Sci Rep. 2024 Jan 12;14(1):1239. doi: 10.1038/s41598-023-50913-8.

ABSTRACT

We focus on drug repurposing in the Ras signaling pathway, considering structural similarities of protein-protein interfaces. The interfaces formed by physically interacting proteins are found from PDB if available and via PRISM (PRotein Interaction by Structural Matching) otherwise. The structural coverage of these interactions has been increased from 21 to 92% using PRISM. Multiple conformations of each protein are used to include protein dynamics and diversity. Next, we find FDA-approved drugs bound to structurally similar protein-protein interfaces. The results suggest that HIV protease inhibitors tipranavir, indinavir, and saquinavir may bind to EGFR and ERBB3/HER3 interface. Tipranavir and indinavir may also bind to EGFR and ERBB2/HER2 interface. Additionally, a drug used in Alzheimer's disease can bind to RAF1 and BRAF interface. Hence, we propose a methodology to find drugs to be potentially used for cancer using a dataset of structurally similar protein-protein interface clusters rather than pockets in a systematic way.

PMID:38216592 | DOI:10.1038/s41598-023-50913-8

Int J Mol Sci. 2024 Jan 3;25(1):609. doi: 10.3390/ijms25010609.

ABSTRACT

Despite significant advances in treatment modalities, colorectal cancer (CRC) remains a poorly understood and highly lethal malignancy worldwide. Cancer stem cells (CSCs) and the tumor microenvironment (TME) have been shown to play critical roles in initiating and promoting CRC progression, metastasis, and treatment resistance. Therefore, a better understanding of the underlying mechanisms contributing to the generation and maintenance of CSCs is crucial to developing CSC-specific therapeutics and improving the current standard of care for CRC patients. To this end, we used a bioinformatics approach to identify increased CD24/SOX4 expression in CRC samples associated with poor prognosis. We also discovered a novel population of tumor-infiltrating CD24+ cancer-associated fibroblasts (CAFs), suggesting that the CD24/SOX4-centered signaling hub could be a potential therapeutic target. Pathway networking analysis revealed a connection between the CD24/SOX4-centered signaling, β-catenin, and DPP4. Emerging evidence indicates that DPP4 plays a role in CRC initiation and progression, implicating its involvement in generating CSCs. Based on these bioinformatics data, we investigated whether sitagliptin, a DPP4 inhibitor and diabetic drug, could be repurposed to inhibit colon CSCs. Using a molecular docking approach, we demonstrated that sitagliptin targeted CD24/SOX4-centered signaling molecules with high affinity. In vitro experimental data showed that sitagliptin treatment suppressed CRC tumorigenic properties and worked in synergy with 5FU and this study thus provided preclinical evidence to support the alternative use of sitagliptin for treating CRC.

PMID:38203779 | DOI:10.3390/ijms25010609

Trends Pharmacol Sci. 2024 Jan 11:S0165-6147(23)00264-X. doi: 10.1016/j.tips.2023.12.006. Online ahead of print.

ABSTRACT

Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.

PMID:38212196 | DOI:10.1016/j.tips.2023.12.006

EXCLI J. 2023 Nov 14;22:1155-1172. doi: 10.17179/excli2023-6427. eCollection 2023.

ABSTRACT

A current clinical challenge in cancer is multidrug resistance (MDR) mediated by ABC transporters. Breast cancer resistance protein (BCRP) or ABCG2 transporter is one of the most important ABC transporters implicated in MDR and the use of inhibitors is a promising approach to overcome the resistance in cancer. This study aimed to characterize the molecular mechanism of ABCG2 inhibitors identified by a repurposing drug strategy using antiviral, anti-inflammatory and antiparasitic agents. Lopinavir and ivermectin can be considered as pan-inhibitors of ABC transporters, since both compounds inhibited ABCG2, P-glycoprotein and MRP1. They inhibited ABCG2 activity showing IC50 values of 25.5 and 23.4 µM, respectively. These drugs were highly cytotoxic and not transported by ABCG2. Additionally, these drugs increased the 5D3 antibody binding and did not affect the mRNA and protein expression levels. Cell-based analysis of the type of inhibition suggested a non-competitive inhibition, which was further corroborated by in silico approaches of molecular docking and molecular dynamics simulations. These results showed an overlap of the lopinavir and ivermectin binding sites on ABCG2, mainly interacting with E446 residue. However, the substrate mitoxantrone occupies a different site, binding to the F436 region, closer to the L554/L555 plug. In conclusion, these results revealed the mechanistic basis of lopinavir and ivermectin interaction with ABCG2. See also the Graphical abstract(Fig. 1).

PMID:38204967 | PMC:PMC10776880 | DOI:10.17179/excli2023-6427

Int J Mol Sci. 2023 Dec 25;25(1):323. doi: 10.3390/ijms25010323.

ABSTRACT

Ovarian cancer metastization is accompanied by the development of malignant ascites, which are associated with poor prognosis. The acellular fraction of this ascitic fluid contains tumor-promoting soluble factors, bioactive lipids, cytokines, and extracellular vesicles, all of which communicate with the tumor cells within this peritoneal fluid. Metabolomic profiling of ovarian cancer ascites has revealed significant differences in the pathways of fatty acids, cholesterol, glucose, and insulin. The proteins involved in these pathways promote tumor growth, resistance to chemotherapy, and immune evasion. Unveiling the key role of this liquid tumor microenvironment is crucial for discovering more efficient treatment options. This review focuses on the cholesterol and insulin pathways in ovarian cancer, identifying statins and metformin as viable treatment options when combined with standard chemotherapy. These findings are supported by clinical trials showing improved overall survival with these combinations. Additionally, statins and metformin are associated with the reversal of T-cell exhaustion, positioning these drugs as potential combinatory strategies to improve immunotherapy outcomes in ovarian cancer patients.

PMID:38203494 | DOI:10.3390/ijms25010323

Molecules. 2023 Dec 31;29(1):225. doi: 10.3390/molecules29010225.

ABSTRACT

Human society is facing the threat of various viruses. Proteases are promising targets for the treatment of viral infections. In this study, we collected and profiled 170 protease sequences from 125 viruses that infect humans. Approximately 73 of them are viral 3-chymotrypsin-like proteases (3CLpro), and 11 are pepsin-like aspartic proteases (PAPs). Their sequences, structures, and substrate characteristics were carefully analyzed to identify their conserved nature for proposing a pan-3CLpro or pan-PAPs inhibitor design strategy. To achieve this, we used computational prediction and modeling methods to predict the binding complex structures for those 73 3CLpro with 4 protease inhibitors of SARS-CoV-2 and 11 protease inhibitors of HCV. Similarly, the complex structures for the 11 viral PAPs with 9 protease inhibitors of HIV were also obtained. The binding affinities between these compounds and proteins were also evaluated to assess their pan-protease inhibition via MM-GBSA. Based on the drugs targeting viral 3CLpro and PAPs, repositioning of the active compounds identified several potential uses for these drug molecules. As a result, Compounds 1-2, modified based on the structures of Ray1216 and Asunaprevir, indicate potential inhibition of DENV protease according to our computational simulation results. These studies offer ideas and insights for future research in the design of broad-spectrum antiviral drugs.

PMID:38202808 | DOI:10.3390/molecules29010225

Cancers (Basel). 2023 Dec 29;16(1):180. doi: 10.3390/cancers16010180.

ABSTRACT

The concept of post-therapy metastatic spread, cancer repopulation and acquired tumor cell resistance (M-CRAC) rationalizes tumor progression because of tumor cell heterogeneity arising from post-therapy genetic damage and subsequent tissue repair mechanisms. Therapeutic strategies designed to specifically address M-CRAC involve tissue editing approaches, such as low-dose metronomic chemotherapy and the use of transcriptional modulators with or without targeted therapies. Notably, tumor tissue editing holds the potential to treat patients, who are refractory to or relapsing (r/r) after conventional chemotherapy, which is usually based on administering a maximum tolerable dose of a cytostatic drugs. Clinical trials enrolling patients with r/r malignancies, e.g., non-small cell lung cancer, Hodgkin's lymphoma, Langerhans cell histiocytosis and acute myelocytic leukemia, indicate that tissue editing approaches could yield tangible clinical benefit. In contrast to conventional chemotherapy or state-of-the-art precision medicine, tissue editing employs a multi-pronged approach targeting important drivers of M-CRAC across various tumor entities, thereby, simultaneously engaging tumor cell differentiation, immunomodulation, and inflammation control. In this review, we highlight the M-CRAC concept as a major factor in resistance to conventional cancer therapies and discusses tissue editing as a potential treatment.

PMID:38201607 | DOI:10.3390/cancers16010180

Int Immunopharmacol. 2024 Jan 9;128:111514. doi: 10.1016/j.intimp.2024.111514. Online ahead of print.

ABSTRACT

BACKGROUND: Diabetic neuropathy (DN) is a serious microvascular complication and a major cause of morbidity and mortality in diabetes mellitus. It is characterized by neurodegeneration of terminal sensory nerve fibers with subsequent pain, loss of sensation, and paresthesia, thus compromising the quality of life of diabetic patients. It is considered the leading cause of non-traumatic amputations worldwide, reflecting the insufficiency of current therapies. Pramipexole (PPX) is a dopamine receptor agonist used for the treatment of Parkinson's disease. The current study aims to investigate the potential neuroprotective effect of PPX in an experimental model of DN.

METHODS: Sprague Dawley rats were randomly assigned into five groups: normal control, Normal + PPX (1 mg/kg) group, STZ control, STZ + PPX (0.25 and 1 mg/kg/day for eight weeks). The neuroprotective effect of PPX in rats was evaluated in terms of sciatic nerve histological alterations, oxidative stress, and protein expression of TLR4/MyD88/IRAK-1/TRAF-6/NF-κB axis and downstream inflammatory mediators.

RESULTS: PPX administration ameliorated histopathological signs of neuronal inflammation and apoptosis. Additionally, PPX attenuated STZ-induced sciatic nerve oxidative stress and downregulated neural tissue expression of TLR4, MyD88, IRAK-1, TRAF-6, NF-κB and downstream mediators (TNF-α, IL-1β and ICAM-1).

CONCLUSION: Collectively, the current study sheds light on PPX as a potential protective medication to alleviate neuropathy progression in diabetic patients. PPX neuroprotective effect can be attributed to modulating TLR4/ MyD88/IRAK-1/TRAF-6/ NF-κB axis signaling in nerve tissues with subsequent attenuation of oxidative stress and inflammation.

PMID:38199193 | DOI:10.1016/j.intimp.2024.111514

Clin Pharmacol. 2024 Jan 4;16:1-25. doi: 10.2147/CPAA.S429064. eCollection 2024.

ABSTRACT

The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a betacoronavirus responsible for the COVID-19 pandemic, causing respiratory disorders, and even death in some individuals, if not appropriately treated in time. To face the pandemic, preventive measures have been taken against contagions and the application of vaccines to prevent severe disease and death cases. For the COVID-19 treatment, antiviral, antiparasitic, anticoagulant and other drugs have been reused due to limited specific medicaments for the disease. Drug repurposing is an emerging strategy with therapies that have already tested safe in humans. One promising alternative for systematic experimental screening of a vast pool of compounds is computational drug repurposing (in silico assay). Using these tools, new uses for approved drugs such as chloroquine, hydroxychloroquine, ivermectin, zidovudine, ribavirin, lamivudine, remdesivir, lopinavir and tenofovir/emtricitabine have been conducted, showing effectiveness in vitro and in silico against SARS-CoV-2 and some of these, also in clinical trials. Additionally, therapeutic options have been sought in natural products (terpenoids, alkaloids, saponins and phenolics) with promising in vitro and in silico results for use in COVID-19 disease. Among these, the most studied are resveratrol, quercetin, hesperidin, curcumin, myricetin and betulinic acid, which were proposed as SARS-CoV-2 inhibitors. Among the drugs reused to control the SARS-CoV2, better results have been observed for remdesivir in hospitalized patients and outpatients. Regarding natural products, resveratrol, curcumin, and quercetin have demonstrated in vitro antiviral activity against SARS-CoV-2 and in vivo, a nebulized formulation has demonstrated to alleviate the respiratory symptoms of COVID-19. This review shows the evidence of drug repurposing efficacy and the potential use of natural products as a treatment for COVID-19. For this, a search was carried out in PubMed, SciELO and ScienceDirect databases for articles about drugs approved or under study and natural compounds recognized for their antiviral activity against SARS-CoV-2.

PMID:38197085 | PMC:PMC10773251 | DOI:10.2147/CPAA.S429064

Intensive Care Med Exp. 2024 Jan 9;12(1):2. doi: 10.1186/s40635-023-00586-4.

ABSTRACT

BACKGROUND: Hemorrhagic shock (HS), which causes insufficient tissue perfusion, can result in multiple organ failure (MOF) and death. This study aimed to evaluate whether doxycycline (DOX) protects cardiovascular, kidney, and liver tissue from damage in a rat model of HS. Immediately before the resuscitation, DOX (10 mg/kg; i.v.) was administered, and its protective effects were assessed 24 h later. Mean arterial pressure, renal blood flow, heart rate, vasoactive drug response, and blood markers such as urea, creatinine, AST, ALT, CPK, CPR, and NOx levels were determined.

RESULTS: We showed that DOX has a significant effect on renal blood flow and on urea, creatinine, AST, ALT, CPK, and NOx. Morphologically, DOX reduced the inflammatory process in the liver tissue.

CONCLUSIONS: We conclude that DOX protects the liver and kidney against injury and dysfunction in a HS model and could be a strategy to reduce organ damage associated with ischemia-and-reperfusion injury.

PMID:38194181 | DOI:10.1186/s40635-023-00586-4

Xenobiotica. 2024 Jan 9:1-13. doi: 10.1080/00498254.2024.2304129. Online ahead of print.

ABSTRACT

Tipepidine, an antitussive drug, has been reported to have central pharmacological effects and can be expected to be safely repositioned as treatment for psychiatric disorders. Since tipepidine requires three doses per day, development of a once-daily medication would be highly beneficial. Previously, we reported that combination use with quinidine, a CYP2D6 inhibitor, prolongs the half-life of tipepidine in chimeric mice with humanized liver.In this study, to predict this combination effect in humans, a physiologically based pharmacokinetic (PBPK) model was developed, and quantitative simulation was conducted. The simulation results indicated that concomitant administration of tipepidine with quinidine increased the predicted Cmax, AUC, and t1/2 of tipepidine in the Japanese population by 3.4-, 6.6-, and 2.4-fold, respectively.Furthermore, to compare with another approach that aims to prolong the half-life, the PK profile of tipepidine administered in hypothetical extended-release form was simulated. Extended-release form was predicted to be more influenced by CYP2D6 genotype than combination with quinidine, and the predicted plasma exposure was markedly increased in poor metabolizers, potentially leading to adverse effects.In conclusion, quantitative simulation using the PBPK model suggests the feasibility of the safe repositioning of tipepidine as a once-daily medication in combination with quinidine.

PMID:38193900 | DOI:10.1080/00498254.2024.2304129

BMC Genomics. 2024 Jan 9;25(1):43. doi: 10.1186/s12864-023-09913-1.

ABSTRACT

BACKGROUND: Drug repurposing plays a significant role in providing effective treatments for certain diseases faster and more cost-effectively. Successful repurposing cases are mostly supported by a classical paradigm that stems from de novo drug development. This paradigm is based on the "one-drug-one-target-one-disease" idea. It consists of designing drugs specifically for a single disease and its drug's gene target. In this article, we investigated the use of biological pathways as potential elements to achieve effective drug repurposing.

METHODS: Considering a total of 4214 successful cases of drug repurposing, we identified cases in which biological pathways serve as the underlying basis for successful repurposing, referred to as DREBIOP. Once the repurposing cases based on pathways were identified, we studied their inherent patterns by considering the different biological elements associated with this dataset, as well as the pathways involved in these cases. Furthermore, we obtained gene-disease association values to demonstrate the diminished significance of the drug's gene target in these repurposing cases. To achieve this, we compared the values obtained for the DREBIOP set with the overall association values found in DISNET, as well as with the drug's target gene (DREGE) based repurposing cases using the Mann-Whitney U Test.

RESULTS: A collection of drug repurposing cases, known as DREBIOP, was identified as a result. DREBIOP cases exhibit distinct characteristics compared with DREGE cases. Notably, DREBIOP cases are associated with a higher number of biological pathways, with Vitamin D Metabolism and ACE inhibitors being the most prominent pathways. Additionally, it was observed that the association values of GDAs in DREBIOP cases were significantly lower than those in DREGE cases (p-value < 0.05).

CONCLUSIONS: Biological pathways assume a pivotal role in drug repurposing cases. This investigation successfully revealed patterns that distinguish drug repurposing instances associated with biological pathways. These identified patterns can be applied to any known repurposing case, enabling the detection of pathway-based repurposing scenarios or the classical paradigm.

PMID:38191292 | DOI:10.1186/s12864-023-09913-1

J Affect Disord. 2024 Jan 6:S0165-0327(24)00043-0. doi: 10.1016/j.jad.2024.01.034. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar disorder (BD) presents significant challenges in drug discovery, necessitating alternative approaches. Drug repurposing, leveraging computational techniques and expanding biomedical data, holds promise for identifying novel treatment strategies.

METHODS: This study utilized gene regulatory networks (GRNs) to identify significant regulatory changes in BD, using network-based signatures for drug repurposing. Employing the PANDA algorithm, we investigated the variations in transcription factor-GRNs between individuals with BD and unaffected individuals, incorporating binding motifs, protein interactions, and gene co-expression data. The differences in edge weights between BD and controls were then used as differential network signatures to identify drugs potentially targeting the disease-associated gene signature, employing the CLUEreg tool in the GRAND database.

RESULTS: Using a large RNA-seq dataset of 216 post-mortem brain samples from the CommonMind consortium, we constructed GRNs based on co-expression for individuals with BD and unaffected controls, involving 15,271 genes and 405 TFs. Our analysis highlighted significant influences of these TFs on immune response, energy metabolism, cell signalling, and cell adhesion pathways in the disorder. By employing drug repurposing, we identified 10 promising candidates potentially repurposed as BD treatments.

LIMITATIONS: Non-drug-naïve transcriptomics data, bulk analysis of BD samples, potential bias of GRNs towards well-studied genes.

CONCLUSIONS: Further investigation into repurposing candidates, especially those with preclinical evidence supporting their efficacy, like kaempferol and pramocaine, is warranted to understand their mechanisms of action and effectiveness in treating BD. Additionally, novel targets such as PARP1 and A2b offer opportunities for future research on their relevance to the disorder.

PMID:38190860 | DOI:10.1016/j.jad.2024.01.034

IEEE/ACM Trans Comput Biol Bioinform. 2024 Jan 8;PP. doi: 10.1109/TCBB.2024.3351079. Online ahead of print.

ABSTRACT

Traditional drug development is often high-risk and time-consuming. A promising alternative is to reuse or relocate approved drugs. Recently, some methods based on graph representation learning have started to be used for drug repositioning. These models learn the low dimensional embeddings of drug and disease nodes from the drug-disease interaction network to predict the potential association between drugs and diseases. However, these methods have strict requirements for the dataset, and if the dataset is sparse, the performance of these methods will be severely affected. At the same time, these methods have poor robustness to noise in the dataset. In response to the above challenges, we propose a drug repositioning model based on self-supervised graph learning with adptive denoising, called SADR. SADR uses data augmentation and contrastive learning strategies to learn feature representations of nodes, which can effectively solve the problems caused by sparse datasets. SADR includes an adaptive denoising training (ADT) component that can effectively identify noisy data during the training process and remove the impact of noise on the model. We have conducted comprehensive experiments on three datasets and have achieved better prediction accuracy compared to multiple baseline models. At the same time, we propose the top 10 new predictive approved drugs for treating two diseases. This demonstrates the ability of our model to identify potential drug candidates for disease indications. The code implementation is available at https://github.com/Soar1998/SADR.

PMID:38190661 | DOI:10.1109/TCBB.2024.3351079

J Biomol Struct Dyn. 2024 Jan 8:1-15. doi: 10.1080/07391102.2023.2300127. Online ahead of print.

ABSTRACT

The aldehyde dehydrogenase 1A1 (ALDH1A1) also known as retinal dehydrogenase, is an enzyme normally involved in the cellular metabolism, development and detoxification processes in healthy cells. However, it's also considered a cancer stem cell marker and its high levels of expression in several cancers, including breast, lung, ovarian, and colon cancer have been associated with poor prognosis and resistance to chemotherapy. Given its crucial role in chemotherapy resistance by detoxification of chemotherapeutic drugs, ALDH1A1 has attracted significant research interest as a potential therapeutic target for cancer. Though a few synthetic inhibitors of ALDH1A1 have been synthesized and their efficacy has been proved in-vitro and in-vivo studies, none of them have passed clinical trials so far. In this scenario, we have performed an in-silico study to verify whether any of the already approved drugs used for various purposes has the ability to inhibit catalytic activity of ALDH1A1, so that they can be repurposed for cancer therapy. Keeping in mind the feasibility of repurposing in a larger population we have selected the approved drugs from five widely used drug categories such as antibiotic, antiviral, antifungal, anti diabetic and antihypertensive for screening. Computational techniques like molecular docking, molecular dynamics simulations and MM-PBSA binding energy calculation have been used in this study to screen the approved drugs. Based on the logical analysis of results, we propose that three drugs - telmisartan, irbesartan and maraviroc can inhibit the catalytic activity of ALDH1A1 and thus can be repurposed to increase chemotherapy sensitivity in cancer cells.Communicated by Ramaswamy H. Sarma.

PMID:38189344 | DOI:10.1080/07391102.2023.2300127

Front Cell Neurosci. 2023 Dec 21;17:1327621. doi: 10.3389/fncel.2023.1327621. eCollection 2023.

ABSTRACT

Glioblastoma (GB) is a highly malignant primary brain tumor with limited treatment options and poor prognosis. Despite current treatment approaches, including surgical resection, radiation therapy, and chemotherapy with temozolomide (TMZ), GB remains mostly incurable due to its invasive growth pattern, limited drug penetration beyond the blood-brain barrier (BBB), and resistance to conventional therapies. One of the main challenges in GB treatment is effectively eliminating infiltrating cancer cells that remain in the brain parenchyma after primary tumor resection. We've reviewed the most recent challenges and surveyed the potential strategies aimed at enhancing local treatment outcomes.

PMID:38188666 | PMC:PMC10767996 | DOI:10.3389/fncel.2023.1327621

Front Immunol. 2023 Dec 21;14:1285345. doi: 10.3389/fimmu.2023.1285345. eCollection 2023.

ABSTRACT

INTRODUCTION: Pro-thrombotic events are one of the prevalent causes of intensive care unit (ICU) admissions among COVID-19 patients, although the signaling events in the stimulated platelets are still unclear.

METHODS: We conducted a comparative analysis of platelet transcriptome data from healthy donors, ICU, and non-ICU COVID-19 patients to elucidate these mechanisms. To surpass previous analyses, we constructed models of involved networks and control cascades by integrating a global human signaling network with transcriptome data. We investigated the control of platelet hyperactivation and the specific proteins involved.

RESULTS: Our study revealed that control of the platelet network in ICU patients is significantly higher than in non-ICU patients. Non-ICU patients require control over fewer proteins for managing platelet hyperactivity compared to ICU patients. Identification of indispensable proteins highlighted key subnetworks, that are targetable for system control in COVID-19-related platelet hyperactivity. We scrutinized FDA-approved drugs targeting indispensable proteins and identified fostamatinib as a potent candidate for preventing thrombosis in COVID-19 patients.

DISCUSSION: Our findings shed light on how SARS-CoV-2 efficiently affects host platelets by targeting indispensable and critical proteins involved in the control of platelet activity. We evaluated several drugs for specific control of platelet hyperactivity in ICU patients suffering from platelet hyperactivation. The focus of our approach is repurposing existing drugs for optimal control over the signaling network responsible for platelet hyperactivity in COVID-19 patients. Our study offers specific pharmacological recommendations, with drug prioritization tailored to the distinct network states observed in each patient condition. Interactive networks and detailed results can be accessed at https://fostamatinib.bioinfo-wuerz.eu/.

PMID:38187394 | PMC:PMC10768010 | DOI:10.3389/fimmu.2023.1285345

Front Pharmacol. 2023 Dec 22;14:1297924. doi: 10.3389/fphar.2023.1297924. eCollection 2023.

ABSTRACT

Purpose: This study introduces a sophisticated computational pipeline, eVir, designed for the discovery of antiviral drugs based on their interactions within the human protein network. There is a pressing need for cost-effective therapeutics for infectious diseases (e.g., COVID-19), particularly in resource-limited countries. Therefore, our team devised an Artificial Intelligence (AI) system to explore repurposing opportunities for currently used oral therapies. The eVir system operates by identifying pharmaceutical compounds that mirror the effects of antiviral peptides (AVPs)-fragments of human proteins known to interfere with fundamental phases of the viral life cycle: entry, fusion, and replication. eVir extrapolates the probable antiviral efficacy of a given compound by analyzing its established and predicted impacts on the human protein-protein interaction network. This innovative approach provides a promising platform for drug repurposing against SARS-CoV-2 or any virus for which peptide data is available. Methods: The eVir AI software pipeline processes drug-protein and protein-protein interaction networks generated from open-source datasets. eVir uses Node2Vec, a graph embedding technique, to understand the nuanced connections among drugs and proteins. The embeddings are input a Siamese Network (SNet) and MLPs, each tailored for the specific mechanisms of entry, fusion, and replication, to evaluate the similarity between drugs and AVPs. Scores generated from the SNet and MLPs undergo a Platt probability calibration and are combined into a unified score that gauges the potential antiviral efficacy of a drug. This integrated approach seeks to boost drug identification confidence, offering a potential solution for detecting therapeutic candidates with pronounced antiviral potency. Once identified a number of compounds were tested for efficacy and toxicity in lung carcinoma cells (Calu-3) infected with SARS-CoV-2. A lead compound was further identified to determine its efficacy and toxicity in K18-hACE2 mice infected with SARS-CoV-2. Computational Predictions: The SNet confidently differentiated between similar and dissimilar drug pairs with an accuracy of 97.28% and AUC of 99.47%. Key compounds identified through these networks included Zinc, Mebendazole, Levomenol, Gefitinib, Niclosamide, and Imatinib. Notably, Mebendazole and Zinc showcased the highest similarity scores, while Imatinib, Levemenol, and Gefitinib also ranked within the top 20, suggesting their significant pharmacological potentials. Further examination of protein binding analysis using explainable AI focused on reverse engineering the causality of the networks. Protein interaction scores for Mebendazole and Imatinib revealed their effects on notable proteins such as CDPK1, VEGF2, ABL1, and several tyrosine protein kinases. Laboratory Studies: This study determined that Mebendazole, Gefitinib, Topotecan and to some extent Carfilzomib showed conventional drug-response curves, with IC50 values near or below that of Remdesivir with excellent confidence all above R2>0.91, and no cytotoxicity at the IC50 concentration in Calu-3 cells. Cyclosporine A showed antiviral activity, but also unconventional drug-response curves and low R2 which are explained by the non-dose dependent toxicity of the compound. Additionally, Niclosamide demonstrated a conventional drug-response curve with high confidence; however, its inherent cytotoxicity may be a confounding element that misrepresents true antiviral efficacy, by reflecting cellular damage rather than a genuine antiviral action. Remdesivir was used as a control compound and was evaluated in parallel with the submitted test article and had conventional drug-response curves validating the overall results of the assay. Mebendazole was identified from the cell studies to have efficacy at non-toxic concentrations and were further evaluated in mice infected with SARS-CoV-2. Mebendazole administered to K18-hACE2 mice infected with SARS-CoV-2, resulted in a 44.2% reduction in lung viral load compared to non-treated placebo control respectively. There were no significant differences in body weight and all clinical chemistry determinations evaluated (i.e., kidney and liver enzymes) between the different treatment groups. Conclusion: This research underscores the potential of repurposing existing compounds for treating COVID-19. Our preliminary findings underscore the therapeutic promise of several compounds, notably Mebendazole, in both in vitro and in vivo settings against SARS-CoV-2. Several of the drugs explored, especially Mebendazole, are off-label medication; their cost-effectiveness position them as economical therapies against SARS-CoV-2.

PMID:38186640 | PMC:PMC10770831 | DOI:10.3389/fphar.2023.1297924

J Inherit Metab Dis. 2024 Jan 7. doi: 10.1002/jimd.12714. Online ahead of print.

ABSTRACT

Off-label repurposing of empagliflozin allows pathomechanism-based treatment of neutropenia/neutrophil-dysfunction in glycogen storage disease type Ib (GSDIb). From a value-based healthcare (VBHC) perspective, we here retrospectively studied patient-reported, clinical and pharmacoeconomic outcomes in 11 GSDIb individuals before and under empagliflozin at two centers (the Netherlands [NL], Austria [AT]), including a budget impact analysis, sensitivity-analysis, and systematic benefit-risk assessment. Under empagliflozin, all GSDIb individuals reported improved quality-of-life-scores. Neutrophil dysfunction related symptoms allowed either granulocyte colony-stimulating factor cessation or tapering. Calculated cost savings per patient per year ranged between € 6482-14 190 (NL) and € 1281-41 231 (AT). The budget impact analysis estimated annual total cost savings ranging between € 75 062-225 716 (NL) and € 37 697-231 790 (AT), based on conservative assumptions. The systematic benefit-risk assessment was favorable. From a VBHC perspective, empagliflozin treatment in GSDIb improved personal and clinical outcomes while saving costs, thereby creating value at multiple pillars. We emphasize the importance to reimburse empagliflozin for GSDIb individuals, further supported by the favorable systematic benefit-risk assessment. These observations in similar directions in two countries/health care systems strongly suggest that our findings can be extrapolated to other geographical areas and health care systems.

PMID:38185897 | DOI:10.1002/jimd.12714

Biomed Pharmacother. 2024 Jan 6;171:116096. doi: 10.1016/j.biopha.2023.116096. Online ahead of print.

ABSTRACT

Drug repurposing can accelerate drug development while reducing the cost and risk of toxicity typically associated with de novo drug design. Several disorders lacking pharmacological solutions and exhibiting poor results in clinical trials - such as Alzheimer's disease (AD) - could benefit from a cost-effective approach to finding new therapeutics. We previously developed a neural network model, Z-LaP Tracker, capable of quantifying behaviors in zebrafish larvae relevant to cognitive function, including activity, reactivity, swimming patterns, and optomotor response in the presence of visual and acoustic stimuli. Using this model, we performed a high-throughput screening of FDA-approved drugs to identify compounds that affect zebrafish larval behavior in a manner consistent with the distinct behavior induced by calcineurin inhibitors. Cyclosporine (CsA) and other calcineurin inhibitors have garnered interest for their potential role in the prevention of AD. We generated behavioral profiles suitable for cluster analysis, through which we identified 64 candidate therapeutics for neurodegenerative disorders.

PMID:38185043 | DOI:10.1016/j.biopha.2023.116096