Appl Biochem Biotechnol. 2023 Jul 1. doi: 10.1007/s12010-023-04608-5. Online ahead of print.

ABSTRACT

Cervical cancer is one of the main causes of cancer death in women globally, and its epidemiology is similar to that of a low-infectious venereal illness. Many sexual partners and early age at first intercourse have been demonstrated to have a significant influence on risk. TGF-β1 is a multifunctional cytokine that is required for cervical carcinoma metastasis, tumor development, progression, and invasion. The TGF-β1 signaling system plays a paradoxical function in cancer formation, suppressing early-stage tumor growth while increasing tumor progression and metastasis. Importantly, TGF-β1 and TGF-β receptor 1 (TGF-βR1), two components of the TGF-β signaling system, are substantially expressed in a range of cancers, including breast cancer, colon cancer, gastric cancer, and hepatocellular carcinoma. The current study aims to investigate possible inhibitors targeting TGF-β1 using molecular docking and dynamic simulations. To target TGF-β1, we used anti-cancer drugs and small molecules. MVD was utilized for virtual screening, and the highest scoring compound was then subjected to MD simulations using Schrodinger software package v2017-1 (Maestro v11.1) to identify the most favorable lead interactions against TGF-β1. The Nilotinib compound has shown the least XP Gscore of -2.581 kcal/mol, 30ns MD simulations revealing that the Nilotinib- TGF-β1 complex possesses the lowest energy of -77784.917 kcal/mol. Multiple parameters, including Root Mean Square Deviation, Root Mean Square Fluctuation, and Intermolecular Interactions, were used to analyze the simulation trajectory. Based on the results; we conclude that the ligand nilotinib appears to be a promising prospective TGF-β1inhibitor for reducing TGF-β1 expression ad halting cervical cancer progression.

PMID:37392324 | DOI:10.1007/s12010-023-04608-5

BMC Bioinformatics. 2023 Jun 30;24(1):272. doi: 10.1186/s12859-023-05373-2.

ABSTRACT

This paper applies different link prediction methods on a knowledge graph generated from biomedical literature, with the aim to compare their ability to identify unknown drug-gene interactions and explain their predictions. Identifying novel drug-target interactions is a crucial step in drug discovery and repurposing. One approach to this problem is to predict missing links between drug and gene nodes, in a graph that contains relevant biomedical knowledge. Such a knowledge graph can be extracted from biomedical literature, using text mining tools. In this work, we compare state-of-the-art graph embedding approaches and contextual path analysis on the interaction prediction task. The comparison reveals a trade-off between predictive accuracy and explainability of predictions. Focusing on explainability, we train a decision tree on model predictions and show how it can aid the understanding of the prediction process. We further test the methods on a drug repurposing task and validate the predicted interactions against external databases, with very encouraging results.

PMID:37391722 | DOI:10.1186/s12859-023-05373-2

Mol Divers. 2023 Jun 30. doi: 10.1007/s11030-023-10673-z. Online ahead of print.

ABSTRACT

Tyrosine Kinase beta (TRKβ), is a type I membrane receptor which plays a major role in various signalling pathways. TRKβ was found to be upregulated in various cancers and contrastingly downregulated in various neurodegenerative disorders. Hitherto, contemporary drug research is oriented towards discovery of TRKβ inhibitors, thus neglecting the development of TRKβ agonists. This research is aimed at identifying FDA approved drugs exhibiting repurposable potential as TRKβ agonists by mapping them with fingerprints of the BDNF/TRKβ interaction interface. Initially, crucial interacting residues were retrieved and a receptor grid was generated around it. TRKβ agonists were retrieved from literature search and a drug library was created for each agonist based on its structural and side effect similarities. Subsequently, molecular docking and dynamics were performed for each library to identify the drugs possessing affinity towards the binding pocket of TRKβ. The study revealed molecular interactions of Perospirone, Droperidol, Urapidil, and Clobenzorex with the crucial amino acids lining the active binding pocket of TRKβ. Subsequent network pharmacological analysis of the above drugs revealed their interactions with key proteins involved in neurotransmitter signalling pathways. Clobenzorex displayed high stability in dynamics simulation and therefore this drug is recommended for further experimental evaluations to attain better mechanistic insights and predict its implications in correcting neuropathological aberrations. This study's focus on the interaction interface between TRKβ and BDNF, combined with the utilization of fingerprint analysis for drug repurposing, contributes to our understanding of neurotrophic signalling and holds potential for identifying new therapeutic options for neurological disorders.

PMID:37389778 | DOI:10.1007/s11030-023-10673-z

Bioinformatics. 2023 Jun 30:btad418. doi: 10.1093/bioinformatics/btad418. Online ahead of print.

ABSTRACT

MOTIVATION: Knowledge graphs (KGs) are a powerful approach for integrating heterogeneous data and making inferences in biology and many other domains, but a coherent solution for constructing, exchanging, and facilitating the downstream use of knowledge graphs is lacking.

RESULTS: Here we present KG-Hub, a platform that enables standardized construction, exchange, and reuse of knowledge graphs. Features include a simple, modular extract-transform-load (ETL) pattern for producing graphs compliant with Biolink Model (a high-level data model for standardizing biological data), easy integration of any OBO (Open Biological and Biomedical Ontologies) ontology, cached downloads of upstream data sources, versioned and automatically updated builds with stable URLs, web-browsable storage of KG artifacts on cloud infrastructure, and easy reuse of transformed subgraphs across projects. Current KG-Hub projects span use cases including COVID-19 research, drug repurposing, microbial-environmental interactions, and rare disease research. KG-Hub is equipped with tooling to easily analyze and manipulate knowledge graphs. KG-Hub is also tightly integrated with graph machine learning (ML) tools which allow automated graph ML, including node embeddings and training of models for link prediction and node classification.

AVAILABILITY AND IMPLEMENTATION: https://kghub.org.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37389415 | DOI:10.1093/bioinformatics/btad418

Sci Rep. 2023 Jun 29;13(1):10583. doi: 10.1038/s41598-023-36342-7.

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a recent pandemic that caused serious global emergency. To identify new and effective therapeutics, we employed a drug repurposing approach. The poly (ADP ribose) polymerase inhibitors were used for this purpose and were repurposed against the main protease (Mpro) target of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The results from these studies were used to design compounds using the 'Grow Scaffold' modules available on Discovery Studio v2018. The three designed compounds, olaparib 1826 and olaparib 1885, and rucaparib 184 demonstrated better CDOCKER docking scores for Mpro than their parent compounds. Moreover, the compounds adhered to Lipinski's rule of five and demonstrated a synthetic accessibility score of 3.55, 3.63, and 4.30 for olaparib 1826, olaparib 1885, and rucaparib 184, respectively. The short-range Coulombic and Lennard-Jones potentials also support the potential binding of the modified compounds to Mpro. Therefore, we propose these three compounds as novel SARS-CoV-2 inhibitors.

PMID:37386052 | DOI:10.1038/s41598-023-36342-7

Trends Parasitol. 2023 Jun 27:S1471-4922(23)00132-0. doi: 10.1016/j.pt.2023.06.001. Online ahead of print.

ABSTRACT

Highly druggable and essential to almost all aspects of cellular life, the protein and phosphoinositide kinase gene families offer a wealth of potential targets for pharmacological modulation for both noncommunicable and infectious diseases. Despite the success of kinase inhibitors in oncology and other disease indications, targeting kinases comes with significant challenges. Key hurdles for kinase drug discovery include selectivity and acquired resistance. The phosphatidylinositol 4-kinase beta inhibitor MMV390048 showed good efficacy in Phase 2a clinical trials, demonstrating the potential of kinase inhibitors for malaria treatment. Here we argue that the potential benefits of Plasmodium kinase inhibitors outweigh the risks, and we highlight the opportunity for designed polypharmacology to reduce the risk of resistance.

PMID:37385921 | DOI:10.1016/j.pt.2023.06.001

Front Oncol. 2023 Jun 13;13:1192208. doi: 10.3389/fonc.2023.1192208. eCollection 2023.

ABSTRACT

INTRODUCTION: Drug resistance is a major obstacle in cancer treatment and can involve a variety of different factors. Identifying effective therapies for drug resistant tumors is integral for improving patient outcomes.

METHODS: In this study, we applied a computational drug repositioning approach to identify potential agents to sensitize primary drug resistant breast cancers. We extracted drug resistance profiles from the I-SPY 2 TRIAL, a neoadjuvant trial for early stage breast cancer, by comparing gene expression profiles of responder and non-responder patients stratified into treatments within HR/HER2 receptor subtypes, yielding 17 treatment-subtype pairs. We then used a rank-based pattern-matching strategy to identify compounds in the Connectivity Map, a database of cell line derived drug perturbation profiles, that can reverse these signatures in a breast cancer cell line. We hypothesize that reversing these drug resistance signatures will sensitize tumors to treatment and prolong survival.

RESULTS: We found that few individual genes are shared among the drug resistance profiles of different agents. At the pathway level, however, we found enrichment of immune pathways in the responders in 8 treatments within the HR+HER2+, HR+HER2-, and HR-HER2- receptor subtypes. We also found enrichment of estrogen response pathways in the non-responders in 10 treatments primarily within the hormone receptor positive subtypes. Although most of our drug predictions are unique to treatment arms and receptor subtypes, our drug repositioning pipeline identified the estrogen receptor antagonist fulvestrant as a compound that can potentially reverse resistance across 13/17 of the treatments and receptor subtypes including HR+ and triple negative. While fulvestrant showed limited efficacy when tested in a panel of 5 paclitaxel resistant breast cancer cell lines, it did increase drug response in combination with paclitaxel in HCC-1937, a triple negative breast cancer cell line.

CONCLUSION: We applied a computational drug repurposing approach to identify potential agents to sensitize drug resistant breast cancers in the I-SPY 2 TRIAL. We identified fulvestrant as a potential drug hit and showed that it increased response in a paclitaxel-resistant triple negative breast cancer cell line, HCC-1937, when treated in combination with paclitaxel.

PMID:37384294 | PMC:PMC10294228 | DOI:10.3389/fonc.2023.1192208

Front Pharmacol. 2023 Jun 13;14:1223200. doi: 10.3389/fphar.2023.1223200. eCollection 2023.

NO ABSTRACT

PMID:37383724 | PMC:PMC10295121 | DOI:10.3389/fphar.2023.1223200

Antimicrob Agents Chemother. 2023 Jun 29:e0050323. doi: 10.1128/aac.00503-23. Online ahead of print.

ABSTRACT

Fungal pathogens like Candida albicans can cause devastating human disease. Treatment of candidemia is complicated by the high rate of resistance to common antifungal therapies. Additionally, there is host toxicity associated with many antifungal compounds due to the conservation between essential mammalian and fungal proteins. An attractive new approach for antimicrobial development is to target virulence factors: non-essential processes that are required for the organism to cause disease in human hosts. This approach expands the potential target space while reducing the selective pressure toward resistance, as these targets are not essential for viability. In C. albicans, a key virulence factor is the ability to transition to hyphal morphology. We developed a high-throughput image analysis pipeline to distinguish between yeast and filamentous growth in C. albicans at the single cell level. Based on this phenotypic assay, we screened the FDA drug repurposing library of 2,017 compounds for their ability to inhibit filamentation and identified 33 compounds that block the hyphal transition in C. albicans with IC50 values ranging from 0.2 to 150 μM. Multiple compounds showed a phenyl sulfone chemotype, prompting further analysis. Of these phenyl sulfones, NSC 697923 displayed the most efficacy, and by selecting for resistant mutants, we identified eIF3 as the target of NSC 697923 in C. albicans.

PMID:37382550 | DOI:10.1128/aac.00503-23

Parasitol Int. 2023 Jun 26:102774. doi: 10.1016/j.parint.2023.102774. Online ahead of print.

ABSTRACT

Babesia gibsoni is mainly transmitted by hard ticks of the genus Rhipicephalus (R. sanguineus) and Haemaphysalis (H. longicornis), and causes canine babesiosis. Clinical manifestations of B. gibsoni infection include fever, hemoglobinemia, hemoglobinuria, and progressive anemia. Traditional antibabesial therapy, such as imidocarb dipropionate or diminazene aceturate, can only alleviate severe clinical manifestations and cannot eliminate parasites in the host. Food and Drug Administration (FDA)-approved drugs are a solid starting point for researching novel therapy strategies for canine babesiosis. In this work, we screened 640 FDA-approved drugs against the growth of B. gibsoni in vitro. Among them, 13 compounds (at 10 μM) exhibited high growth inhibition (>60%), and two compounds, namely idarubicin hydrochloride (idamycin) and vorinostat, were chosen for further investigation. The half-maximal inhibitory concentration (IC50) values of idamycin and vorinostat were determined to be 0.044 ± 0.008 μM and 0.591 ± 0.107 μM, respectively. Viability results indicated that a concentration of 4 × IC50 of vorinostat prevented the regrowth of treated B. gibsoni, whereas parasites treated with 4 × IC50 concentration of idamycin remained viable. The B. gibsoni parasites treated with vorinostat exhibited degeneration within erythrocytes and merozoites, in contrast to the oval or signet-ring shape of normal B. gibsoni parasites. In conclusion, FDA-approved drugs offer a valuable platform for drug repositioning in antibabesiosis research. Particularly, vorinostat demonstrated promising inhibitory effects against B. gibsoni in vitro, and further studies on vorinostat are necessary to elucidate its mechanism as a novel treatment in infected animal models.

PMID:37380124 | DOI:10.1016/j.parint.2023.102774

Int J Biol Macromol. 2023 Jun 26:125587. doi: 10.1016/j.ijbiomac.2023.125587. Online ahead of print.

ABSTRACT

Tyrosinase, a rate-limiting enzyme for melanin production, has been the most efficient target for the development of depigmenting agents. Although hydroquinone, kojic acid, and arbutin are the most well-known tyrosinase inhibitors, their adverse effects are inevitable. In the present study, an in silico drug repositioning combined with experimental validation was performed to search for novel potent tyrosinase inhibitors. Docking-based virtual screening results revealed that, among the 3210 FDA-approved drugs available in the ZINC database, amphotericin B, an antifungal drug exhibited the highest binding efficiency against human tyrosinase. Results from tyrosinase inhibition assay demonstrated that amphotericin B could inhibit the activity of mushroom and cellular tyrosinases, especially from MNT-1 human melanoma cells. Molecular modeling results revealed that amphotericin B/human tyrosinase complex exhibited high stability in an aqueous environment. Melanin assay results demonstrated that amphotericin B significantly suppressed melanin production in α-MSH-induced B16F10 murine melanoma and MNT-1 human melanoma cell lines better than the known inhibitor, kojic acid. Mechanistically, amphotericin B treatment significantly activated ERK and Akt signaling pathways, resulting in the decreased expression of MITF and tyrosinase. The obtained results may pursue pre-clinical and clinical studies to examine the possibility of using amphotericin B as an alternative treatment for hyperpigmentation disorders.

PMID:37379954 | DOI:10.1016/j.ijbiomac.2023.125587

Drug Discov Today. 2023 Jun 26:103684. doi: 10.1016/j.drudis.2023.103684. Online ahead of print.

ABSTRACT

Hurdles in the identification of new drugs for cancer treatment have made drug repurposing an increasingly appealing alternative. The approach involves the use of old drugs for new therapeutic purposes. It is cost-effective and facilitates rapid clinical translation. Given that cancer is also considered a metabolic disease, drugs for metabolic disorders are being actively repurposed for cancer therapeutics. In this review, we discuss the repurposing of such drugs approved for two major metabolic diseases, diabetes and cardiovascular disease (CVD), which have shown potential as anti-cancer treatment. We also highlight the current understanding of the cancer signaling pathways that these drugs target.

PMID:37379903 | DOI:10.1016/j.drudis.2023.103684

BMC Bioinformatics. 2023 Jun 28;24(1):266. doi: 10.1186/s12859-023-05393-y.

ABSTRACT

Pathway-level survival analysis offers the opportunity to examine molecular pathways and immune signatures that influence patient outcomes. However, available survival analysis algorithms are limited in pathway-level function and lack a streamlined analytical process. Here we present a comprehensive pathway-level survival analysis suite, PATH-SURVEYOR, which includes a Shiny user interface with extensive features for systematic exploration of pathways and covariates in a Cox proportional-hazard model. Moreover, our framework offers an integrative strategy for performing Hazard Ratio ranked Gene Set Enrichment Analysis and pathway clustering. As an example, we applied our tool in a combined cohort of melanoma patients treated with checkpoint inhibition (ICI) and identified several immune populations and biomarkers predictive of ICI efficacy. We also analyzed gene expression data of pediatric acute myeloid leukemia (AML) and performed an inverse association of drug targets with the patient's clinical endpoint. Our analysis derived several drug targets in high-risk KMT2A-fusion-positive patients, which were then validated in AML cell lines in the Genomics of Drug Sensitivity database. Altogether, the tool offers a comprehensive suite for pathway-level survival analysis and a user interface for exploring drug targets, molecular features, and immune populations at different resolutions.

PMID:37380943 | DOI:10.1186/s12859-023-05393-y

PLoS Negl Trop Dis. 2023 Jun 28;17(6):e0011443. doi: 10.1371/journal.pntd.0011443. Online ahead of print.

ABSTRACT

Between January and March 2022, WHO conducted a global online survey to collect data on diagnostic capacities and treatment practices in different settings for four implantation mycoses: eumycetoma, actinomycetoma, cutaneous sporotrichosis and chromoblastomycosis. The survey investigated the type of diagnostic methods available in countries at various health system levels (tertiary, secondary, primary level) and the medicines used to treat implantation mycoses, with a view to understanding the level of drug repurposing for treatment of these diseases. 142 respondents from 47 countries, including all continents, contributed data: 60% were from middle-income countries, with 59% working at the tertiary level of the health system and 30% at the secondary level. The results presented in this article provide information on the current diagnostic capacity and treatment trends for both pharmacological and non-pharmacological interventions. In addition, the survey provides insight on refractory case rates, as well as other challenges, such as availability and affordability of medicines, especially in middle-income countries. Although the study has limitations, the survey-collected data confirms that drug repurposing is occurring for all four surveyed implantation mycoses. The implementation of an openly accessible global and/or a national treatment registry for implantation mycoses could contribute to address the gaps in epidemiological information and collect valuable observational data to inform treatment guidelines and clinical research.

PMID:37379338 | DOI:10.1371/journal.pntd.0011443

Bioinformatics. 2023 Jun 28:btad411. doi: 10.1093/bioinformatics/btad411. Online ahead of print.

ABSTRACT

MOTIVATION: Screening new drug-target interactions (DTIs) by traditional experimental methods is costly and time-consuming. Recent advances in knowledge graphs, chemical linear notations, and genomic data enable researchers to develop computational-based DTI models, which play a pivotal role in drug repurposing and discovery. However, there still needs to develop a multimodal fusion DTI model that integrates available heterogeneous data into a unified framework.

RESULTS: We developed MDTips, a Multimodal-data based Drug-Target interaction prediction system, by fusing the knowledge graphs, gene expression profiles, and structural information of drugs/targets. MDTips yielded accurate and robust performance on DTI predictions. We found that multimodal fusion learning can fully consider the importance of each modality and incorporate information from multiple aspects, thus improving model performance. Extensive experimental results demonstrate that deep learning-based encoders (i.e., Attentive FP and Transformer) outperform traditional chemical descriptors/fingerprints, and MDTips outperforms other state-of-the-art prediction models. MDTips is designed to predict the input drugs' candidate targets, side effects, and indications with all available modalities. Via MDTips, we reverse-screened candidate targets of 6,766 drugs, which can be used for drug repurposing and discovery.

AVAILABILITY: https://github.com/XiaoqiongXia/MDTips and https://doi.org/10.5281/zenodo.7560544.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:37379157 | DOI:10.1093/bioinformatics/btad411

Pharmaceutics. 2023 Jun 7;15(6):1673. doi: 10.3390/pharmaceutics15061673.

ABSTRACT

In an era of unparalleled technical advancement, the pharmaceutical industry is struggling to transform data into increased research and development efficiency, and, as a corollary, new drugs for patients. Here, we briefly review some of the commonly discussed issues around this counterintuitive innovation crisis. Looking at both industry- and science-related factors, we posit that traditional preclinical research is front-loading the development pipeline with data and drug candidates that are unlikely to succeed in patients. Applying a first principles analysis, we highlight the critical culprits and provide suggestions as to how these issues can be rectified through the pursuit of a Human Data-driven Discovery (HD3) paradigm. Consistent with other examples of disruptive innovation, we propose that new levels of success are not dependent on new inventions, but rather on the strategic integration of existing data and technology assets. In support of these suggestions, we highlight the power of HD3, through recently published proof-of-concept applications in the areas of drug safety analysis and prediction, drug repositioning, the rational design of combination therapies and the global response to the COVID-19 pandemic. We conclude that innovators must play a key role in expediting the path to a largely human-focused, systems-based approach to drug discovery and research.

PMID:37376121 | DOI:10.3390/pharmaceutics15061673

Pharmaceutics. 2023 May 26;15(6):1600. doi: 10.3390/pharmaceutics15061600.

ABSTRACT

Cancer therapeutics have undergone immense research over the past decade. While chemotherapies remain the mainstay treatments for many cancers, the advent of new molecular techniques has opened doors for more targeted modalities towards cancer cells. Although immune checkpoint inhibitors (ICIs) have demonstrated therapeutic efficacy in treating cancer, adverse side effects related to excessive inflammation are often reported. There is a lack of clinically relevant animal models to probe the human immune response towards ICI-based interventions. Humanized mouse models have emerged as valuable tools for pre-clinical research to evaluate the efficacy and safety of immunotherapy. This review focuses on the establishment of humanized mouse models, highlighting the challenges and recent advances in these models for targeted drug discovery and the validation of therapeutic strategies in cancer treatment. Furthermore, the potential of these models in the process of uncovering novel disease mechanisms is discussed.

PMID:37376049 | DOI:10.3390/pharmaceutics15061600

Pharmaceuticals (Basel). 2023 Jun 1;16(6):826. doi: 10.3390/ph16060826.

ABSTRACT

Chagas disease (ChD), caused by Trypanosoma cruzi, is the most serious parasitosis in the western hemisphere. Benznidazole and nifurtimox, the only two trypanocidal drugs, are expensive, difficult to obtain, and have severe side effects. Nitazoxanide has shown to be effective against protozoa, bacteria, and viruses. This study aimed to evaluate the nitazoxanide efficacy against the Mexican T. cruzi Ninoa strain in mice. Infected animals were orally treated for 30 days with nitazoxanide (100 mg/kg) or benznidazole (10 mg/kg). The clinical, immunological, and histopathological conditions of the mice were evaluated. Nitazoxanide- or benznidazole-treated mice had longer survival and less parasitemia than those without treatment. Antibody production in the nitazoxanide-treated mice was of the IgG1-type and not of the IgG2-type as in the benznidazole-treated mice. Nitazoxanide-treated mice had significantly high IFN-γ levels compared to the other infected groups. Serious histological damage could be prevented with nitazoxanide treatment compared to without treatment. In conclusion, nitazoxanide decreased parasitemia levels, indirectly induced the production of IgG antibodies, and partially prevented histopathological damage; however, it did not show therapeutic superiority compared to benznidazole in any of the evaluated aspects. Therefore, the repositioning of nitazoxanide as an alternative treatment against ChD could be considered, since it did not trigger adverse effects that worsened the pathological condition of the infected mice.

PMID:37375773 | DOI:10.3390/ph16060826

Nutrients. 2023 Jun 18;15(12):2790. doi: 10.3390/nu15122790.

ABSTRACT

Melanoma is the most invasive and lethal skin cancer. Recently, PD-1/PD-L1 pathway modulation has been applied to cancer therapy due to its remarkable clinical efficacy. SH003, a mixture of natural products derived from Astragalus membranaceus, Angelica gigas, and Trichosanthes kirilowii, and formononetin (FMN), an active constituent of SH003, exhibit anti-cancer and anti-oxidant properties. However, few studies have reported on the anti-melanoma activities of SH003 and FMN. This work aimed to elucidate the anti-melanoma effects of SH003 and FMN through the PD-1/PD-L1 pathway, using B16F10 cells and CTLL-2 cells. Results showed that SH003 and FMN reduced melanin content and tyrosinase activity induced by α-MSH. Moreover, SH003 and FMN suppressed B16F10 growth and arrested cells at the G2/M phase. SH003 and FMN also led to cell apoptosis with increases in PARP and caspase-3 activation. The pro-apoptotic effects were further enhanced when combined with cisplatin. In addition, SH003 and FMN reversed the increased PD-L1 and STAT1 phosphorylation levels induced by cisplatin in the presence of IFN-γ. SH003 and FMN also enhanced the cytotoxicity of CTLL-2 cells against B16F10 cells. Therefore, the mixture of natural products SH003 demonstrates therapeutic potential in cancer treatment by exerting anti-melanoma effects through the PD-1/PD-L1 pathway.

PMID:37375695 | DOI:10.3390/nu15122790

Int J Mol Sci. 2023 Jun 12;24(12):10033. doi: 10.3390/ijms241210033.

ABSTRACT

Psoriasis is a chronic skin disease that affects millions of people worldwide. In 2014, psoriasis was recognized by the World Health Organization (WHO) as a serious non-communicable disease. In this study, a systems biology approach was used to investigate the underlying pathogenic mechanism of psoriasis and identify the potential drug targets for therapeutic treatment. The study involved the construction of a candidate genome-wide genetic and epigenetic network (GWGEN) through big data mining, followed by the identification of real GWGENs of psoriatic and non-psoriatic using system identification and system order detection methods. Core GWGENs were extracted from real GWGENs using the Principal Network Projection (PNP) method, and the corresponding core signaling pathways were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Comparing core signaling pathways of psoriasis and non-psoriasis and their downstream cellular dysfunctions, STAT3, CEBPB, NF-κB, and FOXO1 are identified as significant biomarkers of pathogenic mechanism and considered as drug targets for the therapeutic treatment of psoriasis. Then, a deep neural network (DNN)-based drug-target interaction (DTI) model was trained by the DTI dataset to predict candidate molecular drugs. By considering adequate regulatory ability, toxicity, and sensitivity as drug design specifications, Naringin, Butein, and Betulinic acid were selected from the candidate molecular drugs and combined into potential multi-molecule drugs for the treatment of psoriasis.

PMID:37373186 | DOI:10.3390/ijms241210033