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

Multi-Omics Approach Reveals Genes and Pathways Affected in Miller-Dieker Syndrome

Miller-Dieker syndrome (MDS) is a rare neurogenetic disorder resulting from a heterozygous deletion of 26 genes in the MDS locus on human chromosome 17. MDS patients often die in utero and only 10% of those who are born reach 10 years of age. Current treatments mostly prevent complications and control seizures. A detailed understanding of the pathogenesis of MDS through gene expression studies would be useful in developing precise medical approaches toward MDS. To better understand MDS at the molecular level, we performed RNA sequencing on RNA and mass spectrometry on total protein isolated from BJ (non-MDS) cells and GM06097 (MDS) cells, which were derived from a healthy individual and an MDS patient, respectively. Differentially expressed genes (DEGs) at the RNA and protein levels involved genes associated with phenotypic features reported in MDS patients (CACNG4, ADD2, SPTAN1, SHANK2), signaling pathways (GABBR2, CAMK2B, TRAM-1), and nervous system development (CAMK2B, BEX1, ARSA). Functional assays validated enhanced calcium signaling, downregulated protein translation, and cell migration defects in MDS. Interestingly, overexpression of methyltransferase-like protein 16 (METTL16), a protein encoded in the MDS locus, restored defects in protein translation, phosphor states of mTOR (mammalian target of rapamycin) pathway regulators, and cell migration in MDS cells. Although DNA- and RNA-modifying enzymes were among the DEGs and the intracellular SAM/SAH ratio was eightfold lower in MDS cells, global nucleoside modifications remained unchanged. Thus, this study identified specific genes and pathways responsible for the gene expression changes, which could lead to better therapeutics for MDS patients.

Carbonic anhydrase inhibition ameliorates tau toxicity via enhanced tau secretion

Tauopathies are neurodegenerative diseases that manifest with intracellular accumulation and aggregation of tau protein. These include Pick's disease, progressive supranuclear palsy, corticobasal degeneration and argyrophilic grain disease, where tau is believed to be the primary disease driver, as well as secondary tauopathies, such as Alzheimer's disease. There is a need to develop effective pharmacological therapies. Here we tested >1,400 clinically approved compounds using transgenic zebrafish tauopathy models. This revealed that carbonic anhydrase (CA) inhibitors protected against tau toxicity. CRISPR experiments confirmed that CA depletion mimicked the effects of these drugs. CA inhibition promoted faster clearance of human tau by promoting lysosomal exocytosis. Importantly, methazolamide, a CA inhibitor used in the clinic, also reduced total and phosphorylated tau levels, increased neuronal survival and ameliorated neurodegeneration in mouse tauopathy models at concentrations similar to those seen in people. These data underscore the feasibility of in vivo drug screens using zebrafish models and suggest serious consideration of CA inhibitors for treating tauopathies.

Utilization of precision medicine digital twins for drug discovery in Alzheimer's disease

Alzheimer's disease (AD) presents significant challenges in drug discovery and development due to its complex and poorly understood pathology and etiology. Digital twins (DTs) are recently developed virtual real-time representations of physical entities that enable rapid assessment of the bidirectional interaction between the virtual and physical domains. With recent advances in artificial intelligence (AI) and the growing accumulation of multi-omics and clinical data, application of DTs in healthcare is gaining traction. Digital twin technology, in the form of multiscale virtual models of patients or organ systems, can track health status in real time with continuous feedback, thereby driving model updates that enhance clinical decision-making. Here, we posit an additional role for DTs in drug discovery, with particular utility for complex diseases like AD. In this review, we discuss salient challenges in AD drug development, including complex disease pathology and comorbidities, difficulty in early diagnosis, and the current high failure rate of clinical trials. We also review DTs and discuss potential applications for predicting AD progression, discovering biomarkers, identifying new drug targets and opportunities for drug repurposing, facilitating clinical trials, and advancing precision medicine. Despite significant hurdles in this area, such as integration and standardization of dynamic medical data and issues of data security and privacy, DTs represent a promising approach for revolutionizing drug discovery in AD.

Drug repurposing using artificial intelligence, molecular docking, and hybrid approaches: A comprehensive review in general diseases vs Alzheimer's disease

BACKGROUND

Alzheimer's disease (AD), the most prevalent form of dementia, remains enigmatic in its origins despite the widely accepted "amyloid hypothesis," which implicates amyloid-beta peptide aggregates in its pathogenesis and progression. Despite advancements in technology and healthcare, the incidence of AD continues to rise. The traditional drug development process remains time-consuming, often taking years to bring an AD treatment to market. Drug repurposing has emerged as a promising strategy for developing cost-effective and efficient therapeutic options by identifying new uses for existing approved drugs, thus accelerating drug development.

OBJECTIVES

This study aimed to examine two key drug repurposing methodologies in general diseases and specifically in AD, which are artificial intelligent (AI) approach and molecular docking approach. In addition, the hybrid approach that integrates AI with molecular docking techniques will be explored too.

METHODOLOGY

This study systematically compiled a comprehensive collection of relevant academic articles, scientific papers, and research studies which were published up until November 2024 (as of the writing of this review paper). The final selection of papers was filtered to include studies related to Alzheimer's disease and general diseases, and then categorized into three groups: AI articles, molecular docking articles, and hybrid articles.

RESULTS

As a result, 331 papers were identified that employed AI for drug repurposing in general diseases, and 58 papers focused specifically in AD. For molecular docking in drug repurposing, 588 papers addressed general diseases, while 46 papers were dedicated to AD. The hybrid approach combining AI and molecular docking in drug repurposing has 52 papers for general diseases and 9 for AD. A comparative review was done across the methods, results, strengths, and limitations in those studies. Challenges of drug repurposing in AD are explored and future prospects are proposed.

DISCUSSION AND CONCLUSION

Drug repurposing emerges as a compelling and effective strategy within AD research. Both AI and molecular docking methods exhibit significant potential in this domain. AI algorithms yield more precise predictions, thus facilitating the exploration of new therapeutic avenues for existing drugs. Similarly, molecular docking techniques revolutionize drug-target interaction modelling, employing refined algorithms to screen extensive drug databases against specific target proteins. This review offers valuable insights for guiding the utilization of AI, molecular docking, or their hybrid in AD drug repurposing endeavors. The hope is to speed up the timeline of drug discovery which could improve the therapeutic approach to AD.

Repurposing of nervous system drugs for cancer treatment: recent advances, challenges, and future perspectives

The nervous system plays a critical role in developmental biology and oncology, influencing processes from ontogeny to the complex dynamics of cancer progression. Interactions between the nervous system and cancer significantly affect oncogenesis, tumor growth, invasion, metastasis, treatment resistance, inflammation that promotes tumors, and the immune response. A comprehensive understanding of the signal transduction pathways involved in cancer biology is essential for devising effective anti-cancer strategies and overcoming resistance to existing therapies. Recent advances in cancer neuroscience promise to establish a new cornerstone of cancer therapy. Repurposing drugs originally developed for modulating nerve signal transduction represent a promising approach to target oncogenic signaling pathways in cancer treatment. This review endeavors to investigate the potential of repurposing neurological drugs, which target neurotransmitters and neural pathways, for oncological applications. In this context, it aims to bridge the interdisciplinary gap between neurology, psychiatry, internal medicine, and oncology. By leveraging already approved drugs, researchers can utilize existing extensive safety and efficacy data, thereby reducing both the time and financial resources necessary for the development of new cancer therapies. This strategy not only promises to enhance patient outcomes but also to expand the array of available treatments, thereby enriching the therapeutic landscape in oncology.

Computational drug repurposing: approaches, evaluation of in silico resources and case studies

Repurposing of existing drugs for new indications has attracted substantial attention owing to its potential to accelerate drug development and reduce costs. Hundreds of computational resources such as databases and predictive platforms have been developed that can be applied for drug repurposing, making it challenging to select the right resource for a specific drug repurposing project. With the aim of helping to address this challenge, here we overview computational approaches to drug repurposing based on a comprehensive survey of available in silico resources using a purpose-built drug repurposing ontology that classifies the resources into hierarchical categories and provides application-specific information. We also present an expert evaluation of selected resources and three drug repurposing case studies implemented within the Horizon Europe REMEDi4ALL project to demonstrate the practical use of the resources. This comprehensive Review with expert evaluations and case studies provides guidelines and recommendations on the best use of various in silico resources for drug repurposing and establishes a basis for a sustainable and extendable drug repurposing web catalogue.

Neurological Adverse Events Associated with the Use of Janus Kinase Inhibitors: A Pharmacovigilance Study Based on Vigibase

Background: Janus kinase (JAK) inhibitors are a new class of targeted therapies that block cytokines and the signal transduction and activators of transcription (STAT) pathway. However, post-marketing surveillance studies have led to revised recommendations, highlighting potential serious heart-related events and cancer risk of JAK inhibitors. Here, we aimed to determine the neurological adverse events (AEs) of JAK inhibitors (tofacitinib, ruxolitinib, and baricitinib) based on a global real-world database. Methods: We analyzed individual case safety reports from the Uppsala Monitoring Center from January 1968 to 4 April 2022. A disproportionality analysis was performed using the proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC) to detect signals. Signals were classified according to the hierarchy of the Medical Dictionary for Regulatory Activities (MedDRA). Additionally, a stratified disproportionality analysis by age group and sex was performed for major AEs. Results: A total of 30,051,159 reports for all drugs were analyzed in this study. Among 105,798 reports of tofacitinib, 14.1% (14,863 reports) were neurological AEs. For ruxolitinib and baricitinib, 14.5% (6317 reports) and 10.2% (1216 reports) were neurological AEs, respectively. Various neurological AE signals were detected for tofacitinib and ruxolitinib, with memory impairment exhibiting the highest number of reports and a positive signal in the stratified disproportionality analysis by age group. Baricitinib did not reach the signal detection threshold. Conclusions: This study suggests the potential for neurological AEs, including memory impairment, associated with tofacitinib and ruxolitinib use based on a real-world database.

Brain-penetrant histone deacetylase inhibitor RG2833 improves spatial memory in females of an Alzheimer's disease rat model

BACKGROUND

Nearly two-thirds of Alzheimer's disease (AD) patients are women. Therapeutics for women are critical to lowering their elevated risk of developing this major cause of adult dementia. Moreover, targeting epigenetic processes such as histone acetylation that regulate multiple cellular pathways is advantageous given the multifactorial nature of AD. Histone acetylation takes part in memory consolidation, and its disruption is linked to AD.

OBJECTIVE

Determine whether the investigational drug RG2833 has repurposing potential for AD. RG2833 is a histone deacetylase HDAC1/3 inhibitor that is orally bioavailable and permeates the blood-brain-barrier.

METHODS

RG2833 effects were determined on cognition, transcriptome, and AD-like pathology in 11-month TgF344-AD female and male rats. Treatment started early in the course of pathology when therapeutic intervention is predicted to be most effective.

RESULTS

RG2833-treatment of 11-month TgF344-AD rats: (1) Significantly improved hippocampal-dependent spatial memory in females but not males. (2) Upregulated expression of immediate early genes, such as Arc, Egr1 and c-Fos, and other genes involved in synaptic plasticity and memory consolidation in females. Remarkably, out of 17,168 genes analyzed for each sex, no significant changes in gene expression were detected in males at p < 0.05, false discovery rate <0.05, and fold-change equal or > 1.5. (3) Failed to improve amyloid beta accumulation and microgliosis in female and male TgF344-AD rats.

CONCLUSIONS

Our study highlights the potential of histone-modifying therapeutics such as RG2833 to improve cognitive behavior and drive the expression of immediate early, synaptic plasticity and memory consolidation genes, especially in female AD patients.

Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder

Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. Based on genotype data of medication-naïve patients with ADHD who received pharmacological treatments for 12 weeks, the current study performed GWAS using the percentage changes in ADHD-RS score as phenotype. Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-wide P thresholds (E-02, E-03, E-04, E-05) as inputs. The current GWAS results identified two significant loci (rs10880574, P = 2.39E-09; rs2000900, P = 3.31E-09) which implicated two genes, TMEM117 and MYO5B, that were primarily associated with both brain- and gut-related disorders. The convolutional neural network (CNN) model, using variants with genome-wide P values less than E-02 (5516 SNPs), demonstrated the best performance with mean squared error (MSE) equals 0.012 (Accuracy = 0.83; Sensitivity = 0.90; Specificity = 0.75) in the validation dataset, 0.081 in an independent test dataset (Acc = 0.61, Sensitivity = 0.81; Specificity = 0.26). Notably, the variant that contributed most to the CNN model was NKAIN2, an ADHD-related gene, which is also associated with metabolic processes. To conclude, the integration of GWAS and DL methods revealed new genes contribute to ADHD pharmacological treatment responses, and underscored the interplay between neural systems and metabolic processes, potentially providing critical insights into precision treatment. Furthermore, our CNN model exhibited good performance in an independent dataset, encouraged future studies and implied potential clinical applications.

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer's disease

Alzheimer's Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

BIGFormer: A Graph Transformer With Local Structure Awareness for Diagnosis and Pathogenesis Identification of Alzheimer's Disease Using Imaging Genetic Data

Alzheimer's disease (AD) is a highly inheritable neurological disorder, and brain imaging genetics (BIG) has become a rapidly advancing field for comprehensive understanding its pathogenesis. However, most of the existing approaches underestimate the complexity of the interactions among factors that cause AD. To take full appreciate of these complexity interactions, we propose BIGFormer, a graph Transformer with local structural awareness, for AD diagnosis and identification of pathogenic mechanisms. Specifically, the factors interaction graph is constructed with lesion brain regions and risk genes as nodes, where the connection between nodes intuitively represents the interaction between nodes. After that, a perception with local structure awareness is built to extract local structure around nodes, which is then injected into node representation. Then, the global reliance inference component assembles the local structure into higher-order structure, and multi-level interaction structures are jointly aggregated into a classification projection head for disease state prediction. Experimental results show that BIGFormer demonstrated superiority in four classification tasks on the AD neuroimaging initiative dataset and proved to identify biomarkers closely intimately related to AD.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

Drug Repositioning Based on Cerebrospinal Fluid Proteomes Using Connectivity Map Framework

Selecting a fluid near an affected organ can improve the likelihood of identifying a biomarker panel from pathological tissue. Cerebrospinal fluid (CSF), in close contact with the brain, is a valuable source of biomarkers for neurological disorders due to the inaccessibility of brain tissue. Moreover, the altered CSF proteome identified in neurological diseases can facilitate the repurposing of drugs already used for other therapeutic purposes. In this context, Connectivity Map (CMap) is a valuable tool as it provides information on compounds and gene modifications that can be utilized to reverse specific pathological signatures. Analyzing CSF differential proteomics through the CMap framework offers an efficient and cost-effective approach to identifying potential novel therapies for neurodegenerative diseases.

Data-driven discovery of cell-type-directed network-correcting combination therapy for Alzheimer's disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by heterogeneous molecular changes across diverse cell types, posing significant challenges for treatment development. To address this, we introduced a cell-type-specific, multi-target drug discovery strategy grounded in human data and real-world evidence. This approach integrates single-cell transcriptomics, drug perturbation databases, and clinical records. Using this framework, letrozole and irinotecan were identified as a potential combination therapy, each targeting AD-related gene expression changes in neurons and glial cells, respectively. In an AD mouse model, this combination therapy significantly improved memory function and reduced AD-related pathologies compared to vehicle and single-drug treatments. Single-nuclei transcriptomic analysis confirmed that the therapy reversed disease-associated gene networks in a cell-type-specific manner. These results highlight the promise of cell-type-directed combination therapies in addressing multifactorial diseases like AD and lay the groundwork for precision medicine tailored to patient-specific transcriptomic and clinical profiles.

Molecular classification of geriatric breast cancer displays distinct senescent subgroups of prognostic significance

Breast cancer in the elderly presents distinct biological characteristics and clinical treatment responses compared with cancer in younger patients. Comprehensive Geriatric Assessment is recommended for evaluating treatment efficacy in elderly cancer patients based on physiological classification. However, research on molecular classification in older cancer patients remains insufficient. In this study, we identified two subgroups with distinct senescent clusters among geriatric breast cancer patients through multi-omics analysis. Using various machine learning algorithms, we developed a comprehensive scoring model called "Sene_Signature," which more accurately distinguished elderly breast cancer patients compared with existing methods and better predicted their prognosis. The Sene_Signature was correlated with tumor immune cell infiltration, as supported by single-cell transcriptomics, RNA sequencing, and pathological data. Furthermore, we observed increased drug responsiveness in patients with a high Sene_Signature to treatments targeting the epidermal growth factor receptor and cell-cycle pathways. We also established a user-friendly web platform to assist investigators in assessing Sene_Signature scores and predicting treatment responses for elderly breast cancer patients. In conclusion, we developed a novel model for evaluating prognosis and therapeutic responses, providing a potential molecular classification that assists in the pre-treatment assessment of geriatric breast cancer.

Cerebral Small Vessel Disease: a Review of the Pathophysiological Mechanisms

Cerebral small vessel disease (cSVD) refers to the age-dependent pathological processes involving the brain small vessels and leading to vascular cognitive impairment, intracerebral hemorrhage, and acute lacunar ischemic stroke. Despite the significant public health burden of cSVD, disease-specific therapeutics remain unavailable due to the incomplete understanding of the underlying pathophysiological mechanisms. Recent advances in neuroimaging acquisition and processing capabilities as well as findings from cSVD animal models have revealed critical roles of several age-dependent processes in cSVD pathogenesis including arterial stiffness, vascular oxidative stress, low-grade systemic inflammation, gut dysbiosis, and increased salt intake. These factors interact to cause a state of endothelial cell dysfunction impairing cerebral blood flow regulation and breaking the blood brain barrier. Neuroinflammation follows resulting in neuronal injury and cSVD clinical manifestations. Impairment of the cerebral waste clearance through the glymphatic system is another potential process that has been recently highlighted contributing to the cognitive decline. This review details these mechanisms and attempts to explain their complex interactions. In addition, the relevant knowledge gaps in cSVD mechanistic understanding are identified and a systematic approach to future translational and early phase clinical research is proposed in order to reveal new cSVD mechanisms and develop disease-specific therapeutics.

Unlocking therapeutic frontiers: harnessing artificial intelligence in drug discovery for neurodegenerative diseases

Neurodegenerative diseases (NDs) pose serious healthcare challenges with limited therapeutic treatments and high social burdens. The integration of artificial intelligence (AI) into drug discovery has emerged as a promising approach to address these challenges. This review explores the application of AI techniques to unravel therapeutic frontiers for NDs. We examine the current landscape of AI-driven drug discovery and discuss the potentials of AI in accelerating the identification of novel therapeutic targets on ND research and drug development, optimization of drug candidates, and expediating personalized medicine approaches. Finally, we outline future directions and challenges in harnessing AI for the advancement of therapeutics in this critical area by emphasizing the importance of interdisciplinary collaboration and ethical considerations.

Unlocking biological complexity: the role of machine learning in integrative multi-omics

The increasing complexity of biological systems demands advanced analytical approaches to decode the underlying mechanisms of health and disease. Integrative multi-omics approaches use multi-layered datasets such as genomic, transcriptomic, proteomic, and metabolomic data to understand biological processes much more comprehensively compared to the single-omics analysis and to provide a comprehensive view of cellular and molecular processes. However, these integrative approaches have their own computational and analytical challenges due to the large volume and nature of multi-omics data. Machine learning has emerged as a powerful tool to help and resolve these challenges. It offers sophisticated algorithms that can identify and discover hidden patterns and provide insights into complex biological networks. By integrating machine learning in multi-omics, we can enhance our understanding of drug discovery, disease, pathway, and network analysis. Machine learning and ensemble methods allow researchers to model nonlinear relationships and manage high-dimensional data, improving the precision of predictions. This approach paves the way for personalized medicine by identifying unique molecular signatures for individual patients, which can provide valuable insights into treatment planning and support more effective treatment. As machine learning continues to evolve, its role in multi-omics analysis will be pivotal in advancing our ability to interpret biological complexity and translate findings into clinical applications.

Alzheimer's Disease: Exploring the Landscape of Cognitive Decline

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory loss, and impaired daily functioning. The pathology of AD is marked by the accumulation of amyloid beta plaques and tau protein tangles in the brain, along with neuroinflammation and synaptic dysfunction. Genetic factors, such as mutations in APP, PSEN1, and PSEN2 genes, as well as the APOE ε4 allele, contribute to increased risk of acquiring AD. Currently available treatments provide symptomatic relief but do not halt disease progression. Research efforts are focused on developing disease-modifying therapies that target the underlying pathological mechanisms of AD. Advances in identification and validation of reliable biomarkers for AD hold great promise for enhancing early diagnosis, monitoring disease progression, and assessing treatment response in clinical practice in effort to alleviate the burden of this devastating disease. In this paper, we analyze data from the CAS Content Collection to summarize the research progress in Alzheimer's disease. We examine the publication landscape in effort to provide insights into current knowledge advances and developments. We also review the most discussed and emerging concepts and assess the strategies to combat the disease. We explore the genetic risk factors, pharmacological targets, and comorbid diseases. Finally, we inspect clinical applications of products against AD with their development pipelines and efforts for drug repurposing. The objective of this review is to provide a broad overview of the evolving landscape of current knowledge regarding AD, to outline challenges, and to evaluate growth opportunities to further efforts in combating the disease.

Atomoxetine suppresses radioresistance in glioblastoma via circATIC/miR-520d-5p/Notch2-Hey1 axis

BACKGROUND

Resistance acquired after radiotherapy is directly related to the failure of various cancer treatments, including GBM. Because the mechanism for overcoming radioresistance has not yet been clearly identified, the development of diagnostic and therapeutic markers to treat radioresistance is necessary. Since increased expression of stemness- and EMT-related markers are reported to be closely correlated with radioresistance, research is underway to develop new drugs targeting these factors.

METHODS

To develop an anticancer drug that overcomes radioresistance, a library of drugs already approved by the FDA was used. After treating radioresistant GBM cells with each drug, the expression of stemness- and EMT-related markers was confirmed by qRT-PCR, and as a result, Atomoxetine (ATX) was selected. It was confirmed that radioresistance-induced cell migratory, invasive, sphere formation abilities, and tumor growth using a xenograft mouse model were suppressed upon ATX treatment. Using a miRNA prediction tool, we discovered miR-520d-5p, which targets Notch2 and Hey1, key factors in radioresistance, and discovered circATIC targeting this miRNA, revealing its relationship with ATX. We demonstrated the expression regulation mechanism and signaling mechanism between circATIC, miR-520d-5p, Notch2, and Hey1 factors using a luciferase reporter assay. In addition, the results at the cellular level were clinically verified by confirming the correlation between radiation, miR-520d-5p, and circATIC using patient plasma by qRT-PCR.

RESULTS

ATX showed potential as a treatment for radioresistance by suppressing the malignant phenotype by regulating the circATIC/miR-520d-5p/Notch2-Hey1 signaling mechanism in vitro and in vivo using radioresistant GBM cells.

CONCLUSIONS

This study revealed that ATX suppresses radioresistance through the circATIC/miR-520d-5p/Notch2-Hey1 signaling pathway. These results showed the potential of ATX as a new drug that can overcome radioresistance, a major challenge in cancer treatment, and the signaling factors identified in this mechanism suggest the possibility of use as potential targets for the diagnosis and treatment of radioresistance.

DRADTiP: Drug repurposing for aging disease through drug-target interaction prediction

MOTIVATION

The greatest risk factor for many non-communicable diseases is aging. Studies on model organisms have demonstrated that genetic and chemical perturbation alterations can lengthen longevity and overall health. However, finding longevity-enhancing medications and their related targets is difficult.

METHOD

In this work, we designed a novel drug repurposing model by identifying the interaction between aging-related genes or targets and drugs similar to aging disease. Each disease is associated with certain specific genetic factors for the occurrence of that disease. The factors include gene expression, pathway, miRNA, and degree of genes in the protein-protein interaction network. In this paper, we aim to find the drugs that prolong the life span of humans with their aging-related targets using the above-mentioned factors. In addition, the contribution or importance of each factor may vary among drugs and targets. Therefore, we designed a novel multi-layer random walk-based network representation learning model including node and edge weight to learn the features of drugs and targets respectively.

RESULT

The performance of the proposed model is demonstrated using k-fold cross-validation (k = 5). This model achieved better performance with scores of 0.93 and 0.91 for precision and recall respectively. The drugs identified by the system are evaluated to be potential candidates for aging since the degree of interaction between the potential drugs and their gene sets are high. In addition, the genes that are interacting with drugs produce the same biological functions. Hence the life span of the human will be increased or prolonged.

The transformative potential of AI-driven CRISPR-Cas9 genome editing to enhance CAR T-cell therapy

This narrative review examines the promising potential of integrating artificial intelligence (AI) with CRISPR-Cas9 genome editing to advance CAR T-cell therapy. AI algorithms offer unparalleled precision in identifying genetic targets, essential for enhancing the therapeutic efficacy of CAR T-cell treatments. This precision is critical for eliminating negative regulatory elements that undermine therapy effectiveness. Additionally, AI streamlines the manufacturing process, significantly reducing costs and increasing accessibility, thereby encouraging further research and development investment. A key benefit of AI integration is improved safety; by predicting and minimizing off-target effects, AI enhances the specificity of CRISPR-Cas9 edits, contributing to safer CAR T-cell therapy. This advancement is crucial for patient safety and broader clinical adoption. The convergence of AI and CRISPR-Cas9 has transformative potential, poised to revolutionize personalized immunotherapy. These innovations could expand the application of CAR T-cell therapy beyond hematologic malignancies to various solid tumors and other non-hematologic conditions, heralding a new era in cancer treatment that substantially improves patient outcomes.

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Microglia-mediated neuron death requires TNF and is exacerbated by mutant Huntingtin

Microglia, the resident immune cells of the brain, regulate the balance of inflammation in the central nervous system under healthy and pathogenic conditions. Huntington's disease (HD) is a chronic neurodegenerative disease characterized by activated microglia and elevated concentrations of pro-inflammatory cytokines within the brain. Chronic hyperactivation of microglia is associated with brain pathology and eventual neuron death. However, it is unclear which specific cytokines are required for neuron death and whether HD neurons may be hypersensitive to neuroinflammation. We assessed the profile of microglia-secreted proteins in response to LPS and IFNγ, and a conditioned media paradigm was used to examine the effects of these secreted proteins on cultured neuronal cells. STHdhQ7/Q7 and STHdhQ111/Q111 neuronal cells were used to model wild-type and HD neurons, respectively. We determined that STHdhQ111/Q111 cells were hypersensitive to pro-inflammatory factors secreted by microglia, and that TNF was required to induce neuronal death. Microglia-mediated neuronal death could be effectively halted through the use of JAK-STAT or TNF inhibitors which supported the requirement for TNF as well as IFNγ in the process of secondary neurotoxicity. Further data derived from human HD patients as well as HD mice were suggestive of enhanced receptor density for TNF (TNFR1) and IFNγ (IFNGR) which could sensitize the HD brain to these cytokines. This highlights several potential mechanisms by which microglia may induce neuronal death and suggests that these mechanisms may be upregulated in the brain of HD patients.

A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer's disease (AD) and Parkinson's disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.

Machine learning models to identify lead compound and substitution optimization to have derived energetics and conformational stability through docking and MD simulations for sphingosine kinase 1

Sphingosine kinases (SphKs) are a group of important enzymes that circulate at low micromolar concentrations in mammals and have received considerable attention due to the roles they play in a broad array of biological processes including apoptosis, mutagenesis, lymphocyte migration, radio- and chemo-sensitization, and angiogenesis. In the present study, we constructed three classification models by four machine learning (ML) algorithms including naive bayes (NB), support vector machine (SVM), logistic regression, and random forest from 395 compounds. The generated ML models were validated by fivefold cross validation. Five different scaffold hit fragments resulted from SVM model-based virtual screening and docking results indicate that all the five fragments exhibit common hydrogen bond interaction a catalytic residue of SphK1. Further, molecular dynamics (MD) simulations and binding free energy calculation had been carried out with the identified five fragment leads and three cocrystal inhibitors. The best 15 fragments were selected. Molecular dynamics (MD) simulations showed that among these compounds, 7 compounds have favorable binding energy compared with cocrystal inhibitors. Hence, the study showed that the present lead fragments could act as potential inhibitors against therapeutic target of cancers and neurodegenerative disorders.

Emerging paradigms in Alzheimer's therapy

Alzheimer's disease is a neurodegenerative disorder that affects elderly, and its incidence is continuously increasing across the globe. Unfortunately, despite decades of research, a complete cure for Alzheimer's disease continues to elude us. The current medications are mainly symptomatic and slow the disease progression but do not result in reversal of all disease pathologies. The growing body of knowledge on the factors responsible for the onset and progression of the disease has resulted in the identification of new targets that could be targeted for treatment of Alzheimer's disease. This has opened new vistas for treatment of Alzheimer's disease that have moved away from chemotherapeutic agents modulating a single target to biologics and combinations that acted on multiple targets thereby offering better therapeutic outcomes. This review discusses the emerging directions in therapeutic interventions against Alzheimer's disease highlighting their merits that promise to change the treatment paradigm and challenges that limit their clinical translation.

In-silico screening of Acacia pennata and Bridelia retusa reveals pinocembrin-7-O-β-D-glucopyranoside as a promising β-lactamase inhibitor to combat antibiotic resistance

The β-lactamase of Pseudomonas aeruginosa is known to degrade β-lactam antibiotics such as penicillins, cephalosporins, monobactams, and carbapenems. With the discovery of an extended-spectrum β-lactamase in a clinical isolate of P. aeruginosa, the bacterium has become multi-drug resistant. In this study, we aim to identify new β-lactamase inhibitors by virtually screening a total of 43 phytocompounds from two Indian medicinal plants. In the molecular docking studies, pinocembrin-7-O-β-D-glucopyranoside (P7G) (-9.6 kcal/mol) from Acacia pennata and ellagic acid (EA) (-9.2 kcal/mol) from Bridelia retusa had lower binding energy than moxalactam (-8.4 kcal/mol). P7G and EA formed 5 (Ser62, Asn125, Asn163, Thr209, and Ser230) and 4 (Lys65, Ser123, Asn125, and Glu159) conventional hydrogens bonds with the active site residues. 100 ns MD simulations revealed that moxalactam and P7G (but not EA) were able to form a stable complex. The binding free energy calculations further revealed that P7G (-59.6526 kcal/mol) formed the most stable complex with β-lactamase when compared to moxalactam (-46.5669 kcal/mol) and EA (-28.4505 kcal/mol). The HOMO-LUMO and other DFT parameters support the stability and chemical reactivity of P7G at the active site of β-lactamase. P7G passed all the toxicity tests and bioavailability tests indicating that it possesses drug-likeness. Among the studied compounds, we identified P7G of A. pennata as the most promising phytocompound to combat antibiotic resistance by potentially inhibiting the β-lactamase of P. aeruginosa.Communicated by Ramaswamy H. Sarma.

Repurposing of USFDA-approved drugs to identify leads for inhibition of acetylcholinesterase enzyme: a plausible utility as an anti-Alzheimer agent

In the quest to identify new anti-Alzheimer agents, we employed drug repositioning or drug repositioning techniques on approved USFDA small molecules. Herein, we report the structure-based virtual screening (SBVS) of 1880 USFDA-approved drugs. The in silico-based identification was followed by calculating Prime MMGB-SA binding energy and molecular dynamics simulation studies. The cumulative analysis led to identifying domperidone as an identified hit. Domperidone was further corroborated in vitro using anticholinesterase-based assessment, keeping donepezil as a positive control. The analysis revealed that the identified lead (domperidone) could induce an inhibitory effect on AChE in a dose-dependent manner with an IC50 of 3.67 μM as compared to donepezil, which exhibited an IC50 of 1.37 μM. However, as domperidone is known to have poor BBB permeability, we rationally proposed new analogues utilizing the principles of bioisosterism. The bioisostere-clubbed analogues were found to have better BBB permeability, affinity, and stability within the catalytic domain of AChE via molecular docking and dynamics studies. The proposed bioisosteres may be synthesized in the future. They may plausibly be explored for their implication in the developmental progress of new anti-Alzheimer agent achieved via repurposing techniques in future.

Antineoplastics for treating Alzheimer's disease and dementia: Evidence from preclinical and observational studies

As the world population ages, there will be an increasing need for effective therapies for aging-associated neurodegenerative disorders, which remain untreatable. Dementia due to Alzheimer's disease (AD) is one of the leading neurological diseases in the aging population. Current therapeutic approaches to treat this disorder are solely symptomatic, making the need for new molecular entities acting on the causes of the disease extremely urgent. One of the potential solutions is to use compounds that are already in the market. The structures have known pharmacokinetics, pharmacodynamics, toxicity profiles, and patient data available in several countries. Several drugs have been used successfully to treat diseases different from their original purposes, such as autoimmunity and peripheral inflammation. Herein, we divulge the repurposing of drugs in the area of neurodegenerative diseases, focusing on the therapeutic potential of antineoplastics to treat dementia due to AD and dementia. We briefly touch upon the shared pathological mechanism between AD and cancer and drug repurposing strategies, with a focus on artificial intelligence. Next, we bring out the current status of research on the development of drugs, provide supporting evidence from retrospective, clinical, and preclinical studies on antineoplastic use, and bring in new areas, such as repurposing drugs for the prion-like spreading of pathologies in treating AD.

Predicting Alzheimer's Cognitive Resilience Score: A Comparative Study of Machine Learning Models Using RNA-seq Data

Alzheimer's disease (AD) is an important research topic. While amyloid plaques and neurofibrillary tangles are hallmark pathological features of AD, cognitive resilience (CR) is a phenomenon where cognitive function remains preserved despite the presence of these pathological features. This study aimed to construct and compare predictive machine learning models for CR scores using RNA-seq data from the Religious Orders Study and Memory and Aging Project (ROSMAP) and Mount Sinai Brain Bank (MSBB) cohorts. We evaluated support vector regression (SVR), random forest, XGBoost, linear, and transformer-based models. The SVR model exhibited the best performance, with contributing genes identified using Shapley additive explanations (SHAP) scores, providing insights into biological pathways associated with CR. Finally, we developed a tool called the resilience gene analyzer (REGA), which visualizes SHAP scores to interpret the contributions of individual genes to CR. REGA is available at https://igcore.cloud/GerOmics/REsilienceGeneAnalyzer/.

Recent advances in Alzheimer's disease: Mechanisms, clinical trials and new drug development strategies

Alzheimer's disease (AD) stands as the predominant form of dementia, presenting significant and escalating global challenges. Its etiology is intricate and diverse, stemming from a combination of factors such as aging, genetics, and environment. Our current understanding of AD pathologies involves various hypotheses, such as the cholinergic, amyloid, tau protein, inflammatory, oxidative stress, metal ion, glutamate excitotoxicity, microbiota-gut-brain axis, and abnormal autophagy. Nonetheless, unraveling the interplay among these pathological aspects and pinpointing the primary initiators of AD require further elucidation and validation. In the past decades, most clinical drugs have been discontinued due to limited effectiveness or adverse effects. Presently, available drugs primarily offer symptomatic relief and often accompanied by undesirable side effects. However, recent approvals of aducanumab (1) and lecanemab (2) by the Food and Drug Administration (FDA) present the potential in disrease-modifying effects. Nevertheless, the long-term efficacy and safety of these drugs need further validation. Consequently, the quest for safer and more effective AD drugs persists as a formidable and pressing task. This review discusses the current understanding of AD pathogenesis, advances in diagnostic biomarkers, the latest updates of clinical trials, and emerging technologies for AD drug development. We highlight recent progress in the discovery of selective inhibitors, dual-target inhibitors, allosteric modulators, covalent inhibitors, proteolysis-targeting chimeras (PROTACs), and protein-protein interaction (PPI) modulators. Our goal is to provide insights into the prospective development and clinical application of novel AD drugs.

Repositioning of baricitinib for management of memory impairment in ovariectomized/D-galactose treated rats: A potential role of JAK2/STAT3-PI3K/AKT/mTOR signaling pathway

AIMS

Neuroinflammation plays a pivotal role in amyloid β (Aβ) plaques formation which is among the hallmarks of Alzheimer's disease (AD). The present study investigated the potential therapeutic effects of baricitinib (BAR), a selective JAK2/ STAT3 inhibitor, in ovariectomized/ D-galactose (OVX/D-gal) treated rats as a model for AD.

MAIN METHODS

To induce AD, adult female rats (130-180 g) underwent bilateral ovariectomy and were injected daily with 150 mg/kg, i.p. D-gal for 8 consecutive weeks. BAR (10 and 50 mg/kg/day) was then given orally for 14 days.

KEY FINDINGS

BAR in a dose-dependent effect mitigated OVX/D-gal-induced aberrant activation of JAK2/STAT3 signaling pathway resulting in significant decreases in the expression of p-JAK 2, and p-STAT3 levels, along with deactivating AKT/PI3K/mTOR signaling as evidenced by deceased protein expression of p-AKT, p-PI3K, and p-mTOR. As a result, neuroinflammation was diminished as evidenced by decreased NF-κβ, TNF-α, and IL-6 levels. Moreover, oxidative stress biomarkers levels as iNOS, and MDA were reduced, whereas GSH was increased by BAR. BAR administration also succeeded in reverting histopathological alterations caused by OVX/D-gal, increased the number of intact neurons (detected by Nissl stain), and diminished astrocyte hyperactivity assessed as GFAP immunoreactivity. Finally, treatment with BAR diminished the levels of Aβ. These changes culminated in enhancing spatial learning and memory in Morris water maze, and novel object recognition test.

SIGNIFICANCE

BAR could be an effective therapy against neuroinflammation, astrogliosis and cognitive impairment induced by OVX/ D-gal where inhibiting JAK2/STAT3- AKT/PI3K/mTOR seems to play a crucial role in its beneficial effect.

Integrative approach of omics and imaging data to discover new insights for understanding brain diseases

Treatments that can completely resolve brain diseases have yet to be discovered. Omics is a novel technology that allows researchers to understand the molecular pathways underlying brain diseases. Multiple omics, including genomics, transcriptomics and proteomics, and brain imaging technologies, such as MRI, PET and EEG, have contributed to brain disease-related therapeutic target detection. However, new treatment discovery remains challenging. We focused on establishing brain multi-molecular maps using an integrative approach of omics and imaging to provide insights into brain disease diagnosis and treatment. This approach requires precise data collection using omics and imaging technologies, data processing and normalization. Incorporating a brain molecular map with the advanced technologies through artificial intelligence will help establish a system for brain disease diagnosis and treatment through regulation at the molecular level.

Integrating AI in fighting advancing Alzheimer: diagnosis, prevention, treatment, monitoring, mechanisms, and clinical trials

The application of artificial intelligence (AI) in neurology is a growing field offering opportunities to improve accuracy of diagnosis and treatment of complicated neuronal disorders, plus fostering a deeper understanding of the aetiologies of these diseases through AI-based analyses of large omics data. The most common neurodegenerative disease, Alzheimer's disease (AD), is characterized by brain accumulation of specific pathological proteins, accompanied by cognitive impairment. In this review, we summarize the latest progress on the use of AI in different AD-related fields, such as analysis of neuroimaging data enabling early and accurate AD diagnosis; prediction of AD progression, identification of patients at higher risk and evaluation of new treatments; improvement of the evaluation of drug response using AI algorithms to analyze patient clinical and neuroimaging data; the development of personalized AD therapies; and the use of AI-based techniques to improve the quality of daily life of AD patients and their caregivers.

In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease

The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.

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.

DeepSeq2Drug: An expandable ensemble end-to-end anti-viral drug repurposing benchmark framework by multi-modal embeddings and transfer learning

Drug repurposing is promising in multiple scenarios, such as emerging viral outbreak controls and cost reductions of drug discovery. Traditional graph-based drug repurposing methods are limited to fast, large-scale virtual screens, as they constrain the counts for drugs and targets and fail to predict novel viruses or drugs. Moreover, though deep learning has been proposed for drug repurposing, only a few methods have been used, including a group of pre-trained deep learning models for embedding generation and transfer learning. Hence, we propose DeepSeq2Drug to tackle the shortcomings of previous methods. We leverage multi-modal embeddings and an ensemble strategy to complement the numbers of drugs and viruses and to guarantee the novel prediction. This framework (including the expanded version) involves four modal types: six NLP models, four CV models, four graph models, and two sequence models. In detail, we first make a pipeline and calculate the predictive performance of each pair of viral and drug embeddings. Then, we select the best embedding pairs and apply an ensemble strategy to conduct anti-viral drug repurposing. To validate the effect of the proposed ensemble model, a monkeypox virus (MPV) case study is conducted to reflect the potential predictive capability. This framework could be a benchmark method for further pre-trained deep learning optimization and anti-viral drug repurposing tasks. We also build software further to make the proposed model easier to reuse. The code and software are freely available at http://deepseq2drug.cs.cityu.edu.hk.

Hypergraph Structural Information Aggregation Generative Adversarial Networks for Diagnosis and Pathogenetic Factors Identification of Alzheimer's Disease With Imaging Genetic Data

Alzheimer's disease (AD) is a neurodegenerative disease with profound pathogenetic causes. Imaging genetic data analysis can provide comprehensive insights into its causes. To fully utilize the multi-level information in the data, this article proposes a hypergraph structural information aggregation model, and constructs a novel deep learning method named hypergraph structural information aggregation generative adversarial networks (HSIA-GANs) for the automatic sample classification and accurate feature extraction. Specifically, HSIA-GAN is composed of generator and discriminator. The generator has three main functions. First, vertex graph and edge graph are constructed based on the input hypergraph to present the low-order relations. Second, the low-order structural information of hypergraph is extracted by the designed vertex convolution layers and edge convolution layers. Finally, the synthetic hypergraph is generated as the input of the discriminator. The discriminator can extract the high-order structural information directly from hypergraph through vertex-edge convolution, fuse the high and low-order structural information, and finalize the results through the full connection (FC) layers. Based on the data acquired from AD neuroimaging initiative, HSIA-GAN shows significant advantages in three classification tasks, and extracts discriminant features conducive to better disease classification.

DDCM: A Computational Strategy for Drug Repositioning Based on Support-Vector Regression Algorithm

Computational drug-repositioning technology is an effective tool for speeding up drug development. As biological data resources continue to grow, it becomes more important to find effective methods to identify potential therapeutic drugs for diseases. The effective use of valuable data has become a more rational and efficient approach to drug repositioning. The disease-drug correlation method (DDCM) proposed in this study is a novel approach that integrates data from multiple sources and different levels to predict potential treatments for diseases, utilizing support-vector regression (SVR). The DDCM approach resulted in potential therapeutic drugs for neoplasms and cardiovascular diseases by constructing a correlation hybrid matrix containing the respective similarities of drugs and diseases, implementing the SVR algorithm to predict the correlation scores, and undergoing a randomized perturbation and stepwise screening pipeline. Some potential therapeutic drugs were predicted by this approach. The potential therapeutic ability of these drugs has been well-validated in terms of the literature, function, drug target, and survival-essential genes. The method's feasibility was confirmed by comparing the predicted results with the classical method and conducting a co-drug analysis of the sub-branch. Our method challenges the conventional approach to studying disease-drug correlations and presents a fresh perspective for understanding the pathogenesis of diseases.

The broken Alzheimer's disease genome

The complex pathobiology of late-onset Alzheimer's disease (AD) poses significant challenges to therapeutic and preventative interventions. Despite these difficulties, genomics and related disciplines are allowing fundamental mechanistic insights to emerge with clarity, particularly with the introduction of high-resolution sequencing technologies. After all, the disrupted processes at the interface between DNA and gene expression, which we call the broken AD genome, offer detailed quantitative evidence unrestrained by preconceived notions about the disease. In addition to highlighting biological pathways beyond the classical pathology hallmarks, these advances have revitalized drug discovery efforts and are driving improvements in clinical tools. We review genetic, epigenomic, and gene expression findings related to AD pathogenesis and explore how their integration enables a better understanding of the multicellular imbalances contributing to this heterogeneous condition. The frontiers opening on the back of these research milestones promise a future of AD care that is both more personalized and predictive.

JAK1/2 Regulates Synergy Between Interferon Gamma and Lipopolysaccharides in Microglia

Microglia, the resident immune cells of the brain, regulate neuroinflammation which can lead to secondary neuronal damage and cognitive impairment under pathological conditions. Two of the many molecules that can elicit an inflammatory response from microglia are lipopolysaccharide (LPS), a component of gram-negative bacteria, and interferon gamma (IFNγ), an endogenous pro-inflammatory cytokine. We thoroughly examined the concentration-dependent relationship between LPS from multiple bacterial species and IFNγ in cultured microglia and macrophages. We measured the effects that these immunostimulatory molecules have on pro-inflammatory activity of microglia and used a battery of signaling inhibitors to identify the pathways that contribute to the microglial response. We found that LPS and IFNγ interacted synergistically to induce a pro-inflammatory phenotype in microglia, and that inhibition of JAK1/2 completely blunted the response. We determined that this synergistic action of LPS and IFNγ was likely dependent on JNK and Akt signaling rather than typical pro-inflammatory mediators such as NF-κB. Finally, we demonstrated that LPS derived from Escherichia coli, Klebsiella pneumoniae, and Akkermansia muciniphila can elicit different inflammatory responses from microglia and macrophages, but these responses could be consistently prevented using ruxolitinib, a JAK1/2 inhibitor. Collectively, this work reveals a mechanism by which microglia may become hyperactivated in response to the combination of LPS and IFNγ. Given that elevations in circulating LPS and IFNγ occur in a wide variety of pathological conditions, it is critical to understand the pharmacological interactions between these molecules to develop safe and effective treatments to suppress this process.

The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research

BACKGROUND

As global populations age and become susceptible to neurodegenerative illnesses, new therapies for Alzheimer disease (AD) are urgently needed. Existing data resources for drug discovery and repurposing fail to capture relationships central to the disease's etiology and response to drugs.

OBJECTIVE

We designed the Alzheimer's Knowledge Base (AlzKB) to alleviate this need by providing a comprehensive knowledge representation of AD etiology and candidate therapeutics.

METHODS

We designed the AlzKB as a large, heterogeneous graph knowledge base assembled using 22 diverse external data sources describing biological and pharmaceutical entities at different levels of organization (eg, chemicals, genes, anatomy, and diseases). AlzKB uses a Web Ontology Language 2 ontology to enforce semantic consistency and allow for ontological inference. We provide a public version of AlzKB and allow users to run and modify local versions of the knowledge base.

RESULTS

AlzKB is freely available on the web and currently contains 118,902 entities with 1,309,527 relationships between those entities. To demonstrate its value, we used graph data science and machine learning to (1) propose new therapeutic targets based on similarities of AD to Parkinson disease and (2) repurpose existing drugs that may treat AD. For each use case, AlzKB recovers known therapeutic associations while proposing biologically plausible new ones.

CONCLUSIONS

AlzKB is a new, publicly available knowledge resource that enables researchers to discover complex translational associations for AD drug discovery. Through 2 use cases, we show that it is a valuable tool for proposing novel therapeutic hypotheses based on public biomedical knowledge.

Reimagining old drugs with new tricks: Mechanisms, strategies and notable success stories in drug repurposing for neurological diseases

Recent evolution in drug repurposing has brought new anticipation, especially in the conflict against neurodegenerative diseases (NDDs). The traditional approach to developing novel drugs for these complex disorders is laborious, time-consuming, and often abortive. However, drug reprofiling which is the implementation of illuminating novel therapeutic applications of existing approved drugs, has shown potential as a promising strategy to accelerate the hunt for therapeutics. The advancement of computational approaches and artificial intelligence has expedited drug repurposing. These progressive technologies have enabled scientists to analyse extensive datasets and predict potential drug-disease interactions. By prospecting into the existing pharmacological knowledge, scientists can recognise potential therapeutic candidates for reprofiling, saving precious time and resources. Preclinical models have also played a pivotal role in this field, confirming the effectiveness and mechanisms of action of repurposed drugs. Several studies have occurred in recent years, including the discovery of available drugs that demonstrate significant protective effects in NDDs, relieve debilitating symptoms, or slow down the progression of the disease. These findings highlight the potential of repurposed drugs to change the landscape of NDD treatment. Here, we present an overview of recent developments and major advances in drug repurposing intending to provide an in-depth analysis of traditional drug discovery and the strategies, approaches and technologies that have contributed to drug repositioning. In addition, this chapter attempts to highlight successful case studies of drug repositioning in various therapeutic areas related to NDDs and explore the clinical trials, challenges and limitations faced by researchers in the field. Finally, the importance of drug repositioning in drug discovery and development and its potential to address discontented medical needs is also highlighted.

Artificial intelligence for drug discovery and development in Alzheimer's disease

The complex molecular mechanism and pathophysiology of Alzheimer's disease (AD) limits the development of effective therapeutics or prevention strategies. Artificial Intelligence (AI)-guided drug discovery combined with genetics/multi-omics (genomics, epigenomics, transcriptomics, proteomics, and metabolomics) analysis contributes to the understanding of the pathophysiology and precision medicine of the disease, including AD and AD-related dementia. In this review, we summarize the AI-driven methodologies for AD-agnostic drug discovery and development, including de novo drug design, virtual screening, and prediction of drug-target interactions, all of which have shown potentials. In particular, AI-based drug repurposing emerges as a compelling strategy to identify new indications for existing drugs for AD. We provide several emerging AD targets from human genetics and multi-omics findings and highlight recent AI-based technologies and their applications in drug discovery using AD as a prototypical example. In closing, we discuss future challenges and directions in AI-based drug discovery for AD and other neurodegenerative diseases.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

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.

GRELinker: A Graph-Based Generative Model for Molecular Linker Design with Reinforcement and Curriculum Learning

Fragment-based drug discovery (FBDD) is widely used in drug design. One useful strategy in FBDD is designing linkers for linking fragments to optimize their molecular properties. In the current study, we present a novel generative fragment linking model, GRELinker, which utilizes a gated-graph neural network combined with reinforcement and curriculum learning to generate molecules with desirable attributes. The model has been shown to be efficient in multiple tasks, including controlling log P, optimizing synthesizability or predicted bioactivity of compounds, and generating molecules with high 3D similarity but low 2D similarity to the lead compound. Specifically, our model outperforms the previously reported reinforcement learning (RL) built-in method DRlinker on these benchmark tasks. Moreover, GRELinker has been successfully used in an actual FBDD case to generate optimized molecules with enhanced affinities by employing the docking score as the scoring function in RL. Besides, the implementation of curriculum learning in our framework enables the generation of structurally complex linkers more efficiently. These results demonstrate the benefits and feasibility of GRELinker in linker design for molecular optimization and drug discovery.

In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor

O-linked N-acetylglucosamine (O-GlcNAc) is a unique intracellular post-translational glycosylation at the hydroxyl group of serine or threonine residues in nuclear, cytoplasmic and mitochondrial proteins. The enzyme O-GlcNAc transferase (OGT) is responsible for adding GlcNAc, and anomalies in this process can lead to the development of diseases associated with metabolic imbalance, such as diabetes and cancer. Repurposing approved drugs can be an attractive tool to discover new targets reducing time and costs in the drug design. This work focuses on drug repurposing to OGT targets by virtual screening of FDA-approved drugs through consensus machine learning (ML) models from an imbalanced dataset. We developed a classification model using docking scores and ligand descriptors. The SMOTE approach to resampling the dataset showed excellent statistical values in five of the seven ML algorithms to create models from the training set, with sensitivity, specificity and accuracy over 90% and Matthew's correlation coefficient greater than 0.8. The pose analysis obtained by molecular docking showed only H-bond interaction with the OGT C-Cat domain. The molecular dynamics simulation showed the lack of H-bond interactions with the C- and N-catalytic domains allowed the drug to exit the binding site. Our results showed that the non-steroidal anti-inflammatory celecoxib could be a potentially OGT inhibitor.