Alzheimer's disease drug development pipeline: 2024.

INTRODUCTION: New therapies to prevent or delay the onset of symptoms, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. METHODS: We interrogated clinicaltrials.gov including all clinical trials assessing pharmaceutical therapies for AD active in on January 1, 2024. We used the Common Alzheimer's Disease Research Ontology (CADRO) to classify the targets of therapies in the pipeline. RESULTS: There are 164 trials assessing 127 drugs across the 2024 AD pipeline. There were 48 trials in Phase 3 testing 32 drugs, 90 trials in Phase 2 assessing 81 drugs, and 26 trials in Phase 1 testing 25 agents. Of the 164 trials, 34% (N = 56) assess disease-modifying biological agents, 41% (N = 68) test disease-modifying small molecule drugs, 10% (N = 17) evaluate cognitive enhancing agents, and 14% (N = 23) test drugs for the treatment of neuropsychiatric symptoms. DISCUSSION: Compared to the 2023 pipeline, there are fewer trials (164 vs. 187), fewer drugs (127 vs. 141), fewer new chemical entities (88 vs. 101), and a similar number of repurposed agents (39 vs. 40). Highlights: In the 2024 Alzheimer's disease drug development pipeline, there are 164 clinical trials assessing 127 drugs.The 2024 Alzheimer's disease drug development pipeline has contracted compared to the 2023 Alzheimer pipeline with fewer trials, fewer drugs, and fewer new chemical entities.Drugs in the Alzheimer's disease drug development pipeline target a wide array of targets; the most common processes targeted include neurotransmitter receptors, inflammation, amyloid, and synaptic plasticity.The total development time for a potential Alzheimer's disease therapy to progress from nonclinical studies to FDA review is approximately 13 years.

Systematic characterization of multi-omics landscape between gut microbial metabolites and GPCRome in Alzheimer's disease.

Shifts in the magnitude and nature of gut microbial metabolites have been implicated in Alzheimer's disease (AD), but the host receptors that sense and respond to these metabolites are largely unknown. Here, we develop a systems biology framework that integrates machine learning and multi-omics to identify molecular relationships of gut microbial metabolites with non-olfactory G-protein-coupled receptors (termed the "GPCRome"). We evaluate 1.09 million metabolite-protein pairs connecting 408 human GPCRs and 335 gut microbial metabolites. Using genetics-derived Mendelian randomization and integrative analyses of human brain transcriptomic and proteomic profiles, we identify orphan GPCRs (i.e., GPR84) as potential drug targets in AD and that triacanthine experimentally activates GPR84. We demonstrate that phenethylamine and agmatine significantly reduce tau hyperphosphorylation (p-tau181 and p-tau205) in AD patient induced pluripotent stem cell-derived neurons. This study demonstrates a systems biology framework to uncover the GPCR targets of human gut microbiota in AD and other complex diseases if broadly applied.

Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis.

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Sildenafil as a Candidate Drug for Alzheimer's Disease: Real-World Patient Data Observation and Mechanistic Observations from Patient-Induced Pluripotent Stem Cell-Derived Neurons.

Background: Alzheimer's disease (AD) is a chronic neurodegenerative disease needing effective therapeutics urgently. Sildenafil, one of the approved phosphodiesterase-5 inhibitors, has been implicated as having potential effect in AD. Objective: To investigate the potential therapeutic benefit of sildenafil on AD. Methods: We performed real-world patient data analysis using the MarketScan® Medicare Supplemental and the Clinformatics® databases. We conducted propensity score-stratified analyses after adjusting confounding factors (i.e., sex, age, race, and comorbidities). We used both familial and sporadic AD patient induced pluripotent stem cells (iPSC) derived neurons to evaluate the sildenafil's mechanism-of-action. Results: We showed that sildenafil usage is associated with reduced likelihood of AD across four new drug compactor cohorts, including bumetanide, furosemide, spironolactone, and nifedipine. For instance, sildenafil usage is associated with a 54% reduced incidence of AD in MarketScan® (hazard ratio [HR] = 0.46, 95% CI 0.32- 0.66) and a 30% reduced prevalence of AD in Clinformatics® (HR = 0.70, 95% CI 0.49- 1.00) compared to spironolactone. We found that sildenafil treatment reduced tau hyperphosphorylation (pTau181 and pTau205) in a dose-dependent manner in both familial and sporadic AD patient iPSC-derived neurons. RNA-sequencing data analysis of sildenafil-treated AD patient iPSC-derived neurons reveals that sildenafil specifically target AD related genes and pathobiological pathways, mechanistically supporting the beneficial effect of sildenafil in AD. Conclusions: These real-world patient data validation and mechanistic observations from patient iPSC-derived neurons further suggested that sildenafil is a potential repurposable drug for AD. Yet, randomized clinical trials are warranted to validate the causal treatment effects of sildenafil in AD.

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.

Alzheimer's disease-linked risk alleles elevate microglial cGAS-associated senescence and neurodegeneration in a tauopathy model.

The strongest risk factors for Alzheimer's disease (AD) include the χ4 allele of apolipoprotein E (APOE), the R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2), and female sex. Here, we combine APOE4 and TREM2R47H ( R47H ) in female P301S tauopathy mice to identify the pathways activated when AD risk is the strongest, thereby highlighting disease-causing mechanisms. We find that the R47H variant induces neurodegeneration in female APOE4 mice without impacting hippocampal tau load. The combination of APOE4 and R47H amplified tauopathy-induced cell-autonomous microglial cGAS-STING signaling and type-I interferon response, and interferon signaling converged across glial cell types in the hippocampus. APOE4-R47H microglia displayed cGAS- and BAX-dependent upregulation of senescence, showing association between neurotoxic signatures and implicating mitochondrial permeabilization in pathogenesis. By uncovering pathways enhanced by the strongest AD risk factors, our study points to cGAS-STING signaling and associated microglial senescence as potential drivers of AD risk.

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.

Structurally-informed human interactome reveals proteome-wide perturbations by disease mutations.

Human genome sequencing studies have identified numerous loci associated with complex diseases. However, translating human genetic and genomic findings to disease pathobiology and therapeutic discovery remains a major challenge at multiscale interactome network levels. Here, we present a deep-learning-based ensemble framework, termed PIONEER (Protein-protein InteractiOn iNtErfacE pRediction), that accurately predicts protein binding partner-specific interfaces for all known protein interactions in humans and seven other common model organisms, generating comprehensive structurally-informed protein interactomes. We demonstrate that PIONEER outperforms existing state-of-the-art methods. We further systematically validated PIONEER predictions experimentally through generating 2,395 mutations and testing their impact on 6,754 mutation-interaction pairs, confirming the high quality and validity of PIONEER predictions. We show that disease-associated mutations are enriched in PIONEER-predicted protein-protein interfaces after mapping mutations from ~60,000 germline exomes and ~36,000 somatic genomes. We identify 586 significant protein-protein interactions (PPIs) enriched with PIONEER-predicted interface somatic mutations (termed oncoPPIs) from pan-cancer analysis of ~11,000 tumor whole-exomes across 33 cancer types. We show that PIONEER-predicted oncoPPIs are significantly associated with patient survival and drug responses from both cancer cell lines and patient-derived xenograft mouse models. We identify a landscape of PPI-perturbing tumor alleles upon ubiquitination by E3 ligases, and we experimentally validate the tumorigenic KEAP1-NRF2 interface mutation p.Thr80Lys in non-small cell lung cancer. We show that PIONEER-predicted PPI-perturbing alleles alter protein abundance and correlates with drug responses and patient survival in colon and uterine cancers as demonstrated by proteogenomic data from the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium. PIONEER, implemented as both a web server platform and a software package, identifies functional consequences of disease-associated alleles and offers a deep learning tool for precision medicine at multiscale interactome network levels.

Microglial immunometabolism endophenotypes contribute to sex difference in Alzheimer's disease.

INTRODUCTION: The molecular mechanisms that contribute to sex differences, in particular female predominance, in Alzheimer's disease (AD) prevalence, symptomology, and pathology, are incompletely understood. METHODS: To address this problem, we investigated cellular metabolism and immune responses ("immunometabolism endophenotype") across AD individuals as a function of sex with diverse clinical diagnosis of cognitive status at death (cogdx), Braak staging, and Consortium to Establish a Registry for AD (CERAD) scores using human cortex metabolomics and transcriptomics data from the Religious Orders Study / Memory and Aging Project (ROSMAP) cohort. RESULTS: We identified sex-specific metabolites, immune and metabolic genes, and pathways associated with the AD diagnosis and progression. We identified female-specific elevation in glycerophosphorylcholine and N-acetylglutamate, which are AD inflammatory metabolites involved in interleukin (IL)-17 signaling, C-type lectin receptor, interferon signaling, and Toll-like receptor pathways. We pinpointed distinct microglia-specific immunometabolism endophenotypes (i.e., lipid- and amino acid-specific IL-10 and IL-17 signaling pathways) between female and male AD subjects. In addition, female AD subjects showed evidence of diminished excitatory neuron and microglia communications via glutamate-mediated immunometabolism. DISCUSSION: Our results point to new understanding of the molecular basis for female predominance in AD, and warrant future independent validations with ethnically diverse patient cohorts to establish a likely causal relationship of microglial immunometabolism in the sex differences in AD. HIGHLIGHTS: Sex-specific immune metabolites, gene networks and pathways, are associated with Alzheimer's disease pathogenesis and disease progression. Female AD subjects exhibit microglial immunometabolism endophenotypes characterized by decreased glutamate metabolism and elevated interleukin-10 pathway activity. Female AD subjects showed a shift in glutamate-mediated cell-cell communications between excitatory neurons to microglia and astrocyte.

High-throughput target trial emulation for Alzheimer's disease drug repurposing with real-world data.

Target trial emulation is the process of mimicking target randomized trials using real-world data, where effective confounding control for unbiased treatment effect estimation remains a main challenge. Although various approaches have been proposed for this challenge, a systematic evaluation is still lacking. Here we emulated trials for thousands of medications from two large-scale real-world data warehouses, covering over 10 years of clinical records for over 170 million patients, aiming to identify new indications of approved drugs for Alzheimer's disease. We assessed different propensity score models under the inverse probability of treatment weighting framework and suggested a model selection strategy for improved baseline covariate balancing. We also found that the deep learning-based propensity score model did not necessarily outperform logistic regression-based methods in covariate balancing. Finally, we highlighted five top-ranked drugs (pantoprazole, gabapentin, atorvastatin, fluticasone, and omeprazole) originally intended for other indications with potential benefits for Alzheimer's patients.

PDE5 inhibitor drugs for use in dementia.

Alzheimer's disease and related dementias (ADRD) remain a major health-care challenge with few licensed medications. Repurposing existing drugs may afford prevention and treatment. Phosphodiesterase-5 (PDE5) is widely expressed in vascular myocytes, neurons, and glia. Potent, selective, Food and Drug Administration-approved PDE5 inhibitors are already in clinical use (sildenafil, vardenafil, tadalafil) as vasodilators in erectile dysfunction and pulmonary arterial hypertension. Animal data indicate cognitive benefits of PDE5 inhibitors. In humans, real-world patient data suggest that sildenafil and vardenafil are associated with reduced dementia risk. While a recent clinical trial of acute tadalafil on cerebral blood flow was neutral, there may be chronic actions of PDE5 inhibition on cerebrovascular or synaptic function. We provide a perspective on the potential utility of PDE5 inhibitors for ADRD. We conclude that further prospective clinical trials with PDE5 inhibitors are warranted. The choice of drug will depend on brain penetration, tolerability in older people, half-life, and off-target effects. HIGHLIGHTS: Potent phosphodiesterase-5 (PDE5) inhibitors are in clinical use as vasodilators.In animals PDE5 inhibitors enhance synaptic function and cognitive ability.In humans the PDE5 inhibitor sildenafil is associated with reduced risk of Alzheimer's disease.Licensed PDE5 inhibitors have potential for repurposing in dementia.Prospective clinical trials of PDE5 inhibitors are warranted.

GAP43-dependent mitochondria transfer from astrocytes enhances glioblastoma tumorigenicity.

The transfer of intact mitochondria between heterogeneous cell types has been confirmed in various settings, including cancer. However, the functional implications of mitochondria transfer on tumor biology are poorly understood. Here we show that mitochondria transfer is a prevalent phenomenon in glioblastoma (GBM), the most frequent and malignant primary brain tumor. We identified horizontal mitochondria transfer from astrocytes as a mechanism that enhances tumorigenesis in GBM. This transfer is dependent on network-forming intercellular connections between GBM cells and astrocytes, which are facilitated by growth-associated protein 43 (GAP43), a protein involved in neuron axon regeneration and astrocyte reactivity. The acquisition of astrocyte mitochondria drives an increase in mitochondrial respiration and upregulation of metabolic pathways linked to proliferation and tumorigenicity. Functionally, uptake of astrocyte mitochondria promotes cell cycle progression to proliferative G2/M phases and enhances self-renewal and tumorigenicity of GBM. Collectively, our findings reveal a host-tumor interaction that drives proliferation and self-renewal of cancer cells, providing opportunities for therapeutic development.

Alzheimer's disease drug development pipeline: 2023.

Introduction: Drugs that prevent the onset, slow progression, or improve cognitive and behavioral symptoms of Alzheimer's disease (AD) are needed. Methods: We searched ClinicalTrials.gov for all current Phase 1, 2 and 3 clinical trials for AD and mild cognitive impairment (MCI) attributed to AD. We created an automated computational database platform to search, archive, organize, and analyze the derived data. The Common Alzheimer's Disease Research Ontology (CADRO) was used to identify treatment targets and drug mechanisms. Results: On the index date of January 1, 2023, there were 187 trials assessing 141 unique treatments for AD. Phase 3 included 36 agents in 55 trials; 87 agents were in 99 Phase 2 trials; and Phase 1 had 31 agents in 33 trials. Disease-modifying therapies were the most common drugs comprising 79% of drugs in trials. Twenty-eight percent of candidate therapies are repurposed agents. Populating all current Phase 1, 2, and 3 trials will require 57,465 participants. Discussion: The AD drug development pipeline is advancing agents directed at a variety of target processes. HIGHLIGHTS: There are currently 187 trials assessing 141 drugs for the treatment of Alzheimer's disease (AD).Drugs in the AD pipeline address a variety of pathological processes.More than 57,000 participants will be required to populate all currently registered trials.

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.

Altered gene expression in excitatory neurons is associated with Alzheimer's disease and its higher incidence in women.

Introduction: Alzheimer's disease (AD) is a neurodegenerative disorder involving interactions between different cell types in the brain. Previous single-cell and bulk expression Alzheimer's studies have reported conflicting findings about the key cell types and cellular pathways whose expression is primarily altered in this disease. We re-analyzed these data in a uniform, coherent manner aiming to resolve and extend past findings. Our analysis sheds light on the observation that females have higher AD incidence than males. Methods: We re-analyzed three single-cell transcriptomics datasets. We used the software Model-based Analysis of Single-cell Transcriptomics (MAST) to seek differentially expressed genes comparing AD cases to matched controls for both sexes together and each sex separately. We used the GOrilla software to search for enriched pathways among the differentially expressed genes. Motivated by the male/female difference in incidence, we studied genes on the X-chromosome, focusing on genes in the pseudoautosomal region (PAR) and on genes that are heterogeneous across individuals or tissues for X-inactivation. We validated findings by analyzing bulk AD datasets from the cortex in the Gene Expression Omnibus. Results: Our results resolve a contradiction in the literature, showing that by comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types. Synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons. PAR genes and X-chromosome heterogeneous genes, including, for example, BEX1 and ELK1, may contribute to the difference in sex incidence of Alzheimer's disease. GRIN1, stood out as an overexpressed autosomal gene in cases versus controls in all three single-cell datasets and as a functional candidate gene contributing to pathways upregulated in cases. Discussion: Taken together, these results point to a potential linkage between two longstanding questions concerning AD pathogenesis, involving which cell type is the most important and why females have a higher incidence than males. Highlights: By reanalyzing three, published, single-cell RNAseq datasets, we resolved a contradiction in the literature and showed that when comparing AD patients to unaffected controls, excitatory neurons have more differentially expressed genes than do other cell types.Further analysis of the published single-cell datasets showed that synaptic transmission and related pathways are altered in a sex-specific analysis of excitatory neurons.Combining analysis of single-cell datasets and publicly available bulk transcriptomics datasets revealed that X-chromosome genes, such as BEX1, ELK1, and USP11, whose X-inactivation status is heterogeneous may contribute to the higher incidence in females of Alzheimer's disease.

MBOAT7-driven lysophosphatidylinositol acylation in adipocytes contributes to systemic glucose homeostasis.

We previously demonstrated that antisense oligonucleotide-mediated knockdown of Mboat7, the gene encoding membrane bound O-acyltransferase 7, in the liver and adipose tissue of mice promoted high fat diet-induced hepatic steatosis, hyperinsulinemia, and systemic insulin resistance. Thereafter, other groups showed that hepatocyte-specific genetic deletion of Mboat7 promoted striking fatty liver and NAFLD progression in mice but does not alter insulin sensitivity, suggesting the potential for cell autonomous roles. Here, we show that MBOAT7 function in adipocytes contributes to diet-induced metabolic disturbances including hyperinsulinemia and systemic insulin resistance. We generated Mboat7 floxed mice and created hepatocyte- and adipocyte-specific Mboat7 knockout mice using Cre-recombinase mice under the control of the albumin and adiponectin promoter, respectively. Here, we show that MBOAT7 function in adipocytes contributes to diet-induced metabolic disturbances including hyperinsulinemia and systemic insulin resistance. The expression of Mboat7 in white adipose tissue closely correlates with diet-induced obesity across a panel of ∼100 inbred strains of mice fed a high fat/high sucrose diet. Moreover, we found that adipocyte-specific genetic deletion of Mboat7 is sufficient to promote hyperinsulinemia, systemic insulin resistance, and mild fatty liver. Unlike in the liver, where Mboat7 plays a relatively minor role in maintaining arachidonic acid-containing PI pools, Mboat7 is the major source of arachidonic acid-containing PI pools in adipose tissue. Our data demonstrate that MBOAT7 is a critical regulator of adipose tissue PI homeostasis, and adipocyte MBOAT7-driven PI biosynthesis is closely linked to hyperinsulinemia and insulin resistance in mice.

Graph embedding and Gaussian mixture variational autoencoder network for end-to-end analysis of single-cell RNA sequencing data.

Single-cell RNA sequencing (scRNA-seq) is a revolutionary technology to determine the precise gene expression of individual cells and identify cell heterogeneity and subpopulations. However, technical limitations of scRNA-seq lead to heterogeneous and sparse data. Here, we present autoCell, a deep-learning approach for scRNA-seq dropout imputation and feature extraction. autoCell is a variational autoencoding network that combines graph embedding and a probabilistic depth Gaussian mixture model to infer the distribution of high-dimensional, sparse scRNA-seq data. We validate autoCell on simulated datasets and biologically relevant scRNA-seq. We show that interpolation of autoCell improves the performance of existing tools in identifying cell developmental trajectories of human preimplantation embryos. We identify disease-associated astrocytes (DAAs) and reconstruct DAA-specific molecular networks and ligand-receptor interactions involved in cell-cell communications using Alzheimer's disease as a prototypical example. autoCell provides a toolbox for end-to-end analysis of scRNA-seq data, including visualization, clustering, imputation, and disease-specific gene network identification.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.