Computational Drug Repositioning in Cardiorenal Disease: Opportunities, Challenges, and Approaches

PROTEOMICS, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

There is currently increased interest in drug repositioning programs, namely the identification of new therapeutic areas for already approved drugs, both academia as well biotech and pharmaceutical industry. Since 2012, number publications indexed MEDLINE on or repurposing exponentially increasing with a peak year 2021 due to worldwide search options combat COVID-19 pandemic [1]. Drug repositioning, however, not new, companies have ever since been looking additional market opportunities their products, particular when patents expire generics manufacturers enter initial approvals [2, 3]. In pharma world, term indication expansion also often used instead repurposing. patients suffering from rare disease who are lacking any therapies, represents very interesting efficient way bringing treatment patient fast [4]. This has stressed recent position paper International Rare Disease Research Consortium [5]. Several international consortia recognized trend toward Two US-based endeavors focusing Repurposing Hub EveryCure. Researchers Broad Institute created aim construct curate library FDA drugs that can be systematic screenings [6]. EveryCure's mission identify novel diseases via computational repositioning. European initiatives context Repo4EU (https://repo4.eu/) REMEDi4ALL (https://remedi4all.org/) consortia, being public–private partnerships develop tools but apply these selected indications. Next effort COVID-19, there at least three reasons why programs gaining momentum. First, molecular characterization processes continuously improving, we understand more about key pathways disease-modifying proteins, forming basis find counterbalancing dysregulations level. Second, arsenal tools, methods, workflows getting better matching pathobiology mechanism action (MoA), identifying connections thus potential targets intervention. And third, list successful cases longer. Even current blockbuster GLP1 agonists seen positive examples, scientifically commercially. Initially developed diabetes mellitus, this compound class meantime obesity clinical development several indications across different areas. viewpoint article, will discuss (i) experimental approaches discover opportunities, (ii) challenges further discovered compounds, (iii) kidney cardiovascular (CVD). observation-driven trials practice methods such binding assays phenotype screens, systematically opportunities. These make use information direct targets, affected biological mechanisms, side effects, omics signatures action, data electronic health records (EHRs) registries [7-10]. Key given Figure 1 discussed following sections. A major challenge one most crucial early steps discovery selection an appropriate target. For majority target known, always responsible drug's full efficacy potential. Information nevertheless target-based by which causally linked and/or progression [11]. Databases holding drug-target relations include, example, DrugBank [12], Therapeutic Target Database [13], STITCH EMBL-EBI [14]. open-source aiming consolidate sources associate Open Targets platform [15] Pharos [16]. comprehensive integrates types genetic associations, gene expression data, pathway information, results literature-mining prioritize human diseases. The driven researchers collaboration between scientific institutions companies. Users option either evaluating individual proteins (potential targets) searching (phenotype) [15]. web application consolidating established "Illuminating Druggable Genome" consortium Like Targets, users (protein) presented lists ranked respectively. Protein furthermore classified into four categories, Tclin (targets, drug), Tchem activity cutoff <30 nM), Tbio high annotation score based literature antibodies targeting protein), Tdark (targets without known compounds low scores literature). Especially category prime candidates approaches. Si et al. recently identified set promising chronic analysis proteomics transcriptomics combination Mendelian randomization investigation protein–protein interactions colocalization protein-coding genes [17]. might pave beneficially impact course disease. Fu similar approach evaluate relevance medications osteoarthritis [18]. They propose thiazolidinediones agents predicted role PPARG progression. protein structure-based Structure-based cheminformatics than bioinformatics-driven leverage structural similarities same drugs. Known pharmacokinetic profiles safety approach. broadly traditional advanced AI/ML-aided high-quality structures receptor ligands. Traditional high-throughput vitro, whereas machine learning algorithms process facilitates docking simulations, accuracy efficiency [19]. core strategy contrast only focus single compare whole (i.e., patterns abundance changes) various those reverse signature [20]. landmark study was published 2006 Lamb making 164 transcriptome level, dataset they called Connectivity Map [21]. meantime, extended thousands signature-based "connectivity mapping". To generate total over million profiles, focused 1000 encode 80% variation transcriptional creating L1000 [22]. integrated other resources within Library Integrated Network-based Cellular Signatures (LINCS) program, including proteomic, metabolomic, epigenomic [23]. publicly available accessible iLINCS web-based [24]. applications Mergeomics 2.0 R package integrating multi-omics reveal insights pathways, networks, drivers important pathogenesis ultimately predict [25]. functional module PharmOmics matches multi-omics-informed networks [26]. designed enable summary statistics multiomics sets, networks. PharmGWAS database leverages genome-wide association studies (GWAS) candidate repurposing, resistance, (iv) effects broad range GWAS datasets retrieved biobanks consortiums, deposited compound-perturbed CMap2.0 SigCom LINCS [27]. Single-cell Guided Pipeline Aid Drugs (ASGARD) uses scRNA-Seq samples attempts highest respective [28]. aims model complex systems level analyze among entities, diseases, By mapping onto network, measurements connectivity, proximity, cluster formation interactions, regulatory allow predictions silico Guala Sonnhammer tested network crosstalk-based constructed benchmark performance assessment network-based [29]. cross-talk sets disease-related genes, calculated distinct measures. evaluated shortest path every its closest [30], separate estimate crosstalk links two nodes [31-33]. Sadegh proposed (NeDRex) gene, protein, drug, target, annotations relationships [34]. NeDRex incorporates state-of-the-art case exemplified applicability extracting meaningful ovarian cancer starting seed nodes. obtained contained newly connector which, together participate relevant could using alone. Maier Drugst.one provides user-friendly, utilities interactive visualization capabilities [35]. 14 covering protein/gene, tool enrich proteins/genes clinically associations. Other include integration display adjacent project network. Yang DRONet framework effectiveness comparative combining embeddings, generated heterogeneous drug-disease ranking specific utilizing like RankNet, LambdaRank, LambdaMART [36]. Advances DNA/RNA sequencing enabled models derived mapped interactome Cheng Genome-wide Positioning Systems whole-exome approximately 5000 15 taken Cancer Genome Atlas, prioritizing [37]. Validation showed ouabain, cardiac arrhythmia heart failure, exhibited significant antitumor lung adenocarcinoma pathways. Moreover, another group proximity hundreds drug–disease associations [38]. validated against large-scale supported mechanistic vitro demonstrating risk coronary artery carbamazepine decreased hydroxychloroquine, compared levetiracetam. highlights interactome-based uncovering existing understanding broader Fiscon stated footprints randomly scattered colocalized highly interconnected subnetworks [39]. effective off-target adverse should proximal module. frameworks five metrics, breast prostate neoplasms, schizophrenia, liver cirrhosis. Ruiz introduced multiscale interactome, method disease-perturbed functions [40]. Their comprised 17,660 9798 functions, 1661 840 performing random walks evaluates propagation indirect enabling prediction treatments, related alter treatments reactions. comparing diffusion research groups atopic dermatitis [41], [42], non-alcoholic fatty [43], metabolic syndrome [44], cardiorenal later section examples rather just leveraging drug-specific dysregulations. REpurposing BIOlogical Pathways (DREBIOP) exemplary concepts comprehensively utilizes where effect mediated through [45]. authors exemplify beneficial ergocalciferol rickets interference vitamin D pathway. Another example pathway-based subtypes driver overcome resistance [46]. Knowledge-based enhance incorporating domain-specific knowledge graph-based structures, tasks providing deeper interactions. Graph embeddings advance multi-modal nodes, relationships, entire sub-graphs low-dimensional vectors. Himmelstein modeled graph 11 entities 24 relationship types, 29 public cover anatomies, pharmacologic classes, symptoms, entity [47]. paths correlating compound–disease pairs previously shown effective. Additionally, were DrugCentral trial data. eight epilepsy. Jain address issue insufficient specificity work [48]. Diluted coarse clustering limit ability synergies. Therefore, create hypergraphs, hyperedges this, converted modification node2vec algorithm. KG. Seven Alzheimer's Ghorbanali homogeneous features along negative unified latent space [49]. unknown sub-graph. Prediction area under curve ∼90% achieved. coronavirus infection skin-related predicted, conditions having demonstrated studies. Santos provide infrastructure facilitate automatic analysis, visualization, extraction [50]. 26 biomedical databases, literature. It built precision medicine decision-making. Machine graph. Its significantly higher CT45 serous adenocarcinoma, confirming biomarker long-term survival. Amiri Souri DT2Vec+ link [51]. shows predicting degree type proposing cancer-specific biomarkers. Lobentanzer unify fragmented landscape simplify task-specific KGs [52]. Biocypher offers focuses modularity, reproducibility, harmonization, reusability, accessibility. builds top rigid standards allows exchanges, modifications, extensions ontologies map concepts. Multiple act proof concept demonstrate practical use, federated Care-for-Rare project, multiple children's hospitals collaborate train shared while keeping decentralized private locally employed, sharing model's configuration parameters anonymous results. active hold combined ML unravel reported As articles almost tripled last decade continues grow 850,000 abstracts annually, increasingly relies automated text mining natural language processing methods. Deep technologies long short-term memory, convolutional neural bidirectional encoder representations transformers [53]. Challenges abbreviations ambiguity terms, symbols identical common phrases unspecific sub-strings falsely part longer, entries. contrast, rule-based definition stringent domain experts. Although involves manual curation longer cycles, it overall certain [54]. extract ideally causal genes/proteins, form intermediate genes/proteins. commonly resource storing genes/proteins DisGeNet [55]. Similarities Kumar epilepsy paroxetine seizures [56]. Data extracted SIDER [57], however seems no maintained update dates back 2015. events event reporting system still regularly updated. Paci measure reposition CVDs [58, 59]. formulated side-effect distal effects. vast amounts real-world captured EHRs, demographics, diagnoses, outcomes, lab results, explore correlations outcomes originally [60]. Retrospective analyses EHR hand lead de-novo validate findings Metformin, primarily therapy diabetes, found reduce dementia 2 after statistical databases [61]. Deep-learning emulate retrospective large Liu [62]. UK Biobank valuable around half participants deidentified [https://www.ukbiobank.ac.uk/]. 10 years. attractive addressing questions beyond exploration events, comorbidities groups. Main screenings. screen wide manner. High-throughput target-binding screening rely cellular animal prior targets. interact potentially modulate [63]. Phenotype usually performed cell-based determining readouts cell viability, apoptosis, motility, morphology, monitor signaling Asawa al., 8000 viability polycystic cells anti-proliferative [64]. Observations considered serendipitous play field led past [65]. prominent sildenafil initially systemic hypertension erectile dysfunction observed "side effects" [66], minoxidil treat inducing hair regrowth alopecia areata [67], amantadine influenza now symptoms Parkinson's [68]. Most until findings. Accessible Biobank, datasets, paved data-driven too determine biology-driven probability success viable eventually mix complementary applied increase chances success. holds references conducted renal No matter discovered, first step clinic next chapter. Repositioning described shortcut time drastically decrease costs. Widely figures report takes 10000 start end up average 10–17 years comes mean price tag 1.6 2.8 billion USD [69, 70]. exact savings achieved time, risk, money unclear, some conflicting evidence. Some reviews suggest 30% efforts product marketing, 10% (NDAs) general, argue repurposed do necessarily rates limiting factor [71]. Once utilization marketing remains cost-intensive challenge, referred "valley death" basic [72]. Numerous experts state benefit lies availability profile preclinical test even (safety) may skipped Phase II III. criteria, straightforward obvious, as: safe studies, does adequately rec

Language: Английский

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Navigating the Landscape of Translational Medicine of Calcific Aortic Valve Disease

JACC Asia, Journal Year: 2025, Volume and Issue: 5(4), P. 503 - 515

Published: April 1, 2025

Language: Английский

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Pharmacometabolomics for the Study of Lipid-Lowering Therapies: Opportunities and Challenges

International Journal of Molecular Sciences, Journal Year: 2023, Volume and Issue: 24(4), P. 3291 - 3291

Published: Feb. 7, 2023

Lipid-lowering therapies are widely used to prevent the development of atherosclerotic cardiovascular disease (ASCVD) and related mortality worldwide. "Omics" technologies have been successfully applied in recent decades investigate mechanisms action these drugs, their pleiotropic effects, side aiming identify novel targets for future personalized medicine with an improvement efficacy safety associated treatment. Pharmacometabolomics is a branch metabolomics that focused on study drug effects metabolic pathways implicated variation response treatment considering also influences from specific disease, environment, concomitant pharmacological therapies. In this review, we summarized most significant metabolomic studies lipid-lowering therapies, including commonly statins fibrates drugs or nutraceutical approaches. The integration pharmacometabolomics data information obtained other "omics" approaches could help comprehension biological underlying use view defining precision improve reduce

Language: Английский

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Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities

Annual Review of Biomedical Data Science, Journal Year: 2024, Volume and Issue: 7(1), P. 15 - 29

Published: April 10, 2024

Drug repurposing refers to the inference of therapeutic relationships between a clinical indication and existing compounds. As an emerging paradigm in drug development, enables more efficient treatment rare diseases, stratified patient populations, urgent threats public health. However, prioritizing well-suited candidates from among nearly infinite number options continues represent significant challenge development. Over past decade, advances genomic profiling, database curation, machine learning techniques have enabled accurate identification for subsequent evaluation. This review outlines major methodologic classes that these approaches comprise, which rely on (a) protein structure, (b) signatures, (c) biological networks, (d) real-world data. We propose realizing full impact methodologies requires multidisciplinary understanding each method's advantages limitations with respect practice.

Language: Английский

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Tamoxifen may contribute to preserve cardiac function in Duchenne muscular dystrophy

European Journal of Pediatrics, Journal Year: 2024, Volume and Issue: 183(9), P. 4057 - 4062

Published: July 3, 2024

Abstract Duchenne muscular dystrophy is life-limiting. Cardiomyopathy, which mostly ensues in the second decade of life, main cause death. Treatment options are still limited. The TAMDMD (NCT03354039) trial assessed motor function, muscle strength and structure, laboratory biomarkers, safety 79 ambulant boys with genetically confirmed dystrophy, 6.5–12 years age, receiving either daily tamoxifen 20 mg or placebo for 48 weeks. In this post-hoc analysis, available echocardiographic data patients recruited at one study centre were retrieved compared before after treatment. Data from 14 patients, median 11 (interquartile range, IQR, 11–12) age was available. Baseline demographic characteristics similar participants assigned to ( n = 7) 7). Left ventricular end-diastolic diameter group (median IQR) 39 (38–41) mm baseline 43 (38–44) end, while it 44 (41–46) 41 (37–46) treatment group. fractional shortening 35% (32–38%) 33% (32–36%) treatment, 34% (33–34%) (33–35%) end. No signals detected. Conclusion : This hypothesis-generating analysis suggests that over weeks well tolerated may help preserving cardiac structure function dystrophy. Further studies justified. ClinicalTrials.gov Identifier EudraCT 2017–004554–42, NCT03354039 What known: • (DMD) Cardiomyopathy life Tamoxifen reduced fibrosis mice improved cardiomyocyte human-induced pluripotent stem cell-derived cardiomyocytes. new: among boys, treated weeks, well-tolerated. A visual trend left-ventricular dimensions better systolic preservation generates hypothesis a potential beneficial effect DMD cardiomyopathy.

Language: Английский

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