Curr Issues Mol Biol. 2022 Jan 22;44(2):541-549. doi: 10.3390/cimb44020037.

ABSTRACT

Oncostatin M (OSM) is essential in a wide range of inflammatory responses, and most OSM is produced by neutrophils in respiratory diseases. While resveratrol (RES) is regarded as an anti-inflammatory agent in a variety of conditions, the mechanism of OSM inhibition by RES in neutrophils remains to be elucidated. In this study, we investigated whether RES could inhibit OSM production in neutrophil-like differentiated (d)HL-60 cells. The effects of RES were measured by means of an enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and Western blotting. Increases in production and mRNA expression of OSM resulted from the addition of granulocyte-macrophage colony-stimulating factor (GM-CSF) in neutrophil-like dHL-60 cells; however, these increases were downregulated by RES treatment. Exposure to GM-CSF led to elevations of phosphorylation of phosphatidylinositol 3-kinase (PI3K), Akt, and nuclear factor (NF)-kB. Treatment with RES induced downregulation of the phosphorylated levels of PI3K, Akt, and NF-κB in neutrophil-like dHL-60 cells. These results suggest that RES could be applicable to prevent and/or treat inflammatory disorders through blockade of OSM.

PMID:35723323 | DOI:10.3390/cimb44020037

ACS Omega. 2022 Jun 1;7(23):19243-19260. doi: 10.1021/acsomega.2c00518. eCollection 2022 Jun 14.

ABSTRACT

Novel drug development is a time-consuming process with relatively high debilitating costs. To overcome this problem, computational drug repositioning approaches are being used to predict the possible therapeutic scaffolds against different diseases. In the current study, computational drug repositioning approaches were employed to fetch the promising drugs from the pool of FDA-approved drugs against Ewing sarcoma. The binding interaction patterns and conformational behaviors of screened drugs within the active region of Ewing sarcoma protein (EWS) were confirmed through molecular docking profiles. Furthermore, pharmacogenomics analysis was employed to check the possible associations of selected drugs with Ewing sarcoma genes. Moreover, the stability behavior of selected docked complexes (drugs-EWS) was checked by molecular dynamics simulations. Taken together, astemizole, sulfinpyrazone, and pranlukast exhibited a result comparable to pazopanib and can be used as a possible therapeutic agent in the treatment of Ewing sarcoma.

PMID:35721972 | PMC:PMC9202290 | DOI:10.1021/acsomega.2c00518

Front Pharmacol. 2022 Jun 3;13:869461. doi: 10.3389/fphar.2022.869461. eCollection 2022.

ABSTRACT

Docetaxel (DTX) was the first chemotherapeutic agent to demonstrate significant efficacy in the treatment of men with metastatic castration-resistant prostate cancer. However, response to DTX is generally short-lived, and relapse eventually occurs due to emergence of drug-resistance. We previously established two DTX-resistant prostate cancer cell lines, LNCaPR and C4-2BR, derived from the androgen-dependent LNCaP cell line, and from the LNCaP lineage-derived androgen-independent C4-2B sub-line, respectively. Using an unbiased drug screen, we identify itraconazole (ITZ), an oral antifungal drug, as a compound that can efficiently re-sensitize drug-resistant LNCaPR and C4-2BR prostate cancer cells to DTX treatment. ITZ can re-sensitize multiple DTX-resistant cell models, not only in prostate cancer derived cells, such as PC-3 and DU145, but also in docetaxel-resistant breast cancer cells. This effect is dependent on expression of ATP-binding cassette (ABC) transporter protein ABCB1, also known as P-glycoprotein (P-gp). Molecular modeling of ITZ bound to ABCB1, indicates that ITZ binds tightly to the inward-facing form of ABCB1 thereby inhibiting the transport of DTX. Our results suggest that ITZ may provide a feasible approach to re-sensitization of DTX resistant cells, which would add to the life-prolonging effects of DTX in men with metastatic castration-resistant prostate cancer.

PMID:35721223 | PMC:PMC9203833 | DOI:10.3389/fphar.2022.869461

Front Pharmacol. 2022 Jun 3;13:837115. doi: 10.3389/fphar.2022.837115. eCollection 2022.

ABSTRACT

Current anti-HBV therapeutic strategy relies on interferon and nucleos(t)ide-type drugs with the limitation of functional cure, inducing hepatitis B surface antigen (HBsAg) loss in very few patients. Notably, the level of HBsAg has been established as an accurate indicator to evaluate the drug efficacy and predict the disease prognosis, thus exploring a novel drug targeting HBsAg will be of great significance. Herein, by screening 978 compounds from an FDA-approved drug library and determining the inhibitory function of each drug on HBsAg level in HepG2.2.15 cells supernatant, we identified that pimobendan (Pim) has a powerful antiviral activity with relatively low cytotoxicity. The inhibitory effect of Pim on HBsAg as well as other HBV markers was validated in HBV-infected cell models and HBV-transgenic mice. Mechanistically, real-time PCR and dual-luciferase reporter assay were applied to identify the partial correlation of transcription factor CAAT enhancer-binding protein α (C/EBPα) with the cccDNA transcription regulated by Pim. This indicates Pim is an inhibitor of HBV transcription through suppressing HBV promoters to reduce HBV RNAs levels and HBsAg production. In conclusion, Pim was identified to be a transcription inhibitor of cccDNA, thereby inhibiting HBsAg and other HBV replicative intermediates both in vitro and in vivo. This report may provide a promising lead for the development of new anti-HBV agent.

PMID:35721154 | PMC:PMC9204083 | DOI:10.3389/fphar.2022.837115

Front Pharmacol. 2022 Jun 2;13:899469. doi: 10.3389/fphar.2022.899469. eCollection 2022.

ABSTRACT

Background and aim: Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease causing irreparable scarring of lung tissue, with most patients succumbing rapidly after diagnosis. The mevalonate pathway, which is involved in the regulation of cell proliferation, survival, and motility, is targeted by the bisphosphonate zoledronic acid (ZA). The aim of this study was to assess the antifibrotic effects of ZA and to elucidate the mechanisms by which potential IPF treatment occurs. Methods: A series of in vitro and in vivo models were employed to identify the therapeutic potential of ZA in treating IPF. In vitro transwell assays were used to assess the ability of ZA to reduce fibrotic-related immune cell recruitment. Farnesyl diphosphate synthase (FDPS) was screened as a potential antifibrotic target using a bleomycin mouse model. FDPS-targeting siRNA and ZA were administered to mice following the onset of experimentally-induced lung fibrosis. Downstream analyses were conducted on murine lung tissues and lung fluids including 23-plex cytokine array, flow cytometry, histology, Western blotting, immunofluorescent staining, and PCR analysis. Results: In vitro administration of ZA reduced myofibroblast transition and blocked NF-κB signaling in macrophages leading to impaired immune cell recruitment in a transwell assay. FDPS-targeting siRNA administration significantly attenuated profibrotic cytokine production and lung damage in a murine lung fibrosis model. Furthermore, ZA treatment of mice with bleomycin-induced lung damage displayed decreased cytokine levels in the BALF, plasma, and lung tissue, resulting in less histologically visible fibrotic scarring. Bleomycin-induced upregulation of the ZA target, FDPS, was reduced in lung tissue and fibroblasts upon ZA treatment. Confirmatory increases in FDPS immunoreactivity was seen in human IPF resected lung samples compared to control tissue indicating potential translational value of the approach. Additionally, ZA polarized macrophages towards a less profibrotic phenotype contributing to decreased IPF pathogenesis. Conclusion: This study highlights ZA as an expedient and efficacious treatment option against IPF in a clinical setting.

PMID:35721132 | PMC:PMC9201219 | DOI:10.3389/fphar.2022.899469

Front Oncol. 2022 May 26;12:865454. doi: 10.3389/fonc.2022.865454. eCollection 2022.

ABSTRACT

Cyclin-dependent kinases (CDKs) play significant roles in numerous physiological, and are considered an attractive drug target for cancer, neurodegenerative, and inflammatory diseases. In the present study, we have aimed to investigate the binding affinity and inhibitory potential of selonsertib toward CDK6. Using the drug repurposing approach, we performed molecular docking of selonsertib with CDK6 and observed a significant binding affinity. To ascertain, we further performed essential dynamics analysis and free energy calculation, which suggested the formation of a stable selonsertib-CDK6 complex. The in-silico findings were further experimentally validated. The recombinant CDK6 was expressed, purified, and treated with selonsertib. The binding affinity of selonsertib to CDK6 was estimated by fluorescence binding studies and enzyme inhibition assay. The results indicated an appreciable binding of selonsertib against CDK6, which subsequently inhibits its activity with a commendable IC50 value (9.8 μM). We concluded that targeting CDK6 by selonsertib can be an efficient therapeutic approach to cancer and other CDK6-related diseases. These observations provide a promising opportunity to utilize selonsertib to address CDK6-related human pathologies.

PMID:35720007 | PMC:PMC9204300 | DOI:10.3389/fonc.2022.865454

Front Oncol. 2022 Jun 1;12:899009. doi: 10.3389/fonc.2022.899009. eCollection 2022.

ABSTRACT

Salvicine is a new diterpenoid quinone substance from a natural source, specifically in a Chinese herb. It has powerful growth-controlling abilities against a broad range of human cancer cells in both in vitro and in vivo environments. A significant inhibitory effect of salvicine on multidrug-resistant (MDR) cells has also been discovered. Several research studies have examined the activities of salvicine on topoisomerase II (Topo II) by inducing reactive oxygen species (ROS) signaling. As opposed to the well-known Topo II toxin etoposide, salvicine mostly decreases the catalytic activity with a negligible DNA breakage effect, as revealed by several enzymatic experiments. Interestingly, salvicine dramatically reduces lung metastatic formation in the MDA-MB-435 orthotopic lung cancer cell line. Recent investigations have established that salvicine is a new non-intercalative Topo II toxin by interacting with the ATPase domains, increasing DNA-Topo II interaction, and suppressing DNA relegation and ATP hydrolysis. In addition, investigations have revealed that salvicine-induced ROS play a critical role in the anticancer-mediated signaling pathway, involving Topo II suppression, DNA damage, overcoming multidrug resistance, and tumor cell adhesion suppression, among other things. In the current study, we demonstrate the role of salvicine in regulating the ROS signaling pathway and the DNA damage response (DDR) in suppressing the progression of cancer cells. We depict the mechanism of action of salvicine in suppressing the DNA-Topo II complex through ROS induction along with a brief discussion of the anticancer perspective of salvicine.

PMID:35719997 | PMC:PMC9198638 | DOI:10.3389/fonc.2022.899009

Front Cell Infect Microbiol. 2022 Jun 2;12:939813. doi: 10.3389/fcimb.2022.939813. eCollection 2022.

NO ABSTRACT

PMID:35719344 | PMC:PMC9201246 | DOI:10.3389/fcimb.2022.939813

Trends Cancer. 2022 Jun 17:S2405-8033(22)00132-7. doi: 10.1016/j.trecan.2022.06.004. Online ahead of print.

ABSTRACT

Patients with congenital giant nevi (CGN), which can compromise quality of life and progress to melanoma, have limited treatment options. Choi et al. have demonstrated that topical application of a proinflammatory hapten for alopecia treatment [squaric acid dibutylester (SADBE)] caused nevus regression and prevented melanoma in an Nras mouse CGN model. Their results demonstrate the promise of repurposing drugs through precision modeling.

PMID:35718707 | DOI:10.1016/j.trecan.2022.06.004

Infect Genet Evol. 2022 Jun 16:105318. doi: 10.1016/j.meegid.2022.105318. Online ahead of print.

ABSTRACT

COVID-19 pathogenesis is mainly attributed to dysregulated antiviral immune response, the prominent hallmark of COVID-19. As no established drugs are available against SARS-CoV-2 and developing new ones would be a big challenge, repurposing of existing drugs holds promise against COVID-19. Here, we used a signature-based strategy to delve into cellular responses to SARS-CoV-2 infection in order to identify potential host contributors in COVID-19 pathogenesis and to find repurposable drugs using in silico approaches. We scrutinized transcriptomic profile of various human alveolar cell sources infected with SARS-CoV-2 to determine up-regulated genes specific to COVID-19. Enrichment analysis revealed that the up-regulated genes were involved mainly in viral infectious disease, immune system, and signal transduction pathways. Analysis of protein-protein interaction network and COVID-19 molecular pathway resulted in identifying several anti-viral proteins as well as 11 host pro-viral proteins, ADAR, HBEGF, MMP9, USP18, JUN, FOS, IRF2, ICAM1, IFI35, CASP1, and STAT3. Finally, molecular docking of up-regulated proteins and all FDA-approved drugs revealed that both Hydrocortisone and Benzhydrocodone possess high binding affinity for all pro-viral proteins. The suggested repurposed drugs should be subject to complementary in vitro and in vivo experiments in order to be evaluated in detail prior to clinical studies in potential management of COVID-19.

PMID:35718334 | DOI:10.1016/j.meegid.2022.105318

J Pharmacol Sci. 2022 Aug;149(4):198-204. doi: 10.1016/j.jphs.2022.05.005. Epub 2022 May 21.

ABSTRACT

To prevent the onset and aggravation of allergic diseases, it is necessary to modulate excessive Th2-type immune responses. It is well accepted that thymic stromal lymphopoietin (TSLP) plays important roles in the change of Th1/Th2 balance to Th2 dominance and would be a druggable target. In this study, using a drug repositioning strategy, we identified 6-(2-amino-4-phenylpyrimidine-5-yl)-2-isopropylpyridazin-3(2H)-one (FK3453) as a novel inhibitor of TSLP production. FK3453 inhibited constitutive production of TSLP in the KCMH-1 mouse keratinocyte cell line and 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced one in PAM212 cells. FK3453 also inhibited TSLP mRNA expression induced by a mixture of tumor necrosis factor alpha (TNF-α), interleukin (IL)-4, fibroblast-stimulation lipopeptide-1, and protease activated-receptor agonist and TPA in normal human epidermal keratinocytes (NHEKs). Although FK3453 inhibited TPA-induced IL-33 expression in NHEKs in addition to TSLP, it did not inhibit TNF-α and IL-6 production. In addition, FK3453 did not inhibit MAP kinase (ERK) phosphorylation. We have confirmed that topical treatment with FK3453 inhibited TSLP production in the lipopolysaccharide-induced air pouch-type inflammation model. FK3453 could be a lead compound for a novel type of medicine which prevents the onset and aggravation of allergic diseases.

PMID:35717073 | DOI:10.1016/j.jphs.2022.05.005

Int Immunopharmacol. 2022 Jun 15;110:108952. doi: 10.1016/j.intimp.2022.108952. Online ahead of print.

ABSTRACT

Visceral leishmaniasis (VL) is an infectious disease caused by Leishmania infantum (L. infantum). Currently, there are no vaccines and/or prophylactic therapies against VL, and the recentpharmacological approaches come from the drug repositioning strategy. Here, we evaluated the anticancer drug pamidronate (PAM) to identify a new therapeutic option for the treatment of human VL. We assessed its in vitro antileishmanial activity against the promastigote and amastigote forms of L. infantum by evaluating cell cytotoxicity. The antileishmanial and immunomodulatory activities were assessed using human peripheral blood leukocytes ex vivo. PAM induced the formation of vacuoles in the cytoplasm of the promastigotes and alterations in the morphology of the kinetoplast and mitochondria in vitro, which indicates anti-promastigote activity. PAM also reduced the number of infected macrophages and intracellular amastigotes in a concentration-dependent manner, with cell viability above 70%. In ex vivo, PAM reduced the internalized forms of L. infantum in the classical monocyte subpopulation. Furthermore, it enhanced IL-12 and decreased IL-10 and TGF-β by monocytes and neutrophils. Increased IFN-γ and TNF levels for CD8- and CD8+ T lymphocytes and B lymphocytes, respectively, were observed after the treatment with PAM, as well as a reduction in IL-10 by the lymphocyte subpopulations evaluated. Taken together, our results suggest that PAM may be eligible as a potential therapeutic alternative for drug repurposing to treat human visceral leishmaniasis.

PMID:35716482 | DOI:10.1016/j.intimp.2022.108952

J Mol Model. 2022 Jun 18;28(7):193. doi: 10.1007/s00894-022-05185-w.

ABSTRACT

Fungal infections in humans are responsible for mild to severe infections resulting in systemic effects that cause a large amount of mortality. Invasive fungal infections are having similar symptomatic effects to those of COVID-19. The COVID-19 patients are immunocompromised in nature and have a high probability of developing severe fungal infections, resulting in the development of further complications. The existing antifungal therapy has associated problems related to the development of drug resistance, being sub-potent in nature, and the presence of undesirable toxic effects. The fungal dihydrofolate reductase is an essential enzyme involved in the absorption of dietary folic acid and its conversion into tetrahydrofolate, which is a coenzyme required for the biosynthesis of the fungal nucleotides. Thus, in the current study, an attempt has been made to identify potential folate inhibitors of Candida albicans by a computational drug repurposing approach. Based upon the molecular docking simulation-based virtual screening followed by the molecular dynamic simulation of the macromolecular complex, benzbromarone has been identified as a potential anti-folate agent for the development of a novel therapy for the treatment of candidiasis.

PMID:35716240 | DOI:10.1007/s00894-022-05185-w

Nanomedicine. 2022 Jun 14:102576. doi: 10.1016/j.nano.2022.102576. Online ahead of print.

ABSTRACT

Targeting microglial activation is emerging as a clinically promising drug target for neuropathic pain treatment. Fexofenadine, a histamine receptor 1 antagonist, is a clinical drug for the management of allergic reactions as well as pain and inflammation. However, the effect of fexofenadine on microglial activation and pain behaviors remains elucidated. Here, we investigated nanomedicinal approach that targets more preferentially microglia and long-term analgesics. Fexofenadine significantly abolished histamine-induced microglial activation. The fexofenadine-encapsulated poly(lactic-co-glycolic acid) nanoparticles (Fexo NPs) injection reduced the pain sensitivity of spinal nerve ligation rats in a dose-dependent manner. This alleviation was sustained for 4 days, whereas the effective period by direct fexofenadine injection was 3 h. Moreover, Fexo NPs inhibited microglial activation, inflammatory signaling, cytokine release, and a macrophage phenotype shift towards the alternative activated state in the spinal cord. These results show that Fexo NPs exhibit drug repositioning promise as a long-term treatment modality for neuropathic pain.

PMID:35714922 | DOI:10.1016/j.nano.2022.102576

Eur J Pharmacol. 2022 Jun 14:175086. doi: 10.1016/j.ejphar.2022.175086. Online ahead of print.

ABSTRACT

The transient receptor potential (TRP) channel TRPV2 is widely expressed in a variety of different cell types and tissues. However, elucidating the exact biological functions of TRPV2 is significantly hampered by the lack of selective pharmacological tools to modulate channel activity in vitro and in vivo. This study aimed to identify new compounds that modify TRPV2 activity via the use of a plate-based calcium imaging approach to screen a drug repurposing library. Three antihistaminic drugs, loratadine, astemizole and clemizole were identified to reduce calcium-influx evoked by the TRPV2 agonist tetrahydrocannabivarin in HEK293 cells expressing murine TRPV2. Using single-cell calcium-microfluorimetry and whole-cell patch clamp recordings, we further confirmed that all three compounds induced a concentration-dependent block of TRPV2-mediated Ca2+ influx and whole-cell currents, with loratadine being the most potent antagonist of TRPV2. Moreover, this study demonstrated that loratadine was able to block both the human and mouse TRPV2 orthologs, without inhibiting the activity of other closely related members of the TRPV superfamily. Finally, loratadine inhibited TRPV2-dependent responses in a primary culture of mouse endometrial stromal cells and attenuated cell proliferation and migration in in vitro cell proliferation and wound healing assays. Taken together, our study revealed that the antihistaminic drugs loratadine, astemizole and clemizole target TRPV2 in a concentration-dependent manner. The identification of these antihistaminic drugs as blockers of TRPV2 may form a new starting point for the synthesis of more potent and selective TRPV2 antagonists, which could further lead to the unravelling of the physiological role of the channel.

PMID:35714693 | DOI:10.1016/j.ejphar.2022.175086

Methods Mol Biol. 2022;2496:221-235. doi: 10.1007/978-1-0716-2305-3_12.

ABSTRACT

In biomedicine, facts about relations between entities (disease, gene, drug, etc.) are hidden in the large trove of 30 million scientific publications. The curated information is proven to play an important role in various applications such as drug repurposing and precision medicine. Recently, due to the advancement in deep learning a transformer architecture named BERT (Bidirectional Encoder Representations from Transformers) has been proposed. This pretrained language model trained using the Books Corpus with 800M words and English Wikipedia with 2500M words reported state of the art results in various NLP (Natural Language Processing) tasks including relation extraction. It is a widely accepted notion that due to the word distribution shift, general domain models exhibit poor performance in information extraction tasks of the biomedical domain. Due to this, an architecture is later adapted to the biomedical domain by training the language models using 28 million scientific literatures from PubMed and PubMed central. This chapter presents a protocol for relation extraction using BERT by discussing state-of-the-art for BERT versions in the biomedical domain such as BioBERT. The protocol emphasis on general BERT architecture, pretraining and fine tuning, leveraging biomedical information, and finally a knowledge graph infusion to the BERT model layer.

PMID:35713867 | DOI:10.1007/978-1-0716-2305-3_12

Methods Mol Biol. 2022;2496:203-219. doi: 10.1007/978-1-0716-2305-3_11.

ABSTRACT

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) has spread on an unprecedented scale around the globe. Despite of 141,975 published papers on COVID-19 and several hundreds of new studies carried out every day, this pandemic remains as a global challenge. Biomedical literature mining helps the researchers to understand the etiology of the disease and to gain an in-depth knowledge of the disease, potential drugs, vaccines developed and novel therapies. In addition to the available treatments, there is a huge need to address the comorbidity-based disease mortality in case of COVID-19 patients with type 2 diabetes mellitus (T2D), hypertension and cardiovascular disease (CVD). In this chapter, we provide a hybrid protocol based on biomedical literature mining, network analysis of omics data, and deep learning for the identification of most potential drugs for COVID-19.

PMID:35713866 | DOI:10.1007/978-1-0716-2305-3_11

Methods Mol Biol. 2022;2496:123-140. doi: 10.1007/978-1-0716-2305-3_7.

ABSTRACT

The major outcomes and insights of scientific research and clinical study end up in the form of publication or clinical record in an unstructured text format. Due to advancements in biomedical research, the growth of published literature is getting tremendous large in recent years. The scientists and clinical researchers are facing a big challenge to stay current with the knowledge and to extract hidden information from this sheer quantity of millions of published biomedical literature. The potential one-stop automated solution to this problem is biomedical literature mining. One of the long-standing goals in biology is to discover the disease-causing genes and their specific roles in personalized precision medicine and drug repurposing. However, the empirical approaches and clinical affirmation are expensive and time-consuming. In silico approach using text mining to identify the disease causing genes can contribute towards biomarker discovery. This chapter presents a protocol on combining literature mining and machine learning for predicting biomedical discoveries with a special emphasis on gene-disease relation based discovery. The protocol is presented as a literature based discovery (LBD) pipeline for gene-disease based discovery. The protocol includes our web based tools: (1) DNER (Disease Named Entity Recognizer) for disease entity recognition, (2) BCCNER (Bidirectional, Contextual clues Named Entity Tagger) for gene/protein entity recognition, (3) DisGeReExT (Disease-Gene Relation Extractor) for statistically validated results and visualization, and (4) a newly introduced deep learning based method for association discovery. Our proposed deep learning based method can be generalized and applied to other important biomedical discoveries focusing on entities such as drug/chemical, or miRNA.

PMID:35713862 | DOI:10.1007/978-1-0716-2305-3_7

Front Genet. 2022 May 31;13:868015. doi: 10.3389/fgene.2022.868015. eCollection 2022.

ABSTRACT

Target prioritization is essential for drug discovery and repositioning. Applying computational methods to analyze and process multi-omics data to find new drug targets is a practical approach for achieving this. Despite an increasing number of methods for generating datasets such as genomics, phenomics, and proteomics, attempts to integrate and mine such datasets remain limited in scope. Developing hybrid intelligence solutions that combine human intelligence in the scientific domain and disease biology with the ability to mine multiple databases simultaneously may help augment drug target discovery and identify novel drug-indication associations. We believe that integrating different data sources using a singular numerical scoring system in a hybrid intelligent framework could help to bridge these different omics layers and facilitate rapid drug target prioritization for studies in drug discovery, development or repositioning. Herein, we describe our prototype of the StarGazer pipeline which combines multi-source, multi-omics data with a novel target prioritization scoring system in an interactive Python-based Streamlit dashboard. StarGazer displays target prioritization scores for genes associated with 1844 phenotypic traits, and is available via https://github.com/AstraZeneca/StarGazer.

PMID:35711912 | PMC:PMC9197487 | DOI:10.3389/fgene.2022.868015

BMC Bioinformatics. 2022 Jun 16;23(1):232. doi: 10.1186/s12859-022-04753-4.

ABSTRACT

BACKGROUND: The Open Targets (OT) Platform integrates a wide range of data sources on target-disease associations to facilitate identification of potential therapeutic drug targets to treat human diseases. However, due to the complexity that targets are usually functionally pleiotropic and efficacious for multiple indications, challenges in identifying novel target to indication associations remain. Specifically, persistent need exists for new methods for integration of novel target-disease association evidence and biological knowledge bases via advanced computational methods. These offer promise for increasing power for identification of the most promising target-disease pairs for therapeutic development. Here we introduce a novel approach by integrating additional target-disease features with machine learning models to further uncover druggable disease to target indications.

RESULTS: We derived novel target-disease associations as supplemental features to OT platform-based associations using three data sources: (1) target tissue specificity from GTEx expression profiles; (2) target semantic similarities based on gene ontology; and (3) functional interactions among targets by embedding them from protein-protein interaction (PPI) networks. Machine learning models were applied to evaluate feature importance and performance benchmarks for predicting targets with known drug indications. The evaluation results show the newly integrated features demonstrate higher importance than current features in OT. In addition, these also show superior performance over association benchmarks and may support discovery of novel therapeutic indications for highly pursued targets.

CONCLUSION: Our newly generated features can be used to represent additional underlying biological relatedness among targets and diseases to further empower improved performance for predicting novel indications for drug targets through advanced machine learning models. The proposed methodology enables a powerful new approach for systematic evaluation of drug targets with novel indications.

PMID:35710324 | DOI:10.1186/s12859-022-04753-4