PLoS One. 2022 Apr 21;17(4):e0266897. doi: 10.1371/journal.pone.0266897. eCollection 2022.

ABSTRACT

BACKGROUND: For many drugs, mechanisms of action with regard to desired effects and/or unwanted side effects are only incompletely understood. To investigate possible pleiotropic effects and respective molecular mechanisms, we describe here a catalogue of commonly used drugs and their impact on the blood transcriptome.

METHODS AND RESULTS: From a population-based cohort in Germany (LIFE-Adult), we collected genome-wide gene-expression data in whole blood using in Illumina HT12v4 micro-arrays (n = 3,378; 19,974 gene expression probes per individual). Expression profiles were correlated with the intake of active substances as assessed by participants' medication. This resulted in a catalogue of fourteen substances that were identified as associated with differential gene expression for a total of 534 genes. As an independent replication cohort, an observational study of patients with suspected or confirmed stable coronary artery disease (CAD) or myocardial infarction (LIFE-Heart, n = 3,008, 19,966 gene expression probes per individual) was employed. Notably, we were able to replicate differential gene expression for three active substances affecting 80 genes in peripheral blood mononuclear cells (carvedilol: 25; prednisolone: 17; timolol: 38). Additionally, using gene ontology enrichment analysis, we demonstrated for timolol a significant enrichment in 23 pathways, 19 of them including either GPER1 or PDE4B. In the case of carvedilol, we showed that, beside genes with well-established association with hypertension (GPER1, PDE4B and TNFAIP3), the drug also affects genes that are only indirectly linked to hypertension due to their effects on artery walls or their role in lipid biosynthesis.

CONCLUSIONS: Our developed catalogue of blood gene expressions profiles affected by medication can be used to support both, drug repurposing and the identification of possible off-target effects.

PMID:35446883 | DOI:10.1371/journal.pone.0266897

IEEE Trans Neural Netw Learn Syst. 2022 Apr 21;PP. doi: 10.1109/TNNLS.2022.3165618. Online ahead of print.

ABSTRACT

Deep graph networks (DGNs) are a family of machine learning models for structured data which are finding heavy application in life sciences (drug repurposing, molecular property predictions) and on social network data (recommendation systems). The privacy and safety-critical nature of such domains motivates the need for developing effective explainability methods for this family of models. So far, progress in this field has been challenged by the combinatorial nature and complexity of graph structures. In this respect, we present a novel local explanation framework specifically tailored to graph data and DGNs. Our approach leverages reinforcement learning to generate meaningful local perturbations of the input graph, whose prediction we seek an interpretation for. These perturbed data points are obtained by optimizing a multiobjective score taking into account similarities both at a structural level as well as at the level of the deep model outputs. By this means, we are able to populate a set of informative neighboring samples for the query graph, which is then used to fit an interpretable model for the predictive behavior of the deep network locally to the query graph prediction. We show the effectiveness of the proposed explainer by a qualitative analysis on two chemistry datasets, TOX21 and Estimated SOLubility (ESOL) and by quantitative results on a benchmark dataset for explanations, CYCLIQ.

PMID:35446771 | DOI:10.1109/TNNLS.2022.3165618

Front Pharmacol. 2022 Apr 4;13:894310. doi: 10.3389/fphar.2022.894310. eCollection 2022.

NO ABSTRACT

PMID:35444535 | PMC:PMC9013936 | DOI:10.3389/fphar.2022.894310

NPJ Syst Biol Appl. 2022 Apr 20;8(1):12. doi: 10.1038/s41540-022-00221-0.

ABSTRACT

Despite advances in modern medicine that led to improvements in cardiovascular outcomes, cardiovascular disease (CVD) remains the leading cause of mortality and morbidity globally. Thus, there is an urgent need for new approaches to improve CVD drug treatments. As the development time and cost of drug discovery to clinical application are excessive, alternate strategies for drug development are warranted. Among these are included computational approaches based on omics data for drug repositioning, which have attracted increasing attention. In this work, we developed an adjusted similarity measure implemented by the algorithm SAveRUNNER to reposition drugs for cardiovascular diseases while, at the same time, considering the side effects of drug candidates. We analyzed nine cardiovascular disorders and two side effects. We formulated both disease disorders and side effects as network modules in the human interactome, and considered those drug candidates that are proximal to disease modules but far from side-effects modules as ideal. Our method provides a list of drug candidates for cardiovascular diseases that are unlikely to produce common, adverse side-effects. This approach incorporating side effects is applicable to other diseases, as well.

PMID:35443763 | DOI:10.1038/s41540-022-00221-0

BMC Bioinformatics. 2022 Apr 20;23(1):143. doi: 10.1186/s12859-022-04662-6.

ABSTRACT

'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

PMID:35443626 | DOI:10.1186/s12859-022-04662-6

J Assoc Physicians India. 2022 Apr;70(4):11-12.

ABSTRACT

Drug repurposing is considered as a rapid strategy for COVID-19 drug discovery and many drugs have been tried for treatment of COVID-19. Antivirals like favipiravir and remdesivir have become part of the COVID-19 Management Protocol by Ministry of Health and Family Welfare (MOHFW) as well as Maharashtra State guidelines since beginning, after being approved by Drugs Controller General of India (DCGI). Although these drugs have shown promising results, their efficacy is still not proven completely and needs to be studied in large populations. The purpose of our study was to evaluate the clinical outcomes in hospitalised patients with COVID-19 treated with remdesivir and/or favipiravir.

MATERIAL: Retrospective analysis of medical records of 914 adult COVID-19 patients hospitalized in a tertiary care center in Mumbai was conducted. We assessed the following outcomes: severity of disease, need for oxygen supplementation, incidence of complications, discharge from hospital or death, oxygen requirement at the time of discharge, duration of hospital stay. These outcomes were compared between patients who received remdesivir only, patients who received favipiravir only, patients who received both remdesivir and favipiravir and patients who did not receive either remdesivir or favipiravir (control group).

OBSERVATION: Of total 914 patients in our study, 55.79% patients received only remdesivir, 13.45% received only favipiravir, 7.76% received remdesivir plus favipiravir and 22.97% did not receive any antivirals. Higher number of patients in remdesivir only group (60.19%) and remdesivir plus favipiravir groups (56.33%) required supplemental oxygen [vs 32.38 % patients in control group and 24.39% in favipiravir only group]. Highest number of patients (91.05%) in favipiravir only group got discharged under Indian Council of Medical Research (ICMR) guidelines closely followed by 88.73% in remdesivir plus favipiravir group, 88.43% in remdesivir only group and 82.38 % patients in control group. 8.43% patients in remdesivir only group, 6.5% in favipiravir only group, 7.04% in remdesivir plus favipiravir group and 10.47 % patients in control group needed oxygen support after discharge.

CONCLUSION: Majority of patients in our study got discharged under ICMR guidelines with higher proportion of patients in the treatment groups as compared to the control group. Also, lesser number of patients in the treatment groups required oxygen supplementation post discharge as compared to the control group.

PMID:35443501

Sci Rep. 2022 Apr 19;12(1):6454. doi: 10.1038/s41598-022-10393-8.

ABSTRACT

This study aimed to assess the ultrapure cannabidiol (CBD) antibacterial activity and to investigate the antibacterial activity of the combination CBD + polymyxin B (PB) against Gram-negative (GN) bacteria, including PB-resistant Gram-negative bacilli (GNB). We used the standard broth microdilution method, checkerboard assay, and time-kill assay. CBD exhibited antibacterial activity against Gram-positive bacteria, lipooligosaccharide (LOS)-expressing GN diplococcus (GND) (Neisseria gonorrhoeae, Neisseria meningitidis, Moraxella catarrhalis), and Mycobacterium tuberculosis, but not against GNB. For most of the GNB studied, our results showed that low concentrations of PB (≤ 2 µg/mL) allow CBD (≤ 4 µg/mL) to exert antibacterial activity against GNB (e.g., Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii), including PB-resistant GNB. CBD + PB also showed additive and/or synergistic effect against LOS-expressing GND. Time-kill assays results showed that the combination CBD + PB leads to a greater reduction in the number of colony forming units per milliliter compared to CBD and PB alone, at the same concentration used in combination, and the combination CBD + PB was synergistic for all four PB-resistant K. pneumoniae isolates evaluated. Our results show that CBD has translational potential and should be further explored as a repurposed antibacterial agent in clinical trials. The antibacterial efficacy of the combination CBD + PB against multidrug-resistant and extensively drug-resistant GNB, especially PB-resistant K. pneumoniae, is particularly promising.

PMID:35440801 | DOI:10.1038/s41598-022-10393-8

Brief Bioinform. 2022 Apr 21:bbac147. doi: 10.1093/bib/bbac147. Online ahead of print.

ABSTRACT

Target prediction and virtual screening are two powerful tools of computer-aided drug design. Target identification is of great significance for hit discovery, lead optimization, drug repurposing and elucidation of the mechanism. Virtual screening can improve the hit rate of drug screening to shorten the cycle of drug discovery and development. Therefore, target prediction and virtual screening are of great importance for developing highly effective drugs against COVID-19. Here we present D3AI-CoV, a platform for target prediction and virtual screening for the discovery of anti-COVID-19 drugs. The platform is composed of three newly developed deep learning-based models i.e., MultiDTI, MPNNs-CNN and MPNNs-CNN-R models. To compare the predictive performance of D3AI-CoV with other methods, an external test set, named Test-78, was prepared, which consists of 39 newly published independent active compounds and 39 inactive compounds from DrugBank. For target prediction, the areas under the receiver operating characteristic curves (AUCs) of MultiDTI and MPNNs-CNN models are 0.93 and 0.91, respectively, whereas the AUCs of the other reported approaches range from 0.51 to 0.74. For virtual screening, the hit rate of D3AI-CoV is also better than other methods. D3AI-CoV is available for free as a web application at http://www.d3pharma.com/D3Targets-2019-nCoV/D3AI-CoV/index.php, which can serve as a rapid online tool for predicting potential targets for active compounds and for identifying active molecules against a specific target protein for COVID-19 treatment.

PMID:35443040 | DOI:10.1093/bib/bbac147

medRxiv. 2022 Apr 13:2022.04.12.22273802. doi: 10.1101/2022.04.12.22273802. Preprint.

ABSTRACT

BACKGROUND: In response to the challenge to rapidly identify treatment options for COVID-19, several studies reported that statins, as a drug class, reduce mortality in these patients. Here we explored the possibility that different statins might differ in their ability to exert protective effects based on computational predictions.

METHODS: A Bayesian network tool was used to predict drugs that shift the host transcriptomic response to SARS-CoV-2 infection towards a healthy state. Drugs were predicted using 14 RNA-sequencing datasets from 72 autopsy tissues and 465 COVID-19 patient samples or from cultured human cells and organoids infected with SARS-CoV-2, with a total of 2,436 drugs investigated. Top drug predictions included statins, which were tested in Vero E6 cells infected with SARS-CoV-2 and human endothelial cells infected with a related OC43 coronavirus. A database containing over 4,000 COVID-19 patients on statins was also analyzed to determine mortality risk in patients prescribed specific statins versus untreated matched controls.

FINDINGS: Simvastatin was among the most highly predicted compounds (14/14 datasets) and five other statins were predicted to be active in > 50% of analyses. In vitro testing of SARS- CoV-2 infected cells revealed simvastatin to be a potent inhibitor whereas most other statins were less effective. Simvastatin also inhibited OC43 infection and reduced cytokine production in endothelial cells. Analysis of the clinical database revealed that reduced mortality risk was only observed in COVID-19 patients prescribed a subset of statins, including simvastatin and atorvastatin.

INTERPRETATION: Different statins may differ in their ability to sustain the lives of COVID-19 patients despite having a shared target and lipid-modifying mechanism of action. These findings highlight the value of target-agnostic drug prediction coupled with patient databases to identify and validate non-obvious mechanisms and drug repurposing opportunities.

FUNDING: DARPA, Wyss Institute, Hess Research Fund, UCSF Program for Breakthrough Biomedical Research, and NIH.

PMID:35441166 | PMC:PMC9016655 | DOI:10.1101/2022.04.12.22273802

ACS Biomater Sci Eng. 2022 Apr 19. doi: 10.1021/acsbiomaterials.2c00026. Online ahead of print.

ABSTRACT

Dexamethasone (DEX) has been widely used to treat a variety of diseases, including autoimmune diseases, allergies, ocular disorders, cancer, and, more recently, COVID-19. However, DEX usage is often restricted in the clinic due to its poor water solubility. When administered through a systemic route, it can elicit severe side effects, such as hypertension, peptic ulcers, hyperglycemia, and hydro-electrolytic disorders. There is currently much interest in developing efficient DEX-loaded nanoformulations that ameliorate adverse disease effects inhibiting advancements in scientific research. Various nanoparticles have been developed to selectively deliver drugs without destroying healthy cells or organs in recent years. In the present review, we have summarized some of the most attractive applications of DEX-loaded delivery systems, including liposomes, polymers, hydrogels, nanofibers, silica, calcium phosphate, and hydroxyapatite. This review provides our readers with a broad spectrum of nanomedicine approaches to deliver DEX safely.

PMID:35439408 | DOI:10.1021/acsbiomaterials.2c00026

Med Teach. 2022 Apr 19:1-7. doi: 10.1080/0142159X.2022.2058386. Online ahead of print.

ABSTRACT

PURPOSE: Recent findings have suggested that physicians who spend more time participating in their most meaningful job activities (e.g. teaching) are less likely to experience burnout. The current study aimed to expound upon this finding, focusing specifically on the role of teaching in promoting meaning and preventing burnout.

METHOD: A total of 428 physicians at a large academic healthcare institution completed an online survey that included measures of burnout and other relevant variables. In the second part of this study, 20 physicians participated in interviews with the aim of expounding upon and contextualizing the findings from Part 1.

RESULTS: Results from Part 1 suggested that although meaningfulness derived from teaching was associated with reduced burnout, this association was only true for those who indicated that clinical teaching was among the most meaningful parts of being a physician. In addition, physicians were less likely to spend time working on their most meaningful job activity when it was teaching. Part 2 illustrated why teaching in the clinical environment can be so meaningful and protective against burnout.

CONCLUSIONS: Many physicians are unable to teach due to the increasing demands of medical institutions, which may contribute to the increasing levels of burnout in healthcare providers.

PMID:35439099 | DOI:10.1080/0142159X.2022.2058386

Methods Mol Biol. 2022;2486:87-104. doi: 10.1007/978-1-0716-2265-0_6.

ABSTRACT

Viruses can cause many diseases resulting in disabilities and death. Fortunately, advances in systems medicine enable the development of effective therapies for treating viral diseases, of vaccines to prevent viral infections, as well as of diagnostic tools to mitigate the risk and reduce the death toll. Characterizing the SARS-CoV-2 gene sequence and the role of its spike protein in infection informs development of small molecule drugs, antibodies, and vaccines to combat infection and complication, as well as to end the pandemic. Drug repurposing of small molecule drugs is a viable strategy to combat viral diseases; the key concepts include (1) linking a drug candidate's pharmacological network to its pharmacodynamic response in patients; (2) linking a drug candidate's physicochemical properties to its pharmacokinetic characteristics; and (3) optimizing the safe and effective dosing regimen within its therapeutic window. Computational integration of drug-induced signaling pathways with clinical outcomes is useful to inform selection of potential drug candidates with respect to safety and effectiveness. Key pharmacokinetic and pharmacodynamic principles for computational optimization of drug development include a drug candidate's Cminss/IC95 ratio, pharmacokinetic characteristics, and systemic exposure-response relationship, where Cminss is the trough concentration following multiple dosing. In summary, systems medicine approaches play a vital role in global success in combating viral diseases, including global real-time information sharing, development of test kits, drug repurposing, discovery and development of safe, effective therapies, detection of highly transmissible and deadly variants, and development of vaccines.

PMID:35437720 | DOI:10.1007/978-1-0716-2265-0_6

Front Cell Infect Microbiol. 2022 Mar 24;12:845368. doi: 10.3389/fcimb.2022.845368. eCollection 2022.

ABSTRACT

Coronaviruses are the etiologic agents of several diseases. Coronaviruses of critical medical importance are characterized by highly inflammatory pathophysiology, involving severe pulmonary impairment and infection of multiple cell types within the body. Here, we discuss the interplay between coronaviruses and autophagy regarding virus life cycle, cell resistance, and inflammation, highlighting distinct mechanisms by which autophagy restrains inflammatory responses, especially those involved in coronavirus pathogenesis. We also address different autophagy modulators available and the rationale for drug repurposing as an attractive adjunctive therapy. We focused on pharmaceuticals being tested in clinical trials with distinct mechanisms but with autophagy as a common target. These autophagy modulators act in cell resistance to virus infection and immunomodulation, providing a double-strike to prevent or treat severe disease development and death from coronaviruses diseases.

PMID:35433503 | PMC:PMC9010404 | DOI:10.3389/fcimb.2022.845368

New Microbes New Infect. 2022 Apr 11:100979. doi: 10.1016/j.nmni.2022.100979. Online ahead of print.

NO ABSTRACT

PMID:35433012 | PMC:PMC8995196 | DOI:10.1016/j.nmni.2022.100979

Front Pharmacol. 2022 Mar 30;13:879020. doi: 10.3389/fphar.2022.879020. eCollection 2022.

ABSTRACT

Beyond the wide use of tamoxifen in breast cancer chemotherapy due to its estrogen receptor antagonist activity, this drug is being assayed in repurposing strategies against a number of microbial infections. We conducted a literature search on the evidence related with tamoxifen activity in macrophages, since these immune cells participate as a first line-defense against pathogen invasion. Consistent data indicate the existence of estrogen receptor-independent targets of tamoxifen in macrophages that include lipid mediators and signaling pathways, such as NRF2 and caspase-1, which allow these cells to undergo phenotypic adaptation and potentiate the inflammatory response, without the induction of cell death. Thus, these lines of evidence suggest that the widespread antimicrobial activity of this drug can be ascribed, at least in part, to the potentiation of the host innate immunity. This widens our understanding of the pharmacological activity of tamoxifen with relevant therapeutic implications for infections and other clinical indications that may benefit from the immunomodulatory effects of this drug.

PMID:35431927 | PMC:PMC9006819 | DOI:10.3389/fphar.2022.879020

Front Pharmacol. 2022 Apr 1;13:850045. doi: 10.3389/fphar.2022.850045. eCollection 2022.

ABSTRACT

Background: Therapeutic phlebotomy, known as scheduled bloodletting, has been the main method for managing erythrocytosis symptoms and thrombocytosis-associated complications in various blood disorders. One of the major indications for phlebotomy is polycythemia vera (PV). The main goal of current treatment strategies for patients who require phlebotomy is to prevent thrombohemorrhagic complications rather than to prolong survival or lessen the risk of myelofibrotic or leukemic progression. Additional cytoreductive therapy is recommended for high-risk PV, for which the common first-line drug is hydroxyurea. However, recent evidence suggests that phlebotomy may not reduce the risk of thrombosis in patients with PV. Further evidence suggests that patients with PV treated with hydroxyurea who require three or more phlebotomy procedures per year have a higher risk of thrombotic complications. Methods: We hypothesized that a drug-repurposing strategy of utilizing antineoplastic drugs for patients who require phlebotomy would result in greater benefits than would phlebotomy. The antihypertensive hydralazine and the anticonvulsant valproate, which have both been reported to have antineoplastic activity that mimics cytoreductive agents, were selected as candidates for the drug-repositioning strategy in a retrospective cohort study. We measured the hazard ratios (HR) and the frequencies of phlebotomy in patients with prescriptions for hydralazine or valproate or the two drugs in combination by using data from Taiwan's National Health Insurance Research Database from 2000 to 2015 (n = 1,936,512). Results: The HRs of undergoing phlebotomy in groups with hydralazine, valproate, and combination hydralazine-valproate prescriptions were reduced to 0.729 (p = 0.047), 0.887 (p = 0.196), and 0.621 (p = 0.022), respectively. The frequency of undergoing phlebotomy decreased from 2.27 to 1.99, 2.01, and 1.86 per person-year (p = 0.015), respectively. However, no significant differences were observed for the hydralazine group or the hydralazine-valproate combination group. Conclusion: Whether a repurposed drug can serve as a cytoreductive agent for patients who require phlebotomy depends on its risk-benefit balance. We suggest that hydralazine, instead of the hydralazine-valproate combination, is a reasonable alternative for patients who require regular phlebotomy.

PMID:35431926 | PMC:PMC9011102 | DOI:10.3389/fphar.2022.850045

Biomed Pharmacother. 2022 Apr 13;150:112952. doi: 10.1016/j.biopha.2022.112952. Online ahead of print.

ABSTRACT

Mendelian randomization (MR) is an epidemiological method that uses genetic variants to proxy an exposure predicting its causal association with an outcome. It occupies a valuable niche between observational studies and randomized trials. MR applications expanded lately, facilitated by the availability of big data, to include disease risk causation prediction, supporting evidence of prior observational data, identifying new drug targets, and drug repurposing. Concurrently, the last decade witnessed the growth of pharmacogenomics (PGx) research as a cornerstone in precision medicine. PGx research, conducted at discovery and implementation levels, resulted in validated PGx biomarkers and tests. Despite many clinically relevant PGx associations that could be translated into clinical applications, worldwide implementation is lagging far behind. The current review examines the intersection zones between MR and PGx research. MR can provide supporting evidence that allows generalizing PGx findings supporting its implementation. Interchangeability, PGx research can fuel MR studies with libraries of genetic variants of validated biological relevance. Furthermore, PGx and MR exhibit a synergistic relationship in drug discovery that can accelerate identifying new targets and repurposing old drugs. Interdisciplinary research applied by PGx researchers, epidemiologists with MR experience, and data scientists' collaborations can unlock unforeseen opportunities in accelerating precision medicine acquisition.

PMID:35429744 | DOI:10.1016/j.biopha.2022.112952

Cell Mol Life Sci. 2022 Apr 16;79(5):243. doi: 10.1007/s00018-022-04278-2.

ABSTRACT

Bile acids are soluble derivatives of cholesterol produced in the liver that subsequently undergo bacterial transformation yielding a diverse array of metabolites. The bulk of bile acid synthesis takes place in the liver yielding primary bile acids; however, other tissues have also the capacity to generate bile acids (e.g. ovaries). Hepatic bile acids are then transported to bile and are subsequently released into the intestines. In the large intestine, a fraction of primary bile acids is converted to secondary bile acids by gut bacteria. The majority of the intestinal bile acids undergo reuptake and return to the liver. A small fraction of secondary and primary bile acids remains in the circulation and exert receptor-mediated and pure chemical effects (e.g. acidic bile in oesophageal cancer) on cancer cells. In this review, we assess how changes to bile acid biosynthesis, bile acid flux and local bile acid concentration modulate the behavior of different cancers. Here, we present in-depth the involvement of bile acids in oesophageal, gastric, hepatocellular, pancreatic, colorectal, breast, prostate, ovarian cancer. Previous studies often used bile acids in supraphysiological concentration, sometimes in concentrations 1000 times higher than the highest reported tissue or serum concentrations likely eliciting unspecific effects, a practice that we advocate against in this review. Furthermore, we show that, although bile acids were classically considered as pro-carcinogenic agents (e.g. oesophageal cancer), the dogma that switch, as lower concentrations of bile acids that correspond to their serum or tissue reference concentration possess anticancer activity in a subset of cancers. Differences in the response of cancers to bile acids lie in the differential expression of bile acid receptors between cancers (e.g. FXR vs. TGR5). UDCA, a bile acid that is sold as a generic medication against cholestasis or biliary surge, and its conjugates were identified with almost purely anticancer features suggesting a possibility for drug repurposing. Taken together, bile acids were considered as tumor inducers or tumor promoter molecules; nevertheless, in certain cancers, like breast cancer, bile acids in their reference concentrations may act as tumor suppressors suggesting a Janus-faced nature of bile acids in carcinogenesis.

PMID:35429253 | DOI:10.1007/s00018-022-04278-2

Biomed Pharmacother. 2022 Apr 12;150:112930. doi: 10.1016/j.biopha.2022.112930. Online ahead of print.

ABSTRACT

Dupuytren disease (DD) is a hand-localized fibrotic disorder characterized by a scar-like, collagen-rich cord. Treatment usually comprises surgical removal of the cord, but is associated with a high relapse rate, in some cases requiring finger amputation. There is currently no consensual medical approach for treating DD. Numerous preclinical studies have highlighted antifibrotic properties of metformin, and the aim of this study was to assess a potential antifibrotic role of metformin in DD. Fibroblasts from DD cords (DF) and phenotypically normal palmar fascia (PF) were extracted from surgical specimens and cultured. The fibrotic status of DF and PF was compared at baseline, and under profibrotic (TGF-β stimulation) and antifibrotic (metformin stimulation) conditions, using quantitative RT-PCR, western blot, immunocytochemistry, and a functional fibroblast contraction assay. At baseline, DF showed higher levels of fibrotic markers and contraction capacity compared with PF. Both types of fibroblasts responded to TGF-β stimulation. Treatment of DF and PF with metformin did not affect basal levels of fibrotic markers and contraction but largely prevented their induction by TGF-β. In conclusion, our data show that metformin inhibits TGF-β-induced expression of fibrotic markers and contraction in hand-derived fibroblasts. This supports the case for a clinical trial to assess the repurposing of metformin as an adjuvant to surgery, to prevent, reduce, or delay recurrence in at-risk DD patients.

PMID:35427821 | DOI:10.1016/j.biopha.2022.112930

Drug Discov Today. 2022 Apr 12:S1359-6446(22)00155-6. doi: 10.1016/j.drudis.2022.04.008. Online ahead of print.

ABSTRACT

For emerging and re-emerging epidemic infections, researchers face challenges to develop broad-spectrum antivirals as well as reducing development time and costs, and drug resistance. Drug repurposing is a reliable strategy for rapidly discovering potent new antiviral agents, reducing the need for clinical trials. In this review, we outline antiviral drug candidates identified using the drug repurposing approach, with their potential modes of action and biological responses against various epidemic viral infectious diseases. Teaser: With the advent of antivirals identified from pandemic diseases, drug repurposing is an efficient strategy for identifying new therapeutic avenues for underexplored epidemic viral diseases.

PMID:35427764 | DOI:10.1016/j.drudis.2022.04.008