Insights into Computational Drug Repurposing for Neurodegenerative Disease

Identification of Blood Biomarkers Related to Energy Metabolism and Construction of Diagnostic Prediction Model Based on Three Independent Alzheimer's Disease Cohorts

Background

Blood biomarkers are crucial for the diagnosis and therapy of Alzheimer's disease (AD). Energy metabolism disturbances are closely related to AD. However, research on blood biomarkers related to energy metabolism is still insufficient.

Objective

This study aims to explore the diagnostic and therapeutic significance of energy metabolism-related genes in AD.

Methods

AD cohorts were obtained from GEO database and single center. Machine learning algorithms were used to identify key genes. GSEA was used for functional analysis. Six algorithms were utilized to establish and evaluate diagnostic models. Key gene-related drugs were screened through network pharmacology.

Results

We identified 4 energy metabolism genes, NDUFA1, MECOM, RPL26, and RPS27. These genes have been confirmed to be closely related to multiple energy metabolic pathways and different types of T cell immune infiltration. Additionally, the transcription factors INSM2 and 4 lncRNAs were involved in regulating 4 genes. Further analysis showed that all biomarkers were downregulated in the AD cohorts and not affected by aging and gender. More importantly, we constructed a diagnostic prediction model of 4 biomarkers, which has been validated by various algorithms for its diagnostic performance. Furthermore, we found that valproic acid mainly interacted with these biomarkers through hydrogen bonding, salt bonding, and hydrophobic interaction.

Conclusions

We constructed a predictive model based on 4 energy metabolism genes, which may be helpful for the diagnosis of AD. The 4 validated genes could serve as promising blood biomarkers for AD. Their interaction with valproic acid may play a crucial role in the therapy of AD.

Brain organoid as a model to study the role of mitochondria in neurodevelopmental disorders: achievements and weaknesses

Mitochondrial diseases are a group of severe pathologies that cause complex neurodegenerative disorders for which, in most cases, no therapy or treatment is available. These organelles are critical regulators of both neurogenesis and homeostasis of the neurological system. Consequently, mitochondrial damage or dysfunction can occur as a cause or consequence of neurodevelopmental or neurodegenerative diseases. As genetic knowledge of neurodevelopmental disorders advances, associations have been identified between genes that encode mitochondrial proteins and neurological symptoms, such as neuropathy, encephalomyopathy, ataxia, seizures, and developmental delays, among others. Understanding how mitochondrial dysfunction can alter these processes is essential in researching rare diseases. Three-dimensional (3D) cell cultures, which self-assemble to form specialized structures composed of different cell types, represent an accessible manner to model organogenesis and neurodevelopmental disorders. In particular, brain organoids are revolutionizing the study of mitochondrial-based neurological diseases since they are organ-specific and model-generated from a patient's cell, thereby overcoming some of the limitations of traditional animal and cell models. In this review, we have collected which neurological structures and functions recapitulate in the different types of reported brain organoids, focusing on those generated as models of mitochondrial diseases. In addition to advancements in the generation of brain organoids, techniques, and approaches for studying neuronal structures and physiology, drug screening and drug repositioning studies performed in brain organoids with mitochondrial damage and neurodevelopmental disorders have also been reviewed. This scope review will summarize the evidence on limitations in studying the function and dynamics of mitochondria in brain organoids.

Tackling neurodegeneration in vitro with omics: a path towards new targets and drugs

Drug discovery is a generally inefficient and capital-intensive process. For neurodegenerative diseases (NDDs), the development of novel therapeutics is particularly urgent considering the long list of late-stage drug candidate failures. Although our knowledge on the pathogenic mechanisms driving neurodegeneration is growing, additional efforts are required to achieve a better and ultimately complete understanding of the pathophysiological underpinnings of NDDs. Beyond the etiology of NDDs being heterogeneous and multifactorial, this process is further complicated by the fact that current experimental models only partially recapitulate the major phenotypes observed in humans. In such a scenario, multi-omic approaches have the potential to accelerate the identification of new or repurposed drugs against a multitude of the underlying mechanisms driving NDDs. One major advantage for the implementation of multi-omic approaches in the drug discovery process is that these overarching tools are able to disentangle disease states and model perturbations through the comprehensive characterization of distinct molecular layers (i.e., genome, transcriptome, proteome) up to a single-cell resolution. Because of recent advances increasing their affordability and scalability, the use of omics technologies to drive drug discovery is nascent, but rapidly expanding in the neuroscience field. Combined with increasingly advanced in vitro models, which particularly benefited from the introduction of human iPSCs, multi-omics are shaping a new paradigm in drug discovery for NDDs, from disease characterization to therapeutics prediction and experimental screening. In this review, we discuss examples, main advantages and open challenges in the use of multi-omic approaches for the in vitro discovery of targets and therapies against NDDs.

Indole-Based Compounds in the Development of Anti-Neurodegenerative Agents

Neurodegeneration is a gradual decay process leading to the depletion of neurons in both the central and peripheral nervous systems, ultimately resulting in cognitive dysfunctions and the deterioration of brain functions, alongside a decline in motor skills and behavioral capabilities. Neurodegenerative disorders (NDs) impose a substantial socio-economic strain on society, aggravated by the advancing age of the world population and the absence of effective remedies, predicting a negative future. In this context, the urgency of discovering viable therapies is critical and, despite significant efforts by medicinal chemists in developing potential drug candidates and exploring various small molecules as therapeutics, regrettably, a truly effective treatment is yet to be found. Nitrogen heterocyclic compounds, and particularly those containing the indole nucleus, which has emerged as privileged scaffold, have attracted particular attention for a variety of pharmacological applications. This review analyzes the rational design strategy adopted by different research groups for the development of anti-neurodegenerative indole-based compounds which have the potential to modulate various molecular targets involved in NDs, with reference to the most recent advances between 2018 and 2023.

Synergizing drug repurposing and target identification for neurodegenerative diseases

Despite dedicated research efforts, the absence of disease-curing remedies for neurodegenerative diseases (NDDs) continues to jeopardize human society and stands as a challenge. Drug repurposing is an attempt to find new functionality of existing drugs and take it as an opportunity to discourse the clinically unmet need to treat neurodegeneration. However, despite applying this approach to rediscover a drug, it can also be used to identify the target on which a drug could work. The primary objective of target identification is to unravel all the possibilities of detecting a new drug or repurposing an existing drug. Lately, scientists and researchers have been focusing on specific genes, a particular site in DNA, a protein, or a molecule that might be involved in the pathogenesis of the disease. However, the new era discusses directing the signaling mechanism involved in the disease progression, where receptors, ion channels, enzymes, and other carrier molecules play a huge role. This review aims to highlight how target identification can expedite the whole process of drug repurposing. Here, we first spot various target-identification methods and drug-repositioning studies, including drug-target and structure-based identification studies. Moreover, we emphasize various drug repurposing approaches in NDDs, namely, experimental-based, mechanism-based, and in silico approaches. Later, we draw attention to validation techniques and stress on drugs that are currently undergoing clinical trials in NDDs. Lastly, we underscore the future perspective of synergizing drug repurposing and target identification in NDDs and present an unresolved question to address the issue.

Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease

The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.

Identification of Potentially Repurposable Drugs for Lewy Body Dementia Using a Network-Based Approach

The conventional method of one drug being used for one target has not yielded therapeutic solutions for Lewy body dementia (LBD), which is a leading progressive neurological disorder characterized by significant loss of neurons. The age-related disease is marked by memory loss, hallucinations, sleep disorder, mental health deterioration, palsy, and cognitive impairment, all of which have no known effective cure. The present study deploys a network medicine pipeline to repurpose drugs having considerable effect on the genes and proteins related to the diseases of interest. We utilized the novel SAveRUNNER algorithm to quantify the proximity of all drugs obtained from DrugBank with the disease associated gene dataset obtained from Phenopedia and targets in the human interactome. We found that most of the 154 FDA-approved drugs predicted by SAveRUNNER were used to treat nervous system disorders, but some off-label drugs like quinapril and selegiline were interestingly used to treat hypertension and Parkinson's disease (PD), respectively. Additionally, we performed gene set enrichment analysis using Connectivity Map (CMap) and pathway enrichment analysis using EnrichR to validate the efficacy of the drug candidates obtained from the pipeline approach. The investigation enabled us to identify the significant role of the synaptic vesicle pathway in our disease and accordingly finalize 8 suitable antidepressant drugs from the 154 drugs initially predicted by SAveRUNNER. These potential anti-LBD drugs are either selective or non-selective inhibitors of serotonin, dopamine, and norepinephrine transporters. The validated selective serotonin and norepinephrine inhibitors like milnacipran, protriptyline, and venlafaxine are predicted to manage LBD along with the affecting symptomatic issues.

The contemporary nexus of medicines security and bioprospecting: a future perspective for prioritizing the patient

Reacting to the challenges presented by the evolving nexus of environmental change, defossilization, and diversified natural product bioprospecting is vitally important for advancing global healthcare and placing patient benefit as the most important consideration. This overview emphasizes the importance of natural and synthetic medicines security and proposes areas for global research action to enhance the quality, safety, and effectiveness of sustainable natural medicines. Following a discussion of some contemporary factors influencing natural products, a rethinking of the paradigms in natural products research is presented in the interwoven contexts of the Fourth and Fifth Industrial Revolutions and based on the optimization of the valuable assets of Earth. Following COP28, bioprospecting is necessary to seek new classes of bioactive metabolites and enzymes for chemoenzymatic synthesis. Focus is placed on those performance and practice modifications which, in a sustainable manner, establish the patient, and the maintenance of their prophylactic and treatment needs, as the priority. Forty initiatives for natural products in healthcare are offered for the patient and the practitioner promoting global action to address issues of sustainability, environmental change, defossilization, quality control, product consistency, and neglected diseases to assure that quality natural medicinal agents will be accessible for future generations.

IUPHAR review - Data-driven computational drug repurposing approaches for opioid use disorder

Opioid Use Disorder (OUD) is a chronic and relapsing condition characterized by the misuse of opioid drugs, causing significant morbidity and mortality in the United States. Existing medications for OUD are limited, and there is an immediate need to discover treatments with enhanced safety and efficacy. Drug repurposing aims to find new indications for existing medications, offering a time-saving and cost-efficient alternative strategy to traditional drug discovery. Computational approaches have been developed to further facilitate the drug repurposing process. In this paper, we reviewed state-of-the-art data-driven computational drug repurposing approaches for OUD and discussed their advantages and potential challenges. We also highlighted promising repurposed candidate drugs for OUD that were identified by computational drug repurposing techniques and reviewed studies supporting their potential mechanisms of action in treating OUD.

Pyroptosis in neurodegenerative diseases: from bench to bedside

The central nervous system regulates all aspects of physiology to some extent. Neurodegenerative diseases (NDDs) lead to the progressive loss and dysfunction of neurons, which are particularly evident in Alzheimer's disease, Parkinson's disease, and many other conditions. NDDs are multifactorial diseases with complex pathogeneses, and there has been a rapid increase in the prevalence of NDDs. However, none of these diseases can be cured, making the development of novel treatment strategies an urgent necessity. Numerous studies have indicated how pyroptosis induces inflammation and affects many aspects of NDD. Therefore, components related to pyroptosis are potential therapeutic candidates and are attracting increasing attention. Here, we review the role of pyroptosis in the pathogenesis of NDDs and potential treatment options. Additionally, several of the current drugs and relevant inhibitors are discussed. Through this article, we provide theoretical support for exploring new therapeutic targets and updating clinical treatment strategies for NDDs. Notably, pyroptosis, a recently widely studied mode of cell death, is still under-researched compared to other traditional forms of cell death. Moreover, the focus of research has been on the onset and progression of NDDs, and the lack of organ-specific target discovery and drug development is a common problem for many basic studies. This urgent problem requires scientists and companies worldwide to collaborate in order to develop more effective drugs against NDDs.

Expediting the drug discovery for ideal leads against SARS-CoV-2 via molecular docking of repurposed drugs

SARS-CoV-2, the novel coronavirus spreading worldwide urges the need to repurpose drugs that can quickly enter clinical trials to combat the on-going global pandemic. A cluster of proteins are encoded for by the viral genome, each assuming a critical role in pathogen endurance inside the host. To handle the adverse circumstances, robust virtual strategies such as repurposing are coming to the fore due to being economical, efficient and rapid. Five FDA approved repurposed drugs proposed to act as inhibitors by targeting SARS-CoV-2 were used for initial evaluation via molecular docking. Moreover, a comparative analysis of the selected SARS-CoV-2 proteins against five ligands (Clemizole hydrochloride, Exemestane, Nafamostat, Pregnenolone and Umifenovir) was designed. In this regard, non-structural proteins (nsp3, nsp5, nsp10, nsp12 and nsp15), structural proteins (Spike, Nucleocapsid protein) and accessory proteins (ORF 3a, ORF 7a and ORF 9 b) were selected. Here, we aim to expedite the search for a potential drug from the five FDA approved repurposing drugs already in use for treatment of multiple diseases. Based on docking analysis, Umifenovir and Pregnenolone are suggested to show potential inhibitory effects against most of the SARS-CoV-2 proteins. These drugs are noteworthy since they exhibit high binding towards target proteins and should be used as lead compounds towards in vitro and in vivo studies.Communicated by Ramaswamy H. Sarma.

Drug repurposing using real-world data

The use of real-world data in drug repurposing has emerged due to well-established advantages of drug repurposing in supplementing de novo drug discovery and incentives in incorporating real-world evidence in regulatory approvals. We conducted a scoping review to characterize repurposing studies using real-world data and discuss their potential challenges and solutions. A total of 250 studies met the inclusion criteria, of which 36 were original studies on hypothesis generation, 101 on hypothesis validation, and seven on safety assessment. Key challenges that should be addressed for future progress in using real-world data for repurposing include isolated data sources with poor clinical granularity, false-positive signals from data mining, the sensitivity of hypothesis validation to bias and confounding, and the lack of clear regulatory guidance.

The evolving role of extracellular vesicles (exosomes) as biomarkers in traumatic brain injury: Clinical perspectives and therapeutic implications

Developing effective disease-modifying therapies for neurodegenerative diseases (NDs) requires reliable diagnostic, disease activity, and progression indicators. While desirable, identifying biomarkers for NDs can be difficult because of the complex cytoarchitecture of the brain and the distinct cell subsets seen in different parts of the central nervous system (CNS). Extracellular vesicles (EVs) are heterogeneous, cell-derived, membrane-bound vesicles involved in the intercellular communication and transport of cell-specific cargos, such as proteins, Ribonucleic acid (RNA), and lipids. The types of EVs include exosomes, microvesicles, and apoptotic bodies based on their size and origin of biogenesis. A growing body of evidence suggests that intercellular communication mediated through EVs is responsible for disseminating important proteins implicated in the progression of traumatic brain injury (TBI) and other NDs. Some studies showed that TBI is a risk factor for different NDs. In terms of therapeutic potential, EVs outperform the alternative synthetic drug delivery methods because they can transverse the blood–brain barrier (BBB) without inducing immunogenicity, impacting neuroinflammation, immunological responses, and prolonged bio-distribution. Furthermore, EV production varies across different cell types and represents intracellular processes. Moreover, proteomic markers, which can represent a variety of pathological processes, such as cellular damage or neuroinflammation, have been frequently studied in neurotrauma research. However, proteomic blood-based biomarkers have short half-lives as they are easily susceptible to degradation. EV-based biomarkers for TBI may represent the complex genetic and neurometabolic abnormalities that occur post-TBI. These biomarkers are not caught by proteomics, less susceptible to degradation and hence more reflective of these modifications (cellular damage and neuroinflammation). In the current narrative and comprehensive review, we sought to discuss the contemporary knowledge and better understanding the EV-based research in TBI, and thus its applications in modern medicine. These applications include the utilization of circulating EVs as biomarkers for diagnosis, developments of EV-based therapies, and managing their associated challenges and opportunities.

The Time Sequence of Gene Expression Changes after Spinal Cord Injury

Gene expression changes following spinal cord injury (SCI) are time-dependent, and an accurate understanding of these changes can be crucial in determining time-based treatment options in a clinical setting. We performed RNA sequencing of the contused spinal cord of rats at five different time points from the very acute to chronic stages (1 hour, 1 day, 1 week, 1 month, and 3 months) following SCI. We identified differentially expressed genes (DEGs) and Gene Ontology (GO) terms at each time point, and 14,257 genes were commonly expressed at all time points. The biological process of the inflammatory response was increased at 1 hour and 1 day, and the cellular component of the integral component of the synaptic membrane was increased at 1 day. DEGs associated with cell activation and the innate immune response were highly enriched at 1 week and 1 month, respectively. A total of 2841 DEGs were differentially expressed at any of the five time points, and 18 genes (17 upregulated and 1 downregulated) showed common expression differences at all time points. We found that interleukin signaling, neutrophil degranulation, eukaryotic translation, collagen degradation, LGI–ADAM interactions, GABA receptor, and L1CAM-ankyrin interactions were prominent after SCI depending on the time post injury. We also performed gene–drug network analysis and found several potential antagonists and agonists which can be used to treat SCI. We expect to discover effective treatments in the clinical field through further studies revealing the efficacy and safety of potential drugs.

Deficiency of autism-related Scn2a gene in mice disrupts sleep patterns and circadian rhythms

Autism spectrum disorder (ASD) affects ~2% of the population in the US, and monogenic forms of ASD often result in the most severe manifestation of the disorder. Recently, SCN2A has emerged as a leading gene associated with ASD, of which abnormal sleep pattern is a common comorbidity. SCN2A encodes the voltage-gated sodium channel NaV1.2. Predominantly expressed in the brain, NaV1.2 mediates the action potential firing of neurons. Clinical studies found that a large portion of children with SCN2A deficiency have sleep disorders, which severely impact the quality of life of affected individuals and their caregivers. The underlying mechanism of sleep disturbances related to NaV1.2 deficiency, however, is not known. Using a gene-trap Scn2a-deficient mouse model (Scn2atrap), we found that Scn2a deficiency results in increased wakefulness and reduced non-rapid-eye-movement (NREM) sleep. Brain region-specific Scn2a deficiency in the suprachiasmatic nucleus (SCN) containing region, which is involved in circadian rhythms, partially recapitulates the sleep disturbance phenotypes. At the cellular level, we found that Scn2a deficiency disrupted the firing pattern of spontaneously firing neurons in the SCN region. At the molecular level, RNA-sequencing analysis revealed differentially expressed genes in the circadian entrainment pathway including core clock genes Per1 and Per2. Performing a transcriptome-based compound discovery, we identified dexanabinol (HU-211), a putative glutamate receptor modulator, that can partially reverse the sleep disturbance in mice. Overall, our study reveals possible molecular and cellular mechanisms underlying Scn2a deficiency-related sleep disturbances, which may inform the development of potential pharmacogenetic interventions for the affected individuals.

Computational drug repurposing based on electronic health records: a scoping review

Computational drug repurposing methods adapt Artificial intelligence (AI) algorithms for the discovery of new applications of approved or investigational drugs. Among the heterogeneous datasets, electronic health records (EHRs) datasets provide rich longitudinal and pathophysiological data that facilitate the generation and validation of drug repurposing. Here, we present an appraisal of recently published research on computational drug repurposing utilizing the EHR. Thirty-three research articles, retrieved from Embase, Medline, Scopus, and Web of Science between January 2000 and January 2022, were included in the final review. Four themes, (1) publication venue, (2) data types and sources, (3) method for data processing and prediction, and (4) targeted disease, validation, and released tools were presented. The review summarized the contribution of EHR used in drug repurposing as well as revealed that the utilization is hindered by the validation, accessibility, and understanding of EHRs. These findings can support researchers in the utilization of medical data resources and the development of computational methods for drug repurposing.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

Novel use of FDA-approved drugs identified by cluster analysis of behavioral profiles

Repurposing FDA-approved drugs is an efficient and cost-effective approach in the development of therapeutics for a broad range of diseases. However, prediction of function can be challenging, especially in the brain. We screened a small-molecule library with FDA-approved drugs for effects on behavior. The studies were carried out using zebrafish larvae, imaged in a 384-well format. We found that various drugs affect activity, habituation, startle responses, excitability, and optomotor responses. The changes in behavior were organized in behavioral profiles, which were examined by hierarchical cluster analysis. One of the identified clusters includes the calcineurin inhibitors cyclosporine (CsA) and tacrolimus (FK506), which are immunosuppressants and potential therapeutics in the prevention of Alzheimer's disease. The calcineurin inhibitors form a functional cluster with seemingly unrelated drugs, including bromocriptine, tetrabenazine, rosiglitazone, nebivolol, sorafenib, cabozantinib, tamoxifen, meclizine, and salmeterol. We propose that drugs with 'CsA-type' behavioral profiles are promising candidates for the prevention and treatment of Alzheimer's disease.

A Study on Methodologies of Drug Repositioning Using Biomedical Big Data: A Focus on Diabetes Mellitus

Drug repositioning is a strategy for identifying new applications of an existing drug that has been previously proven to be safe. Based on several examples of drug repositioning, we aimed to determine the methodologies and relevant steps associated with drug repositioning that should be pursued in the future. Reports on drug repositioning, retrieved from PubMed from January 2011 to December 2020, were classified based on an analysis of the methodology and reviewed by experts. Among various drug repositioning methods, the network-based approach was the most common (38.0%, 186/490 cases), followed by machine learning/deep learningbased (34.3%, 168/490 cases), text mining-based (7.1%, 35/490 cases), semantic-based (5.3%, 26/490 cases), and others (15.3%, 75/490 cases). Although drug repositioning offers several advantages, its implementation is curtailed by the need for prior, conclusive clinical proof. This approach requires the construction of various databases, and a deep understanding of the process underlying repositioning is quintessential. An in-depth understanding of drug repositioning could reduce the time, cost, and risks inherent to early drug development, providing reliable scientific evidence. Furthermore, regarding patient safety, drug repurposing might allow the discovery of new relationships between drugs and diseases.

An integrated chemo-informatics and in vitro experimental approach repurposes acarbose as a post-ischemic neuro-protectant

The increasing prevalence of ischemic stroke combined with limited therapeutic options highlights the compelling need for continued research into the development of future neuro-therapeutics. Death-Associated Protein Kinase 1 (DAPK1) and p53 protein-protein interaction serve as a signaling point for the convergence of apoptosis and necrosis in cerebral ischemia. In this study, we used an integrated chemo-informatics and in vitro experimental drug repurposing strategy to screen potential small-molecule inhibitors of DAPK1-p53 interaction from the United States of America Food and Drug Administration (FDA) approved drug database exhibiting post-ischemic neuroprotective and neuro-regenerative efficacy and mechanisms. The computational docking and molecular dynamics simulation of FDA-approved drugs followed by an in vitro experimental validation identified acarbose, an anti-diabetic medication and caloric restriction mimetic as a potential inhibitor of DAPK1-p53 interaction. The evaluation of post-ischemic neuroprotective and regenerative efficacy and mechanisms of action for acarbose was carried out using a set of experimental methods, including cell viability, proliferation and differentiation assays, fluorescence staining, and gene expression analysis. Post-ischemic administration of acarbose conferred significant neuroprotection against ischemia-reperfusion injury in vitro. The reduced fluorescence emission in cells stained with _pS²⁰ supported the potential of acarbose in inhibiting the DAPK1-p53 interaction. Acarbose prevented mitochondrial and lysosomal dysfunction, and favorably modulated gene expression related to cell survival, inflammation, and regeneration. BrdU staining and neurite outgrowth assay showed a significant increase in cell proliferation and differentiation in acarbose-treated group. This is the first study known to provide mechanistic insight into the post-ischemic neuroprotective and neuro-regenerative potential of acarbose. Our results provide a strong basis for preclinical studies to evaluate the safety and neuroprotective efficacy of acarbose against ischemic stroke.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-022-03130-5.

Multi-Targets: An Unconventional Drug Development Strategy for Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that eventually leads to dementia and death of the patient. Despite the enormous amounts of resources and efforts for AD drug development during the last three decades, no effective treatments have been developed that can slow or halt the progression of the disease. Currently available drugs for treating AD can only improve clinical symptoms temporarily with moderate efficacies. In recent years, the scientific community has realized these challenges and reconsidered the future directions of AD drug development. The most significant recent changes in AD drug development strategy include shifting from amyloid-based targets to other targets, such as tau, and efforts toward better designs for clinical trials. However, most AD drug development is still focused on a single mechanism or target, which is the conventional strategy for drug development. Although multifactorial mechanisms and, on this basis, multi-target strategies have been proposed in recent years, this approach has not been widely recognized and accepted by the mainstream of AD drug development. Here, we emphasize the multifactorial mechanisms of AD and discuss the urgent need for a paradigm shift in AD drug development from a single target to multiple targets, either with the multi-target–directed ligands approach or the combination therapy approach. We hope this article will increase the recognition of the multifactorial nature of AD and promote this paradigm shift. We believe that such a shift will facilitate successful development of effective AD therapies.

Artificial intelligence for COVID-19: battling the pandemic with computational intelligence

The new coronavirus disease 2019 (COVID-19) has become a global pandemic leading to over 180 million confirmed cases and nearly 4 million deaths until June 2021, according to the World Health Organization. Since the initial report in December 2019 , COVID-19 has demonstrated a high transmission rate (with an R₀ > 2), a diverse set of clinical characteristics (e.g., high rate of hospital and intensive care unit admission rates, multi-organ dysfunction for critically ill patients due to hyperinflammation, thrombosis, etc.), and a tremendous burden on health care systems around the world. To understand the serious and complex diseases and develop effective control, treatment, and prevention strategies, researchers from different disciplines have been making significant efforts from different aspects including epidemiology and public health, biology and genomic medicine, as well as clinical care and patient management. In recent years, artificial intelligence (AI) has been introduced into the healthcare field to aid clinical decision-making for disease diagnosis and treatment such as detecting cancer based on medical images, and has achieved superior performance in multiple data-rich application scenarios. In the COVID-19 pandemic, AI techniques have also been used as a powerful tool to overcome the complex diseases. In this context, the goal of this study is to review existing studies on applications of AI techniques in combating the COVID-19 pandemic. Specifically, these efforts can be grouped into the fields of epidemiology, therapeutics, clinical research, social and behavioral studies and are summarized. Potential challenges, directions, and open questions are discussed accordingly, which may provide new insights into addressing the COVID-19 pandemic and would be helpful for researchers to explore more related topics in the post-pandemic era.

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.

Perspective insights of repurposing the pleiotropic efficacy of statins in neurodegenerative disorders: An expository appraisal

Neurodegenerative disorders which affects a larger population pose a great clinical challenge. These disorders impact the quality of life of an individual by damaging the neurons, which are the unit cells of the brain. Clinicians are faced with the grave challenge of inhibiting the progression of these diseases as available treatment options fail to meet the clinical demand. Thus, treating the disease/disorder symptomatically is the Hobson's choice. The goal of the researchers is to introduce newer therapies in this segment and introducing a new molecule will take long years of development. Hence, drug repurposing/repositioning can be a better substitute in comparison to time consuming and expensive drug discovery and development cycle. Presently, a paradigm shift towards the re-purposing of drugs can be witnessed. Statins which have been previously approved as anti-hyperlipidemic agents are in the limelight of research for re-purposed drugs. Owing to their anti-inflammatory and antioxidant nature, statins act as neuroprotective in several brain disorders. Further they attenuate the amyloid plaques and protein aggregation which are the triggering factors in the Alzheimer's and Parkinson's respectively. In case of Huntington disease and Multiple sclerosis they help in improving the psychomotor symptoms and stimulate remyelination thus acting as neuroprotective. This article reviews the potential of statins in treating neurodegenerative disorders along with a brief discussion on the safety concerns associated with use of statins and human clinical trial data linked with re-tasking statins for neurodegenerative disorders along with the regulatory perspectives involved with the drug repositioning.

Advances in the computational landscape for repurposed drugs against COVID-19

The COVID-19 pandemic has caused millions of deaths and massive societal distress worldwide. Therapeutic solutions are urgently needed, but de novo drug development remains a lengthy process. One promising alternative is computational drug repurposing, which enables the prioritization of existing compounds through fast in silico analyses. Recent efforts based on molecular docking, machine learning, and network analysis have produced actionable predictions. Some predicted drugs, targeting viral proteins and pathological host pathways are undergoing clinical trials. Here, we review this work, highlight drugs with high predicted efficacy and classify their mechanisms of action. We discuss the strengths and limitations of the published methodologies and outline possible future directions. Finally, we curate a list of COVID-19 data portals and other repositories that could be used to accelerate future research.

Sex-Specific Cross Tissue Meta-Analysis Identifies Immune Dysregulation in Women With Alzheimer's Disease

Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia in the United States. In spite of evidence of females having a greater lifetime risk of developing Alzheimer's Disease (AD) and greater apolipoprotein E4-related (APOE ε4) AD risk compared to males, molecular signatures underlying these differences remain elusive. Methods: We took a meta-analysis approach to study gene expression in the brains of 1,084 AD patients and age-matched controls and whole blood from 645 AD patients and age-matched controls in seven independent datasets. Sex-specific gene expression patterns were investigated through use of gene-based, pathway-based and network-based approaches. The ability of a sex-specific AD gene expression signature to distinguish Alzheimer's disease from healthy controls was assessed using a linear support vector machine model. Cell type deconvolution from whole blood gene expression data was performed to identify differentially regulated cells in males and females with AD. Results: Strikingly gene-expression, network-based analysis and cell type deconvolution approaches revealed a consistent immune signature in the brain and blood of female AD patients that was absent in males. In females, network-based analysis revealed a coordinated program of gene expression involving several zinc finger nuclease genes related to Herpes simplex viral infection whose expression was modulated by the presence of the APOE ε4 allele. Interestingly, this gene expression program was missing in the brains of male AD patients. Cell type deconvolution identified an increase in neutrophils and naïve B cells and a decrease in M2 macrophages, memory B cells, and CD8+ T cells in AD samples compared to controls in females. Interestingly, among males with AD, no significant differences in immune cell proportions compared to controls were observed. Machine learning-based classification of AD using gene expression from whole blood in addition to clinical features produced an improvement in classification accuracy upon stratifying by sex, achieving an AUROC of 0.91 for females and 0.80 for males. Conclusion: These results help identify sex and APOE ε4 genotype-specific transcriptomic signatures of AD and underscore the importance of considering sex in the development of biomarkers and therapeutic strategies for AD.

Recent trends in artificial intelligence-driven identification and development of anti-neurodegenerative therapeutic agents

Neurological disorders affect various aspects of life. Finding drugs for the central nervous system is a very challenging and complex task due to the involvement of the blood-brain barrier, P-glycoprotein, and the drug's high attrition rates. The availability of big data present in online databases and resources has enabled the emergence of artificial intelligence techniques including machine learning to analyze, process the data, and predict the unknown data with high efficiency. The use of these modern techniques has revolutionized the whole drug development paradigm, with an unprecedented acceleration in the central nervous system drug discovery programs. Also, the new deep learning architectures proposed in many recent works have given a better understanding of how artificial intelligence can tackle big complex problems that arose due to central nervous system disorders. Therefore, the present review provides comprehensive and up-to-date information on machine learning/artificial intelligence-triggered effort in the brain care domain. In addition, a brief overview is presented on machine learning algorithms and their uses in structure-based drug design, ligand-based drug design, ADMET prediction, de novo drug design, and drug repurposing. Lastly, we conclude by discussing the major challenges and limitations posed and how they can be tackled in the future by using these modern machine learning/artificial intelligence approaches.

Drug Repurposing: A Strategy for Discovering Inhibitors against Emerging Viral Infections

BACKGROUND

Viral diseases are responsible for several deaths around the world. Over the past few years, the world has seen several outbreaks caused by viral diseases that, for a long time, seemed to possess no risk. These are diseases that have been forgotten for a long time and, until nowadays, there are no approved drugs or vaccines, leading the pharmaceutical industry and several research groups to run out of time in the search for new pharmacological treatments or prevention methods. In this context, drug repurposing proves to be a fast and economically viable technique, considering the fact that it uses drugs that have a well-established safety profile. Thus, in this review, we present the main advances in drug repurposing and their benefit for searching new treatments against emerging viral diseases.

METHODS

We conducted a search in the bibliographic databases (Science Direct, Bentham Science, PubMed, Springer, ACS Publisher, Wiley, and NIH's COVID-19 Portfolio) using the keywords "drug repurposing", "emerging viral infections" and each of the diseases reported here (CoV; ZIKV; DENV; CHIKV; EBOV and MARV) as an inclusion/exclusion criterion. A subjective analysis was performed regarding the quality of the works for inclusion in this manuscript. Thus, the selected works were those that presented drugs repositioned against the emerging viral diseases presented here by means of computational, high-throughput screening or phenotype-based strategies, with no time limit and of relevant scientific value.

RESULTS

291 papers were selected, 24 of which were CHIKV; 52 for ZIKV; 43 for DENV; 35 for EBOV; 10 for MARV; and 56 for CoV and the rest (72 papers) related to the drugs repurposing and emerging viral diseases. Among CoV-related articles, most were published in 2020 (31 papers), updating the current topic. Besides, between the years 2003 - 2005, 10 articles were created, and from 2011 - 2015, there were 7 articles, portraying the outbreaks that occurred at that time. For ZIKV, similar to CoV, most publications were during the period of outbreaks between the years 2016 - 2017 (23 articles). Similarly, most CHIKV (13 papers) and DENV (14 papers) publications occur at the same time interval. For EBOV (13 papers) and MARV (4 papers), they were between the years 2015 - 2016. Through this review, several drugs were highlighted that can be evolved in vivo and clinical trials as possible used against these pathogens showed that remdesivir represent potential treatments against CoV. Furthermore, ribavirin may also be a potential treatment against CHIKV; sofosbuvir against ZIKV; celgosivir against DENV, and favipiravir against EBOV and MARV, representing new hopes against these pathogens.

CONCLUSION

The conclusions of this review manuscript show the potential of the drug repurposing strategy in the discovery of new pharmaceutical products, as from this approach, drugs could be used against emerging viral diseases. Thus, this strategy deserves more attention among research groups and is a promising approach to the discovery of new drugs against emerging viral diseases and also other diseases.

Reimagining Innovation Amid the COVID-19 Pandemic: Insights From the WISH Innovation Programme

The World Innovation Summit for Health (WISH) hosts two innovation competitions as part of its biennial healthcare conference. During the COVID-19 pandemic, WISH received more than 350 applications for both competitions, of which 31 were shortlisted to showcase at the WISH 2020 virtual summit. Of the 31 showcasing innovations, 11 (35.5%) had suggested an alternative use to their innovation as a contribution to the global fight against COVID-19. As such, this article explores the apparent and urgent need for the repurposing of healthcare innovations to reduce the costs and time associated with the conventional approach, in order to best respond to the demands of the global pandemic.

Tackling Alzheimer's Disease with Existing Drugs: A Promising Strategy for Bypassing Obstacles

The unmet need for the development of effective drugs to treat Alzheimer 's disease has been steadily growing, representing a major challenge in drug discovery. In this context, drug repurposing, namely the identification of novel therapeutic indications for approved or investigational compounds, can be seen as an attractive attempt to obtain new medications reducing both the time and the economic burden usually required for research and development programs. In the last years, several classes of drugs have evidenced promising beneficial effects in neurodegenerative diseases, and for some of them, preliminary clinical trials have been started. This review aims to illustrate some of the most recent examples of drugs reprofiled for Alzheimer's disease, considering not only the finding of new uses for existing drugs but also the new hypotheses on disease pathogenesis that could promote previously unconsidered therapeutic regimens. Moreover, some examples of structural modifications performed on existing drugs in order to obtain multifunctional compounds will also be described.

Diagnostic and Therapeutic Potential of Exosomal MicroRNAs for Neurodegenerative Diseases

Neurodegenerative disorders (NDs) are characterized by a gradual loss of neurons and functions that eventually leads to progressive neurological impairment. In view of the heavy burden on the healthcare system, efficient and reliable biomarkers for early diagnosis and therapeutic treatments to reverse the progression of NDs are in urgent need. There has been an increasing interest in using exosomal miRNAs as biomarkers or targeted therapies for neurological diseases recently. In this review, we overviewed the updated studies on exosomal miRNAs as biomarkers and potential therapeutic approaches in NDs, as well as their association with the pathophysiology of this group of disorders, especially Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD). The exosomal miRNAs that are commonly dysregulated across different NDs or are commonly used as therapeutic candidates were also identified and summarized. In summary, the feasibility of exosomal miRNAs as biomarkers and potential targeted therapy for NDs has been verified. However, due to the limitations of existing studies and the discrepancies across different studies, high quality laboratory and clinical investigations are still required.

[The Use of Artificial Intelligence in Alzheimer's Disease - Personalized Diagnostics and Therapy]

Using the example of dementia in Alzheimer's disease, it is shown which opportunities but also risks are posed by newer methodological approaches of artificial intelligence (AI) for the diagnosis and treatment of Alzheimer's dementia (AD). In addition, AI is examined in the context of an ethical-philosophical critique of technology.

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.

Framework for identifying drug repurposing candidates from observational healthcare data

OBJECTIVE

Observational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs.

MATERIALS AND METHODS

Our framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database.

RESULTS

We demonstrate the utility of the framework in a case study of Parkinson's disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates.

DISCUSSION

Estimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases.

CONCLUSION

Our framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.

The Application of Artificial Intelligence in the Genetic Study of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disease in which genetic factors contribute approximately 70% of etiological effects. Studies have found many significant genetic and environmental factors, but the pathogenesis of AD is still unclear. With the application of microarray and next-generation sequencing technologies, research using genetic data has shown explosive growth. In addition to conventional statistical methods for the processing of these data, artificial intelligence (AI) technology shows obvious advantages in analyzing such complex projects. This article first briefly reviews the application of AI technology in medicine and the current status of genetic research in AD. Then, a comprehensive review is focused on the application of AI in the genetic research of AD, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation, gene expression profile, gene-gene interaction in AD, and genetic analysis of AD based on a knowledge base. Although many studies have yielded some meaningful results, they are still in a preliminary stage. The main shortcomings include the limitations of the databases, failing to take advantage of AI to conduct a systematic biology analysis of multilevel databases, and lack of a theoretical framework for the analysis results. Finally, we outlook the direction of future development. It is crucial to develop high quality, comprehensive, large sample size, data sharing resources; a multi-level system biology AI analysis strategy is one of the development directions, and computational creativity may play a role in theory model building, verification, and designing new intervention protocols for AD.

cando.py: Open Source Software for Predictive Bioanalytics of Large Scale Drug-Protein-Disease Data

Traditional drug discovery methods focus on optimizing the efficacy of a drug against a single biological target of interest for a specific disease. However, evidence supports the multitarget theory, i.e., drugs work by exerting their therapeutic effects via interaction with multiple biological targets, which have multiple phenotypic effects. Analytics of drug-protein interactions on a large proteomic scale provides insight into disease systems while also allowing for prediction of putative therapeutics against specific indications. We present a Python package for analysis of drug-proteome and drug-disease relationships implementing the Computational Analysis of Novel Drug Opportunities (CANDO) platform. The CANDO package allows for rapid drug similarity assessment, most notably via an in-house interaction scoring protocol where billions of drug-protein interactions are rapidly scored and the similarity of drug-proteome interaction signatures is calculated. The package also implements a variety of benchmarking protocols for shotgun drug discovery and repurposing, i.e., to determine how every known drug is related to every other in the context of the indications/diseases for which they are approved. Drug predictions are generated through consensus scoring of the most similar compounds to drugs known to treat a particular indication. Support for comparing and ranking novel chemical entities, as well as machine learning modules for both benchmarking and putative drug candidate prediction is also available. The CANDO Python package is available on GitHub at https://github.com/ram-compbio/CANDO, through the Conda Python package installer, and at http://compbio.org/software/.

Molecular profiling in sarcoidosis

PURPOSE OF REVIEW

Sarcoidosis is a systemic disease characterized by granulomatous inflammation of unknown cause. There is extensive heterogeneity between patients with respect to the number and types of organs involved, disease course, and response to therapy. Recent research in the field has leveraged 'omics' techniques such as transcriptomics to identify important 'molecular profiles' in the disease. These tools may help in identifying clinically useful biomarkers and targets for therapy.

RECENT FINDINGS

Several studies have used gene expression profiling of predesignated lists or the entire genome to find genes and markers that differentiate sarcoidosis from healthy controls, but only a few have compared sarcoidosis patients based on disease phenotypes and organ involvement. The common gene pathways that have been repeatedly identified include those related to the interferon response, T-cell receptor signaling, and the major histocompatibility complex.

SUMMARY

While the molecular profiling studies to date offer the ability to compare sarcoidosis and health as well as across tissues, further longitudinal studies that include sarcoidosis patients with varying outcomes with respect to organ involvement and response to treatment are needed to identify clinically important phenotypes in the disease that can then be differentiated based on molecular features.

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.

Digital Pharmaceutical Sciences

Artificial intelligence (AI) and machine learning, in particular, have gained significant interest in many fields, including pharmaceutical sciences. The enormous growth of data from several sources, the recent advances in various analytical tools, and the continuous developments in machine learning algorithms have resulted in a rapid increase in new machine learning applications in different areas of pharmaceutical sciences. This review summarizes the past, present, and potential future impacts of machine learning technologies on different areas of pharmaceutical sciences, including drug design and discovery, preformulation, and formulation. The machine learning methods commonly used in pharmaceutical sciences are discussed, with a specific emphasis on artificial neural networks due to their capability to model the nonlinear relationships that are commonly encountered in pharmaceutical research. AI and machine learning technologies in common day-to-day pharma needs as well as industrial and regulatory insights are reviewed. Beyond traditional potentials of implementing digital technologies using machine learning in the development of more efficient, fast, and economical solutions in pharmaceutical sciences are also discussed.

Translational Genomics in Neurocritical Care: a Review

Translational genomics represents a broad field of study that combines genome and transcriptome-wide studies in humans and model systems to refine our understanding of human biology and ultimately identify new ways to treat and prevent disease. The approaches to translational genomics can be broadly grouped into two methodologies, forward and reverse genomic translation. Traditional (forward) genomic translation begins with model systems and aims at using unbiased genetic associations in these models to derive insight into biological mechanisms that may also be relevant in human disease. Reverse genomic translation begins with observations made through human genomic studies and refines these observations through follow-up studies using model systems. The ultimate goal of these approaches is to clarify intervenable processes as targets for therapeutic development. In this review, we describe some of the approaches being taken to apply translational genomics to the study of diseases commonly encountered in the neurocritical care setting, including hemorrhagic and ischemic stroke, traumatic brain injury, subarachnoid hemorrhage, and status epilepticus, utilizing both forward and reverse genomic translational techniques. Further, we highlight approaches in the field that could be applied in neurocritical care to improve our ability to identify new treatment modalities as well as to provide important information to patients about risk and prognosis.

Single Cell and Single Nucleus RNA-Seq Reveal Cellular Heterogeneity and Homeostatic Regulatory Networks in Adult Mouse Stria Vascularis

The stria vascularis (SV) generates the endocochlear potential (EP) in the inner ear and is necessary for proper hair cell mechanotransduction and hearing. While channels belonging to SV cell types are known to play crucial roles in EP generation, relatively little is known about gene regulatory networks that underlie the ability of the SV to generate and maintain the EP. Using single cell and single nucleus RNA-sequencing, we identify and validate known and rare cell populations in the SV. Furthermore, we establish a basis for understanding molecular mechanisms underlying SV function by identifying potential gene regulatory networks as well as druggable gene targets. Finally, we associate known deafness genes with adult SV cell types. This work establishes a basis for dissecting the genetic mechanisms underlying the role of the SV in hearing and will serve as a basis for designing therapeutic approaches to hearing loss related to SV dysfunction.