Big data to smart data in Alzheimer's disease: Real-world examples of advanced modeling and simulation

Addressing the Discrepancies Between Animal Models and Human Alzheimer's Disease Pathology: Implications for Translational Research

Animal models, particularly transgenic mice, are extensively used in Alzheimer's disease (AD) research to emulate key disease hallmarks, such as amyloid plaques and neurofibrillary tangles formation. Although these models have contributed to our understanding of AD pathogenesis and can be helpful in testing potential therapeutic interventions, their reliability is dubious. While preclinical studies have shown promise, clinical trials often yield disappointing results, highlighting a notable gap and disparity between animal models and human AD pathology. Existing models frequently overlook early-stage human pathologies and other key AD characteristics, thereby limiting their application in identifying optimal therapeutic interventions. Enhancing model reliability necessitates rigorous study design, comprehensive behavioral evaluations, and biomarker utilization. Overall, a nuanced understanding of each model's neuropathology, its fidelity to human AD, and its limitations is essential for accurate interpretation and successful translation of findings. This article analyzes the discrepancies between animal models and human AD pathology that complicate the translation of findings from preclinical studies to clinical applications. We also delve into AD pathogenesis and attributes to propose a new perspective on this pathology and deliberate over the primary limitations of key experimental models. Additionally, we discuss several fundamental problems that may explain the translational failures and suggest some possible directions for more effective preclinical studies.

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Biomarkers: Our Path Towards a Cure for Alzheimer Disease

Over the last decade, biomarkers have significantly improved our understanding of the pathophysiology of Alzheimer disease (AD) and provided valuable tools to examine different disease mechanisms and their progression over time. While several markers of amyloid, tau, neuronal, synaptic, and axonal injury, inflammation, and immune dysregulation in AD have been identified, there is a relative paucity of biomarkers which reflect other disease mechanisms such as oxidative stress, mitochondrial injury, vascular or endothelial injury, and calcium-mediated excitotoxicity. Importantly, there is an urgent need to standardize methods for biomarker assessments across different centers, and to identify dynamic biomarkers which can monitor disease progression over time and/or response to potential disease-modifying treatments. The updated research framework for AD, proposed by the National Institute of Aging- Alzheimer’s Association (NIA-AA) Work Group, emphasizes the importance of incorporating biomarkers in AD research and defines AD as a biological construct consisting of amyloid, tau, and neurodegeneration which spans pre-symptomatic and symptomatic stages. As results of clinical trials of AD therapeutics have been disappointing, it has become increasingly clear that the success of future AD trials will require the incorporation of biomarkers in participant selection, prognostication, monitoring disease progression, and assessing response to treatments. We here review the current state of fluid AD biomarkers, and discuss the advantages and limitations of the updated NIA-AA research framework. Importantly, the integration of biomarker data with clinical, cognitive, and imaging domains through a systems biology approach will be essential to adequately capture the molecular, genetic, and pathological heterogeneity of AD and its spatiotemporal evolution over time.

Standardized Data Structures in Rare Diseases: CDISC User Guides for Duchenne Muscular Dystrophy and Huntington's Disease

Interest in drug development for rare diseases has expanded dramatically since the Orphan Drug Act was passed in 1983, with 40% of new drug approvals in 2019 targeting orphan indications. However, limited quantitative understanding of natural history and disease progression hinders progress and increases the risks associated with rare disease drug development. Use of international data standards can assist in data harmonization and enable data exchange, integration into larger datasets, and a quantitative understanding of disease natural history. The US Food and Drug Administration (FDA) requires the use of Clinical Data Interchange Consortium (CDISC) Standards in new drug submissions to help the agency efficiently and effectively receive, process, review, and archive submissions, as well as to help integrate data to answer research questions. Such databases have been at the core of biomarker qualification efforts and fit‐for‐purpose models endorsed by the regulators. We describe the development of CDISC therapeutic area user guides for Duchenne muscular dystrophy and Huntington’s disease through Critical Path Institute consortia. These guides describe formalized data structures and controlled terminology to map and integrate data from different sources. This will result in increased standardization of data collection and allow integration and comparison of data from multiple studies. Integration of multiple data sets enables a quantitative understanding of disease progression, which can help overcome common challenges in clinical trial design in these and other rare diseases. Ultimately, clinical data standardization will lead to a faster path to regulatory approval of urgently needed new therapies for patients.

The path to biomarker-based diagnostic criteria for the spectrum of neurodegenerative diseases

Introduction: The postmortem examination still represents the reference standard for detecting the pathological nature of chronic neurodegenerative diseases (NDD). This approach displays intrinsic conceptual limitations since NDD represent a dynamic spectrum of partially overlapping phenotypes, shared pathomechanistic alterations that often give rise to mixed pathologies.Areas covered: We scrutinized the international clinical diagnostic criteria of NDD and the literature to provide a roadmap toward a biomarker-based classification of the NDD spectrum. A few pathophysiological biomarkers have been established for NDD. These are time-consuming, invasive, and not suitable for preclinical detection. Candidate screening biomarkers are gaining momentum. Blood neurofilament light-chain represents a robust first-line tool to detect neurodegeneration tout court and serum progranulin helps detect genetic frontotemporal dementia. Ultrasensitive assays and retinal scans may identify Aβ pathology early, in blood and the eye, respectively. Ultrasound also represents a minimally invasive option to investigate the substantia nigra. Protein misfolding amplification assays may accurately detect α-synuclein in biofluids.Expert opinion: Data-driven strategies using quantitative rather than categorical variables may be more reliable for quantification of contributions from pathophysiological mechanisms and their spatial-temporal evolution. A systems biology approach is suitable to untangle the dynamics triggering loss of proteostasis, driving neurodegeneration and clinical evolution.

Data Dissemination: Shortening the Long Tail of Traumatic Brain Injury Dark Data

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

Regulatory-accepted drug development tools are needed to accelerate innovative CNS disease treatments

Central Nervous System (CNS) diseases represent one of the most challenging therapeutic areas for successful drug approvals. Developing quantitative biomarkers as Drug Development Tools (DDTs) can catalyze the path to innovative treatments, and improve the chances of drug approvals. Drug development and healthcare management requires sensitive, reliable, validated, and regulatory accepted biomarkers and endpoints. This review highlights the regulatory paths and considerations for developing DDTs required to advance biomarker and endpoint use in clinical development (e.g., consensus CDISC [Clinical Data Interchange Standards Consortium] data standards, precompetitive sharing of anonymized patient-level data, and continual alignment with regulators). Summarized is the current landscape of biomarkers in a range of CNS diseases including Alzheimer disease, Parkinson Disease, Amyotrophic Lateral Sclerosis, Autism Spectrum Disorders, Depression, Huntington's disease, Multiple Sclerosis and Traumatic Brain Injury. Advancing DDTs for these devastating diseases that are both validated and qualified will require an integrated, cross-consortium approach to accelerate the delivery of innovative CNS therapeutics.

Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology

The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Blood-Based Biomarker Screening with Agnostic Biological Definitions for an Accurate Diagnosis Within the Dimensional Spectrum of Neurodegenerative Diseases

The discovery, development, and validation of novel candidate biomarkers in Alzheimer's disease (AD) and other neurodegenerative diseases (NDs) are increasingly gaining momentum. As a result, evolving diagnostic research criteria of NDs are beginning to integrate biofluid and neuroimaging indicators of pathophysiological mechanisms. More than 10% of people aged over 65 suffer from NDs. There is an urgent need for a refined two-stage diagnostic model to first initiate an early, sensitive, and noninvasive process in primary care settings. Individuals that meet detection criteria will then be channeled to more specific, costly (positron-emission tomography), and invasive (cerebrospinal fluid) assessment methods for confirmatory biological characterization and diagnosis.A reliable and sensitive blood test for AD and other NDs is not yet established; however, it would provide the golden screening gate for an efficient primary care management. A limitation to the development of a large-scale blood-screening biomarker-based test is the traditional application of clinically descriptive criteria for the categorization of single late-stage ND constructs. These are genetically and biologically heterogeneous, reflected in multiple pathophysiological mechanisms and subsequent pathologies throughout a dimensional continuum. Evidence suggests that a shared, "open-source" integrated multilevel categorization of NDs that clusters individuals based on descriptive clinical phenotypes and pathophysiological biomarker signatures will provide the next incremental step toward an improved diagnostic process of NDs. This intermediate objective toward unbiased biomarker-guided early detection of individuals at risk for NDs is currently carried out by the international pilot Alzheimer Precision Medicine Initiative Cohort Program (APMI-CP).

Towards the virtual human patient. Quantitative Systems Pharmacology in Alzheimer's disease

Development of successful therapeutic interventions in Central Nervous Systems (CNS) disorders is a daunting challenge with a low success rate. Probable reasons include the lack of translation from preclinical animal models, the individual variability of many pathological processes converging upon the same clinical phenotype, the pharmacodynamical interaction of various comedications and last but not least the complexity of the human brain. This paper argues for a re-engineering of the pharmaceutical CNS Research & Development strategy using ideas focused on advanced computer modeling and simulation from adjacent engineering-based industries. We provide examples that such a Quantitative Systems Pharmacology approach based on computer simulation of biological processes and that combines the best of preclinical research with actual clinical outcomes can enhance translation to the clinical situation. We will expand upon (1) the need to go from Big Data to Smart Data and develop predictive and quantitative algorithms that are actionable for the pharma industry, (2) using this platform as a "knowledge machine" that captures community-wide expertise in an active hypothesis-testing approach, (3) learning from failed clinical trials and (4) the need to go beyond simple linear hypotheses and embrace complex non-linear hypotheses. We will propose a strategy for applying these concepts to the substantial individual variability of AD patient subgroups and the treatment of neuropsychiatric problems in AD. Quantitative Systems Pharmacology is a new 'humanized' tool for supporting drug discovery and development in general and CNS disorders in particular.

Towards a systems approach for chronic diseases, based on health state modeling

Rising pressure from chronic diseases means that we need to learn how to deal with challenges at a different level, including the use of systems approaches that better connect across fragments, such as disciplines, stakeholders, institutions, and technologies. By learning from progress in leading areas of health innovation (including oncology and AIDS), as well as complementary indications (Alzheimer's disease), I try to extract the most enabling innovation paradigms, and discuss their extension to additional areas of application within a systems approach. To facilitate such work, a Precision, P4 or Systems Medicine platform is proposed, which is centered on the representation of health states that enable the definition of time in the vision to provide the right intervention for the right patient at the right time and dose. Modeling of such health states should allow iterative optimization, as longitudinal human data accumulate. This platform is designed to facilitate the discovery of links between opportunities related to a) the modernization of diagnosis, including the increased use of omics profiling, b) patient-centric approaches enabled by technology convergence, including digital health and connected devices, c) increasing understanding of the pathobiological, clinical and health economic aspects of disease progression stages, d) design of new interventions, including therapies as well as preventive measures, including sequential intervention approaches. Probabilistic Markov models of health states, e.g. those used for health economic analysis, are discussed as a simple starting point for the platform. A path towards extension into other indications, data types and uses is discussed, with a focus on regenerative medicine and relevant pathobiology.

Drug development in Alzheimer's disease: the path to 2025

The global impact of Alzheimer's disease (AD) continues to increase, and focused efforts are needed to address this immense public health challenge. National leaders have set a goal to prevent or effectively treat AD by 2025. In this paper, we discuss the path to 2025, and what is feasible in this time frame given the realities and challenges of AD drug development, with a focus on disease-modifying therapies (DMTs). Under the current conditions, only drugs currently in late Phase 1 or later will have a chance of being approved by 2025. If pipeline attrition rates remain high, only a few compounds at best will meet this time frame. There is an opportunity to reduce the time and risk of AD drug development through an improvement in trial design; better trial infrastructure; disease registries of well-characterized participant cohorts to help with more rapid enrollment of appropriate study populations; validated biomarkers to better detect disease, determine risk and monitor disease progression as well as predict disease response; more sensitive clinical assessment tools; and faster regulatory review. To implement change requires efforts to build awareness, educate and foster engagement; increase funding for both basic and clinical research; reduce fragmented environments and systems; increase learning from successes and failures; promote data standardization and increase wider data sharing; understand AD at the basic biology level; and rapidly translate new knowledge into clinical development. Improved mechanistic understanding of disease onset and progression is central to more efficient AD drug development and will lead to improved therapeutic approaches and targets. The opportunity for more than a few new therapies by 2025 is small. Accelerating research and clinical development efforts and bringing DMTs to market sooner would have a significant impact on the future societal burden of AD. As these steps are put in place and plans come to fruition, e.g., approval of a DMT, it can be predicted that momentum will build, the process will be self-sustaining, and the path to 2025, and beyond, becomes clearer.