IEEE/ACM Trans Comput Biol Bioinform. 2023 Jul 7;PP. doi: 10.1109/TCBB.2023.3292883. Online ahead of print.

ABSTRACT

Due to the lengthy and costly process of new drug discovery, increasing attention has been paid to drug repositioning, i.e., identifying new drug-disease associations. Current machine learning methods for drug repositioning mainly leverage matrix factorization or graph neural networks, and have achieved impressive performance. However, they often suffer from insufficient training labels of inter-domain associations, while ignore the intra-domain associations. Moreover, they often neglect the importance of tail nodes that have few known associations, which limits their effectiveness in drug repositioning. In this paper, we propose a novel multi-label classification model with dual Tail-Node Augmentation for Drug Repositioning (TNA-DR). We incorporate disease-disease similarity and drug-drug similarity information into k-nearest neighbor ( kNN) augmentation module and contrastive augmentation module, respectively, which effectively complements the weak supervision of drug-disease associations. Furthermore, before employing the two augmentation modules, we filter the nodes by their degrees, so that the two modules are only applied to tail nodes. We conduct 10-fold cross validation experiments on four different real-world datasets, and our model achieves the state-of-the-art performance on all the four datasets. We also demonstrate our model's capability of identifying drug candidates for new diseases and discovering potential new links between existing drugs and diseases.

PMID:37418410 | DOI:10.1109/TCBB.2023.3292883

Mol Biol Rep. 2023 Jul 7. doi: 10.1007/s11033-023-08568-1. Online ahead of print.

ABSTRACT

Antiepileptic drugs are versatile drugs with the potential to be used in functional drug formulations with drug repurposing approaches. In the present review, we investigated the anticancer properties of antiepileptic drugs and interlinked cancer and epileptic pathways. Our focus was primarily on those drugs that have entered clinical trials with positive results and those that provided good results in preclinical studies. Many contributing factors make cancer therapy fail, like drug resistance, tumor heterogeneity, and cost; exploring all alternatives for efficient treatment is important. It is crucial to find new drug targets to find out new antitumor molecules from the already clinically validated and approved drugs utilizing drug repurposing methods. The advancements in genomics, proteomics, and other computational approaches speed up drug repurposing. This review summarizes the potential of antiepileptic drugs in different cancers and tumor progression in the brain. Valproic acid, oxcarbazepine, lacosamide, lamotrigine, and levetiracetam are the drugs that showed potential beneficial outcomes against different cancers. Antiepileptic drugs might be a good option for adjuvant cancer therapy, but there is a need to investigate further their efficacy in cancer therapy clinical trials.

PMID:37418080 | DOI:10.1007/s11033-023-08568-1

Clin Ther. 2023 Jun;45(6):527-534. doi: 10.1016/j.clinthera.2023.03.007.

ABSTRACT

PURPOSE: Interest in the use of cannabis as a medicine has markedly increased during the last decade, with an unprecedented number of patients now seeking advice or prescriptions for medicinal cannabis. Unlike other medicines prescribed by physicians, many medicinal cannabis products have not undergone standard clinical trial development required by regulatory authorities. Different formulations with varying strengths and ratios of tetrahydrocannabinol and cannabidiol are available, and this diversity of medicinal cannabis products available for a myriad of therapeutic indications adds to the complexity. Physicians face challenges and barriers in their clinical decision making with medicinal cannabis because of current evidence limitations. Research efforts to address evidence limitations are ongoing; in the interim, educational resources and clinical guidance are being developed to address the gap in clinical information and support the needs of health professionals.

METHODS: This article provides an overview of various resources that health professionals may use when seeking information about medicinal cannabis in the absence of high-quality evidence and clinical guidelines. It also identifies examples of international evidence-based resources that support clinical decision making with medicinal cannabis.

FINDINGS: Similarities and differences between international examples of guidance and guideline documents are identified and summarized.

IMPLICATIONS: Guidance can help guide physicians in the individualized choice and dose of medicinal cannabis. Before quality clinical trials and regulator-approved products with risk management programs, safety data require clinical and academic collaborative pharmacovigilance.

PMID:37414503 | DOI:10.1016/j.clinthera.2023.03.007

J Mycol Med. 2023 Jun 30;33(3):101411. doi: 10.1016/j.mycmed.2023.101411. Online ahead of print.

ABSTRACT

The increasing resistance to antifungal agents associated with toxicity and interactions turns therapeutic management of fungal infections difficult. This scenario emphasizes the importance of drug repositioning, such as nitroxoline - a urinary antibacterial agent that has shown potential antifungal activity. The aims of this study were to discover the possible therapeutic targets of nitroxoline using an in silico approach, and to determine the in vitro antifungal activity of the drug against the fungal cell wall and cytoplasmic membrane. We explored the biological activity of nitroxoline using PASS, SwissTargetPrediction and Cortellis Drug Discovery Intelligence web tools. After confirmation, the molecule was designed and optimized in HyperChem software. GOLD 2020.1 software was used to predict the interactions between the drug and the target proteins. In vitro investigation evaluated the effect of nitroxoline on the fungal cell wall through sorbitol protection assay. Ergosterol binding assay was carried out to assess the effect of the drug on the cytoplasmic membrane. In silico investigation revealed biological activity with alkane 1-monooxygenase and methionine aminopeptidase enzymes, showing nine and five interactions in the molecular docking, respectively. In vitro results exhibited no effect on the fungal cell wall or cytoplasmic membrane. Finally, nitroxoline has potential as an antifungal agent due to the interaction with alkane 1-monooxygenase and methionine aminopeptidase enzymes, which are not the main human therapeutic targets. These results have potentially revealed a new biological target for the treatment of fungal infections. We also consider that further studies are required to confirm the biological activity of nitroxoline on fungal cells, mainly the confirmation of the alkB gene.

PMID:37413753 | DOI:10.1016/j.mycmed.2023.101411

BMC Bioinformatics. 2023 Jul 6;24(1):278. doi: 10.1186/s12859-023-05387-w.

ABSTRACT

MOTIVATION: Accurate identification of Drug-Target Interactions (DTIs) plays a crucial role in many stages of drug development and drug repurposing. (i) Traditional methods do not consider the use of multi-source data and do not consider the complex relationship between data sources. (ii) How to better mine the hidden features of drug and target space from high-dimensional data, and better solve the accuracy and robustness of the model.

RESULTS: To solve the above problems, a novel prediction model named VGAEDTI is proposed in this paper. We constructed a heterogeneous network with multiple sources of information using multiple types of drug and target dataIn order to obtain deeper features of drugs and targets, we use two different autoencoders. One is variational graph autoencoder (VGAE) which is used to infer feature representations from drug and target spaces. The second is graph autoencoder (GAE) propagating labels between known DTIs. Experimental results on two public datasets show that the prediction accuracy of VGAEDTI is better than that of six DTIs prediction methods. These results indicate that model can predict new DTIs and provide an effective tool for accelerating drug development and repurposing.

PMID:37415176 | DOI:10.1186/s12859-023-05387-w

OMICS. 2023 Jul 6. doi: 10.1089/omi.2023.0066. Online ahead of print.

ABSTRACT

Idiopathic pulmonary arterial hypertension (IPAH) is a progressive disease that affects the pulmonary arteries, resulting in increased pulmonary vascular resistance and right ventricular dysfunction, which can ultimately lead to heart failure and death. The molecular substrates of IPAH are poorly understood while diagnostics and therapeutics innovation remain as unmet needs for this debilitating disease. In this study, a network-based methodology was used to uncover the salient molecular mechanisms of IPAH to inform drug and diagnostic discovery, and personalized medicine. Expression profiling datasets associated with IPAH were obtained from the Gene Expression Omnibus database: GSE15197, GSE113439, GSE53408, and GSE67597. The comparative analysis of mRNA and miRNA expression data and the modular analysis of a transcriptome-based weighted gene coexpression network unraveled disease-specific gene and miRNA signatures. DEAD-box helicase 52 (DDx52), ESF1 nucleolar pre-RNA processing protein (ESF1), heterogeneous nuclear ribonuclearprotein A3 (MNRNPA3), Myosin VA (MYO5A), replication factor C subunit 1 (RFC1), and arginine and serine rich coiled coil 1 (RSRC1) were detected as the salient genes for IPAH. In addition, the salient gene-based drug repositioning analysis identified alvespimycin, tanespimycin, geldanamycin, LY294002, cephaeline, digoxigenin, lanatoside C, helveticoside, trichostatin A, phenoxybenzamine, genistein, pioglitazone, and rosiglitazone as potential drug candidates for IPAH. In conclusion, this study provides new molecular signatures in relation to IPAH and attendant potential drug candidates for further experimental and translational clinical research for patients with IPAH.

PMID:37410515 | DOI:10.1089/omi.2023.0066

Methods Mol Biol. 2023;2684:59-99. doi: 10.1007/978-1-0716-3291-8_4.

ABSTRACT

Delivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.

PMID:37410228 | DOI:10.1007/978-1-0716-3291-8_4

Microbiol Spectr. 2023 Jul 6:e0139323. doi: 10.1128/spectrum.01393-23. Online ahead of print.

ABSTRACT

Schistosomiasis is a parasitic disease that afflicts approximately 250 million people worldwide. There is an urgent demand for new antiparasitic agents because praziquantel, the only drug available for the treatment of schistosomiasis, is not universally effective and may derail current progress toward the WHO goal of eliminating this disease as a public health problem by 2030. Nifuroxazide (NFZ), an oral nitrofuran antibiotic, has recently been explored to be repurposed for parasitic diseases. Here, in vitro, in vivo, and in silico studies were conducted to evaluate the activity of NFZ on Schistosoma mansoni. The in vitro study showed significant antiparasitic activity, with 50% effective concentration (EC50) and 90% effective concentration (EC90) values of 8.2 to 10.8 and 13.7 to 19.3 μM, respectively. NFZ also affected worm pairing and egg production and induced severe damage to the tegument of schistosomes. In vivo, a single oral dose of NFZ (400 mg/kg of body weight) to mice harboring either prepatent or patent S. mansoni infection significantly reduced the total worm burden (~40%). In patent infection, NFZ achieved a high reduction in the number of eggs (~80%), but the drug caused a low reduction in the egg burden of animals with prepatent infection. Finally, results from in silico target fishing methods predicted that serine/threonine kinases could be one of the potential targets for NFZ in S. mansoni. Overall, the present study revealed that NFZ possesses antischistosomal properties, mainly in terms of egg burden reduction in animals with patent S. mansoni infection. IMPORTANCE The increasing recognition of the burden imposed by helminthiasis, associated with the limited therapeutic arsenal, has led to initiatives and strategies to research and develop new drugs for the treatment of schistosomiasis. One of these strategies is drug repurposing, which considers low-risk compounds with potentially reduced costs and shorter time for development. In this study, nifuroxazide (NFZ) was evaluated for its anti-Schistosoma mansoni potential through in vitro, in vivo, and in silico studies. In vitro, NFZ affected worm pairing and egg production and induced severe damage to the tegument of schistosomes. In vivo, a single oral dose of NFZ (400 mg/kg) to mice harboring either prepatent or patent S. mansoni infection significantly reduced the total worm burden and egg production. In silico investigations have identified serine/threonine kinases as a molecular target for NFZ. Collectively, these results implied that NFZ might be a potential therapeutic candidate for the treatment of schistosomiasis.

PMID:37409934 | DOI:10.1128/spectrum.01393-23

Cancer Res. 2023 Jul 5;83(13):2091-2092. doi: 10.1158/0008-5472.CAN-23-1066.

ABSTRACT

Statins are a class of cholesterol-lowering drugs that inhibit 3-hydroxy-3-methylglutaryl-CoA reductase, the rate-limiting enzyme of the mevalonate pathway. Evidence suggests that certain cancers depend on the mevalonate pathway for growth and survival, and thus blocking the mevalonate pathway with statins may offer a viable therapeutic approach for treating cancer, or at least enhance the efficacy of existing cancer drugs. In this issue of Cancer Research, Tran and colleagues showed that caffeine works jointly with FOXM1 inhibition to enhance the antitumor activity of statins in neuroblastoma cells. They found that caffeine synergizes with statins by suppressing statin-induced feedback activation of the mevalonate pathway. Here, we reflect on the potential of combining caffeine and statin drugs as a strategy for potentiating anticancer activity. See related article by Tran et al., p. 2248.

PMID:37403629 | DOI:10.1158/0008-5472.CAN-23-1066

BMC Bioinformatics. 2023 Jul 5;24(1):276. doi: 10.1186/s12859-023-05401-1.

ABSTRACT

BACKGROUND: In many applications of bioinformatics, data stem from distinct heterogeneous sources. One of the well-known examples is the identification of drug-target interactions (DTIs), which is of significant importance in drug discovery. In this paper, we propose a novel framework, manifold optimization based kernel preserving embedding (MOKPE), to efficiently solve the problem of modeling heterogeneous data. Our model projects heterogeneous drug and target data into a unified embedding space by preserving drug-target interactions and drug-drug, target-target similarities simultaneously.

RESULTS: We performed ten replications of ten-fold cross validation on four different drug-target interaction network data sets for predicting DTIs for previously unseen drugs. The classification evaluation metrics showed better or comparable performance compared to previous similarity-based state-of-the-art methods. We also evaluated MOKPE on predicting unknown DTIs of a given network. Our implementation of the proposed algorithm in R together with the scripts that replicate the reported experiments is publicly available at https://github.com/ocbinatli/mokpe .

PMID:37407927 | DOI:10.1186/s12859-023-05401-1

OMICS. 2023 Jul 4. doi: 10.1089/omi.2023.0075. Online ahead of print.

ABSTRACT

Human cytochrome P450 (CYP450) enzymes play a crucial role in drug metabolism and pharmacokinetics. CYP450 inhibition can lead to toxicity, in particular when drugs are co-administered with other drugs and xenobiotics or in the case of polypharmacy. Predicting CYP450 inhibition is also important for rational drug discovery and development, and precision in drug repurposing. In this overarching context, digital transformation of drug discovery and development, for example, using machine and deep learning approaches, offers prospects for prediction of CYP450 inhibition through computational models. We report here the development of a majority-voting machine learning framework to classify inhibitors and noninhibitors for seven major human liver CYP450 isoforms (CYP1A2, CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). For the machine learning models reported herein, we employed interaction fingerprints that were derived from molecular docking simulations, thus adding an additional layer of information for protein-ligand interactions. The proposed machine learning framework is based on the structure of the binding site of isoforms to produce predictions beyond previously reported approaches. Also, we carried out a comparative analysis so as to identify which representation of test compounds (molecular descriptors, molecular fingerprints, or protein-ligand interaction fingerprints) affects the predictive performance of the models. This work underlines the ways in which the structure of the enzyme catalytic site influences machine learning predictions and the need for robust frameworks toward better-informed predictions.

PMID:37406257 | DOI:10.1089/omi.2023.0075

Drug Des Devel Ther. 2023 Jun 28;17:1933-1943. doi: 10.2147/DDDT.S417563. eCollection 2023.

ABSTRACT

Moving a new drug from bench to bedside is a long and arduous process. The tactic of drug repurposing, which solves "new" diseases with "old" existing drugs, is more efficient and economical than conventional ab-initio way for drug development. Information technology has dramatically changed the paradigm of biomedical research in the new century, and drug repurposing studies have been significantly accelerated by implementing informatics techniques related to genomics, systems biology and biophysics during the past few years. A series of remarkable achievements in this field comes with the practical applications of in silico approaches including transcriptomic signature matching, gene-connection-based scanning, and simulated structure docking in repositioning drug therapies against breast cancer. In this review, we systematically curated these impressive accomplishments with summarization of the main findings on potentially repurposable drugs, and provide our insights into the current issues as well as future directions of the field. With the prospective improvement in reliability, the computer-assisted repurposing strategy will play a more critical role in drug research and development.

PMID:37405253 | PMC:PMC10315146 | DOI:10.2147/DDDT.S417563

Front Oncol. 2023 Jun 19;13:1202585. doi: 10.3389/fonc.2023.1202585. eCollection 2023.

ABSTRACT

INTRODUCTION: Medulloblastoma (MB), the most common malignant pediatric brain tumor, is currently treated with surgery followed by radiation and chemotherapy, which is accompanied by severe side effects, raising the need for innovative therapies. Disruption of the microcephaly-related gene Citron kinase (CITK) impairs the expansion of xenograft models as well as spontaneous MB arising in transgenic mice. No specific CITK inhibitors are available.

METHODS: Lestaurtinib, a Staurosporine derivative also known as CEP-701, inhibits CITK with IC50 of 90 nM. We therefore tested the biological effects of this molecule on different MB cell lines, as well as in vivo, injecting the drug in MBs arising in SmoA1 transgenic mice.

RESULTS: Similar to CITK knockdown, treatment of MB cells with 100 nM Lestaurtinib reduces phospho-INCENP levels at the midbody and leads to late cytokinesis failure. Moreover, Lestaurtinib impairs cell proliferation through CITK-sensitive mechanisms. These phenotypes are accompanied by accumulation of DNA double strand breaks, cell cycle block and TP53 superfamily activation in vitro and in vivo. Lestaurtinib treatment reduces tumor growth and increases mice survival.

DISCUSSION: Our data indicate that Lestaurtinib produces in MB cells poly-pharmacological effects extending beyond the inhibition of its validated targets, supporting the possibility of repositioning this drug for MB treatment.

PMID:37404750 | PMC:PMC10315473 | DOI:10.3389/fonc.2023.1202585

Clin Transl Med. 2023 Jul;13(7):e1320. doi: 10.1002/ctm2.1320.

ABSTRACT

BACKGROUND: The immune system plays a pivotal role in the initiation, evolution, invasion and metastasis of cancer. Therapeutics aiming at modulating or boosting anticancer immune responses have experienced immense advances during the past decades, for example, anti-PD-1/PD-L1 monoclonal antibodies.

MAIN BODY: Concomitant with advancements in the understanding of novel mechanisms of action, conventional or emerging drugs bearing the potential to be repurposed for enhancing anticancer immunity have been identified. Meanwhile, ongoing advances in drug delivery systems enable us to utilise novel therapeutic strategies and impart drugs with fresh modes of action in tumour immunology.

CONCLUSION: Herein, we systemically review these kinds of drugs and delivery systems that can unleash the anticancer response through various aspects, including immune recognition, activation, infiltration and tumour killing. We also discuss the current caveats and future directions of these emerging strategies.

PMID:37403792 | DOI:10.1002/ctm2.1320

J Enzyme Inhib Med Chem. 2023 Dec;38(1):2231169. doi: 10.1080/14756366.2023.2231169.

ABSTRACT

Trypanosoma cruzi is the causative agent of American trypanosomiasis, which mainly affects populations in Latin America. Benznidazole is used to control the disease, with severe effects in patients receiving this chemotherapy. Previous studies have demonstrated the inhibition of triosephosphate isomerase from T. cruzi, but cellular enzyme inhibition has yet to be established. This study demonstrates that rabeprazole inhibits both cell viability and triosephosphate isomerase activity in T. cruzi epimastigotes. Our results show that rabeprazole has an IC50 of 0.4 µM, which is 14.5 times more effective than benznidazole. Additionally, we observed increased levels of methyl-glyoxal and advanced glycation end products after the inhibition of cellular triosephosphate isomerase by rabeprazole. Finally, we demonstrate that the inactivation mechanisms of rabeprazole on triosephosphate isomerase of T. cruzi can be achieved through the derivatization of three of its four cysteine residues. These results indicate that rabeprazole is a promising candidate against American trypanosomiasis.

PMID:37401012 | DOI:10.1080/14756366.2023.2231169

Clin Exp Pediatr. 2023 Jun 28. doi: 10.3345/cep.2023.00535. Online ahead of print.

ABSTRACT

Most rare diseases(orphan diseases) still lack approved treatments despite major advances in research providing the tools to understand their molecular basis, as well as legislation providing regulatory and economic incentives to expedite the development of specific therapies. Addressing this translational gap is a multifaceted challenge, for which a key aspect is the selection of the optimal therapeutic modality for translating advances in rare disease knowledge into potential medicines, known as orphan drugs. There are several strategies for the development of orphan drugs for rare genetic disorders including protein replacement therapies, small molecule therapies(e.g. substrate reduction therapy, chemical chaperone therapy, cofactor therapy, expression modification therapy, read through therapy), monoclonal antibodies, antisense oligonucleotide, small interfering RNA or exon skipping therapies, gene replacement and direct genome editing therapies, mRNA therapy, and cell therapy as well as drug repurposing. Each strategy has its strength and limitations for orphan drug development. Furthermore, numerous hurdles are present in clinical trials in rare genetic disease because of difficulty in patient recruitment, unknown molecular physiology and natural history of the disease, ethical concerns about pediatric patients, and regulatory challenges. To address these barriers, the rare genetic diseases community including academic institutions, industry, patient advocacy groups, foundations, payers and government regulatory and research organizations, must be engaged as a partnership in discussions about the issues.

PMID:37402468 | DOI:10.3345/cep.2023.00535

EBioMedicine. 2023 Jul 1;94:104674. doi: 10.1016/j.ebiom.2023.104674. Online ahead of print.

ABSTRACT

BACKGROUND: The identification of new uses for existing drug therapies has the potential to identify treatments for comorbid conditions that have the added benefit of glycemic control while also providing a rapid, low-cost approach to drug (re)discovery.

METHODS: We developed and tested a genetically-informed drug-repurposing pipeline for diabetes management. This approach mapped genetically-predicted gene expression signals from the largest genome-wide association study for type 2 diabetes mellitus to drug targets using publicly available databases to identify drug-gene pairs. These drug-gene pairs were then validated using a two-step approach: 1) a self-controlled case-series (SCCS) using electronic health records from a discovery and replication population, and 2) Mendelian randomization (MR).

FINDINGS: After filtering on sample size, 20 candidate drug-gene pairs were validated and various medications demonstrated evidence of glycemic regulation including two anti-hypertensive classes: angiotensin-converting enzyme inhibitors as well as calcium channel blockers (CCBs). The CCBs demonstrated the strongest evidence of glycemic reduction in both validation approaches (SCCS HbA1c and glucose reduction: -0.11%, p = 0.01 and -0.85 mg/dL, p = 0.02, respectively; MR: OR = 0.84, 95% CI = 0.81, 0.87, p = 5.0 x 10-25).

INTERPRETATION: Our results support CCBs as a strong candidate medication for blood glucose reduction in addition to cardiovascular disease reduction. Further, these results support the adaptation of this approach for use in future drug-repurposing efforts for other conditions.

FUNDING: National Institutes of Health, Medical Research Council Integrative Epidemiology Unit at the University of Bristol, UK Medical Research Council, American Heart Association, and Department of Veterans Affairs (VA) Informatics and Computing Infrastructure and VA Cooperative Studies Program.

PMID:37399599 | DOI:10.1016/j.ebiom.2023.104674

Appl Health Econ Health Policy. 2023 Jul 3. doi: 10.1007/s40258-023-00816-6. Online ahead of print.

ABSTRACT

Drug repurposing is the process of identifying a new use for an existing drug or active substance in an indication outside the scope of the original indication. Drug repurposing has important advantages including reduced development time and costs, and potentially large societal healthcare cost savings. However, current generic drug repurposing research faces a number of challenges in obtaining research funds. Furthermore, regardless of the success of a repurposing trial, commercial parties often lack interest in pursuing marketing authorisation for financial reasons, and academic researchers lack the knowledge, time and funding. Therefore, the new indication of a repurposed drug often does not make it 'on label'. We propose a large increase in public funding for generic drug repurposing research, including funds for the marketing authorisation process when a trial is successful, and a reduction in the regulatory burden of the marketing authorisation process for repurposed generic drugs.

PMID:37398987 | DOI:10.1007/s40258-023-00816-6

medRxiv. 2023 Jun 17:2023.06.08.23291168. doi: 10.1101/2023.06.08.23291168. Preprint.

ABSTRACT

Understanding the genetic basis of biological aging in multi-organ systems is vital for elucidating age-related disease mechanisms and identifying therapeutic interventions. This study characterized the genetic architecture of the biological age gap (BAG) across nine human organ systems in 377,028 individuals of European ancestry from the UK Biobank. We discovered 393 genomic loci, including 143 novel loci, associated with the BAG of the brain, eye, cardiovascular, hepatic, immune, metabolic, musculoskeletal, pulmonary, and renal systems. We also observed BAG-organ specificity and inter-organ crosstalk. Genetic variants associated with the nine BAGs are predominantly specific to the respective organ system while exerting pleiotropic effects on traits linked to multiple organ systems. A gene-drug-disease network confirmed the involvement of the metabolic BAG-associated genes in drugs targeting various metabolic disorders. Genetic correlation analyses supported Cheverud's Conjecture 1 - the genetic correlation between BAGs mirrors their phenotypic correlation. A causal network revealed potential causal effects linking chronic diseases (e.g., Alzheimer's disease), body weight, and sleep duration to the BAG of multiple organ systems. Our findings shed light on promising therapeutic interventions to enhance human organ health within a complex multi-organ network, including lifestyle modifications and potential drug repositioning strategies for treating chronic diseases. All results are publicly available at: https://labs.loni.usc.edu/medicine .

ONE-SENTENCE SUMMARIES: Across nine human organ systems, the genetic architectures of the biological age gap (BAG) revealed BAG-organ specificity and inter-organ crosstalk, highlighting the interconnections among multiple organ systems, chronic diseases, body weight, and lifestyle factors.

PMID:37398441 | PMC:PMC10312870 | DOI:10.1101/2023.06.08.23291168

medRxiv. 2023 Jun 3:2023.05.25.23290531. doi: 10.1101/2023.05.25.23290531. Preprint.

ABSTRACT

INTRODUCTION: Drug repurposing involves finding new therapeutic uses for already approved drugs, which can save costs as their pharmacokinetics and pharmacodynamics are already known. Predicting efficacy based on clinical endpoints is valuable for designing phase 3 trials and making Go/No-Go decisions, given the potential for confounding effects in phase 2.

OBJECTIVES: This study aims to predict the efficacy of the repurposed Heart Failure (HF) drugs for the Phase 3 Clinical Trial.

METHODS: Our study presents a comprehensive framework for predicting drug efficacy in phase 3 trials, which combines drug-target prediction using biomedical knowledgebases with statistical analysis of real-world data. We developed a novel drug-target prediction model that uses low-dimensional representations of drug chemical structures and gene sequences, and biomedical knowledgebase. Furthermore, we conducted statistical analyses of electronic health records to assess the effectiveness of repurposed drugs in relation to clinical measurements (e.g., NT-proBNP).

RESULTS: We identified 24 repurposed drugs (9 with a positive effect and 15 with a non-positive) for heart failure from 266 phase 3 clinical trials. We used 25 genes related to heart failure for drug-target prediction, as well as electronic health records (EHR) from the Mayo Clinic for screening, which contained over 58,000 heart failure patients treated with various drugs and categorized by heart failure subtypes. Our proposed drug-target predictive model performed exceptionally well in all seven tests in the BETA benchmark compared to the six cutting-edge baseline methods (i.e., best performed in 266 out of 404 tasks). For the overall prediction of the 24 drugs, our model achieved an AUCROC of 82.59% and PRAUC (average precision) of 73.39%.

CONCLUSION: The study demonstrated exceptional results in predicting the efficacy of repurposed drugs for phase 3 clinical trials, highlighting the potential of this method to facilitate computational drug repurposing.

PMID:37398384 | PMC:PMC10312819 | DOI:10.1101/2023.05.25.23290531