AMB Express. 2024 Jan 6;14(1):5. doi: 10.1186/s13568-024-01660-0.

ABSTRACT

Staphylococcus aureus is a major threat in infectious diseases due to its varied infection types and increased resistance. S. aureus could form persister cells under certain condition and could also attach on medical apparatus to form biofilms, which exhibited extremely high resistance to antibiotics. 3-Acetyl-11-keto-beta-boswellic acid (AKBA) is a well-studied anti-tumor and antioxidant drug. This study is aimed to determine the antimicrobial effects of AKBA against S. aureus and its persister cells and biofilms. The in vitro antimicrobial susceptibility of AKBA was assessed by micro-dilution assay, disc diffusion assay and time-killing assay. Drug combination between AKBA and conventional antibiotics was detected by checkerboard assay. And the antibiofilm effects of AKBA against S. aureus were explored by crystal violet staining combined with SYTO/PI probes staining. Next, RBC lysis activity and CCK-8 kit were used to determine the cytotoxicity of AKBA. In addition, murine subcutaneous abscess model was used to assess the antimicrobial effects of AKBA in vivo. Our results revealed that AKBA was found to show effective antimicrobial activity against methicillin-resistant S. aureus (MRSA) with the minimal inhibitory concentration of 4-8 µg/mL with undetectable cytotoxicity. And no resistant mutation was induced by AKBA after 20 days of consecutive passage. Further, we found that AKBA could be synergy with gentamycin or amikacin against S. aureus and its clinical isolates. By crystal violet and SYTO9/PI staining, AKBA exhibited strong biofilm inhibitory and eradication effects at the concentration of 1 ~ 4 µg/mL. In addition, the effective antimicrobial effect was verified in vivo in a mouse model. And no detectable in vivo toxicity was found. These results indicated that AKBA has great potential to development as an alternative treatment for the refractory S. aureus infections.

PMID:38184513 | DOI:10.1186/s13568-024-01660-0

Life Sci. 2024 Jan 4:122411. doi: 10.1016/j.lfs.2023.122411. Online ahead of print.

ABSTRACT

AIMS: The cellular defense system against oxidative stress is important for the survival ability and sensitization in chemotherapy; however, the regulatory mechanisms remain unknown in triple-negative breast cancer (TNBC) cells. This study aimed to investigate the relationship between ferroptosis and autophagy by targeting the defense of oxidative stress through the cystine transporter (xCT) using sulfasalazine (SASP), which is a widely employed xCT inhibitor.

MAIN METHODS: We analyzed the cell death process of SASP in human TNBC cells, and examined the effects of SASP on tumor progression by using xenograft mouse model.

KEY FINDINGS: TNBC cells demonstrated a high defense capacity against reactive oxidative species through xCT. SASP significantly attenuated oxidative stress resistance in MDA-MB-231, which is a generally used model cell as TNBC, through decreased glutathione levels, causing a marked iron-dependent ferroptotic cell death induction. Moreover, autophagy was required to trigger efficient SASP-induced ferroptosis at the early stage of cell death. Tamoxifen, which is currently in clinical use as the gold standard for endocrine therapy of estrogen receptor-positive breast cancer, was a beneficial tool as an autophagy regulator under ferroptotic cell death by SASP. Additionally, SASP suppressed tumor growth and metastasis progression through total glutathione reduction in the primary tumor, indicating high anticancer activity against TNBC without liver injury in vivo.

SIGNIFICANCE: We revealed that SASP can efficiently induce ferroptosis associated with autophagy and that an understanding of the mechanism of cell death regulation by SASP is a promising new strategy for TNBC therapy and drug repositioning.

PMID:38184272 | DOI:10.1016/j.lfs.2023.122411

Brief Funct Genomics. 2024 Jan 5:elad058. doi: 10.1093/bfgp/elad058. Online ahead of print.

ABSTRACT

The traditional method of drug reuse or repurposing has significantly contributed to the identification of new antiviral compounds and therapeutic targets, enabling rapid response to developing infectious illnesses. This article presents an overview of how modern computational methods are used in drug repurposing for the treatment of viral infectious diseases. These methods utilize data sets that include reviewed information on the host's response to pathogens and drugs, as well as various connections such as gene expression patterns and protein-protein interaction networks. We assess the potential benefits and limitations of these methods by examining monkeypox as a specific example, but the knowledge acquired can be applied to other comparable disease scenarios.

PMID:38183212 | DOI:10.1093/bfgp/elad058

Comput Methods Programs Biomed. 2023 Dec 31;244:108003. doi: 10.1016/j.cmpb.2023.108003. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Recent studies have emphasized the significance of computational in silico drug-target binding affinity (DTA) prediction in the field of drug discovery and drug repurposing. However, existing DTA prediction approaches suffer from two major deficiencies that impede their progress. Firstly, while most methods primarily focus on the feature representations of drug-target binding affinity pairs, they fail to consider the long-distance relationships of proteins. Furthermore, many deep learning-based DTA predictors simply model the interaction of drug-target pairs through concatenation, which hampers the ability to enhance prediction performance.

METHODS: To address these issues, this study proposes a novel framework named TransVAE-DTA, which combines the transformer and variational autoencoder (VAE). Inspired by the early success of VAEs, we aim to further investigate the feasibility of VAEs for drug structure encoding, while utilizing the transformer architecture for target feature representation. Additionally, an adaptive attention pooling (AAP) module is designed to fuse the drug and target encoded features. Notably, TransVAE-DTA is proven to maximize the lower bound of the joint likelihood of drug, target, and their DTAs.

RESULTS: Experimental results demonstrate the superiority of TransVAE-DTA in drug-target binding affinity prediction assignments on two public Davis and KIBA datasets.

CONCLUSIONS: In this research, the developed TransVAE-DTA opens a new avenue for engineering drug-target interactions.

PMID:38181572 | DOI:10.1016/j.cmpb.2023.108003

Pharmazie. 2023 Dec 4;78(11):217-224. doi: 10.1691/ph.2023.3578.

ABSTRACT

The global pandemic of COVID-19 disease is caused by the pathogenic factor called SARS-CoV-2. Meanwhile, a series of vaccines and small-molecule drugs, including the mRNA vaccines and Paxlovid®, have been approved, but their efficacy is decreased significantly due to the constant emergence of mutant viral strains. The R&D of host-directed therapeutics has great potential to overcome such limitations and provide new prevention and therapy options for patients with COVID-19 or high-risk group for SARS-CoV-2 infections. Transmembrane serine protease 2 (TMPRSS2) is belonging to a protein family with highly conserved serine protease domain whose crucial role in viral entry is to activate the spike protein of viruses to induce the fusion between host cells and viruses. In this review, we sketch the critical position of TMPRSS2 in the SARS-CoV-2 viral entry and summarize the advanced research and development of TMPRSS2 inhibitors, including repurposed drugs, as a new way to fight COVID-19.

PMID:38178286 | DOI:10.1691/ph.2023.3578

Int J Biol Macromol. 2024 Jan 2:129167. doi: 10.1016/j.ijbiomac.2023.129167. Online ahead of print.

ABSTRACT

Apolipoprotein E (ApoE), a pivotal contributor to lipid metabolism and neurodegenerative disorders, emerges as an attractive target for therapeutic intervention. Within this study, we deployed an integrated in-silico strategy, harnessing structure-based virtual screening, to identify potential compounds from the expansive DrugBank database. Employing molecular docking, we unveil initial hits by evaluating their binding efficiency with ApoE. This first tier of screening narrows our focus to compounds that exhibit a strong propensity to bind with ApoE. Further, a detailed interaction analysis was carried out to explore the binding patterns of the selected hits towards the ApoE binding site. The selected compounds were then evaluated for the biological properties in PASS analysis, which showed anti-neurodegenerative properties. Building upon this foundation, we delve deeper, employing all-atom molecular dynamics (MD) simulations extending over an extensive 500 ns (ns). In particular, Ergotamine and Dihydroergocristine emerge as noteworthy candidates, binding to ApoE in a competitive mode. This intriguing binding behavior positions these compounds as potential candidates warranting further analysis in the pursuit of novel therapeutics targeting complex diseases associated with lipid metabolism and neurodegeneration. This approach holds the promise of catalyzing advancements in therapeutic intervention for complex disorders, thereby reporting a meaningful pace towards improved healthcare outcomes.

PMID:38176507 | DOI:10.1016/j.ijbiomac.2023.129167

Virol J. 2024 Jan 4;21(1):5. doi: 10.1186/s12985-023-02271-0.

ABSTRACT

Chikungunya virus (CHIKV) infection causes chikungunya, a viral disease that currently has no specific antiviral treatment. Several repurposed drug candidates have been investigated for the treatment of the disease. In order to improve the efficacy of the known drugs, combining drugs for treatment is a promising approach. The current study was undertaken to explore the antiviral activity of a combination of repurposed drugs that were reported to have anti-CHIKV activity. We explored the effect of different combinations of six effective drugs (2-fluoroadenine, emetine, lomibuvir, enalaprilat, metyrapone and resveratrol) at their non-toxic concentrations against CHIKV under post infection treatment conditions in Vero cells. Focus-forming unit assay, real time RT-PCR, immunofluorescence assay, and western blot were used to determine the virus titre. The results revealed that the combination of 2-fluoroadenine with either metyrapone or emetine or enalaprilat exerted inhibitory activity against CHIKV under post-infection treatment conditions. The effect of these drug combinations was additive in nature compared to the effect of the individual drugs. The results suggest an additive anti-viral effect of these drug combinations against CHIKV. The findings could serve as an outline for the development of an innovative therapeutic approach in the future to treat CHIKV-infected patients.

PMID:38178163 | DOI:10.1186/s12985-023-02271-0

Neurotherapeutics. 2022 Jan;19(1):305-324. doi: 10.1007/s13311-022-01182-2. Epub 2024 Jan 1.

ABSTRACT

Marketed drugs for Parkinson's disease (PD) treat disease motor symptoms but are ineffective in stopping or slowing disease progression. In the quest of novel pharmacological approaches that may target disease progression, drug-repurposing provides a strategy to accelerate the preclinical and clinical testing of drugs already approved for other medical indications. Here, we targeted the inflammatory component of PD pathology, by testing for the first time the disease-modifying properties of the immunomodulatory imide drug (IMiD) pomalidomide in a translational rat model of PD neuropathology based on the intranigral bilateral infusion of toxic preformed oligomers of human α-synuclein (H-αSynOs). The neuroprotective effect of pomalidomide (20 mg/kg; i.p. three times/week 48 h apart) was tested in the first stage of disease progression by means of a chronic two-month administration, starting 1 month after H-αSynOs infusion, when an already ongoing neuroinflammation is observed. The intracerebral infusion of H-αSynOs induced an impairment in motor and coordination performance that was fully rescued by pomalidomide, as assessed via a battery of motor tests three months after infusion. Moreover, H-αSynOs-infused rats displayed a 40-45% cell loss within the bilateral substantia nigra, as measured by stereological counting of TH + and Nissl-stained neurons, that was largely abolished by pomalidomide. The inflammatory response to H-αSynOs infusion and the pomalidomide treatment was evaluated both in CNS affected areas and peripherally in the serum. A reactive microgliosis, measured as the volume occupied by the microglial marker Iba-1, was present in the substantia nigra three months after H-αSynOs infusion as well as after H-αSynOs plus pomalidomide treatment. However, microglia differed for their phenotype among experimental groups. After H-αSynOs infusion, microglia displayed a proinflammatory profile, producing a large amount of the proinflammatory cytokine TNF-α. In contrast, pomalidomide inhibited the TNF-α overproduction and elevated the anti-inflammatory cytokine IL-10. Moreover, the H-αSynOs infusion induced a systemic inflammation with overproduction of serum proinflammatory cytokines and chemokines, that was largely mitigated by pomalidomide. Results provide evidence of the disease modifying potential of pomalidomide in a neuropathological rodent model of PD and support the repurposing of this drug for clinical testing in PD patients.

PMID:38176798 | DOI:10.1007/s13311-022-01182-2

Neurol India. 2023 Nov-Dec;71(6):1132-1141. doi: 10.4103/0028-3886.391397.

ABSTRACT

BACKGROUND: Disease-modifying agents like Pioglitazone have shown promising effects on neuroinflammation and homeostasis of amyloid plaques, but there is a lack of research papers providing conclusive evidence.

OBJECTIVES: This study is aimed to determine the safety and efficacy of Pioglitazone in improving cognitive function in patients with mild-moderate Alzheimer's disease (AD).

MATERIALS AND METHODS: Trials published in the last 12 years were identified from PubMed, Scopus, Cochrane Central, and other trial registries. Five hundred twenty-five records were obtained, from which five studies were included for quantitative analysis. Studies comparing Pioglitazone with a suitable placebo or other oral hypoglycemic agent were considered for review. Data was extracted using a pretested form, which was followed by a risk of bias assessment (ROB) with Cochrane's ROB assessment tool.

RESULTS: This meta-analysis included studies where Pioglitazone (15-30 mg) was compared to other oral hypoglycemic agents, placebo, or diabetic diet for a minimum duration of 6 months. Pioglitazone did not show a statistically significant improvement in Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) scores [mean difference (MD): -1.16; 95% confidence interval (CI): -4.14-1.81]. By conducting sensitivity analysis with the removal of one study, significant efficacy was obtained [MD: -2.75; 95% CI: -4.84--0.66]. The Wechsler Memory Scale-Revised logical memory I (WMS-R) scores had a significant improvement in the Pioglitazone group [MD: 2.02; 95% CI: 0.09-3.95].

CONCLUSION: Pioglitazone is a safe medication that has a promising effect in slowing the advancement of AD.

PMID:38174446 | DOI:10.4103/0028-3886.391397

Biomed Pharmacother. 2024 Jan 2;171:116055. doi: 10.1016/j.biopha.2023.116055. Online ahead of print.

ABSTRACT

BACKGROUND: During the first wave of COVID-19, hydroxychloroquine (HCQ) was used off-label despite the absence of evidence documenting its clinical benefits. Since then, a meta-analysis of randomised trials showed that HCQ use was associated with an 11% increase in the mortality rate. We aimed to estimate the number of HCQ-related deaths worldwide.

METHODS AND FINDINGS: We estimated the worldwide in-hospital mortality attributable to HCQ use by combining the mortality rate, HCQ exposure, number of hospitalised patients, and the increased relative risk of death with HCQ. The mortality rate in hospitalised patients for each country was calculated using pooled prevalence estimated by a meta-analysis of published cohorts. The HCQ exposure was estimated using median and extreme estimates from the same systematic review. The number of hospitalised patients during the first wave was extracted from dedicated databases. The systematic review included 44 cohort studies (Belgium: k = 1, France: k = 2, Italy: k = 12, Spain: k = 6, Turkey: k = 3, USA: k = 20). HCQ prescription rates varied greatly from one country to another (range 16-84%). Overall, using median estimates of HCQ use in each country, we estimated that 16,990 HCQ-related in-hospital deaths (range 6267-19256) occurred in the countries with available data. The median number of HCQ-related deaths in Belgium, Turkey, France, Italy, Spain, and the USA was 240 (range not estimable), 95 (range 92-128), 199 (range not estimable), 1822 (range 1170-2063), 1895 (range 1475-2094) and 12739 (3244- 15570), respectively.

CONCLUSIONS: Although our estimates are limited by their imprecision, these findings illustrate the hazard of drug repurposing with low-level evidence.

PMID:38171239 | DOI:10.1016/j.biopha.2023.116055

ArXiv. 2023 Dec 13:arXiv:2312.07899v1. Preprint.

ABSTRACT

Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high-throughput. These efforts have facilitated understanding of compound mechanism-of-action (MOA), drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.

PMID:38168460 | PMC:PMC10760198

bioRxiv. 2023 Dec 12:2023.10.13.562216. doi: 10.1101/2023.10.13.562216. Preprint.

ABSTRACT

To cope with the rapid growth of scientific publications and data in biomedical research, knowledge graphs (KGs) have emerged as a powerful data structure for integrating large volumes of heterogeneous data to facilitate accurate and efficient information retrieval and automated knowledge discovery (AKD). However, transforming unstructured content from scientific literature into KGs has remained a significant challenge, with previous methods unable to achieve human-level accuracy. In this study, we utilized an information extraction pipeline that won first place in the LitCoin NLP Challenge to construct a large-scale KG using all PubMed abstracts. The quality of the large-scale information extraction rivals that of human expert annotations, signaling a new era of automatic, high-quality database construction from literature. Our extracted information markedly surpasses the amount of content in manually curated public databases. To enhance the KG's comprehensiveness, we integrated relation data from 40 public databases and relation information inferred from high-throughput genomics data. The comprehensive KG enabled rigorous performance evaluation of AKD, which was infeasible in previous studies. We designed an interpretable, probabilistic-based inference method to identify indirect causal relations and achieved unprecedented results for drug target identification and drug repurposing. Taking lung cancer as an example, we found that 40% of drug targets reported in literature could have been predicted by our algorithm about 15 years ago in a retrospective study, demonstrating that substantial acceleration in scientific discovery could be achieved through automated hypotheses generation and timely dissemination. A cloud-based platform ( https://www.biokde.com ) was developed for academic users to freely access this rich structured data and associated tools.

PMID:38168218 | PMC:PMC10760044 | DOI:10.1101/2023.10.13.562216

Alzheimers Res Ther. 2024 Jan 2;16(1):3. doi: 10.1186/s13195-023-01372-w.

ABSTRACT

BACKGROUND: Recently, single-nucleus RNA-seq (snRNA-seq) analyses have revealed important cellular and functional features of Alzheimer's disease (AD), a prevalent neurodegenerative disease. However, our knowledge regarding intercellular communication mediated by dysregulated ligand-receptor (LR) interactions remains very limited in AD brains.

METHODS: We systematically assessed the intercellular communication networks by using a discovery snRNA-seq dataset comprising 69,499 nuclei from 48 human postmortem prefrontal cortex (PFC) samples. We replicated the findings using an independent snRNA-seq dataset of 56,440 nuclei from 18 PFC samples. By integrating genetic signals from AD genome-wide association studies (GWAS) summary statistics and whole genome sequencing (WGS) data, we prioritized AD-associated Gene Ontology (GO) terms containing dysregulated LR interactions. We further explored drug repurposing for the prioritized LR pairs using the Therapeutic Targets Database.

RESULTS: We identified 190 dysregulated LR interactions across six major cell types in AD PFC, of which 107 pairs were replicated. Among the replicated LR signals, we found globally downregulated communications in the astrocytes-to-neurons signaling axis, characterized, for instance, by the downregulation of APOE-related and Calmodulin (CALM)-related LR interactions and their potential regulatory connections to target genes. Pathway analyses revealed 44 GO terms significantly linked to AD, highlighting Biological Processes such as 'amyloid precursor protein processing' and 'ion transmembrane transport,' among others. We prioritized several drug repurposing candidates, such as cromoglicate, targeting the identified dysregulated LR pairs.

CONCLUSIONS: Our integrative analysis identified key dysregulated LR interactions in a cell type-specific manner and the associated GO terms in AD, offering novel insights into potential therapeutic targets involved in disrupted cell-cell communication in AD.

PMID:38167548 | DOI:10.1186/s13195-023-01372-w

Expert Opin Ther Pat. 2024 Jan 2:1-25. doi: 10.1080/13543776.2023.2299349. Online ahead of print.

ABSTRACT

INTRODUCTION: Tropane-derived medications have historically played a substantial role in pharmacotherapy. Both natural and synthetic derivatives of tropane find application in addressing diverse medical conditions. Prominent examples of tropane-based drugs include hyoscine butylbromide, recognized for its antispasmodic properties, atropine, employed as a mydriatic, maraviroc, known for its antiviral effects. trospium chloride, utilized as a spasmolytic for overactive bladder, and ipratropium, a bronchodilator.

AREAS COVERED: We compiled patents pertaining to the biological activity of substances containing tropane up to the year 2023 and categorized them according to the specific type of biological activity they exhibit. ScienceFinder, ScienceDirect, and Patent Guru were used to search for scientific articles and patent literature up to 2023.

EXPERT OPINION: Pharmaceutical researchers in academic and industrial settings have shown considerable interest in tropane derivatives. Despite this, there remains a substantial amount of work to be undertaken. A focused approach is warranted for the exploration and advancement of both natural and synthetic bioactive molecules containing tropane, facilitated through collaborative efforts between academia and industry. Leveraging contemporary techniques and technologies in medicinal and synthetic chemistry, including high throughput screening, drug repurposing,and biotechnological engineering, holds the potential to unveil novel possibilities and accelerate the drug discovery process for innovative tropane-based pharmaceuticals.

PMID:38165255 | DOI:10.1080/13543776.2023.2299349

Front Oncol. 2023 Dec 14;13:1320710. doi: 10.3389/fonc.2023.1320710. eCollection 2023.

ABSTRACT

INTRODUCTION: Drug repurposing is a promising strategy to develop new treatments for glioblastoma. In this phase II clinical trial, we evaluated the addition of chlorpromazine to temozolomide in the adjuvant phase of the standard first-line therapeutic protocol in patients with unmethylated MGMT gene promoter.

METHODS: This was a multicenter phase II single-arm clinical trial. The experimental procedure involved the combination of CPZ with standard treatment with TMZ in the adjuvant phase of the Stupp protocol in newly-diagnosed GBM patients carrying an unmethylated MGMT gene promoter. Progression-free survival was the primary endpoint. Secondary endpoints were overall survival and toxicity.

RESULTS: Forty-one patients were evaluated. Twenty patients (48.7%) completed 6 cycles of treatment with TMZ+CPZ. At 6 months, 27 patients (65.8%) were without progression, achieving the primary endpoint. Median PFS was 8.0 months (95% CI: 7.0-9.0). Median OS was 15.0 months (95% CI: 13.1-16.9). Adverse events led to reduction or interruption of CPZ dosage in 4 patients (9.7%).

DISCUSSION: The addition of CPZ to standard TMZ in the first-line treatment of GBM patients with unmethylated MGMT gene promoter was safe and led to a longer PFS than expected in this population of patients. These findings provide proof-of-concept for the potential of adding CPZ to standard TMZ treatment in GBM patients with unmethylated MGMT gene promoter.

CLINICAL TRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT04224441, identifier NCT04224441.

PMID:38162492 | PMC:PMC10755935 | DOI:10.3389/fonc.2023.1320710

Comput Biol Med. 2023 Dec 28;169:107911. doi: 10.1016/j.compbiomed.2023.107911. Online ahead of print.

ABSTRACT

Extracting expressive molecular features is essential for molecular property prediction. Sequence-based representation is a common representation of molecules, which ignores the structure information of molecules. While molecular graph representation has a weak ability in expressing the 3D structure. In this article, we try to make use of the advantages of different type representations simultaneously for molecular property prediction. Thus, we propose a fusion model named DLF-MFF, which integrates the multi-type molecular features. Specifically, we first extract four different types of features from molecular fingerprints, 2D molecular graph, 3D molecular graph and molecular image. Then, in order to learn molecular features individually, we use four essential deep learning frameworks, which correspond to four distinct molecular representations. The final molecular representation is created by integrating the four feature vectors and feeding them into prediction layer to predict molecular property. We compare DLF-MFF with 7 state-of-the-art methods on 6 benchmark datasets consisting of multiple molecular properties, the experimental results show that DLF-MFF achieves state-of-the-art performance on 6 benchmark datasets. Moreover, DLF-MFF is applied to identify potential anti-SARS-CoV-2 inhibitor from 2500 drugs. We predict probability of each drug being inferred as a 3CL protease inhibitor and also calculate the binding affinity scores between each drug and 3CL protease. The results show that DLF-MFF product better performance in the identification of anti-SARS-CoV-2 inhibitor. This work is expected to offer novel research perspectives for accurate prediction of molecular properties and provide valuable insights into drug repurposing for COVID-19.

PMID:38160501 | DOI:10.1016/j.compbiomed.2023.107911

Pac Symp Biocomput. 2024;29:306-321.

ABSTRACT

Recently, drug repurposing has emerged as an effective and resource-efficient paradigm for AD drug discovery. Among various methods for drug repurposing, network-based methods have shown promising results as they are capable of leveraging complex networks that integrate multiple interaction types, such as protein-protein interactions, to more effectively identify candidate drugs. However, existing approaches typically assume paths of the same length in the network have equal importance in identifying the therapeutic effect of drugs. Other domains have found that same length paths do not necessarily have the same importance. Thus, relying on this assumption may be deleterious to drug repurposing attempts. In this work, we propose MPI (Modeling Path Importance), a novel network-based method for AD drug repurposing. MPI is unique in that it prioritizes important paths via learned node embeddings, which can effectively capture a network's rich structural information. Thus, leveraging learned embeddings allows MPI to effectively differentiate the importance among paths. We evaluate MPI against a commonly used baseline method that identifies anti-AD drug candidates primarily based on the shortest paths between drugs and AD in the network. We observe that among the top-50 ranked drugs, MPI prioritizes 20.0% more drugs with anti-AD evidence compared to the baseline. Finally, Cox proportional-hazard models produced from insurance claims data aid us in identifying the use of etodolac, nicotine, and BBB-crossing ACE-INHs as having a reduced risk of AD, suggesting such drugs may be viable candidates for repurposing and should be explored further in future studies.

PMID:38160288

Pac Symp Biocomput. 2024;29:261-275.

ABSTRACT

The drug development pipeline for a new compound can last 10-20 years and cost over $10 billion. Drug repurposing offers a more time- and cost-effective alternative. Computational approaches based on network graph representations, comprising a mixture of disease nodes and their interactions, have recently yielded new drug repurposing hypotheses, including suitable candidates for COVID-19. However, these interactomes remain aggregate by design and often lack disease specificity. This dilution of information may affect the relevance of drug node embeddings to a particular disease, the resulting drug-disease and drug-drug similarity scores, and therefore our ability to identify new targets or drug synergies. To address this problem, we propose constructing and learning disease-specific hypergraphs in which hyperedges encode biological pathways of various lengths. We use a modified node2vec algorithm to generate pathway embeddings. We evaluate our hypergraph's ability to find repurposing targets for an incurable but prevalent disease, Alzheimer's disease (AD), and compare our ranked-ordered recommendations to those derived from a state-of-the-art knowledge graph, the multiscale interactome. Using our method, we successfully identified 7 promising repurposing candidates for AD that were ranked as unlikely repurposing targets by the multiscale interactome but for which the existing literature provides supporting evidence. Additionally, our drug repositioning suggestions are accompanied by explanations, eliciting plausible biological pathways. In the future, we plan on scaling our proposed method to 800+ diseases, combining single-disease hypergraphs into multi-disease hypergraphs to account for subpopulations with risk factors or encode a given patient's comorbidities to formulate personalized repurposing recommendations.Supplementary materials and code: https://github.com/ayujain04/psb_supplement.

PMID:38160285

Pac Symp Biocomput. 2024;29:232-246.

ABSTRACT

Drug repurposing (DR) intends to identify new uses for approved medications outside their original indication. Computational methods for finding DR candidates usually rely on prior biological and chemical information on a specific drug or target but rarely utilize real-world observations. In this work, we propose a simple and effective systematic screening approach to measure medication impact on hospitalization risk based on large-scale observational data. We use common classification systems to group drugs and diseases into broader functional categories and test for non-zero effects in each drug-disease category pair. Treatment effects on the hospitalization risk of an individual disease are obtained by combining widely used methods for causal inference and time-to-event modelling. 6468 drug-disease pairs were tested using data from the UK Biobank, focusing on cardiovascular, metabolic, and respiratory diseases. We determined key parameters to reduce the number of spurious correlations and identified 7 statistically significant associations of reduced hospitalization risk after correcting for multiple testing. Some of these associations were already reported in other studies, including new potential applications for cardioselective beta-blockers and thiazides. We also found evidence for proton pump inhibitor side effects and multiple possible associations for anti-diabetic drugs. Our work demonstrates the applicability of the present screening approach and the utility of real-world data for identifying potential DR candidates.

PMID:38160283

Pac Symp Biocomput. 2024;29:226-231.

ABSTRACT

This PSB 2024 session discusses the many broad biological, computational, and statistical approaches currently being used for therapeutic drug target identification and repurposing of existing treatments. Drug repurposing efforts have the potential to dramatically improve the treatment landscape by more rapidly identifying drug targets and alternative strategies for untreated or poorly managed diseases. The overarching theme for this session is the use and integration of real-world data to identify drug-disease pairs with potential therapeutic use. These drug-disease pairs may be identified through genomic, proteomic, biomarkers, protein interaction analyses, electronic health records, and chemical profiling. Taken together, this session combines novel applications of methods and innovative modeling strategies with diverse real-world data to suggest new pharmaceutical treatments for human diseases.

PMID:38160282