Bioinformatics. 2021 Nov 11:btab767. doi: 10.1093/bioinformatics/btab767. Online ahead of print.

ABSTRACT

SUMMARY: DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research, etc. To improve the performance of DeepKG, a cascaded hybrid information extraction framework (CHIEF) is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144,900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7,980 entities and 43,760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform.

AVAILABILITY: Free to all users: http://covidkg.ai/.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:34788369 | DOI:10.1093/bioinformatics/btab767

Transl Psychiatry. 2021 Nov 16;11(1):591. doi: 10.1038/s41398-021-01724-w.

ABSTRACT

Drug repurposing is an invaluable strategy to identify new uses for existing drug therapies that overcome many of the time and financial costs associated with novel drug development. The COVID-19 pandemic has driven an unprecedented surge in the development and use of bioinformatic tools to identify candidate repurposable drugs. Using COVID-19 as a case study, we discuss examples of machine-learning and signature-based approaches that have been adapted to rapidly identify candidate drugs. The Library of Integrated Network-based Signatures (LINCS) and Connectivity Map (CMap) are commonly used repositories and have the advantage of being amenable to use by scientists with limited bioinformatic training. Next, we discuss how these recent advances in bioinformatic drug repurposing approaches might be adapted to identify repurposable drugs for CNS disorders. As the development of novel therapies that successfully target the cause of neuropsychiatric and neurological disorders has stalled, there is a pressing need for innovative strategies to treat these complex brain disorders. Bioinformatic approaches to identify repurposable drugs provide an exciting avenue of research that offer promise for improved treatments for CNS disorders.

PMID:34785660 | DOI:10.1038/s41398-021-01724-w

J Biomol Struct Dyn. 2021 Nov 16:1-17. doi: 10.1080/07391102.2021.2000498. Online ahead of print.

ABSTRACT

COVID-19, for which no confirmed therapeutic agents are available, has claimed over 48,14,000 lives globally. A feasible and quicker method to resolve this problem may be 'drug repositioning'. We investigated selected FDA and WHO-EML approved drugs based on their previously promising potential as antivirals, antibacterials or antifungals. These drugs were docked onto the nsp12 protein, which reigns the RNA-dependent RNA polymerase activity of SARS-CoV-2, a key therapeutic target for coronaviruses. Docked complexes were reevaluated using MM-GBSA analysis and the top three inhibitor-protein complexes were subjected to 100 ns long molecular dynamics simulation followed by another round of MM-GBSA analysis. The RMSF plots, binding energies and the mode of physicochemical interaction of the active site of the protein with the drugs were evaluated. Suramin, Penciclovir, and Anidulafungin were found to bind to nsp12 with similar binding energies as that of Remdesivir, which has been used as a therapy for COVID-19. In addition, recent experimental evidences indicate that these drugs exhibit antiviral efficacy against SARS-CoV-2. Such evidence, along with the significant and varied physical interactions of these drugs with the key viral enzyme outlined in this investigation, indicates that they might have a prospective therapeutic potential in the treatment of COVID-19 as monotherapy or combination therapy with Remdesivir.

PMID:34784490 | DOI:10.1080/07391102.2021.2000498

J Chem Inf Model. 2021 Nov 16. doi: 10.1021/acs.jcim.1c01285. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE.

PMID:34783553 | DOI:10.1021/acs.jcim.1c01285

J Control Release. 2021 Nov 12:S0168-3659(21)00610-6. doi: 10.1016/j.jconrel.2021.11.015. Online ahead of print.

ABSTRACT

Lung cancer (LC) is one of the leading causes of mortality accounting for almost 25% of cancer deaths throughout the world. The shortfall of affordable and effective first-line chemotherapeutics, the existence of resistant tumors, and the non-optimal route of administration contribute to poor prognosis and high mortality in LC. Administration of repurposed non-oncology drugs (RNODs) loaded in nanocarriers (NCs) via inhalation may prove as an effective alternative strategy to treat LC. Furthermore, their site-specific release through inhalation route using an appropriate inhalation device would offer improved therapeutic efficacy, thereby reducing mortality and improving patients' quality of life. The current manuscript offers a comprehensive overview on use of RNODs in LC treatment with an emphasis on their inhalation delivery and the associated challenges. The role of NCs to improve lung deposition and targeting of RNODs via inhalation are also elaborated. In addition, information about various RNODs in clinical trials for the treatment of LC, possibility for repurposing phytoceuticals against LC via inhalation and the bottlenecks associated with repurposing RNODs against cancer are also highlighted. Based on the reported studies covered in this manuscript, it was understood that delivery of RNODs via inhalation has emerged as a propitious approach. Hence, it is anticipated to provide effective first-line treatment at an affordable cost in debilitating LC from low and middle-income countries (LMIC).

PMID:34780880 | DOI:10.1016/j.jconrel.2021.11.015

OMICS. 2021 Nov 15. doi: 10.1089/omi.2021.0170. Online ahead of print.

ABSTRACT

Genomic medicine has made important strides over the past several decades, but as new insights and technologies emerge, the applications of genomics in medicine and planetary health continue to evolve and expand. An important grand challenge is harnessing and making sense of the genomic big data in ways that best serve public and planetary health. Because human health is inextricably intertwined with the health of planetary ecosystems and nonhuman animals, genomic medicine is in need of high throughput bioinformatics analyses to harness and integrate human and ecological multiomics big data. It is in this overarching context that artificial intelligence (AI), particularly machine learning and deep learning, offers enormous potentials to advance genomic medicine in a spirit of One Health. This expert review offers an analysis of the rapidly emerging role of AI in genomic medicine, including its current drivers, levers, opportunities, and challenges. The scope of AI applications in genomic medicine is broad, ranging from efficient and automated data analysis to drug repurposing and precision medicine, as with its challenges such as veracity of the big data that AI sorely depends on, social biases that the AI-driven algorithms can introduce, and how best to incorporate AI with human intelligence. The road ahead for AI in genomic medicine is complex and arduous and yet worthy of cautious optimism as we face future pandemics and ecological crises in the 21st century. Now is a good time to think about the role of AI in genomic medicine and planetary health.

PMID:34780300 | DOI:10.1089/omi.2021.0170

Patterns (N Y). 2021 Nov 9:100396. doi: 10.1016/j.patter.2021.100396. Online ahead of print.

ABSTRACT

We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorisation algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 Repositioning Explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.

PMID:34778851 | PMC:PMC8576113 | DOI:10.1016/j.patter.2021.100396

Front Med (Lausanne). 2021 Oct 27;8:744697. doi: 10.3389/fmed.2021.744697. eCollection 2021.

ABSTRACT

SARS-CoV2 infection results in a range of symptoms from mild pneumonia to cardiac arrhythmias, hyperactivation of the immune response, systemic organ failure and death. However, the mechanism of action has been hard to establish. Analysis of symptoms associated with COVID-19, the activity of repurposed drugs associated with lower death rates or antiviral activity in vitro and a small number of studies describing interventions, point to the importance of electrolyte, and particularly potassium, homeostasis at both the cellular, and systemic level. Elevated urinary loss of potassium is associated with disease severity, and the response to electrolyte replenishment correlates with progression toward recovery. These findings suggest possible diagnostic opportunities and therapeutic interventions. They provide insights into comorbidities and mechanisms associated with infection by SARS-CoV2 and other RNA viruses that target the ACE2 receptor, and/or activate cytokine-mediated immune responses in a potassium-dependent manner.

PMID:34778307 | PMC:PMC8578622 | DOI:10.3389/fmed.2021.744697

Cogn Neurodyn. 2021 Nov 5:1-9. doi: 10.1007/s11571-021-09727-5. Online ahead of print.

ABSTRACT

The novel coronavirus disease, COVID-19, has rapidly spread worldwide. Developing methods to identify the therapeutic activity of drugs based on phenotypic data can improve the efficiency of drug development. Here, a state-of-the-art machine-learning method was used to identify drug mechanism of actions (MoAs) based on the cell image features of 1105 drugs in the LINCS database. As the multi-dimensional features of cell images are affected by non-experimental factors, the characteristics of similar drugs vary considerably, and it is difficult to effectively identify the MoA of drugs as there is substantial noise. By applying the supervised information theoretic metric-learning (ITML) algorithm, a linear transformation made drugs with the same MoA aggregate. By clustering drugs to communities and performing enrichment analysis, we found that transferred image features were more conducive to the recognition of drug MoAs. Image features analysis showed that different features play important roles in identifying different drug functions. Drugs that significantly affect cell survival or proliferation, such as cyclin-dependent kinase inhibitors, were more likely to be enriched in communities, whereas other drugs might be decentralized. Chloroquine and clomiphene, which block the entry of virus, were clustered into the same community, indicating that similar MoA could be reflected by the cell image. Overall, the findings of the present study laid the foundation for the discovery of MoAs of new drugs, based on image data. In addition, it provided a new method of drug repurposing for COVID-19.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11571-021-09727-5.

PMID:34777628 | PMC:PMC8570398 | DOI:10.1007/s11571-021-09727-5

Front Microbiol. 2021 Oct 28;12:739684. doi: 10.3389/fmicb.2021.739684. eCollection 2021.

ABSTRACT

Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

PMID:34777286 | PMC:PMC8581544 | DOI:10.3389/fmicb.2021.739684

Acta Biomater. 2021 Nov 10:S1742-7061(21)00749-2. doi: 10.1016/j.actbio.2021.11.008. Online ahead of print.

ABSTRACT

Transcutaneous drug delivery is a promising method in terms of drug repositioning and reformulation because of its non-invasive and easy-to-use features. To overcome the skin barrier, which is the biggest challenge in transcutaneous drug delivery, a number of techniques, such as microemulsion, solid-in-oil dispersions and liposomes, have been studied extensively. However, the low viscosity of these formulations limits drug retention on the skin and reduces patient acceptability. Although viscosity can be increased by adding a thickening reagent, such an addition often alters formulation nanostructures and drug solubility, and importantly, decreases skin permeability. In this study, a gel-like lyotropic liquid crystal (LLC) was used as a tool to enhance skin permeability. In particular, we prepared 1-monolinolein (ML)-based LLCs with different water contents. All LLCs significantly enhanced skin permeation of a peptide drug, an epitope peptide of melanoma, despite their high viscoelasticity. Fourier transform infra-red spectroscopic analysis of the skin surface treated with the LLCs revealed that the gyroid geometry more strongly interacted with the lamellar structure inside the stratum corneum (SC) than the diamond geometry. Finally, as the result of the in vivo tumor challenge experiment using B16F10 melanoma-bearing mice, the LLC with the gyroid geometry showed stronger vaccine effect against tumor than a subcutaneous injection. Collectively, ML-based LLCs, especially with the gyroid geometry, are a promising strategy to deliver biomacromolecules into skin. STATEMENT OF SIGNIFICANCE: Transcutaneous drug delivery is a promising method for drug repositioning and reformulation because of its non-invasive and easy-to-use features. To overcome the skin barrier, which is the biggest challenge in transcutaneous drug delivery, we used a gel-like lyotropic liquid crystal (LLC) as a novel tool to enhance skin permeability. In this paper, we demonstrated that an LLC with a specific liquid crystalline structure has the highest skin permeation enhancement effect for a peptide antigen as a model drug. Moreover, the peptide antigen-loaded LLC showed a vaccine effect that was comparable to a subcutaneous injection in vivo. This study provides a basis for designing a transcutaneous delivery system of peptide drugs with LLC.

PMID:34774785 | DOI:10.1016/j.actbio.2021.11.008

BMC Nephrol. 2021 Nov 13;22(1):381. doi: 10.1186/s12882-021-02586-6.

ABSTRACT

BACKGROUND: Kidney dysfunction occurs in severe COVID-19, and is a predictor of COVID-19 mortality. Whether kidney dysfunction causes severe COVID-19, and hence is a target of intervention, or whether it is a symptom, is unclear because conventional observational studies are open to confounding. To obtain unconfounded estimates, we used Mendelian randomization to examine the role of kidney function in severe COVID-19.

METHODS: We used genome-wide significant, uncorrelated genetic variants to predict kidney function, in terms of estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR), and then assessed whether people with genetically instrumented higher eGFR or lower UACR, an indication of better kidney function, had a lower risk of severe COVID-19 (8779 cases, 1,001,875 controls), using the largest available cohorts with extensive genotyping. For comprehensiveness, we also examined their role in COVID-19 hospitalization (24,274 cases, 2,061,529 controls) and all COVID-19 (1,12,612 cases, 2,474,079 controls).

RESULTS: Genetically instrumented higher eGFR was associated with lower risk of severe COVID-19 (odds ratio (OR) 0.90, 95% confidence interval (CI) 0.83, 0.98) but not related to COVID-19 hospitalization or infection. Genetically instrumented UACR was not related to COVID-19.

CONCLUSIONS: Kidney function appears to be one of the key targets for severe COVID-19 treatment. Use of available medications to improve kidney function, such as antihypertensives, might be beneficial for COVID-19 treatment, with relevance to drug repositioning.

PMID:34774005 | DOI:10.1186/s12882-021-02586-6

Mol Neurodegener. 2021 Nov 12;16(1):77. doi: 10.1186/s13024-021-00497-6.

ABSTRACT

BACKGROUND: Parkinson's disease is a disabling neurodegenerative movement disorder characterized by dopaminergic neuron loss induced by α-synuclein oligomers. There is an urgent need for disease-modifying therapies for Parkinson's disease, but drug discovery is challenged by lack of in vivo models that recapitulate early stages of neurodegeneration. Invertebrate organisms, such as the nematode worm Caenorhabditis elegans, provide in vivo models of human disease processes that can be instrumental for initial pharmacological studies.

METHODS: To identify early motor impairment of animals expressing α-synuclein in dopaminergic neurons, we first used a custom-built tracking microscope that captures locomotion of single C. elegans with high spatial and temporal resolution. Next, we devised a method for semi-automated and blinded quantification of motor impairment for a population of simultaneously recorded animals with multi-worm tracking and custom image processing. We then used genetic and pharmacological methods to define the features of early motor dysfunction of α-synuclein-expressing C. elegans. Finally, we applied the C. elegans model to a drug repurposing screen by combining it with an artificial intelligence platform and cell culture system to identify small molecules that inhibit α-synuclein oligomers. Screen hits were validated using in vitro and in vivo mammalian models.

RESULTS: We found a previously undescribed motor phenotype in transgenic α-synuclein C. elegans that correlates with mutant or wild-type α-synuclein protein levels and results from dopaminergic neuron dysfunction, but precedes neuronal loss. Together with artificial intelligence-driven in silico and in vitro screening, this C. elegans model identified five compounds that reduced motor dysfunction induced by α-synuclein. Three of these compounds also decreased α-synuclein oligomers in mammalian neurons, including rifabutin which has not been previously investigated for Parkinson's disease. We found that treatment with rifabutin reduced nigrostriatal dopaminergic neurodegeneration due to α-synuclein in a rat model.

CONCLUSIONS: We identified a C. elegans locomotor abnormality due to dopaminergic neuron dysfunction that models early α-synuclein-mediated neurodegeneration. Our innovative approach applying this in vivo model to a multi-step drug repurposing screen, with artificial intelligence-driven in silico and in vitro methods, resulted in the discovery of at least one drug that may be repurposed as a disease-modifying therapy for Parkinson's disease.

PMID:34772429 | DOI:10.1186/s13024-021-00497-6

Cancers (Basel). 2021 Oct 31;13(21):5492. doi: 10.3390/cancers13215492.

ABSTRACT

Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

PMID:34771654 | DOI:10.3390/cancers13215492

Int J Environ Res Public Health. 2021 Nov 8;18(21):11711. doi: 10.3390/ijerph182111711.

ABSTRACT

Pandemic of coronavirus disease (COVID-19) is still pressing the healthcare systems worldwide. Thus far, the lack of available COVID-19-targeted treatments has led scientists to look through drug repositioning practices and exploitation of available scientific evidence for potential efficient drugs that may block biological pathways of SARS-CoV-2. Till today, several molecules have emerged as promising pharmacological agents, and more than a few medication protocols are applied during hospitalization. On the other hand, given the criticality of the disease, it is important for healthcare providers, especially those in COVID-19 clinics (i.e., nursing personnel and treating physicians), to recognize potential drug interactions that may lead to adverse drug reactions that may negatively impact the therapeutic outcome. In this review, focusing on patients with respiratory diseases (i.e., asthma or chronic obstructive pulmonary disease) that are treated also for COVID-19, we discuss possible drug interactions, their underlying pharmacological mechanisms, and possible clinical signs that healthcare providers in COVID-19 clinics may need to acknowledge as adverse drug reactions due to drug-drug interactions.

PMID:34770225 | DOI:10.3390/ijerph182111711

Int J Mol Sci. 2021 Nov 8;22(21):12074. doi: 10.3390/ijms222112074.

ABSTRACT

Tumor-associated cell-free DNAs (cfDNA) play an important role in the promotion of metastases. Previous studies proved the high antimetastatic potential of bovine pancreatic DNase I and identified short interspersed nuclear elements (SINEs) and long interspersed nuclear elements (LINEs)and fragments of oncogenes in cfDNA as the main molecular targets of enzyme in the bloodstream. Here, recombinant human DNase I (commercial name Pulmozyme®), which is used for the treatment of cystic fibrosis in humans, was repurposed for the inhibition of lung metastases in the B16 melanoma model in mice. We found that Pulmozyme® strongly reduced migration and induced apoptosis of B16 cells in vitro and effectively inhibited metastases in lungs and liver in vivo. Pulmozyme® was shown to be two times more effective when administered intranasally (i.n.) than bovine DNase I, but intramuscular (i.m.) administration forced it to exhibit as high an antimetastatic activity as bovine DNase I. Both DNases administered to mice either i.m. or i.n. enhanced the DNase activity of blood serum to the level of healthy animals, significantly decreased cfDNA concentrations, efficiently degraded SINE and LINE repeats and c-Myc fragments in the bloodstream and induced apoptosis and disintegration of neutrophil extracellular traps in metastatic foci; as a result, this manifested as the inhibition of metastases spread. Thus, Pulmozyme®, which is already an approved drug, can be recommended for use in the treatment of lung metastases.

PMID:34769514 | DOI:10.3390/ijms222112074

Int J Mol Sci. 2021 Nov 5;22(21):11984. doi: 10.3390/ijms222111984.

ABSTRACT

Cell-penetrating peptides (CPPs) are small peptide sequences used mainly as cellular delivery agents that are able to efficiently deliver cargo into cells. Some CPPs also demonstrate intrinsic anticancer properties. Previously, our group developed a new family of CPP2-thiazole conjugates that have been shown to effectively reduce the proliferation of different cancer cells. This work aimed to combine these CPP2-thiazole conjugates with paclitaxel (PTX) and 5-fluorouracil (5-FU) in PC-3 prostate and HT-29 colon cancer cells, respectively, to evaluate the cytotoxic effects of these combinations. We also combined these CPP2-thiazole conjugates with clotrimazole (CLZ), an antifungal agent that has been shown to decrease cancer cell proliferation. Cell viability was evaluated using MTT and SRB assays. Drug interaction was quantified using the Chou-Talalay method. We determined that CPP2 did not have significant activity in these cells and demonstrate that N-terminal modification of this peptide enhanced its anticancer activity in both cell lines. Our results also showed an uneven response between cell lines to the proposed combinations. PC-3 cells were more responsive to the combination of CPP2-thiazole conjugates with CLZ than PTX and were more sensitive to these combinations than HT-29 cells. In addition, the interaction of drugs resulted in more synergism in PC-3 cells. These results suggest that N-terminal modification of CPP2 results in the enhanced anticancer activity of the peptide and demonstrates the potential of CPPs as adjuvants in cancer therapy. These results also validate that CLZ has significant anticancer activity both alone and in combination and support the strategy of drug repurposing coupled to drug combination for prostate cancer therapy.

PMID:34769414 | DOI:10.3390/ijms222111984

Int J Mol Sci. 2021 Nov 4;22(21):11970. doi: 10.3390/ijms222111970.

ABSTRACT

INTRODUCTION: Introducing new drugs for clinical application is a very difficult, long, drawn-out, and costly process, which is why drug repositioning is increasingly gaining in importance. The aim of this study was to analyze the cytotoxic properties of ciprofloxacin and levofloxacin on bladder and prostate cell lines in vitro.

METHODS: Bladder and prostate cancer cell lines together with their non-malignant counterparts were used in this study. In order to evaluate the cytotoxic effect of both drugs on tested cell lines, MTT assay, real-time cell growth analysis, apoptosis detection, cell cycle changes, molecular analysis, and 3D cultures were examined.

RESULTS: Both fluoroquinolones exhibited a toxic effect on all of the tested cell lines. In the case of non-malignant cell lines, the cytotoxic effect was weaker, which was especially pronounced in the bladder cell line. A comparison of both fluoroquinolones showed the advantage of ciprofloxacin (lower doses of drug caused a stronger cytotoxic effect). Both fluoroquinolones led to an increase in late apoptotic cells and an inhibition of cell cycle mainly in the S phase. Molecular analysis showed changes in BAX, BCL2, TP53, and CDKN1 expression in tested cell lines following incubation with ciprofloxacin and levofloxacin. The downregulation of topoisomerase II genes (TOP2A and TOP2B) was noticed. Three-dimensional (3D) cell culture analysis confirmed the higher cytotoxic effect of tested fluoroquinolone against cancer cell lines.

CONCLUSIONS: Our results suggest that both ciprofloxacin and levofloxacin may have great potential, especially in the supportive therapy of bladder cancer treatment. Taking into account the low costs of such therapy, fluoroquinolones seem to be ideal candidates for repositioning into bladder cancer therapeutics.

PMID:34769400 | DOI:10.3390/ijms222111970

Methods. 2021 Nov 9:S1046-2023(21)00251-6. doi: 10.1016/j.ymeth.2021.11.002. Online ahead of print.

ABSTRACT

In the past 20 years, there have been several infectious disease outbreaks in humans for which the causative agent has been a zoonotic coronavirus. Novel infectious disease outbreaks, as illustrated by the current coronavirus disease 2019 (COVID-19) pandemic, demand a rapid response in terms of identifying effective treatments for seriously ill patients. The repurposing of approved drugs from other therapeutic areas is one of the most practical routes through which to approach this. Here, we present a systematic network-based drug repurposing methodology, which interrogates virus-human, human protein-protein and drug-protein interactome data. We identified 196 approved drugs that are appropriate for repurposing against COVID-19 and 102 approved drugs against a related coronavirus, severe acute respiratory syndrome (SARS-CoV). We constructed a protein-protein interaction (PPI) network based on disease signatures from COVID-19 and SARS multi-omics datasets. Analysis of this PPI network uncovered key pathways. Of the 196 drugs predicted to target COVID-19 related pathways, 44 (hypergeometric p-value: 1.98e-04) are already in COVID-19 clinical trials, demonstrating the validity of our approach. Using an artificial neural network, we provide information on the mechanism of action and therapeutic value for each of the identified drugs, to facilitate their rapid repurposing into clinical trials.

PMID:34767922 | DOI:10.1016/j.ymeth.2021.11.002

MedComm (Beijing). 2021 Jul 15;2(3):453-466. doi: 10.1002/mco2.83. eCollection 2021 Sep.

ABSTRACT

Colorectal cancer (CRC) is one of the most common malignancies currently. Despite advances in drug development, the survival and response rates in CRC patients are still poor. In our previous study, a library comprised of 1056 bioactive compounds was used for screening of drugs that could suppress CRC. Lomerizine 2HCl, which is an approved prophylactic drug for migraines, was selected for our studies. The results of in vitro and in vivo assays suggested that lomerizine 2HCl suppresses cell growth and promotes apoptosis in CRC cells. Moreover, lomerizine 2HCl inhibits cell migration and invasion of CRC. RNA sequencing analysis and Western blotting confirmed that lomerizine 2HCl can inhibit cell growth, migration, and invasion through PI3K/AKT/mTOR signaling pathway and induces protective autophagy in CRC. Meanwhile, autophagy inhibition by 3-methyladenine (3-MA) increases lomerizine 2HCl-induced cell apoptosis. Taken together, these results imply that lomerizine 2HCl is a potential anticancer agent, and the combination of lomerizine 2HCl and autophagy inhibitors may serve as a novel strategy to increase the antitumor efficacy of agents in the treatment of CRC.

PMID:34766155 | PMC:PMC8554656 | DOI:10.1002/mco2.83