Rational drug repurposing for alzheimer's treatment using in-silico ligand and structure-based approaches

Abstract Alzheimer's disease is a devastating neurodegenerative disorder characterized by memory loss and cognitive decline. New AD treatments are essential, and drug repositioning is a promising approach. In this study, we combined ligand-based and structure-based approaches to identify potential candidates among FDA-approved drugs for AD treatment. We used the human acetylcholinesterase receptor structure (PDB ID: 4EY7) and applied Rapid Overlay of Chemical Structures and Swiss Similarity for ligand-based screening.Computational shape-based screening revealed 20 out of 760 FDA approved drugs with promising structural similarity to Donepezil, an AD treatment AChE inhibitor and query molecule. The screened hits were further analyzed using docking analysis with Autodock Vina and Schrodinger glide. Predicted binding affinities of hits to AChE receptor guided prioritization of potential drug candidates. Doxazosin, Oxypertine, Cyclopenthiazide, Mestranol, and Terazosin exhibited favorable properties in shape similarity, docking energy, and molecular dynamics stability.Molecular dynamics simulations confirmed the stability of the complexes over 100 ns. Binding free energy analysis using MM-GBSA indicated favourable binding energies for the selected drugs. ADME, formulation studies offered insights into therapeutic applications and predicted toxicity.This comprehensive computational approach identified potential FDA-approved drugs (especially Doxazosin) as candidates for repurposing in AD treatment, warranting further investigation and clinical assessment.

A Novel Method for Differential Prognosis of Brain Degenerative Diseases Using Radiomics-Based Textural Analysis and Ensemble Learning Classifiers.

We propose a novel approach to develop a computer-aided decision support system for radiologists to help them classify brain degeneration process as physiological or pathological, aiding in early prognosis of brain degenerative diseases. Our approach applies computational and mathematical formulations to extract quantitative information from biomedical images. Our study explores the longitudinal OASIS-3 dataset, which consists of 4096 brain MRI scans collected over a period of 15 years. We perform feature extraction using Pyradiomics python package that quantizes brain MRI images using different texture analysis methods. Studies indicate that Radiomics has rarely been used for analysis of brain cognition; hence, our study is also a novel effort to determine the efficiency of Radiomics features extracted from structural MRI scans for classification of brain degenerative diseases and to create awareness about Radiomics. For classification tasks, we explore various ensemble learning classification algorithms such as random forests, bagging-based ensemble classifiers, and gradient-boosted ensemble classifiers such as XGBoost and AdaBoost. Such ensemble learning classifiers have not been used for biomedical image classification. We also propose a novel texture analysis matrix, Decreasing Gray-Level Matrix or DGLM. The features extracted from this filter helped to further improve the accuracy of our decision support system. The proposed system based on XGBoost ensemble learning classifiers achieves an accuracy of 97.38%, with sensitivity 99.82% and specificity 97.01%.

Cellular and pathological heterogeneity of primary tauopathies.

Microtubule-associated protein tau is abnormally aggregated in neuronal and glial cells in a range of neurodegenerative diseases that are collectively referred to as tauopathies. Multiple studies have suggested that pathological tau species may act as a seed that promotes aggregation of endogenous tau in naïve cells and contributes to propagation of tau pathology. While they share pathological tau aggregation as a common feature, tauopathies are distinct from one another with respect to predominant tau isoforms that accumulate and the selective vulnerability of brain regions and cell types that have tau inclusions. For instance, primary tauopathies present with glial tau pathology, while it is mostly neuronal in Alzheimer's disease (AD). Also, morphologies of tau inclusions can greatly vary even within the same cell type, suggesting distinct mechanisms or distinct tau conformers in each tauopathy. Neuropathological heterogeneity across tauopathies challenges our understanding of pathophysiology behind tau seeding and aggregation, as well as our efforts to develop effective therapeutic strategies for AD and other tauopathies. In this review, we describe diverse neuropathological features of tau inclusions in neurodegenerative tauopathies and discuss what has been learned from experimental studies with mouse models, advanced transcriptomics, and cryo-electron microscopy (cryo-EM) on the biology underlying cell type-specific tau pathology.

An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms.

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.

Estimating the prevalence of Niemann-Pick disease type C (NPC) in the United States.

BACKGROUND: Niemann-Pick Disease Type C (NPC) is an ultra-rare progressive neurodegenerative disease caused by autosomal recessive mutations in the NPC1 or NPC2 genes that lead to premature death, with most individuals dying between 10 and 25 years of age. NPC can present at any age and many individuals with NPC may be misdiagnosed or undiagnosed. A key challenge with recognizing NPC is the heterogeneous and nonspecific clinical presentation. Currently, there are no approved treatments for NPC in the United States; miglustat, an FDA-approved treatment for Gaucher disease, is used off-label for NPC and GM1 gangliosidosis. OBJECTIVES: To estimate the number of people in the United States that 1) have an NPC diagnosis 2) have an NPC diagnosis and/or are treated off-label with miglustat for NPC and 3) are likely to have NPC. METHODS: For the first two objectives, patients were identified using the Symphony Integrated DataVerse database (Oct 2015-Jan 2020). To identify the number of people with NPC for Objective 1, cases of NPC were defined as any patients with an ICD-10 code of E75.242 (NPC) during the study period. Objective 2 expands upon Objective 1, including (a) patients from Objective 1 and (b) patients with documented miglustat use (NDC 43975-0310 or 10,148-0201) who did not have any claim with Gaucher disease (ICD-10 E75.22) or GM1 gangliosidosis (ICD-10 E75.1) during the study period. For the third objective, published NPC incidence (1 per 89,000 live births) and expected mortality estimates were applied to the 2018 United States birth rate (11.6 per 1000) and population size (326.7 million). RESULTS: A total of 308 million unique individuals were represented in the database. Of these, 294 individuals had an NPC diagnosis, yielding an identified NPC prevalence of 0.95 per million people in the United States. 305 individuals were diagnosed with NPC and/or were treated with miglustat without having a diagnosis for either Gaucher or GM1 gangliosidosis, yielding an NPC diagnosed or treated prevalence of 0.99 per million people in the United States. Based on the published literature, there are an estimated 42 new NPC cases per year. Applying this number to the distribution of NPC type (based on age of neurologic symptom onset) and corresponding mortality estimates generates an estimated 943 prevalent cases of NPC, or 2.9 cases of NPC per million people in the United States. CONCLUSIONS: NPC is an ultra-rare, progressive neurodegenerative disease with approximately 1 per million people in the United States diagnosed with or treated off-label for NPC. Given that NPC is often misdiagnosed or undiagnosed, the estimated prevalence from the epidemiology calculations (2.9 per million) approximates the number of NPC cases if disease awareness, screening and diagnosis efforts were enhanced.

TCRD and Pharos 2021: mining the human proteome for disease biology.

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Peryton: a manual collection of experimentally supported microbe-disease associations.

We present Peryton (https://dianalab.e-ce.uth.gr/peryton/), a database of experimentally supported microbe-disease associations. Its first version constitutes a novel resource hosting more than 7900 entries linking 43 diseases with 1396 microorganisms. Peryton's content is exclusively sustained by manual curation of biomedical articles. Diseases and microorganisms are provided in a systematic, standardized manner using reference resources to create database dictionaries. Information about the experimental design, study cohorts and the applied high- or low-throughput techniques is meticulously annotated and catered to users. Several functionalities are provided to enhance user experience and enable ingenious use of Peryton. One or more microorganisms and/or diseases can be queried at the same time. Advanced filtering options and direct text-based filtering of results enable refinement of returned information and the conducting of tailored queries suitable to different research questions. Peryton also provides interactive visualizations to effectively capture different aspects of its content and results can be directly downloaded for local storage and downstream analyses. Peryton will serve as a valuable source, enabling scientists of microbe-related disease fields to form novel hypotheses but, equally importantly, to assist in cross-validation of findings.

Development of a Brief Screen to Detect Cognitive Impairment in Older Adults: The QuickSort.

BACKGROUND: Sorting tests detect cognitive decline in older adults who have a neurodegenerative disorder, such as Alzheimer's and Parkinson's disease. Although equally effective at detecting impairment as other cognitive screens (e.g. Mini-Mental State Examination (MMSE)), sorting tests are not commonly used in this context. This study examines the QuickSort, which is a new brief sorting test that is designed to screen older adults for cognitive impairment. DESIGN: Observational cohort study. SETTING: General community and inpatients, Australia. PARTICIPANTS: Older (≥60 years) community-dwelling adults (n = 187) and inpatients referred for neuropsychological assessment (n = 78). A normative subsample (n = 115), screened for cognitive and psychological disorders, was formed from the community sample. MEASUREMENTS: Participants were administered the QuickSort, MMSE, Frontal Assessment Battery (FAB), and Depression Anxiety and Stress Scale-21. The QuickSort requires people to sort nine stimuli by color, shape, and number, and to explain the basis for their correct sorts. Sorting (range = 0-12), Explanation (range = 0-6), and Total (range = 0-18) scores were calculated for the QuickSort. RESULTS: The Cognitively Healthy subsample completed the QuickSort within 2 minutes, 50% had errorless performance, and 95% had Total scores of 10 or greater. The likelihood of community-dwelling older adults and inpatients (n = 260) being impaired on either the MMSE or FAB, or both, increased by a factor of 3.75 for QuickSort Total scores of less than 10 and reduced by a factor of 0.23 for scores of 10 or greater. CONCLUSION: The QuickSort provides a quick, reliable, and valid alternative to lengthier cognitive screens (e.g., MMSE and FAB) when screening older adults for cognitive impairment. The QuickSort performance of an older adult can be compared with a cognitively healthy normative sample and used to estimate the likelihood they will be impaired on either the MMSE or FAB, or both. Clinicians can also use evidence-based modeling to customize the QuickSort for their setting.

Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model.

Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aß42), Aß40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aß40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.