Medicine (Baltimore). 2022 Mar 18;101(11):e29107. doi: 10.1097/MD.0000000000029107.

ABSTRACT

Given the high disability rate of multiple sclerosis (MS), there is a need for safer and more effective therapeutic agents. Existing literature highlights the prominent roles of miRNA in MS pathophysiology. Nevertheless, there are few studies that have explored the usefulness of existing drugs in treating MS through potential miRNA-modulating abilities.The current investigation identifies genes that may exacerbate the risk of MS due to their respective miRNA associations. These findings were then used to determine potential drug candidates through the construction of miRNA-regulated drug-pathway network through genes. We uncovered a total of 48 MS risk pathways, 133 MS risk miRNAs, and 186 drugs that can affect these pathways. Potential MS risk miRNAs that are also regulated by therapeutic candidates were hsa05215 and hsa05152. We analyzed the properties of the miRNA-regulated drug-pathway network through genes and uncovered a number of novel MS agents by assessing their respective Z-values.A total of 20 likely drug candidates were identified, including human immunoglobulin, aspirin, alemtuzumab, minocycline, abciximab, alefacept, palivizumab, bevacizumab, efalizumab, tositumomab, minocycline, etanercept, catumaxomab, and sarilumab. Each of these agents were then explored with regards to their likely mechanism of action in treating MS.The current investigation provides a fresh perspective on MS biological mechanisms as well as likely treatment strategies.

PMID:35356949 | DOI:10.1097/MD.0000000000029107

Front Oncol. 2022 Mar 9;12:808174. doi: 10.3389/fonc.2022.808174. eCollection 2022.

ABSTRACT

Daemonorops draco Blume (DD), also called dragon's blood, has been used as a traditional Korean medicine, especially for relieving pain caused by wound infection. Recently, it has been described that DD has antibacterial and analgesic effects. In this study, the underlying anticancer effect of DD associated with apoptosis was investigated in acute myeloid leukemia cell lines U937 and THP-1. DD exhibited cytotoxic effects and induced apoptosis in U937 and THP-1 cells. Moreover, DD treatment significantly reduced mitochondrial membrane potential (ΔΨ). The protein expression of cleaved poly(ADP-ribose) polymerase, cleaved caspase-3, p-H2A.X, CCAAT/enhancer-binding protein (CHOP), and activating transcription factor 4 was upregulated by DD treatment. Consistently, DD-treated cells had increased reactive oxygen species (ROS) level in a concentration-dependent manner via miR-216b activation in association with c-Jun inhibition. N-acetyl-L-cysteine pretreatment reversed the cytotoxic effect of DD treatment as well as prevented ROS accumulation. Collectively, the results of this study suggest that the anticancer effect of DD in AML was mediated by CHOP-dependent apoptosis along with ROS accumulation and included upregulation of miR-216b followed by a decrease in c-Jun.

PMID:35356209 | PMC:PMC8959842 | DOI:10.3389/fonc.2022.808174

Front Genet. 2022 Mar 8;13:792090. doi: 10.3389/fgene.2022.792090. eCollection 2022.

ABSTRACT

The need for systematic drug repurposing has seen a steady increase over the past decade and may be particularly valuable to quickly remedy unexpected pandemics. The abundance of functional interaction data has allowed mapping of substantial parts of the human interactome modeled using functional association networks, favoring network-based drug repurposing. Network crosstalk-based approaches have never been tested for drug repurposing despite their success in the related and more mature field of pathway enrichment analysis. We have, therefore, evaluated the top performing crosstalk-based approaches for drug repurposing. Additionally, the volume of new interaction data as well as more sophisticated network integration approaches compelled us to construct a new benchmark for performance assessment of network-based drug repurposing tools, which we used to compare network crosstalk-based methods with a state-of-the-art technique. We find that network crosstalk-based drug repurposing is able to rival the state-of-the-art method and in some cases outperform it.

PMID:35350247 | PMC:PMC8958038 | DOI:10.3389/fgene.2022.792090

Arthritis Rheumatol. 2022 Mar 29. doi: 10.1002/art.42129. Online ahead of print.

ABSTRACT

OBJECTIVE: Juvenile Idiopathic Arthritis (JIA) is the most common chronic immune-mediated joint disease among children, encompassing a heterogeneous group of immune-mediated joint disorders, being classified into seven subtypes based on clinical presentation. However, phenotypic overlap and biological evidence suggest shared mechanistic basis between subtypes. In this study, we aimed to systematically understand the shared genetic underpinnings of JIA subtypes.

METHODS: We performed heterogeneity-sensitive genome-wide association studies (hsGWAS) encompassing a total of 1,245 JIA cases classified into 7 subtypes and 9,250 controls, followed by fine-mapping of candidate causal variants at each genome-wide significant locus, functional annotation, as well as pathway and network analysis. We further identified candidate drug targets with drug repurposing opportunities by in silico analyses.

RESULTS: In addition to the MHC locus, we uncovered 15 genome-wide significant loci which were shared between at least two JIA subtypes, including 10 novel loci. Functional annotation indicates that candidate genes at these loci are expressed in diverse immune cell types.

CONCLUSION: This study identified novel genetic loci shared by JIA subtypes and nominated candidate mechanisms underlying JIA subtypes and candidate targets with possible drug repositioning opportunities for JIA treatment.

PMID:35347896 | DOI:10.1002/art.42129

J Mol Model. 2022 Mar 26;28(4):105. doi: 10.1007/s00894-022-05093-z.

ABSTRACT

Increased expression and activation of tumor necrosis factor-α (TNF-α) could lead to recurrent implantation failure (RIF). Therefore, TNF-α inhibition may be a strategic way to enhance the implantation rate in women with RIF. Nowadays, monoclonal antibodies are considered an effective therapeutic method for TNF-α inhibition. Unfortunately, monoclonal antibody treatments have several disadvantages. Thus, the design of small molecules capable of inhibiting TNF-α has become critical in recent years. In silico drug repurposing of FDA-approved drugs for TNF-α inhibition was used in this study. PyRx tools were employed for virtual screening. Additionally, the free energy of binding, the number of hydrogen bonds, and the number of drug contacts with the protein were calculated using the molecular dynamics (MD) simulation method. Virtual screening results reveal that 17 of 2471 FDA-approved drugs benefited from favorable binding energy with TNF-α (delta G < - 10 kcal/mol). Two of the 17 drugs, progesterone and prednisone, were the most frequently used without adverse effects during pregnancy. As a result, MD simulation was used to investigate these two drugs further. According to the MD simulation results, prednisone appears to have a higher affinity for TNF-α than progesterone, and consequently, the prednisone complex stability is higher. For the first time, this study examined the possible role of prednisone and progesterone in inhibiting TNF-α using in silico methods.

PMID:35347442 | DOI:10.1007/s00894-022-05093-z

Aging (Albany NY). 2022 Mar 29;14(undefined). doi: 10.18632/aging.203960. Online ahead of print.

ABSTRACT

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

PMID:35347083 | DOI:10.18632/aging.203960

iScience. 2022 Mar 10;25(4):104052. doi: 10.1016/j.isci.2022.104052. eCollection 2022 Apr 15.

ABSTRACT

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

PMID:35345455 | PMC:PMC8957031 | DOI:10.1016/j.isci.2022.104052

Aging Cell. 2022 Mar 27:e13596. doi: 10.1111/acel.13596. Online ahead of print.

ABSTRACT

Common chronic diseases represent the greatest driver of rising healthcare costs, as well as declining function, independence, and quality of life. Geroscience-guided approaches seek to delay the onset and progression of multiple chronic conditions by targeting fundamental biological pathways of aging. This approach is more likely to improve overall health and function in old age than treating individual diseases, by addressing aging the largest and mostly ignored risk factor for the leading causes of morbidity in older adults. Nevertheless, challenges in repurposing existing and moving newly discovered interventions from the bench to clinical care have impeded the progress of this potentially transformational paradigm shift. In this article, we propose the creation of a standardized process for evaluating FDA-approved medications for their geroscience potential. Criteria for systematically evaluating the existing literature that spans from animal models to human studies will permit the prioritization of efforts and financial investments for translating geroscience and allow immediate progress on the design of the next Targeting Aging with MEtformin (TAME)-like study involving such candidate gerotherapeutics.

PMID:35343051 | DOI:10.1111/acel.13596

Math Biosci Eng. 2022 Jan 21;19(4):3269-3284. doi: 10.3934/mbe.2022151.

ABSTRACT

Research on the relationship between drugs and targets is the key to precision medicine. Ion channel is a kind of important drug targets. Aiming at the urgent needs of corona virus disease 2019 (COVID-19) treatment and drug development, this paper designed a mixed graph network model to predict the affinity between ion channel targets of COVID-19 and drugs. According to the simplified molecular input line entry specification (SMILES) code of drugs, firstly, the atomic features were extracted to construct the point sets, and edge sets were constructed according to atomic bonds. Then the undirected graph with atomic features was generated by RDKit tool and the graph attention layer was used to extract the drug feature information. Five ion channel target proteins were screened from the whole SARS-CoV-2 genome sequences of NCBI database, and the protein features were extracted by convolution neural network (CNN). Using attention mechanism and graph convolutional network (GCN), the extracted drug features and target features information were connected. After two full connection layers operation, the drug-target affinity was output, and model was obtained. Kiba dataset was used to train the model and determine the model parameters. Compared with DeepDTA, WideDTA, graph attention network (GAT), GCN and graph isomorphism network (GIN) models, it was proved that the mean square error (MSE) of the proposed model was decreased by 0.055, 0.04, 0.001, 0.046, 0.013 and the consistency index (CI) was increased by 0.028, 0.016, 0.003, 0.03 and 0.01, respectively. It can predict the drug-target affinity more accurately. According to the prediction results of drug-target affinity of SARS-CoV-2 ion channel targets, seven kinds of small molecule drugs acting on five ion channel targets were obtained, namely SCH-47112, Dehydroaltenusin, alternariol 5-o-sulfate, LPA1 antagonist 1, alternariol, butin, and AT-9283.These drugs provide a reference for drug repositioning and precise treatment of COVID-19.

PMID:35341251 | DOI:10.3934/mbe.2022151

PeerJ. 2022 Mar 21;10:e13090. doi: 10.7717/peerj.13090. eCollection 2022.

ABSTRACT

BACKGROUND: Chikungunya virus (CHIKV) is a mosquito-borne pathogen, within the Alphavirus genus of the Togaviridae family, that causes ~1.1 million human infections annually. CHIKV uses Aedes albopictus and Aedes aegypti mosquitoes as insect vectors. Human infections can develop arthralgia and myalgia, which results in debilitating pain for weeks, months, and even years after acute infection. No therapeutic treatments or vaccines currently exist for many alphaviruses, including CHIKV. Targeting the phagocytosis of CHIKV by macrophages after mosquito transmission plays an important role in early productive viral infection in humans, and could reduce viral replication and/or symptoms.

METHODS: To better characterize the transcriptional response of macrophages during early infection, we generated RNA-sequencing data from a CHIKV-infected human macrophage cell line at eight or 24 hours post-infection (hpi), together with mock-infected controls. We then calculated differential gene expression, enriched functional annotations, modulated intracellular signaling pathways, and predicted therapeutic drugs from these sequencing data.

RESULTS: We observed 234 pathways were significantly affected 24 hpi, resulting in six potential pharmaceutical treatments to modulate the affected pathways. A subset of significant pathways at 24 hpi includes AGE-RAGE, Fc epsilon RI, Chronic myeloid leukemia, Fc gamma R-mediated phagocytosis, and Ras signaling. We found that the MAPK1 and MAPK3 proteins are shared among this subset of pathways and that Telmisartan and Dasatinib are strong candidates for repurposed small molecule therapeutics that target human processes. The results of our analysis can be further characterized in the wet lab to contribute to the development of host-based prophylactics and therapeutics.

PMID:35341048 | PMC:PMC8944338 | DOI:10.7717/peerj.13090

EBioMedicine. 2022 Mar 24;78:103963. doi: 10.1016/j.ebiom.2022.103963. Online ahead of print.

ABSTRACT

BACKGROUND: The response rates of the clinical chemotherapies are still low in clear cell renal cell carcinoma (ccRCC). Computational drug repositioning is a promising strategy to discover new uses for existing drugs to treat patients who cannot get benefits from clinical drugs.

METHODS: We proposed a systematic approach which included the target prediction based on the co-expression network analysis of transcriptomics profiles of ccRCC patients and drug repositioning for cancer treatment based on the analysis of shRNA- and drug-perturbed signature profiles of human kidney cell line.

FINDINGS: First, based on the gene co-expression network analysis, we identified two types of gene modules in ccRCC, which significantly enriched with unfavorable and favorable signatures indicating poor and good survival outcomes of patients, respectively. Then, we selected four genes, BUB1B, RRM2, ASF1B and CCNB2, as the potential drug targets based on the topology analysis of modules. Further, we repurposed three most effective drugs for each target by applying the proposed drug repositioning approach. Finally, we evaluated the effects of repurposed drugs using an in vitro model and observed that these drugs inhibited the protein levels of their corresponding target genes and cell viability.

INTERPRETATION: These findings proved the usefulness and efficiency of our approach to improve the drug repositioning researches for cancer treatment and precision medicine.

FUNDING: This study was funded by Knut and Alice Wallenberg Foundation and Bash Biotech Inc., San Diego, CA, USA.

PMID:35339898 | DOI:10.1016/j.ebiom.2022.103963

Virology. 2022 Mar 10;570:9-17. doi: 10.1016/j.virol.2022.02.006. Online ahead of print.

ABSTRACT

The repurposing of marketed drugs for new indications is an elegant strategy to quickly and cost-efficiently address unmet medical needs. The hepatitis C virus (HCV) RNA-dependent RNA polymerase (RdRp) has been shown to be a valid drug target. We performed structure-based virtual screening to assess the off-label utilization of existing drugs as novel HCV inhibitors. The virtual screen showed that tigecycline could potentially dock with high affinity to the palm site of the HCV RdRp. In vitro validation showed that tigecycline had therapeutic indexes (CC50/EC50) greater than 13 and 6.5 against infectious HCV and subgenomic HCV replicons, respectively. Furthermore, tigecycline displayed synergistic activity with sofosbuvir and daclatasvir against HCV. In silico screening identified tigecycline as a putative inhibitor of HCV RdRp, which was validated in vitro and demonstrated synergistic effects in combination with first-line anti-HCV therapies.

PMID:35338891 | DOI:10.1016/j.virol.2022.02.006

Mol Inform. 2022 Mar 25. doi: 10.1002/minf.202200001. Online ahead of print.

ABSTRACT

Identification of disease-drug associations is an effective strategy for drug repurposing, especially in searching old drugs for newly emerged diseases like COVID-19. In this study, we put forward a network-based method named NEDNBI to predict disease-drug associations based on a gene-disease-drug tripartite network, which could be applied in drug repurposing. The novelty of our method lies in the fact that no negative data are required, and new disease could be added into the disease-drug network with gene as the bridge. The comprehensive evaluation results showed that the proposed method had good performance, with AUC value 0.948 ± 0.009 for 10-fold cross validation. In a case study, 8 of the 20 predicted old drugs have been tested clinically for the treatment of COVID-19, which illustrated the usefulness of our method in drug repurposing. The source code and data of the method are available at https://github.com/Qli97/NEDNBI.

PMID:35338586 | DOI:10.1002/minf.202200001

Appl Microbiol Biotechnol. 2022 Mar 26. doi: 10.1007/s00253-022-11878-2. Online ahead of print.

ABSTRACT

Staphylococcus aureus is a major human pathogen, and the appearance of methicillin-resistant S. aureus (MRSA) renders S. aureus infections more challenging to treat. Therefore, new antimicrobial drugs are urgently needed to combat MRSA infections. Drug repurposing is an effective and feasible strategy. Here, we reported that the clinically approved anti-hepatitis C virus drug simeprevir had strong antibacterial activity against MRSA, with a minimum inhibitory concentration of 2-8 µg/mL. Simeprevir did not easily induce in vitro resistance. In addition, simeprevir significantly prevented S. aureus biofilm formation. Furthermore, simeprevir displayed limited toxicity in in vitro and in vivo assays. Moreover, simeprevir showed synergistic antimicrobial effects against both type and clinical strains of S. aureus. Simeprevir combined with gentamicin effectively reduced the bacterial burden in an MRSA-infected subcutaneous abscess mouse model. Results from a series of experiments, including membrane permeability assay, membrane potential assay, intracellular ATP level assay, and electron microscope observation, demonstrated that the action of simeprevir may be by disrupting bacterial cell membranes. Collectively, these results demonstrated the potential of simeprevir as an antimicrobial agent for the treatment of MRSA infections. KEY POINTS: • Simeprevir showed strong antibacterial activity against MRSA. • The antibacterial mechanism of simeprevir was mediated by membrane disruption and intracellular ATP depletion. • In vitro and in vivo synergistic antimicrobial efficacy between simeprevir and gentamicin was found.

PMID:35338386 | DOI:10.1007/s00253-022-11878-2

NPJ Genom Med. 2022 Mar 25;7(1):25. doi: 10.1038/s41525-022-00290-4.

ABSTRACT

There have been no new drugs for the treatment of schizophrenia in several decades and treatment resistance represents a major unmet clinical need. The drugs that exist are based on serendipitous clinical observations rather than an evidence-based understanding of disease pathophysiology. In the present review, we address these bottlenecks by integrating common, rare, and expression-related schizophrenia risk genes with knowledge of the druggability of the human genome as a whole. We highlight novel drug repurposing opportunities, clinical trial candidates which are supported by genetic evidence, and unexplored therapeutic opportunities in the lesser-known regions of the schizophrenia genome. By identifying translational gaps and opportunities across the schizophrenia disease space, we discuss a framework for translating increasingly well-powered genetic association studies into personalized treatments for schizophrenia and initiating the vital task of characterizing clinically relevant drug targets in underexplored regions of the human genome.

PMID:35338153 | DOI:10.1038/s41525-022-00290-4

Biol Sex Differ. 2022 Mar 25;13(1):13. doi: 10.1186/s13293-022-00420-8.

ABSTRACT

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

PMID:35337371 | DOI:10.1186/s13293-022-00420-8

Viruses. 2022 Mar 15;14(3):611. doi: 10.3390/v14030611.

ABSTRACT

The novel coronavirus SARS-CoV-2 is responsible for the ongoing COVID-19 pandemic and has caused a major health and economic burden worldwide. Understanding how SARS-CoV-2 viral proteins behave in host cells can reveal underlying mechanisms of pathogenesis and assist in development of antiviral therapies. Here, the cellular impact of expressing SARS-CoV-2 viral proteins was studied by global proteomic analysis, and proximity biotinylation (BioID) was used to map the SARS-CoV-2 virus-host interactome in human lung cancer-derived cells. Functional enrichment analyses revealed previously reported and unreported cellular pathways that are associated with SARS-CoV-2 proteins. We have established a website to host the proteomic data to allow for public access and continued analysis of host-viral protein associations and whole-cell proteomes of cells expressing the viral-BioID fusion proteins. Furthermore, we identified 66 high-confidence interactions by comparing this study with previous reports, providing a strong foundation for future follow-up studies. Finally, we cross-referenced candidate interactors with the CLUE drug library to identify potential therapeutics for drug-repurposing efforts. Collectively, these studies provide a valuable resource to uncover novel SARS-CoV-2 biology and inform development of antivirals.

PMID:35337019 | DOI:10.3390/v14030611

Viruses. 2022 Mar 10;14(3):573. doi: 10.3390/v14030573.

ABSTRACT

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the Coronavirus family which caused the worldwide pandemic of human respiratory illness coronavirus disease 2019 (COVID-19). Presumably emerging at the end of 2019, it poses a severe threat to public health and safety, with a high incidence of transmission, predominately through aerosols and/or direct contact with infected surfaces. In 2020, the search for vaccines began, leading to the obtaining of, to date, about twenty COVID-19 vaccines approved for use in at least one country. However, COVID-19 continues to spread and new genetic mutations and variants have been discovered, requiring pharmacological treatments. The most common therapies for COVID-19 are represented by antiviral and antimalarial agents, antibiotics, immunomodulators, angiotensin II receptor blockers, bradykinin B2 receptor antagonists and corticosteroids. In addition, nutraceuticals, vitamins D and C, omega-3 fatty acids and probiotics are under study. Finally, drug repositioning, which concerns the investigation of existing drugs for new therapeutic target indications, has been widely proposed in the literature for COVID-19 therapies. Considering the importance of this ongoing global public health emergency, this review aims to offer a synthetic up-to-date overview regarding diagnoses, variants and vaccines for COVID-19, with particular attention paid to the adopted treatments.

PMID:35336980 | DOI:10.3390/v14030573

Biology (Basel). 2022 Feb 26;11(3):372. doi: 10.3390/biology11030372.

ABSTRACT

NF-κB is a central mediator of inflammation, response to DNA damage and oxidative stress. As a result of its central role in so many important cellular processes, NF-κB dysregulation has been implicated in the pathology of important human diseases. NF-κB activation causes inappropriate inflammatory responses in diseases including rheumatoid arthritis (RA) and multiple sclerosis (MS). Thus, modulation of NF-κB signaling is being widely investigated as an approach to treat chronic inflammatory diseases, autoimmunity and cancer. The emergence of COVID-19 in late 2019, the subsequent pandemic and the huge clinical burden of patients with life-threatening SARS-CoV-2 pneumonia led to a massive scramble to repurpose existing medicines to treat lung inflammation in a wide range of healthcare systems. These efforts continue and have proven to be controversial. Drug repurposing strategies are a promising alternative to de novo drug development, as they minimize drug development timelines and reduce the risk of failure due to unexpected side effects. Different experimental approaches have been applied to identify existing medicines which inhibit NF-κB that could be repurposed as anti-inflammatory drugs.

PMID:35336746 | DOI:10.3390/biology11030372

Pharmaceutics. 2022 Mar 11;14(3):625. doi: 10.3390/pharmaceutics14030625.

ABSTRACT

Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a different disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug-target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.

PMID:35336000 | DOI:10.3390/pharmaceutics14030625