Drug Repositioning for Alzheimer's Disease Based on Systematic 'omics' Data Mining

Neuro-immune interactions in health and disease: Insights from FENS-Hertie 2022 Winter School

In a great partnership, the Federation of European Neuroscience Societies (FENS) and the Hertie Foundation organized the FENS-Hertie 2022 Winter School on 'Neuro-immune interactions in health and disease'. The school selected 27 PhD students and 13 postdoctoral fellows from 20 countries and involved 14 faculty members experts in the field. The Winter School focused on a rising field of research, the interactions between the nervous and both innate and adaptive immune systems under pathological and physiological conditions. A fine-tuned neuro-immune crosstalk is fundamental for healthy development, while disrupted neuro-immune communication might play a role in neurodegeneration, neuroinflammation and aging. However, much is yet to be understood about the underlying mechanisms of these neuro-immune interactions in the healthy brain and under pathological scenarios. In addition to new findings in this emerging field, novel methodologies and animal models were presented to foment research on neuro-immunology. The FENS-Hertie 2022 Winter School provided an insightful knowledge exchange between students and faculty focusing on the latest discoveries in the biology of neuro-immune interactions while fostering great academic and professional opportunities for early-career neuroscientists from around the world.

Statistical integration of multi-omics and drug screening data from cell lines

Data integration methods are used to obtain a unified summary of multiple datasets. For multi-modal data, we propose a computational workflow to jointly analyze datasets from cell lines. The workflow comprises a novel probabilistic data integration method, named POPLS-DA, for multi-omics data. The workflow is motivated by a study on synucleinopathies where transcriptomics, proteomics, and drug screening data are measured in affected LUHMES cell lines and controls. The aim is to highlight potentially druggable pathways and genes involved in synucleinopathies. First, POPLS-DA is used to prioritize genes and proteins that best distinguish cases and controls. For these genes, an integrated interaction network is constructed where the drug screen data is incorporated to highlight druggable genes and pathways in the network. Finally, functional enrichment analyses are performed to identify clusters of synaptic and lysosome-related genes and proteins targeted by the protective drugs. POPLS-DA is compared to other single- and multi-omics approaches. We found that HSPA5, a member of the heat shock protein 70 family, was one of the most targeted genes by the validated drugs, in particular by AT1-blockers. HSPA5 and AT1-blockers have been previously linked to α-synuclein pathology and Parkinson's disease, showing the relevance of our findings. Our computational workflow identified new directions for therapeutic targets for synucleinopathies. POPLS-DA provided a larger interpretable gene set than other single- and multi-omic approaches. An implementation based on R and markdown is freely available online.

Structure-Activity Studies on Bis-Sulfonamide SHIP1 Activators

The SH2-containing inositol polyphosphate 5-phosphatase 1 (SHIP1) enzyme opposes the activity of PI3K and therefore is of interest in the treatment of inflammatory disorders. Recent results also indicate that SHIP1 promotes phagolysosomal degradation of lipids by microglia, suggesting that the enzyme may be a target for the treatment of Alzheimer's disease. Therefore, small molecules that increase SHIP1 activity may have benefits in these areas. Recently we discovered a bis-sulfonamide that increases the enzymatic activity of SHIP1. A series of similar SHIP1 activators have been synthesized and evaluated to determine structure-activity relationships and improve in vivo stability. Some new analogs have now been found with improved potency. In addition, both the thiophene and the thiomorpholine in the parent structure can be replaced by groups without a low valent sulfur atom, which provides a way to access activators that are less prone to oxidative degradation.

SHIP1 and its role for innate immune regulation-Novel targets for immunotherapy

Phosphoinositide-3-kinase/AKT (PI3K/AKT) signaling plays key roles in the regulation of cellular activity in both health and disease. In immune cells, this PI3K/AKT pathway is critically regulated by the phosphoinositide phosphatase SHIP1, which has been reported to modulate the function of most immune subsets. In this review, we summarize our current knowledge of SHIP1 with a focus on innate immune cells, where we reflect on the most pertinent aspects described in the current literature. We also present several small-molecule agonists and antagonists of SHIP1 developed over the last two decades, which have led to improved outcomes in several preclinical models of disease. We outline these promising findings and put them in relation to human diseases with unmet medical needs, where we discuss the most attractive targets for immune therapies based on SHIP1 modulation.

Drug repurposing for Alzheimer's disease from 2012-2022-a 10-year literature review

Background: Alzheimer's disease (AD) is a debilitating neurodegenerative condition with few treatment options available. Drug repurposing studies have sought to identify existing drugs that could be repositioned to treat AD; however, the effectiveness of drug repurposing for AD remains unclear. This review systematically analyzes the progress made in drug repurposing for AD throughout the last decade, summarizing the suggested drug candidates and analyzing changes in the repurposing strategies used over time. We also examine the different types of data that have been leveraged to validate suggested drug repurposing candidates for AD, which to our knowledge has not been previous investigated, although this information may be especially useful in appraising the potential of suggested drug repurposing candidates. We ultimately hope to gain insight into the suggested drugs representing the most promising repurposing candidates for AD. Methods: We queried the PubMed database for AD drug repurposing studies published between 2012 and 2022. 124 articles were reviewed. We used RxNorm to standardize drug names across the reviewed studies, map drugs to their constituent ingredients, and identify prescribable drugs. We used the Anatomical Therapeutic Chemical (ATC) Classification System to group drugs. Results: 573 unique drugs were proposed for repurposing in AD over the last 10 years. These suggested repurposing candidates included drugs acting on the nervous system (17%), antineoplastic and immunomodulating agents (16%), and drugs acting on the cardiovascular system (12%). Clozapine, a second-generation antipsychotic medication, was the most frequently suggested repurposing candidate (N = 6). 61% (76/124) of the reviewed studies performed a validation, yet only 4% (5/124) used real-world data for validation. Conclusion: A large number of potential drug repurposing candidates for AD has accumulated over the last decade. However, among these drugs, no single drug has emerged as the top candidate, making it difficult to establish research priorities. Validation of drug repurposing hypotheses is inconsistently performed, and real-world data has been critically underutilized for validation. Given the urgent need for new AD therapies, the utility of real-world data in accelerating identification of high-priority candidates for AD repurposing warrants further investigation.

Plasma proteomic profiling identifies CD33 as a marker of HIV control in natural infection and after therapeutic vaccination

BACKGROUND

Biomarkers predicting the outcome of HIV-1 virus control in natural infection and after therapeutic interventions in HIV-1 cure trials remain poorly defined. The BCN02 trial (NCT02616874), combined a T-cell vaccine with romidepsin (RMD), a cancer-drug that was used to promote HIV-1 latency reversal and which has also been shown to have beneficial effects on neurofunction. We conducted longitudinal plasma proteomics analyses in trial participants to define biomarkers associated with virus control during monitored antiretroviral pause (MAP) and to identify novel therapeutic targets that can improve future cure strategies.

METHODS

BCN02 was a phase I, open-label, single-arm clinical trial in early-treated, HIV infected individuals. Longitudinal plasma proteomes were analyzed in 11 BCN02 participants, including 8 participants that showed a rapid HIV-1 plasma rebound during a monitored antiretroviral pause (MAP-NC, 'non-controllers') and 3 that remained off ART with sustained plasma viremia <2000 copies/ml (MAP-C, 'controllers'). Inflammatory and neurological proteomes in plasma were evaluated and integration data analysis (viral and neurocognitive parameters) was performed. Validation studies were conducted in a cohort of untreated HIV-1+ individuals (n = 96) and in vitro viral replication assays using an anti-CD33 antibody were used for functional validation.

FINDINGS

Inflammatory plasma proteomes in BCN02 participants showed marked longitudinal alterations. Strong proteome differences were also observed between MAP-C and MAP-NC, including in baseline timepoints. CD33/Siglec-3 was the unique plasma marker with the ability to discriminate between MAPC-C and MAP-NC at all study timepoints and showed positive correlations with viral parameters. Analyses in an untreated cohort of PLWH confirmed the positive correlation between viral parameters and CD33 plasma levels, as well as PBMC gene expression. Finally, adding an anti-CD33 antibody to in vitro virus cultures significantly reduced HIV-1 replication and proviral levels in T cells and macrophages.

INTERPRETATION

This study indicates that CD33/Siglec-3 may serve as a predictor of HIV-1 control and as potential therapeutic tool to improve future cure strategies.

FUNDING

Spanish Science and Innovation Ministry (SAF2017-89726-R and PID2020-119710RB-I00), NIH (P01-AI131568), European Commission (GA101057548) and a Grifols research agreement.

Linagliptin and Empagliflozin Inhibit Microtubule Affinity Regulatory Kinase 4: Repurposing Anti-Diabetic Drugs in Neurodegenerative Disorders Using In Silico and In Vitro Approaches

Type 2 diabetes mellitus (T2DM) and Alzheimer's disease (AD) are significant public health burdens. Many studies have revealed the possibility of common pathophysiology between T2DM and AD. Thus, in recent years, studies deciphering the action mechanism of anti-diabetic drugs with their future use in AD and related pathologies are on high demand. Drug repurposing is a safe and effective approach owing to its low cost and time-saving attributes. Microtubule affinity regulating kinase 4 (MARK4) is a druggable target for various diseases and is found to be linked with AD and diabetes mellitus. MARK4 plays a vital role in energy metabolism and regulation and thus serves as an irrefutable target to treat T2DM. The present study was intended to identify the potent MARK4 inhibitors among FDA-approved anti-diabetic drugs. We performed structure-based virtual screening of FDA-approved drugs to identify the top hits against MARK4. We identified five FDA-approved drugs having an appreciable affinity and specificity toward the binding pocket of MARK4. Among these identified hits, two drugs, linagliptin, and empagliflozin, favorably bind to the MARK4 binding pocket, interacting with its critical residues and thus subjected to detailed analysis. All-atom detailed molecular dynamics (MD) simulations revealed the dynamics of binding of linagliptin and empagliflozin with MARK4. Kinase assay showed significant inhibition of MARK4 kinase activity in the presence of these drugs, implying them as potent MARK4 inhibitors. In conclusion, linagliptin and empagliflozin may be promising MARK4 inhibitors, which can further be exploited as potential lead molecules against MARK4-directed neurodegenerative diseases.

In silico analysis of a novel protein in CAR T- cell therapy for the treatment of hematologic cancer through molecular modelling, docking, and dynamics approach

Cellular therapy has emerged as a key tool in the treatment of hematological malignancies. An advanced cell therapy known as chimeric antigen receptor T cell therapy (CAR T-cell therapy) has been approved by the United States Food and Drug Administration (FDA) as KYMRIAH by Novartis and YESCARTA by Gilead/Kite pharma in the year 2017. A chimeric receptor is composed of an extracellular antigen recognition site along with some co-stimulating and signalling domains. On the whole, it turns out to be one of the most potent receptors on T cells targeting a specific type of cancer cell based on its antigenic marker. CD19 CAR T-cell therapy is the first clinically approved therapy for lymphoma with remarkable results in complete remission of B cell lymphoblastic leukemia up to 90%. The high rate of effectiveness of the CAR T-cell therapy against B-ALL justifies the investigation and application of this therapy for fatal diseases like all types of hematological malignancies. The most critical aspect of chimeric receptor therapy is designing and building an artificial receptor that is specific to a given type of cancer. For this reason, the in silico technique is an appropriate model to investigate the integrity and effectiveness of the engineered chimeric receptor prior to commencing in vitro experiments followed by clinical trials. This computerized experimental study aids in predicting the molecular mechanism of chimeric protein and how it interacts with both ligands. We have anticipated various features of the chimeric protein in terms of qualitative analysis (structure, protein modelling, physiological properties) and functional analysis (antigenicity, allergenicity, its receptor-ligand binding ability, involving signalling pathways). Furthermore, the reliability and validation of the binding mode of the chimeric protein against receptors were performed through a complex molecular dynamics simulation for a 100 ns timeframe in an aqueous environment. The obtained simulation study showed that CD30 was a better approachable marker as compared to CD20 due to its better binding energy score and also binding conformations stability.

Therapeutic drug repositioning with special emphasis on neurodegenerative diseases: Threats and issues

Drug repositioning or repurposing is the process of discovering leading-edge indications for authorized or declined/abandoned molecules for use in different diseases. This approach revitalizes the traditional drug discovery method by revealing new therapeutic applications for existing drugs. There are numerous studies available that highlight the triumph of several drugs as repurposed therapeutics. For example, sildenafil to aspirin, thalidomide to adalimumab, and so on. Millions of people worldwide are affected by neurodegenerative diseases. According to a 2021 report, the Alzheimer's disease Association estimates that 6.2 million Americans are detected with Alzheimer's disease. By 2030, approximately 1.2 million people in the United States possibly acquire Parkinson's disease. Drugs that act on a single molecular target benefit people suffering from neurodegenerative diseases. Current pharmacological approaches, on the other hand, are constrained in their capacity to unquestionably alter the course of the disease and provide patients with inadequate and momentary benefits. Drug repositioning-based approaches appear to be very pertinent, expense- and time-reducing strategies for the enhancement of medicinal opportunities for such diseases in the current era. Kinase inhibitors, for example, which were developed for various oncology indications, demonstrated significant neuroprotective effects in neurodegenerative diseases. This review expounds on the classical and recent examples of drug repositioning at various stages of drug development, with a special focus on neurodegenerative disorders and the aspects of threats and issues viz. the regulatory, scientific, and economic aspects.

Symptomatic and Disease-Modifying Therapy Pipeline for Alzheimer’s Disease: Towards a Personalized Polypharmacology Patient-Centered Approach

Since 1906, when Dr. Alois Alzheimer first described in a patient “a peculiar severe disease process of the cerebral cortex”, people suffering from this pathology have been waiting for a breakthrough therapy. Alzheimer’s disease (AD) is an irreversible, progressive neurodegenerative brain disorder and the most common form of dementia in the elderly with a long presymptomatic phase. Worldwide, approximately 50 million people are living with dementia, with AD comprising 60–70% of cases. Pathologically, AD is characterized by the deposition of amyloid β-peptide (Aβ) in the neuropil (neuritic plaques) and blood vessels (amyloid angiopathy), and by the accumulation of hyperphosphorylated tau in neurons (neurofibrillary tangles) in the brain, with associated loss of synapses and neurons, together with glial activation, and neuroinflammation, resulting in cognitive deficits and eventually dementia. The current competitive landscape in AD consists of symptomatic treatments, of which there are currently six approved medications: three AChEIs (donepezil, rivastigmine, and galantamine), one NMDA-R antagonist (memantine), one combination therapy (memantine/donepezil), and GV-971 (sodium oligomannate, a mixture of oligosaccharides derived from algae) only approved in China. Improvements to the approved therapies, such as easier routes of administration and reduced dosing frequencies, along with the developments of new strategies and combined treatments are expected to occur within the next decade and will positively impact the way the disease is managed. Recently, Aducanumab, the first disease-modifying therapy (DMT) has been approved for AD, and several DMTs are in advanced stages of clinical development or regulatory review. Small molecules, mAbs, or multimodal strategies showing promise in animal studies have not confirmed that promise in the clinic (where small to moderate changes in clinical efficacy have been observed), and therefore, there is a significant unmet need for a better understanding of the AD pathogenesis and the exploration of alternative etiologies and therapeutic effective disease-modifying therapies strategies for AD. Therefore, a critical review of the disease-modifying therapy pipeline for Alzheimer’s disease is needed.

Targeting Microglia in Alzheimer’s Disease: From Molecular Mechanisms to Potential Therapeutic Targets for Small Molecules

Alzheimer’s disease (AD) is a common, progressive, and devastating neurodegenerative disorder that mainly affects the elderly. Microglial dysregulation, amyloid-beta (Aβ) plaques, and intracellular neurofibrillary tangles play crucial roles in the pathogenesis of AD. In the brain, microglia play roles as immune cells to provide protection against virus injuries and diseases. They have significant contributions in the development of the brain, cognition, homeostasis of the brain, and plasticity. Multiple studies have confirmed that uncontrolled microglial function can result in impaired microglial mitophagy, induced Aβ accumulation and tau pathology, and a chronic neuroinflammatory environment. In the brain, most of the genes that are associated with AD risk are highly expressed by microglia. Although it was initially regarded that microglia reaction is incidental and induced by dystrophic neurites and Aβ plaques. Nonetheless, it has been reported by genome-wide association studies that most of the risk loci for AD are located in genes that are occasionally uniquely and highly expressed in microglia. This finding further suggests that microglia play significant roles in early AD stages and they be targeted for the development of novel therapeutics. In this review, we have summarized the molecular pathogenesis of AD, microglial activities in the adult brain, the role of microglia in the aging brain, and the role of microglia in AD. We have also particularly focused on the significance of targeting microglia for the treatment of AD.

Metabolic Profile and Pathological Alterations in the Muscle of Patients with Early-Stage Amyotrophic Lateral Sclerosis

Diverse biomarkers and pathological alterations have been found in muscle of patients with Amyotrophic lateral sclerosis (ALS), but the relation between such alterations and dysfunction in energetic metabolism remains to be investigated. We established the metabolome of muscle and serum of ALS patients and correlated these findings with the clinical status and pathological alterations observed in the muscle. We obtained data from 20 controls and 17 ALS patients (disease duration: 9.4 ± 6.8 months). Multivariate metabolomics analysis identified a distinct serum metabolome for ALS compared to controls (p-CV-ANOVA < 0.035) and revealed an excellent discriminant profile for muscle metabolome (p-CV-ANOVA < 0.0012). Citramalate was discriminant for both muscle and serum. High lauroylcarnitine levels in muscle were associated with low Forced Vital Capacity. Transcriptomics analysis of key antioxidant enzymes showed an upregulation of SOD3 (p = 0.0017) and GLRX2(1) (p = 0.0022) in ALS muscle. Analysis of mitochondrial enzymatic activity in muscle revealed higher complex II/CS (p = 0.04) and lower LDH (p = 0.03) activity in ALS than in controls. Our study showed, for the first time, a global dysfunction in the muscle of early-stage ALS patients. Furthermore, we identified novel metabolites to be employed as biomarkers for diagnosis and prognosis of ALS patients.

Discovery of a novel SHIP1 agonist that promotes degradation of lipid-laden phagocytic cargo by microglia

Here, we describe the use of artificial intelligence to identify novel agonists of the SH2-containing 5′ inositol phosphatase 1 (SHIP1). One of the compounds, K306, represents the most potent agonist identified to date. We find that K306 exhibits selectivity for SHIP1 vs. the paralog enzyme SHIP2, and this activation does not require the C2 domain of SHIP1 which other known SHIP1 agonists require. Thus, K306 represents a new class of SHIP1 agonists with a novel mode of agonism. Importantly, we find that K306 can suppress induction of inflammatory cytokines and iNOS in macrophages or microglia, but not by their SHIP1-deficient counterparts. K306 also reduces TNF-α production in vivo in an LPS-induced endotoxemia assay. Finally, we show that K306 enhances phagolysosomal degradation of synaptosomes and dead neurons by microglia revealing a novel function for SHIP1 that might be exploited therapeutically in dementia.

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Discovery of a potent SHIP1 selective agonist (K306) via artificial intelligence

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SHIP1 agonism via K306 is independent of the C2 domain and increases PI(3,4)P₂ levels

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K306 reduces IL-6, TNF-α, and iNOS induction in microglia and macrophages

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K306 promotes phagocytic degradation of lipid-laden but not protein cargo in microglia

Investigating a Curcumin-Loaded PLGA-PEG-PLGA Thermo-Sensitive Hydrogel for the Prevention of Alzheimer’s Disease

In Alzheimer’s disease (AD), the most common cause of dementia, patients generally forget to take pills or skip medication due to side effects, affecting the treatment efficacy. In this study, we combined a poly(lactic-co-glycolic acid), (PLGA)-poly(ethylene glycol), and (PEG)-PLGA thermo-sensitive hydrogel with curcumin (PGC) to deliver an intramuscular injection that could continuously release curcumin and maintain it at a constant level in blood to prevent AD development or progression. We evaluated the drug release profile and cytotoxicity of PGC and its effects on AD pathology through in vitro and in vivo studies and on cognitive function through an aluminum-chloride-induced AD rat model. In the in vitro study, PGC exhibited a lack of cytotoxicity, excellent anti-inflammatory and antioxidant properties, and microglial modulation. In the Morris water maze test, the PGC injection-administered AD rats presented well-focused searching behavior with the shortest swimming path and longest retention times in the quadrant where the platform was initially located. Furthermore, PGC reduced amyloid-beta aggregation and deposition and significantly increased hippocampal activity. This study demonstrated that intramuscular PGC injection can effectively prevent AD development or progression in rats without inducing toxicity; therefore, this strategy could help overcome the present challenges in AD management in humans.

Deep learning-based identification of genetic variants: application to Alzheimer's disease classification

Deep learning is a promising tool that uses nonlinear transformations to extract features from high-dimensional data. Deep learning is challenging in genome-wide association studies (GWAS) with high-dimensional genomic data. Here we propose a novel three-step approach (SWAT-CNN) for identification of genetic variants using deep learning to identify phenotype-related single nucleotide polymorphisms (SNPs) that can be applied to develop accurate disease classification models. In the first step, we divided the whole genome into nonoverlapping fragments of an optimal size and then ran convolutional neural network (CNN) on each fragment to select phenotype-associated fragments. In the second step, using a Sliding Window Association Test (SWAT), we ran CNN on the selected fragments to calculate phenotype influence scores (PIS) and identify phenotype-associated SNPs based on PIS. In the third step, we ran CNN on all identified SNPs to develop a classification model. We tested our approach using GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) including (N = 981; cognitively normal older adults (CN) = 650 and AD = 331). Our approach identified the well-known APOE region as the most significant genetic locus for AD. Our classification model achieved an area under the curve (AUC) of 0.82, which was compatible with traditional machine learning approaches, random forest and XGBoost. SWAT-CNN, a novel deep learning-based genome-wide approach, identified AD-associated SNPs and a classification model for AD and may hold promise for a range of biomedical applications.

Overcoming Drug Resistance in Advanced Prostate Cancer by Drug Repurposing

Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical’s mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

Learning multi-scale heterogenous network topologies and various pairwise attributes for drug-disease association prediction

MOTIVATION

Identifying new therapeutic effects for the approved drugs is beneficial for effectively reducing the drug development cost and time. Most of the recent computational methods concentrate on exploiting multiple kinds of information about drugs and disease to predict the candidate associations between drugs and diseases. However, the drug and disease nodes have neighboring topologies with multiple scales, and the previous methods did not fully exploit and deeply integrate these topologies.

RESULTS

We present a prediction method, multi-scale topology learning for drug-disease (MTRD), to integrate and learn multi-scale neighboring topologies and the attributes of a pair of drug and disease nodes. First, for multiple kinds of drug similarities, multiple drug-disease heterogenous networks are constructed respectively to integrate the similarities and associations related to drugs and diseases. Moreover, each heterogenous network has its specific topology structure, which is helpful for learning the corresponding specific topology representation. We formulate the topology embeddings for each drug node and disease node by random walking on each heterogeneous network, and the embeddings cover the neighboring topologies with different scopes. Because the multi-scale topology embeddings have context relationships, we construct Bi-directional long short-term memory-based module to encode these embeddings and their relationships and learn the neighboring topology representation. We also design the attention mechanisms at feature level and at scale level to obtain the more informative pairwise features and topology embeddings. A module based on multi-layer convolutional networks is constructed to learn the representative attributes of the drug-disease node pair according to their related similarity and association information. Comprehensive experimental results indicate that MTRD achieves the superior performance than several state-of-the-art methods for predicting drug-disease associations. MTRD also retrieves more actual drug-disease associations in the top-ranked candidates of the prediction result. Case studies on five drugs further demonstrate MTRD's ability in discovering the potential candidate diseases for the interested drugs.

DOTA: Deep Learning Optimal Transport Approach to Advance Drug Repositioning for Alzheimer's Disease

Alzheimer's disease (AD) is the leading cause of age-related dementia, affecting over 5 million people in the United States and incurring a substantial global healthcare cost. Unfortunately, current treatments are only palliative and do not cure AD. There is an urgent need to develop novel anti-AD therapies; however, drug discovery is a time-consuming, expensive, and high-risk process. Drug repositioning, on the other hand, is an attractive approach to identify drugs for AD treatment. Thus, we developed a novel deep learning method called DOTA (Drug repositioning approach using Optimal Transport for Alzheimer's disease) to repurpose effective FDA-approved drugs for AD. Specifically, DOTA consists of two major autoencoders: (1) a multi-modal autoencoder to integrate heterogeneous drug information and (2) a Wasserstein variational autoencoder to identify effective AD drugs. Using our approach, we predict that antipsychotic drugs with circadian effects, such as quetiapine, aripiprazole, risperidone, suvorexant, brexpiprazole, olanzapine, and trazadone, will have efficacious effects in AD patients. These drugs target important brain receptors involved in memory, learning, and cognition, including serotonin 5-HT2A, dopamine D2, and orexin receptors. In summary, DOTA repositions promising drugs that target important biological pathways and are predicted to improve patient cognition, circadian rhythms, and AD pathogenesis.

Review of Current Strategies for Delivering Alzheimer's Disease Drugs Across the Blood-Brain Barrier

(Appeared originally in the International Journal of Molecular Sciences 2019; 20:381) Reprinted under Creative Commons CC-BY license.

ABCA7, a Genetic Risk Factor Associated with Alzheimer's Disease Risk in African Americans

African American/Black adults are twice as likely to have Alzheimer's disease (AD) compared to non-Hispanic White adults. Genetics partially contributes to this disparity in AD risk, among other factors, as there are several genetic variants associated with AD that are more prevalent in individuals of African or European ancestry. The phospholipid-transporting ATPase ABCA7 (ABCA7) gene has stronger associations with AD risk in individuals with African ancestry than in individuals with European ancestry. In fact, ABCA7 has been shown to have a stronger effect size than the apolipoprotein E (APOE) ɛ4 allele in African American/Black adults. ABCA7 is a transmembrane protein involved in lipid homeostasis and phagocytosis. ABCA7 dysfunction is associated with increased amyloid-beta production, reduced amyloid-beta clearance, impaired microglial response to inflammation, and endoplasmic reticulum stress. This review explores the impact of ABCA7 mutations that increase AD risk in African American/Black adults on ABCA7 structure and function and their contributions to AD pathogenesis. The combination of biochemical/biophysical and 'omics-based studies of these variants needed to elucidate their downstream impact and molecular contributions to AD pathogenesis is highlighted.

Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

Microglia in Alzheimer's Disease: A Target for Therapeutic Intervention

Alzheimer’s disease (AD) is one of the most common types of age-related dementia worldwide. In addition to extracellular amyloid plaques and intracellular neurofibrillary tangles, dysregulated microglia also play deleterious roles in the AD pathogenesis. Numerous studies have demonstrated that unbridled microglial activity induces a chronic neuroinflammatory environment, promotes β-amyloid accumulation and tau pathology, and impairs microglia-associated mitophagy. Thus, targeting microglia may pave the way for new therapeutic interventions. This review provides a thorough overview of the pathophysiological role of the microglia in AD and illustrates the potential avenues for microglia-targeted therapies, including microglial modification, immunoreceptors, and anti-inflammatory drugs.

A Maximum Flow-Based Approach to Prioritize Drugs for Drug Repurposing of Chronic Diseases

The discovery of new drugs is required in the time of global aging and increasing populations. Traditional drug development strategies are expensive, time-consuming, and have high risks. Thus, drug repurposing, which treats new/other diseases using existing drugs, has become a very admired tactic. It can also be referred to as the re-investigation of the existing drugs that failed to indicate the usefulness for the new diseases. Previously published literature used maximum flow approaches to identify new drug targets for drug-resistant infectious diseases but not for drug repurposing. Therefore, we are proposing a maximum flow-based protein-protein interactions (PPIs) network analysis approach to identify new drug targets (proteins) from the targets of the FDA (Food and Drug Administration) drugs and their associated drugs for chronic diseases (such as breast cancer, inflammatory bowel disease (IBD), and chronic obstructive pulmonary disease (COPD)) treatment. Experimental results showed that we have successfully turned the drug repurposing into a maximum flow problem. Our top candidates of drug repurposing, Guanidine, Dasatinib, and Phenethyl Isothiocyanate for breast cancer, IBD, and COPD were experimentally validated by other independent research as the potential candidate drugs for these diseases, respectively. This shows the usefulness of the proposed maximum flow approach for drug repurposing.

Characterization of Metabolites in a Zebrafish Model of Alzheimer's Disease Supplemented with Mussel-Derived Plasmalogens by Ultraperformance Liquid Chromatography Q-Exactive Orbitrap Mass Spectrometry-Based Unbiased Metabolomics

Plasmalogens (Pls) are bioactive substances enriched in the brain with a regulatory effect on Alzheimer's disease (AD), while their metabolomic influence accompanying AD and the underlying mechanisms remain unclear. Here, we extracted and purified Pls (purity of ≥90%) from mussels and applied unbiased metabolomics using ultraperformance liquid chromatography Q-Exactive Orbitrap mass spectrometry to analyze the variation of metabolites in the major metabolic pathways of AD and revealed the cognitive improvement effect of Pls using an experimental AD zebrafish model. The results showed that 37 differential endogenous metabolites were identified, among which glycerophosphocholine, choline, S-adenosylmethionine (SAM), l-glutamine, linoleic acid, 9(S)-HPODE, methionine, and creatine were the major abnormally regulated metabolites, and the first four metabolites were viewed as potential endogenous markers. This study suggested that systemic metabolic profiling could reveal the potential metabolic networks of AD and illuminated the protective effect of Pls on AD through biochemistry mechanisms and metabolic pathways.

Ten Years of EWAS

Epigenome-wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next-generation, and third-generation sequencing technologies have prepared a high-quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

Phytotherapeutics Against Alzheimer's Disease: Mechanism, Molecular Targets and Challenges for Drug Development

Alzheimer's disease is inflating worldwide and is combatted by only a few approved drugs. At best, these drugs treat symptomatic conditions by targeting cholinesterase and N-methyl-D-aspartate receptors. Most of the clinical trials in progress are focused to develop disease-modifying agents that aim single targets. The 'one drug-one target' approach is failing in the case of Alzheimer's disease due to its labyrinth etiopathogenesis. Traditional medicinal systems like ayurveda uses a holistic approach encompassing legion of medicinal plants exhibiting multimodal activity. Recent advances in high-throughput technologies have catapulted the research in the arena of ayurveda, specifically in identifying plants with potent anti-Alzheimer's disease properties and their phytochemical characterization. Nonetheless, clinical trials of very few herbal medicines are in progress. This review is a compendium of Indian plants and ayurvedic medicines against Alzheimer's disease and their paraphernalia. A record of 230 plants that are found in India with anti-Alzheimer's disease potential and about 500 phytochemicals from medicinal plants has been solicited with the hope of exploring the unexplored. Further, the molecular targets of phytochemicals isolated from commonly used medicinal plants such as Acorus calamus, Bacopa monnieri, Convolvulus pluricaulis, Tinospora cordifolia and Withania somnifera have been reviewed with respect to their multidimensional property such as antioxidant, anti-inflammation, anti-aggregation, synaptic plasticity modulation, cognition and memory enhancing activity. In addition, the strengths, and challenges in ayurvedic medicine that limit its use as mainstream therapy is discussed and a framework for the development of herbal medicine has been proposed.

Repurposing FDA-Approved Compounds for the Discovery of Glutaminyl Cyclase Inhibitors as Drugs Against Alzheimer's Disease

Alzheimer's disease (AD) is one of the most common neurodegenerative causes of dementia, the pathology of which is still not much clear. It's challenging to discover the disease modifying agents for the prevention and treatment of AD over the years. Emerging evidence has been accumulated to reveal the crucial role of up-regulated glutaminyl cyclase (QC) in the initiation of AD. In the current study, the QC inhibitory potency of a library consisting of 1621 FDA-approved compounds was assessed. A total of 54 hits, 3.33 % of the pool, exhibited QC inhibitory activities. The Ki of the top 5 compounds with the highest QC inhibitory activities were measured. Among these selected hits, compounds affecting neuronal signaling pathways and other mechanisms were recognized. Moreover, several polyphenol derivatives with QC inhibitory activities were also identified. Frameworks and subsets contained in these hits were analyzed. Taken together, our results may contribute to the discovery and development of novel QC inhibitors as potential anti-AD agents.

Artificial Intelligence for Alzheimer's Disease: Promise or Challenge?

Decades of experimental and clinical research have contributed to unraveling many mechanisms in the pathogenesis of Alzheimer's disease (AD), but the puzzle is still incomplete. Although we can suppose that there is no complete set of puzzle pieces, the recent growth of open data-sharing initiatives collecting lifestyle, clinical, and biological data from AD patients has provided a potentially unlimited amount of information about the disease, far exceeding the human ability to make sense of it. Moreover, integrating Big Data from multi-omics studies provides the potential to explore the pathophysiological mechanisms of the entire biological continuum of AD. In this context, Artificial Intelligence (AI) offers a wide variety of methods to analyze large and complex data in order to improve knowledge in the AD field. In this review, we focus on recent findings and future challenges for AI in AD research. In particular, we discuss the use of Computer-Aided Diagnosis tools for AD diagnosis and the use of AI to potentially support clinical practices for the prediction of individual risk of AD conversion as well as patient stratification in order to finally develop effective and personalized therapies.

Computational Drug Repurposing for Alzheimer's Disease Using Risk Genes From GWAS and Single-Cell RNA Sequencing Studies

Background: Traditional therapeutics targeting Alzheimer's disease (AD)-related subpathologies have so far proved ineffective. Drug repurposing, a more effective strategy that aims to find new indications for existing drugs against other diseases, offers benefits in AD drug development. In this study, we aim to identify potential anti-AD agents through enrichment analysis of drug-induced transcriptional profiles of pathways based on AD-associated risk genes identified from genome-wide association analyses (GWAS) and single-cell transcriptomic studies. Methods: We systematically constructed four gene lists (972 risk genes) from GWAS and single-cell transcriptomic studies and performed functional and genes overlap analyses in Enrichr tool. We then used a comprehensive drug repurposing tool Gene2Drug by combining drug-induced transcriptional responses with the associated pathways to compute candidate drugs from each gene list. Prioritized potential candidates (eight drugs) were further assessed with literature review. Results: The genomic-based gene lists contain late-onset AD associated genes (BIN1, ABCA7, APOE, CLU, and PICALM) and clinical AD drug targets (TREM2, CD33, CHRNA2, PRSS8, ACE, TKT, APP, and GABRA1). Our analysis identified eight AD candidate drugs (ellipticine, alsterpaullone, tomelukast, ginkgolide A, chrysin, ouabain, sulindac sulfide and lorglumide), four of which (alsterpaullone, ginkgolide A, chrysin and ouabain) have shown repurposing potential for AD validated by their preclinical evidence and moderate toxicity profiles from literature. These support the value of pathway-based prioritization based on the disease risk genes from GWAS and scRNA-seq data analysis. Conclusion: Our analysis strategy identified some potential drug candidates for AD. Although the drugs still need further experimental validation, the approach may be applied to repurpose drugs for other neurological disorders using their genomic information identified from large-scale genomic studies.

Drug Repurposing Using Biological Networks

Drug repositioning is a strategy to identify new uses for existing, approved, or research drugs that are outside the scope of its original medical indication. Drug repurposing is based on the fact that one drug can act on multiple targets or that two diseases can have molecular similarities, among others. Currently, thanks to the rapid advancement of high-performance technologies, a massive amount of biological and biomedical data is being generated. This allows the use of computational methods and models based on biological networks to develop new possibilities for drug repurposing. Therefore, here, we provide an in-depth review of the main applications of drug repositioning that have been carried out using biological network models. The goal of this review is to show the usefulness of these computational methods to predict associations and to find candidate drugs for repositioning in new indications of certain diseases.

Therapeutic Targeting of Repurposed Anticancer Drugs in Alzheimer's Disease: Using the Multiomics Approach

AIM/HYPOTHESIS

The complexity and heterogeneity of multiple pathological features make Alzheimer's disease (AD) a major culprit to global health. Drug repurposing is an inexpensive and reliable approach to redirect the existing drugs for new indications. The current study aims to study the possibility of repurposing approved anticancer drugs for AD treatment. We proposed an in silico pipeline based on "omics" data mining that combines genomics, transcriptomics, and metabolomics studies. We aimed to validate the neuroprotective properties of repurposed drugs and to identify the possible mechanism of action of the proposed drugs in AD.

RESULTS

We generated a list of AD-related genes and then searched DrugBank database and Therapeutic Target Database to find anticancer drugs related to potential AD targets. Specifically, we researched the available approved anticancer drugs and excluded the information of investigational and experimental drugs. We developed a computational pipeline to prioritize the anticancer drugs having a close association with AD targets. From data mining, we generated a list of 2914 AD-related genes and obtained 49 potential druggable targets by functional enrichment analysis. The protein-protein interaction (PPI) studies for these genes revealed 641 interactions. We found that 15 AD risk/direct PPI genes were associated with 30 approved oncology drugs. The computational validation of candidate drug-target interactions, structural and functional analysis, investigation of related molecular mechanisms, and literature-based analysis resulted in four repurposing candidates, of which three drugs were epidermal growth factor receptor (EGFR) inhibitors.

CONCLUSION

Our computational drug repurposing approach proposed EGFR inhibitors as potential repurposing drugs for AD. Consequently, our proposed framework could be used for drug repurposing for different indications in an economical and efficient way.

Surface Functionalization of PLGA Nanoparticles to Increase Transport across the BBB for Alzheimer’s Disease

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that accounts for about 60% of all diagnosed cases of dementia worldwide. Although there are currently several drugs marketed for its treatment, none are capable of slowing down or stopping the progression of AD. The role of the blood-brain barrier (BBB) plays a key role in the design of a successful treatment for this neurodegenerative disease. Nanosized particles have been proposed as suitable drug delivery systems to overcome BBB with the purpose of increasing bioavailability of drugs in the brain. Biodegradable poly (lactic-co-glycolic acid) nanoparticles (PLGA-NPs) have been particularly regarded as promising drug delivery systems as they can be surface-tailored with functionalized molecules for site-specific targeting. In this review, a thorough discussion about the most recent functionalization strategies based on PLGA-NPs for AD and their mechanisms of action is provided, together with a description of AD pathogenesis and the role of the BBB in brain targeting.

Sialometabolism in Brain Health and Alzheimer's Disease

Sialic acids refer to a unique family of acidic sugars with a 9-carbon backbone that are mostly found as terminal residues in glycan structures of glycoconjugates including both glycoproteins and glycolipids. The highest levels of sialic acids are expressed in the brain where they regulate neuronal sprouting and plasticity, axon myelination and myelin stability, as well as remodeling of mature neuronal connections. Moreover, sialic acids are the sole ligands for microglial Siglecs (sialic acid-binding immunoglobulin-type lectins), and sialic acid-Siglec interactions have been indicated to play a critical role in the regulation of microglial homeostasis in a healthy brain. The recent discovery of CD33, a microglial Siglec, as a novel genetic risk factor for late-onset Alzheimer's disease (AD), highlights the potential role of sialic acids in the development of microglial dysfunction and neuroinflammation in AD. Apart from microglia, sialic acids have been found to be involved in several other major changes associated with AD. Elevated levels of serum sialic acids have been reported in AD patients. Alterations in ganglioside (major sialic acid carrier) metabolism have been demonstrated as an aggravating factor in the formation of amyloid pathology in AD. Polysialic acids are linear homopolymers of sialic acids and have been implicated to be an important regulator of neurogenesis that contributes to neuronal repair and recovery from neurodegeneration such as in AD. In summary, this article reviews current understanding of neural functions of sialic acids and alterations of sialometabolism in aging and AD brains. Furthermore, we discuss the possibility of looking at sialic acids as a promising novel therapeutic target for AD intervention.

Data Mining in Healthcare: Applying Strategic Intelligence Techniques to Depict 25 Years of Research Development

In order to identify the strategic topics and the thematic evolution structure of data mining applied to healthcare, in this paper, a bibliometric performance and network analysis (BPNA) was conducted. For this purpose, 6138 articles were sourced from the Web of Science covering the period from 1995 to July 2020 and the SciMAT software was used. Our results present a strategic diagram composed of 19 themes, of which the 8 motor themes ('NEURAL-NETWORKS', 'CANCER', 'ELETRONIC-HEALTH-RECORDS', 'DIABETES-MELLITUS', 'ALZHEIMER'S-DISEASE', 'BREAST-CANCER', 'DEPRESSION', and 'RANDOM-FOREST') are depicted in a thematic network. An in-depth analysis was carried out in order to find hidden patterns and to provide a general perspective of the field. The thematic network structure is arranged thusly that its subjects are organized into two different areas, (i) practices and techniques related to data mining in healthcare, and (ii) health concepts and disease supported by data mining, embodying, respectively, the hotspots related to the data mining and medical scopes, hence demonstrating the field's evolution over time. Such results make it possible to form the basis for future research and facilitate decision-making by researchers and practitioners, institutions, and governments interested in data mining in healthcare.

Machine learning identifies candidates for drug repurposing in Alzheimer's disease

Clinical trials of novel therapeutics for Alzheimer's Disease (AD) have consumed a large amount of time and resources with largely negative results. Repurposing drugs already approved by the Food and Drug Administration (FDA) for another indication is a more rapid and less expensive option. We present DRIAD (Drug Repurposing In AD), a machine learning framework that quantifies potential associations between the pathology of AD severity (the Braak stage) and molecular mechanisms as encoded in lists of gene names. DRIAD is applied to lists of genes arising from perturbations in differentiated human neural cell cultures by 80 FDA-approved and clinically tested drugs, producing a ranked list of possible repurposing candidates. Top-scoring drugs are inspected for common trends among their targets. We propose that the DRIAD method can be used to nominate drugs that, after additional validation and identification of relevant pharmacodynamic biomarker(s), could be readily evaluated in a clinical trial.

The complexity of Alzheimer's disease: an evolving puzzle

The history of Alzheimer's disease (AD) started in 1907, but we needed to wait until the end of the century to identify the components of pathological hallmarks and genetic subtypes and to formulate the first pathogenic hypothesis. Thanks to biomarkers and new technologies, the concept of AD then rapidly changed from a static view of an amnestic dementia of the presenium to a biological entity that could be clinically manifested as normal cognition or dementia of different types. What is clearly emerging from studies is that AD is heterogeneous in each aspect, such as amyloid composition, tau distribution, relation between amyloid and tau, clinical symptoms, and genetic background, and thus it is probably impossible to explain AD with a single pathological process. The scientific approach to AD suffers from chronological mismatches between clinical, pathological, and technological data, causing difficulty in conceiving diagnostic gold standards and in creating models for drug discovery and screening. A recent mathematical computer-based approach offers the opportunity to study AD in real life and to provide a new point of view and the final missing pieces of the AD puzzle.

Is There Justification to Treat Neurodegenerative Disorders by Repurposing Drugs? The Case of Alzheimer's Disease, Lithium, and Autophagy

Lithium is the prototype mood-stabilizer used for acute and long-term treatment of bipolar disorder. Cumulated translational research of lithium indicated the drug's neuroprotective characteristics and, thereby, has raised the option of repurposing it as a drug for neurodegenerative diseases. Lithium's neuroprotective properties rely on its modulation of homeostatic mechanisms such as inflammation, mitochondrial function, oxidative stress, autophagy, and apoptosis. This myriad of intracellular responses are, possibly, consequences of the drug's inhibition of the enzymes inositol-monophosphatase (IMPase) and glycogen-synthase-kinase (GSK)-3. Here we review lithium's neurobiological properties as evidenced by its neurotrophic and neuroprotective properties, as well as translational studies in cells in culture, in animal models of Alzheimer's disease (AD) and in patients, discussing the rationale for the drug's use in the treatment of AD.

Broad-spectrum therapeutics: A new antimicrobial class

There are currently no emergency treatments for pandemics, yet drug repositioning has emerged as the foremost treatment development strategy for COVID-19, with an aim to identify successful antiviral therapeutics from safe, non-antiviral candidates. These therapeutics include antibiotics such as azithromycin and the antiparasitic nitazoxanide, both of which exhibit antiviral activity. Broad-spectrum therapeutics (BSTs) are a class of antimicrobials active against multiple pathogen types. Establishment of a developmental framework for BSTs will markedly improve global preparedness for future health emergencies.

Structure-Based Scaffold Repurposing toward the Discovery of Novel Cholinesterase Inhibitors

Cholinesterases (ChE) are well-known drug targets for the treatment of Alzheimer's disease (AD). In continuation of work to develop novel cholinesterase inhibitors, we utilized a structure-based scaffold repurposing approach and discovered six novel ChE inhibitors from our recently developed DNA gyrase inhibitor library. Among the identified hits, two compounds (denoted 3 and 18) were found to be the most potent inhibitor of acetylcholinesterase (AChE, IC₅₀ = 6.10 ± 1.01 μM) and butyrylcholinesterase (BuChE, IC₅₀ = 5.50 ± 0.007 μM), respectively. Compound 3 was responsible for the formation of H-bond and π-π stacking interactions within the active site of AChE. In contrast, compound 18 was well fitted in the choline-binding pocket and catalytic site of BuChE. Results obtained from in vitro cytotoxicity assays and in silico derived physicochemical and absorption, distribution, metabolism, and excretion (ADME) properties indicate that repurposed scaffold 3 and 18 could be potential drug candidates for further development as novel ChE inhibitors.

Brain Drug Delivery: Overcoming the Blood-brain Barrier to Treat Tauopathies

Tauopathies are neurodegenerative disorders characterized by the deposition of abnormal tau protein in the brain. The application of potentially effective therapeutics for their successful treatment is hampered by the presence of a naturally occurring brain protection layer called the blood-brain barrier (BBB). BBB represents one of the biggest challenges in the development of therapeutics for central nervous system (CNS) disorders, where sufficient BBB penetration is inevitable. BBB is a heavily restricting barrier regulating the movement of molecules, ions, and cells between the blood and the CNS to secure proper neuronal function and protect the CNS from dangerous substances and processes. Yet, these natural functions possessed by BBB represent a great hurdle for brain drug delivery. This review is concentrated on summarizing the available methods and approaches for effective therapeutics' delivery through the BBB to treat neurodegenerative disorders with a focus on tauopathies. It describes the traditional approaches but also new nanotechnology strategies emerging with advanced medical techniques. Their limitations and benefits are discussed.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

Repurposed agents in the Alzheimer's disease drug development pipeline

BACKGROUND

Treatments are needed to address the growing prevalence of Alzheimer's disease (AD). Clinical trials have failed to produce any AD drugs for Food and Drug Administration (FDA) approval since 2003, and the pharmaceutical development process is both time-consuming and costly. Drug repurposing provides an opportunity to accelerate this process by investigating the AD-related effects of agents approved for other indications. These drugs have known safety profiles, pharmacokinetic characterization, formulations, doses, and manufacturing processes.

METHODS

We assessed repurposed AD therapies represented in Phase I, Phase II, and Phase III of the current AD pipeline as registered on ClinicalTrials.gov as of February 27, 2020.

RESULTS

We identified 53 clinical trials involving 58 FDA-approved agents. Seventy-eight percent of the agents in trials had putative disease-modifying mechanisms of action. Of the repurposed drugs in the pipeline 20% are hematologic-oncologic agents, 18% are drugs derived from cardiovascular indications, 14% are agents with psychiatric uses, 12% are drug used to treat diabetes, 10% are neurologic agents, and the remaining 26% of drugs fall under other conditions. Intellectual property strategies utilized in these programs included using the same drug but altering doses, routes of administration, or formulations. Most repurposing trials were supported by Academic Medical Centers and were not funded through the biopharmaceutical industry. We compared our results to a European trial registry and found results similar to those derived from ClinicalTrials.gov.

CONCLUSIONS

Drug repurposing is a common approach to AD drug development and represents 39% of trials in the current AD pipeline. Therapies from many disease areas provide agents potentially useful in AD. Most of the repurposed agents are generic and a variety of intellectual property strategies have been adopted to enhance their economic value.

Structural advances of Siglecs: insight into synthetic glycan ligands for immunomodulation

Sialic acid-binding immunoglobulin-like lectins (Siglecs) are transmembrane proteins of the immunoglobulin (Ig) superfamily predominantly expressed on the cells of our immune system. Siglecs recognize sialic acid via their terminal V-set domain. In mammals, sialic acid-terminated glycolipids and glycoproteins are the ligands of Siglecs, and the monomeric affinity of Siglecs for their sialic acid-containing ligands is weak. Significant efforts have been devoted toward the development of chemically modified sialoside ligands to target Siglecs with higher affinity and selectivity. In this review we discuss natural and synthetic sialoside ligands for each human Siglec, emphasizing the ligand binding determinants uncovered from recent advances in protein structural information. Potential therapeutic applications of these ligands are also discussed.

Computational Drug Repositioning: Current Progress and Challenges

Novel drug discovery is time-consuming, costly, and a high-investment process due to the high attrition rate. Therefore, many trials are conducted to reuse existing drugs to treat pressing conditions and diseases, since their safety profiles and pharmacokinetics are already available. Drug repositioning is a strategy to identify a new indication of existing or already approved drugs, beyond the scope of their original use. Various computational and experimental approaches to incorporate available resources have been suggested for gaining a better understanding of disease mechanisms and the identification of repurposed drug candidates for personalized pharmacotherapy. In this review, we introduce publicly available databases for drug repositioning and summarize the approaches taken for drug repositioning. We also highlight and compare their characteristics and challenges, which should be addressed for the future realization of drug repositioning.

Fingolimod Affects Transcription of Genes Encoding Enzymes of Ceramide Metabolism in Animal Model of Alzheimer's Disease

The imbalance in sphingolipid signaling may be critically linked to the upstream events in the neurodegenerative cascade of Alzheimer’s disease (AD). We analyzed the influence of mutant (V717I) amyloid β precursor protein (AβPP) transgene on sphingolipid metabolism enzymes in mouse hippocampus. At 3 months of age AβPP/Aβ presence upregulated enzymes of ceramide turnover on the salvage pathway: ceramide synthases (CERS2, CERS4, CERS6) and also ceramidase ACER3. At 6 months, only CERS6 was elevated, and no ceramide synthase was increased at 12 months. However, sphingomyelin synthases, which utilize ceramide on the sphingomyelinase pathway, were reduced (SGMS1 at 12 and SGMS2 at 6 months). mRNAs for sphingomyelin synthases SGMS1 and SGMS2 were also significantly downregulated in human AD hippocampus and neocortex when compared with age-matched controls. Our findings suggest early-phase deregulation of sphingolipid homeostasis in favor of ceramide signaling. Fingolimod (FTY720), a modulator of sphingosine-1-phosphate receptors countered the AβPP-dependent upregulation of hippocampal ceramide synthase CERS2 at 3 months. Moreover, at 12 months, FTY720 increased enzymes of ceramide-sphingosine turnover: CERS4, ASAH1, and ACER3. We also observed influence of fingolimod on the expression of the sphingomyelinase pathway enzymes. FTY720 counteracted the AβPP-linked reduction of sphingomyelin synthases SGMS1/2 (at 12 and 6 months, respectively) and led to elevation of sphingomyelinase SMPD2 (at 6 and 12 months). Therefore, our results demonstrate potentially beneficial, age-specific effects of fingolimod on transcription of sphingolipid metabolism enzymes in an animal model of AD.

Key role of MIF-related neuroinflammation in neurodegeneration and cognitive impairment in Alzheimer's disease

Background

Macrophage Migration Inhibitory Factor (MIF) is a potent proinflammatory cytokine that promotes the production of other immune mediators. MIF is produced by most cell types in the brain including microglia, astrocytes and neurons. Enhanced expression of MIF might contribute to the persistent activation of glial, chronic neuroinflammation and neurodegeneration. Here, we investigated the effect of MIF on inflammatory markers and spatial learning in a mouse model of sporadic AD and on tau pathology in AD patients.

Methods

We examined the effects of MIF deficiency and pharmacological MIF inhibition in vitro and in vivo. In vitro, quantitative PCR and ELISA were used to assess cytokine production of STZ-treated glial cells. In vivo, C57BL/6 mice were subjected to intracerebroventricular streptozotocin injection (3 mg/kg, ICV-STZ). Neuroinflammation and contextual learning performance were assessed using quantitative PCR and fear conditioning, respectively. Pharmacological MIF inhibition was achieved with intraperitoneal injections of ISO-1 (daily, IP, 20 mg/kg in 5% DMSO in 0.9% NaCl) for 4 weeks following ICV-STZ injection. The findings from ISO-1 treated mice were confirmed in MIF knockout C57BL/6. To assess the role of MIF in human AD, cerebrospinal fluid levels of MIF and hyperphosphorylated tau were measured using ELISA.

Results

Administration ICV-STZ resulted in hippocampal dependent cognitive impairment. MIF inhibition with ISO-1 significantly improved the STZ-induced impairment in contextual memory performance, indicating MIF-related inflammation as a major contributor to ICV-STZ-induced memory deficits. Furthermore, inhibition of the MIF resulted in reduced cytokine production in vitro and in vivo.

In human subjects with AD at early clinical stages, cerebrospinal fluid levels of MIF were increased in comparison with age-matched controls, and correlated with biomarkers of tau hyper-phosphorylation and neuronal injury hinting at MIF levels as a potential biomarker for early-stage AD.

Conclusions

The present study indicates the key role of MIF in controlling the chronic cytokine release in neuroinflammation related to tau hyperphosphorylation, neurodegeneration, and clinical manifestations of AD, suggesting the potential of MIF inhibition as therapeutic strategy to slow down neurodegeneration and clinical disease progression.

Drug repositioning: a brief overview

OBJECTIVES

Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery.

KEY FINDINGS

Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value. The first examples of successful drug repositioning mainly came about through serendipity like acetylsalicylic acid, thalidomide, sildenafil or dimethylfumarate.

CONCLUSION

The history of great-repositioned drugs has given some solutions to various pathologies. Serendipity is not yet useful to find repositioning drugs. Drug repositioning is of growing interest. Nowadays, a more rational approach to the identification of drug candidates for repositioning is possible, especially using data mining.

A Proteotranscriptomic-Based Computational Drug-Repositioning Method for Alzheimer's Disease

Numerous clinical trials of drug candidates for Alzheimer’s disease (AD) have failed, and computational drug repositioning approaches using omics data have been proposed as effective alternative approaches to the discovery of drug candidates. However, little multi-omics data is available for AD, due to limited availability of brain tissues. Even if omics data exist, systematic drug repurposing study for AD has suffered from lack of big data, insufficient clinical information, and difficulty in data integration on account of sample heterogeneity derived from poor diagnosis or shortage of qualified post-mortem tissue. In this study, we developed a proteotranscriptomic-based computational drug repositioning method named Drug Repositioning Perturbation Score/Class (DRPS/C) based on inverse associations between disease- and drug-induced gene and protein perturbation patterns, incorporating pharmacogenomic knowledge. We constructed a Drug-induced Gene Perturbation Signature Database (DGPSD) comprised of 61,019 gene signatures perturbed by 1,520 drugs from the Connectivity Map (CMap) and the L1000 CMap. Drugs were classified into three DRPCs (High, Intermediate, and Low) according to DRPSs that were calculated using drug- and disease-induced gene perturbation signatures from DGPSD and The Cancer Genome Atlas (TCGA), respectively. The DRPS/C method was evaluated using the area under the ROC curve, with a prescribed drug list from TCGA as the gold standard. Glioblastoma had the highest AUC. To predict anti-AD drugs, DRPS were calculated using DGPSD and AD-induced gene/protein perturbation signatures generated from RNA-seq, microarray and proteomic datasets in the Synapse database, and the drugs were classified into DRPCs. We predicted 31 potential anti-AD drug candidates commonly belonged to high DRPCs of transcriptomic and proteomic signatures. Of these, four drugs classified into the nervous system group of Anatomical Therapeutic Chemical (ATC) system are voltage-gated sodium channel blockers (bupivacaine, topiramate) and monamine oxidase inhibitors (selegiline, iproniazid), and their mechanism of action was inferred from a potential anti-AD drug perspective. Our approach suggests a shortcut to discover new efficacy of drugs for AD.

Pan-SHIP1/2 inhibitors promote microglia effector functions essential for CNS homeostasis

We show here that both SHIP1 (Inpp5d) and its paralog SHIP2 (Inppl1) are expressed at protein level in microglia. To examine whether targeting of SHIP paralogs might influence microglial physiology and function, we tested the capacity of SHIP1-selective, SHIP2-selective and pan-SHIP1/2 inhibitors for their ability to impact on microglia proliferation, lysosomal compartment size and phagocytic function. We find that highly potent pan-SHIP1/2 inhibitors can significantly increase lysosomal compartment size, and phagocytosis of dead neurons and amyloid beta (Aβ)1-42 by microglia in vitro We show that one of the more-potent and water-soluble pan-SHIP1/2 inhibitors, K161, can penetrate the blood-brain barrier. Consistent with this, K161 increases the capacity of CNS-resident microglia to phagocytose Aβ and apoptotic neurons following systemic administration. These findings provide the first demonstration that small molecule modulation of microglia function in vivo is feasible, and suggest that dual inhibition of the SHIP1 and 2 paralogs can provide a novel means to enhance basal microglial homeostatic functions for therapeutic purposes in Alzheimer's disease and, possibly, other types of dementia where increased microglial function could be beneficial.