Drug repurposing for Alzheimer's disease based on transcriptional profiling of human iPSC-derived cortical neurons

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.

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.

PharmGWAS: a GWAS-based knowledgebase for drug repurposing

Leveraging genetics insights to promote drug repurposing has become a promising and active strategy in pharmacology. Indeed, among the 50 drugs approved by FDA in 2021, two-thirds have genetically supported evidence. In this regard, the increasing amount of widely available genome-wide association studies (GWAS) datasets have provided substantial opportunities for drug repurposing based on genetics discoveries. Here, we developed PharmGWAS, a comprehensive knowledgebase designed to identify candidate drugs through the integration of GWAS data. PharmGWAS focuses on novel connections between diseases and small-molecule compounds derived using a reverse relationship between the genetically-regulated expression signature and the drug-induced signature. Specifically, we collected and processed 1929 GWAS datasets across a diverse spectrum of diseases and 724 485 perturbation signatures pertaining to a substantial 33609 molecular compounds. To obtain reliable and robust predictions for the reverse connections, we implemented six distinct connectivity methods. In the current version, PharmGWAS deposits a total of 740 227 genetically-informed disease-drug pairs derived from drug-perturbation signatures, presenting a valuable and comprehensive catalog. Further equipped with its user-friendly web design, PharmGWAS is expected to greatly aid the discovery of novel drugs, the exploration of drug combination therapies and the identification of drug resistance or side effects. PharmGWAS is available at https://ngdc.cncb.ac.cn/pharmgwas.

Ethical Aspects of Human Induced Pluripotent Stem Cells and Alzheimer's Disease: Potentials and Challenges of a Seemingly Harmless Method

Dementia currently affects more than 55 million people worldwide, and scientists predict that this number will continue to rise. The most common form is Alzheimer's disease (AD), which is triggered, among other things, by dysfunctional cells in the human brain. Stem cell research attempts to counteract neurodegenerative processes, for example by replacing or treating diseased cells. In addition to human embryonic stem cells, since the successes of Takahashi and Yamanaka in 2006, there has been an increased focus on human induced pluripotent stem cells (hiPS cells). These cells avoid ethically challenging questions about the moral status of human embryos, but there are numerous problems, such as high production costs, side effects from the reprogramming process, or a potentially new moral status. These ethical issues will be examined primarily in relation to AD. The first part will be a discussion of hiPS cells and their importance for stem cell research, after which the focus turns to AD. Based on scientific studies, the relationship between hiPS cells and AD will be outlined as well as ethical implications presented. While potential limitations of hiPS cells have been discussed by numerous authors, an ethical perspective on the link between hiPS cells and AD seems to be neglected in the scientific community. The following risk analysis aims to identify a possible research agenda. In conclusion, the focus on individuals with AD may help to adopt an ethical stance that recognizes existing limitations and constructively engages with the possibilities of research.

Neurodegenerative Disease Associated Pathways in the Brains of Triple Transgenic Alzheimer's Model Mice Are Reversed Following Two Weeks of Peripheral Administration of Fasudil

The pan Rho-associated coiled-coil-containing protein kinase (ROCK) inhibitor fasudil acts as a vasodilator and has been used as a medication for post-cerebral stroke for the past 29 years in Japan and China. More recently, based on the involvement of ROCK inhibition in synaptic function, neuronal survival, and processes associated with neuroinflammation, it has been suggested that the drug may be repurposed for neurodegenerative diseases. Indeed, fasudil has demonstrated preclinical efficacy in many neurodegenerative disease models. To facilitate an understanding of the wider biological processes at play due to ROCK inhibition in the context of neurodegeneration, we performed a global gene expression analysis on the brains of Alzheimer's disease model mice treated with fasudil via peripheral IP injection. We then performed a comparative analysis of the fasudil-driven transcriptional profile with profiles generated from a meta-analysis of multiple neurodegenerative diseases. Our results show that fasudil tends to drive gene expression in a reverse sense to that seen in brains with post-mortem neurodegenerative disease. The results are most striking in terms of pathway enrichment analysis, where pathways perturbed in Alzheimer's and Parkinson's diseases are overwhelmingly driven in the opposite direction by fasudil treatment. Thus, our results bolster the repurposing potential of fasudil by demonstrating an anti-neurodegenerative phenotype in a disease context and highlight the potential of in vivo transcriptional profiling of drug activity.

Drug repositioning targeting glutaminase reveals drug candidates for the treatment of Alzheimer's disease patients

BACKGROUND

Despite numerous clinical trials and decades of endeavour, there is still no effective cure for Alzheimer's disease. Computational drug repositioning approaches may be employed for the development of new treatment strategies for Alzheimer's patients since an extensive amount of omics data has been generated during pre-clinical and clinical studies. However, targeting the most critical pathophysiological mechanisms and determining drugs with proper pharmacodynamics and good efficacy are equally crucial in drug repurposing and often imbalanced in Alzheimer's studies.

METHODS

Here, we investigated central co-expressed genes upregulated in Alzheimer's disease to determine a proper therapeutic target. We backed our reasoning by checking the target gene's estimated non-essentiality for survival in multiple human tissues. We screened transcriptome profiles of various human cell lines perturbed by drug induction (for 6798 compounds) and gene knockout using data available in the Connectivity Map database. Then, we applied a profile-based drug repositioning approach to discover drugs targeting the target gene based on the correlations between these transcriptome profiles. We evaluated the bioavailability, functional enrichment profiles and drug-protein interactions of these repurposed agents and evidenced their cellular viability and efficacy in glial cell culture by experimental assays and Western blotting. Finally, we evaluated their pharmacokinetics to anticipate to which degree their efficacy can be improved.

RESULTS

We identified glutaminase as a promising drug target. Glutaminase overexpression may fuel the glutamate excitotoxicity in neurons, leading to mitochondrial dysfunction and other neurodegeneration hallmark processes. The computational drug repurposing revealed eight drugs: mitoxantrone, bortezomib, parbendazole, crizotinib, withaferin-a, SA-25547 and two unstudied compounds. We demonstrated that the proposed drugs could effectively suppress glutaminase and reduce glutamate production in the diseased brain through multiple neurodegeneration-associated mechanisms, including cytoskeleton and proteostasis. We also estimated the human blood-brain barrier permeability of parbendazole and SA-25547 using the SwissADME tool.

CONCLUSIONS

This study method effectively identified an Alzheimer's disease marker and compounds targeting the marker and interconnected biological processes by use of multiple computational approaches. Our results highlight the importance of synaptic glutamate signalling in Alzheimer's disease progression. We suggest repurposable drugs (like parbendazole) with well-evidenced activities that we linked to glutamate synthesis hereby and novel molecules (SA-25547) with estimated mechanisms for the treatment of Alzheimer's patients.

Potential Ototoxicity of Insulin-like Growth Factor 1 Receptor Signaling Inhibitors: An In Silico Drug Repurposing Study of the Regenerating Cochlear Neuron Transcriptome

Spiral ganglion neurons (SGNs) connect cochlear hair cells with higher auditory pathways and their degeneration due to drug toxicity (ototoxicity) contributes to hearing loss. This study aimed to identify drug classes that are negatively correlated with the transcriptome of regenerating SGNs. Human orthologs of differentially expressed genes within the regenerating neonatal mouse SGN transcriptome were entered into CMap and the LINCS unified environment and perturbation-driven gene expression was analyzed. The CMap connectivity scores ranged from 100 (positive correlation) to -100 (negative correlation). Insulin-like growth factor 1/receptor (IGF-1/R) inhibitors were highly negatively correlated with the regenerating SGN transcriptome (connectivity score: -98.87). A systematic literature review of clinical trials and observational studies reporting otologic adverse events (AEs) with IGF-1/R inhibitors identified 108 reports (6141 treated patients). Overall, 16.9% of the treated patients experienced any otologic AE; the rate was highest for teprotumumab (42.9%). In a meta-analysis of two randomized placebo-controlled trials of teprotumumab, there was a significantly higher risk of hearing-related (pooled Peto OR [95% CI]: 7.95 [1.57, 40.17]) and of any otologic AEs (3.56 [1.35, 9.43]) with teprotumumab vs. a placebo, whether or not dizziness/vertigo AEs were included. These results call for close audiological monitoring during IGF-1-targeted treatment, with prompt referral to an otolaryngologist should otologic AEs develop.

Biomedical discovery through the integrative biomedical knowledge hub (iBKH)

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.

Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric syndrome with genetic and environmental influences. In addition to neuroanatomical and circuit-level disturbances, dysregulation of the brain transcriptome is a key phenotypic signature of MDD. Postmortem brain gene expression data are uniquely valuable resources for identifying this signature and key genomic drivers in human depression; however, the scarcity of brain tissue limits our capacity to observe the dynamic transcriptional landscape of MDD. It is therefore crucial to explore and integrate depression and stress transcriptomic data from numerous, complementary perspectives to construct a richer understanding of the pathophysiology of depression. In this review, we discuss multiple approaches for exploring the brain transcriptome reflecting dynamic stages of MDD: predisposition, onset, and illness. We next highlight bioinformatic approaches for hypothesis-free, genome-wide analyses of genomic and transcriptomic data and their integration. Last, we summarize the findings of recent genetic and transcriptomic studies within this conceptual framework.

CREB3L2-ATF4 heterodimerization defines a transcriptional hub of Alzheimer's disease gene expression linked to neuropathology

Gene expression is changed by disease, but how these molecular responses arise and contribute to pathophysiology remains less understood. We discover that β-amyloid, a trigger of Alzheimer's disease (AD), promotes the formation of pathological CREB3L2-ATF4 transcription factor heterodimers in neurons. Through a multilevel approach based on AD datasets and a novel chemogenetic method that resolves the genomic binding profile of dimeric transcription factors (ChIPmera), we find that CREB3L2-ATF4 activates a transcription network that interacts with roughly half of the genes differentially expressed in AD, including subsets associated with β-amyloid and tau neuropathologies. CREB3L2-ATF4 activation drives tau hyperphosphorylation and secretion in neurons, in addition to misregulating the retromer, an endosomal complex linked to AD pathogenesis. We further provide evidence for increased heterodimer signaling in AD brain and identify dovitinib as a candidate molecule for normalizing β-amyloid-mediated transcriptional responses. The findings overall reveal differential transcription factor dimerization as a mechanism linking disease stimuli to the development of pathogenic cellular states.

Synthesize heterogeneous biological knowledge via representation learning for Alzheimer's disease drug repurposing

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in reducing oxidative stress and safety in maintaining neuronal viability and morphology. Our neuronal response experiments confirmed several biologically efficacious drug combinations. This pipeline showed that harmonizing heterogeneous and complementary data/knowledge, including human interactome, transcriptome patterns, experimental efficacy, and real-world patient data shed light on the drug development of complex diseases.

Network Proximity-based computational pipeline identifies drug candidates for different pathological stages of Alzheimer's disease

Despite the massive investment in Alzheimer's disease (AD), there are still no disease-modifying treatments (DMTs) for AD. One major reason is attributed to the limitation of clinical "one-size-fits-all" approach, since the same AD treatment solely based on clinical diagnosis was unlikely to achieve good clinical efficacy. In recent years, computational approaches based on multiomics data have provided an unprecedented opportunity for drug discovery since they can substantially lower the costs and boost the efficiency. In this study, we intended to identify potential drug candidates for different pathological stages of AD by computationally repurposing Food and Drug Administration (FDA) approved drugs. First, we assembled gene expression data from three different AD pathological stages, which include mild cognitive impairment (MCI) and early and late stages of AD (EAD, LAD). We next quantified the network distances between drug target networks and AD modules by utilizing a network proximity approach, and identified 193 candidates that possessed significant associations with AD. After searching for previous literature evidence, 63 out of 193 (32.6%) predicted drugs were demonstrated to exert therapeutic effects on AD. We further explored the novel mechanism of action (MOA) for these drug candidates by determining the specific brain cells they might function on based on AD patient single cell transcriptomic data. Additionally, we selected several promising candidates that could cross the blood brain barrier together with confirmed neuroprotective effects, and subsequently determined the antioxidative activity of these compounds. Experimental results showed that azathioprine decreased the reactive oxygen species (ROS) and malondialdehyde (MDA) levels and improved the superoxide dismutase (SOD) activity in APP-SH-SY5Y cells. Finally, we deciphered the potential MOA of azathioprine against AD via network analysis and validated several apoptosis-related proteins (Caspase 3, Cleaved Caspase 3, Bax, Bcl2) through western blotting. In summary, this study presented an effective computational strategy utilizing omics data for AD drug repurposing, which provides a new perspective for drug discovery and development.

The relationships between neuroglial and neuronal changes in Alzheimer's disease, and the related controversies II:

Since the original description of Alzheimer´s disease (AD), research into this condition has mainly focused on assessing the alterations to neurons associated with dementia, and those to the circuits in which they are involved. In most of the studies on human brains and in many models of AD, the glial cells accompanying these neurons undergo concomitant alterations that aggravate the course of neurodegeneration. As a result, these changes to neuroglial cells are now included in all the "pathogenic cascades" described in AD. Accordingly, astrogliosis and microgliosis, the main components of neuroinflammation, have been integrated into all the pathogenic theories of this disease, as discussed in this part of the two-part monograph that follows an accompanying article on gliopathogenesis and glioprotection. This initial reflection verified the implication of alterations to the neuroglia in AD, suggesting that these cells may also represent therapeutic targets to prevent neurodegeneration. In this second part of the monograph, we will analyze the possibilities of acting on glial cells to prevent or treat the neurodegeneration that is the hallmark of AD and other pathologies. Evidence of the potential of different pharmacological, non-pharmacological, cell and gene therapies (widely treated) to prevent or treat this disease is now forthcoming, in most cases as adjuncts to other therapies. A comprehensive AD multimodal therapy is proposed in which neuronal and neuroglial pharmacological treatments are jointly considered, as well as the use of new cell and gene therapies and non-pharmacological therapies that tend to slow down the progress of dementia.

Endotype reversal as a novel strategy for screening drugs targeting familial Alzheimer's disease

While amyloid-β (Aβ) plaques are considered a hallmark of Alzheimer's disease, clinical trials focused on targeting gamma secretase, an enzyme involved in aberrant Aβ peptide production, have not led to amelioration of AD symptoms or synaptic dysregulation. Screening strategies based on mechanistic, multi-omics approaches that go beyond pathological readouts can aid in the evaluation of therapeutics. Using early-onset Alzheimer's (EOFAD) disease patient lineage PSEN1^A246E iPSC-derived neurons, we performed RNA-seq to characterize AD-associated endotypes, which are in turn used as a screening evaluation metric for two gamma secretase drugs, the inhibitor Semagacestat and the modulator BPN-15606. We demonstrate that drug treatment partially restores the neuronal state while concomitantly inhibiting cell cycle re-entry and dedifferentiation endotypes to different degrees depending on the mechanism of gamma secretase engagement. Our endotype-centric screening approach offers a new paradigm by which candidate AD therapeutics can be evaluated for their overall ability to reverse disease endotypes.

Emerging therapeutic developments in neurodegenerative diseases: A clinical investigation

Despite a century of intensive research, there is still a lack of disease-modifying treatments for neurodegenerative diseases that pose a threat to human society. A well-documented knowledge and resource gap has impeded the translation of fundamental research into promising therapies. In addition, the analysis of extensive preclinical data to allow the improved selection of therapeutic technologies and clinical candidates for further development is challenging. To address this need, we describe technologies that have emerged over the past decade that have enabled the development of novel, high-quality, cost-effective treatments for major neurodegenerative diseases, including Alzheimer's disease and Parkinson's disease. Moreover, we benchmark emerging technologies that have been adopted by top pharmaceutical companies looking to bridge the gap between drug discovery and drug development in neurodegenerative disease.

Dipyridamole for tracking amyloidogenic proteins aggregation and enhancing polyubiquitination

Dipyridamole is currently used as a medication that inhibits blood clot formation and it is also investigated in the context of neurodegenerative and other amyloid related diseases. Here, we propose this molecule as a new diagnostic tool to follow the aggregation properties of three different amyloidogenic proteins tested (insulin, amylin and amyloid β peptide 1-40). Results show that dipyridamole is sensitive to early stage amyloid formation undetected by thioflavin T, giving a different response for the aggregation of the three different proteins. In addition, we show that dipyridamole is also able to enhance ubiquitin chain growth, paving the way to its potential application as therapeutic agent in neurodegenerative diseases.

Mechanistic Insights for Drug Repurposing and the Design of Hybrid Drugs for Alzheimer's Disease

The heterogeneity and complex nature of Alzheimer's disease (AD) is attributed to several genetic risk factors and molecular culprits. The slow pace and increasing failure rate of conventional drug discovery has led to the exploration of complementary strategies based on repurposing approved drugs to treat AD. Drug repurposing (DR) is a cost-effective, low-risk, and efficient approach for identifying novel therapeutic candidates for AD treatment. Similarly, hybrid drug design through the integration of distinct pharmacophores from known or failed drugs and natural products is an interesting strategy to target the multifactorial nature of AD. In this Perspective, we discuss the potential of DR and highlight promising drug candidates that can be advanced for clinical trials, backed by a detailed discussion on their plausible mechanisms of action. Our article fosters research on the hidden potential of DR and hybrid drug design with the goal of unravelling new drugs and targets to tackle AD.

Simple, fast, and flexible framework for matrix completion with infinite width neural networks

Matrix completion is a fundamental problem in machine learning that arises in various applications. We envision that our infinite width neural network framework for matrix completion will be easily deployable and produce strong baselines for a wide range of applications at limited computational costs. We demonstrate the flexibility of our framework through competitive results on virtual drug screening and image inpainting/reconstruction. Simplicity and speed are showcased by the fact that most results in this work require only a central processing unit and commodity hardware. Through its connection to semisupervised learning, our framework provides a principled approach for matrix completion that can be easily applied to problems well beyond those of image completion and virtual drug screening considered in this paper.

Matrix completion problems arise in many applications including recommendation systems, computer vision, and genomics. Increasingly larger neural networks have been successful in many of these applications but at considerable computational costs. Remarkably, taking the width of a neural network to infinity allows for improved computational performance. In this work, we develop an infinite width neural network framework for matrix completion that is simple, fast, and flexible. Simplicity and speed come from the connection between the infinite width limit of neural networks and kernels known as neural tangent kernels (NTK). In particular, we derive the NTK for fully connected and convolutional neural networks for matrix completion. The flexibility stems from a feature prior, which allows encoding relationships between coordinates of the target matrix, akin to semisupervised learning. The effectiveness of our framework is demonstrated through competitive results for virtual drug screening and image inpainting/reconstruction. We also provide an implementation in Python to make our framework accessible on standard hardware to a broad audience.

A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease

Insulin resistance (IR) contributes to the pathophysiology of diabetes, dementia, viral infection, and cardiovascular disease. Drug repurposing (DR) may identify treatments for IR; however, barriers include uncertainty whether in vitro transcriptomic assays yield quantitative pharmacological data, or how to optimise assay design to best reflect in vivo human disease. We developed a clinical-based human tissue IR signature by combining lifestyle-mediated treatment responses (>500 human adipose and muscle biopsies) with biomarkers of disease status (fasting IR from >1200 biopsies). The assay identified a chemically diverse set of >130 positively acting compounds, highly enriched in true positives, that targeted 73 proteins regulating IR pathways. Our multi-gene RNA assay score reflected the quantitative pharmacological properties of a set of epidermal growth factor receptor-related tyrosine kinase inhibitors, providing insight into drug target specificity; an observation supported by deep learning-based genome-wide predicted pharmacology. Several drugs identified are suitable for evaluation in patients, particularly those with either acute or severe chronic IR.

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.

From serendipity to rational drug design in brain disorders: in silico, in vitro, and in vivo approaches

Prolonged life expectancy and stressful lifestyles have increased the risk of developing neurological disorders, including neurodegenerative and psychiatric illnesses. Despite obvious and immediate needs for effective treatment, drug discovery for neurological disorders has been largely serendipitous, whereas hypothesis-driven drug development programs have been remarkably poor. This may be partly due to insufficient knowledge of molecular mechanisms underlying disease pathophysiology, complex genetic and environmental risk factors, and oversimplified diagnostic criteria. Here, we review recent progress in cell type-specific investigations, bioinformatics analyses, and large reference databases, the integration of which, when combined with effective use of animal models, provides novel insights into disease mechanisms, suggests innovative drug development, and ultimately promises superior treatments for patients suffering from neurological disorders.

Emerging hiPSC Models for Drug Discovery in Neurodegenerative Diseases

Neurodegenerative diseases affect millions of people worldwide and are characterized by the chronic and progressive deterioration of neural function. Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), and Huntington's disease (HD), represent a huge social and economic burden due to increasing prevalence in our aging society, severity of symptoms, and lack of effective disease-modifying therapies. This lack of effective treatments is partly due to a lack of reliable models. Modeling neurodegenerative diseases is difficult because of poor access to human samples (restricted in general to postmortem tissue) and limited knowledge of disease mechanisms in a human context. Animal models play an instrumental role in understanding these diseases but fail to comprehensively represent the full extent of disease due to critical differences between humans and other mammals. The advent of human-induced pluripotent stem cell (hiPSC) technology presents an advantageous system that complements animal models of neurodegenerative diseases. Coupled with advances in gene-editing technologies, hiPSC-derived neural cells from patients and healthy donors now allow disease modeling using human samples that can be used for drug discovery.

Intuitive repositioning of an anti-depressant drug in combination with tivozanib: precision medicine for breast cancer therapy

Despite the existing therapies and lack of receptors such as HER-2, estrogen receptor and progesterone receptor, triple-negative breast cancer is one of the most aggressive subtypes of breast cancer. TNBCs are known for their highly aggressive metastatic behavior and typically migrate to brain and bone for secondary site propagation. Many diseases share similar molecular pathology exposing new avenues in molecular signaling for engendering innovative therapies. Generation of newer therapies and novel drugs are time consuming associated with very high resources. In order to provide personalized or precision medicine, drug repositioning will contribute in a cost-effective manner. In our study, we have repurposed and used a neoteric combination of two drug molecules namely, fluvoxamine and tivozanib, to target triple-negative breast cancer growth and progression. Our combination regime significantly targets two diverse but significant pathways in TNBCs. Subsequent analysis on migratory, invasive, and angiogenic properties showed the significance of our repurposed drug combination. Molecular array data resulted in identifying the specific and key players participating in cancer progression when the drug combination was used. The innovative combination of fluvoxamine and tivozanib reiterates the use of drug repositioning for precision medicine and subsequent companion diagnostic development.

Recent Trends in the Management of Alzheimer's Disease: Current Therapeutic Options and Drug Repurposing Approaches

Alzheimer's disease is one of the most progressive forms of dementia, ultimately leading to death in aged populations. The major hallmarks of Alzheimer's disease include deposition of extracellular amyloid senile plaques and intracellular neurofibrillary tangles in brain neuronal cells. Although there are classical therapeutic options available for the treatment of the diseases, however, they provide only a symptomatic relief and do not modify the molecular pathophysiological course of the disease. Recent research advances in Alzheimer's disease have highlighted the potential role of anti-amyloid, anti-tau, and anti-inflammatory therapies. However, these therapies are still in different phases of pre-clinical/clinical development. In addition, drug repositioning/repurposing is another interesting and promising approach to explore rationalized options for the treatment of Alzheimer's disease. This review discusses the different aspects of the pathophysiological mechanism involved in the progression of Alzheimer's disease along with the limitations of current therapies. Furthermore, this review also highlights emerging investigational drugs along with recent drug repurposing approaches for Alzheimer's disease.

A muscle growth-promoting treatment based on the attenuation of activin/myostatin signalling results in long-term testicular abnormalities

Activin/myostatin signalling acts to induce skeletal muscle atrophy in adult mammals by inhibiting protein synthesis as well as promoting protein and organelle turnover. Numerous strategies have been successfully developed to attenuate the signalling properties of these molecules, which result in augmenting muscle growth. However, these molecules, in particular activin, play major roles in tissue homeostasis in numerous organs of the mammalian body. We have recently shown that although the attenuation of activin/myostatin results in robust muscle growth, it also has a detrimental impact on the testis. Here, we aimed to discover the long-term consequences of a brief period of exposure to muscle growth-promoting molecules in the testis. We demonstrate that muscle hypertrophy promoted by a soluble activin type IIB ligand trap (sActRIIB) is a short-lived phenomenon. In stark contrast, short-term treatment with sActRIIB results in immediate impact on the testis, which persists after the sessions of the intervention. Gene array analysis identified an expansion in aberrant gene expression over time in the testis, initiated by a brief exposure to muscle growth-promoting molecules. The impact on the testis results in decreased organ size as well as quantitative and qualitative impact on sperm. Finally, we have used a drug-repurposing strategy to exploit the gene expression data to identify a compound - N ⁶-methyladenosine - that may protect the testis from the impact of the muscle growth-promoting regime. This work indicates the potential long-term harmful effects of strategies aimed at promoting muscle growth by attenuating activin/myostatin signalling. Furthermore, we have identified a molecule that could, in the future, be used to overcome the detrimental impact of sActRIIB treatment on the testis.

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

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

Signature-based approaches for informed drug repurposing: targeting CNS disorders

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing - both a less expensive and time-efficient practice compared to de novo drug development - has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data ("omics") have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

Drug repositioning and repurposing for Alzheimer disease

Drug repositioning and repurposing can enhance traditional drug development efforts and could accelerate the identification of new treatments for individuals with Alzheimer disease (AD) dementia and mild cognitive impairment. Transcriptional profiling offers a new and highly efficient approach to the identification of novel candidates for repositioning and repurposing. In the future, novel AD transcriptional signatures from cells isolated at early stages of disease, or from human neurons or microglia that carry mutations that increase the risk of AD, might be used as probes to identify additional candidate drugs. Phase II trials assessing repurposed agents must consider the best target population for a specific candidate therapy as well as the mechanism of action of the treatment. In this Review, we highlight promising compounds to prioritize for clinical trials in individuals with AD, and discuss the value of Delphi consensus methodology and evidence-based reviews to inform this prioritization process. We also describe emerging work, focusing on the potential value of transcript signatures as a cost-effective approach to the identification of novel candidates for repositioning.

Transcription-based drug repurposing for COVID-19

We have utilised the transcriptional response of lung epithelial cells following infection by the original Severe Acute Respiratory Syndrome coronavirus (SARS) to identify repurposable drugs for COVID-19. Drugs best able to recapitulate the infection profile are highly enriched for antiviral activity. Nine of these have been tested against SARS-2 and found to potently antagonise SARS-2 infection/replication, with a number now being considered for clinical trials. It is hoped that this approach may serve to broaden the spectrum of approved drugs that should be further assessed as potential anti-COVID-19 agents and may help elucidate how this seemingly disparate collection of drugs are able to inhibit SARS-2 infection/replication.

Whole transcriptome in silico screening implicates cardiovascular and infectious disease in the mechanism of action underlying atypical antipsychotic side effects

BACKGROUND

Stroke/thromboembolic events, infections, and death are all significantly increased by antipsychotics in dementia but little is known about why they can be harmful. Using a novel application of a drug repurposing paradigm, we aimed to identify potential mechanisms underlying adverse events.

METHODS

Whole transcriptome signatures were generated for SH-SY5Y cells treated with amisulpride, risperidone, and volinanserin using RNA sequencing. Bioinformatic analysis was performed that scored the association between antipsychotic signatures and expression data from 415,252 samples in the National Center for Biotechnology Information Gene Expression Omnibus (NCBI GEO) repository.

RESULTS

Atherosclerosis, venous thromboembolism, and influenza NCBI GEO-derived samples scored positively against antipsychotic signatures. Pathways enriched in antipsychotic signatures were linked to the cardiovascular and immune systems (eg, brain derived neurotrophic factor [BDNF], platelet derived growth factor receptor [PDGFR]-beta, tumor necrosis factor [TNF], transforming growth factor [TGF]-beta, selenoamino acid metabolism, and influenza infection).

CONCLUSIONS

These findings for the first time mechanistically link antipsychotics to specific cardiovascular and infectious diseases which are known side effects of their use in dementia, providing new information to explain related adverse events.