyloid β; AD, Alzheimer’s disease; ALS, amyotrophic lateral sclerosis; APOE, apolipoprotein E ε4 allele; APP, amyloid protein precursor; bvFTD, behavioral variant FTD; CBD, corticobasal degeneration; CJD, Creutzfeldt-Jakob disease; DLB, dementia with Lewy bodies; FTD, frontotemporal dementia; FTD-MND, FTD with concurrent motor neuron disease; FTLD, frontotemporal lobar degeneration; FTLD-FUS, FTLD associated with inclusions of protein fused in sarcoma; FTLD-tau, FTLD associated with inclusions of microtubule associated protein tau; FUS, RNA-binding protein FUS; GSS, Gerstmann-Sträussler-Scheinker disease; LRRK2, leucine rich repeat kinase 2; MAPT, microtubule associated protein tau; NFTs, neurofibrillary tangles; PARKIN, parkin RBR E3 ubiquitin-protein ligase; PD, Parkinson’s disease; PGRN, progranulin; PICALM, phosphatidylinositol binding clathrin assembly protein; PINK1, PTEN induced putative kinase 1; PNFA, nonfluent variant primary progressive aphasia; PPD, paranoid personality disorder; PS1, presenilin-1; PS2, presenilin-2; PSP, progressive supranuclear palsy; SD, semantic dementia; SNCA, alpha-synuclein; TDP-43, transactive response DNA-binding protein 43.