Drug Repurposing: A Review of Old and New Antibiotics for the Treatment of Malaria: Identifying Antibiotics with a Fast Onset of Antiplasmodial Action.
Ontology highlight
ABSTRACT:
SUBMITTER: Pessanha de Carvalho L
PROVIDER: S-EPMC8071546 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:In tropical and subtropical areas, malaria stands as a profound public health challenge, causing an estimated 247 million cases worldwide annually. Given the absence of a viable vaccine, the timely and effective treatment of malaria remains a critical priority. However, the growing resistance of parasites to currently utilized drugs underscores the critical need for the identification of new antimalarial therapies. Here, we aimed to identify potential new drug candidates against Plasmodium falciparum, the main causative agent of malaria, by analyzing the transcriptomes of different life stages of the parasite and identifying highly expressed genes. We searched for genes that were expressed in all stages of the parasite's life cycle, including the asexual blood stage, gametocyte stage, liver stage, and sexual stages in the insect vector, using transcriptomics data from publicly available databases. From this analysis, we found 674 overlapping genes, including 409 essential ones. By searching through drug target databases, we discovered 70 potential drug targets and 75 associated bioactive compounds. We sought to expand this analysis to similar compounds to known drugs. So, we found a list of 1557 similar compounds, which we predicted as actives and inactives using previously developed machine learning models against five life stages of Plasmodium spp. From this analysis, two compounds were selected, and the reactions were experimentally evaluated. The compounds HSP-990 and silvestrol aglycone showed potent inhibitory activity at nanomolar concentrations against the P. falciparum 3D7 strain asexual blood stage. Moreover, silvestrol aglycone exhibited low cytotoxicity in mammalian cells, transmission-blocking potential, and inhibitory activity comparable to those of established antimalarials. These findings warrant further investigation of silvestrol aglycone as a potential dual-acting antimalarial and transmission-blocking candidate for malaria control.
| S-EPMC10515587 | biostudies-literature
Project description:BACKGROUND:Artemisinin-based combination therapy (ACT) is used as the first-line treatment of uncomplicated malaria caused by the Plasmodium falciparum parasite and chloroquine-resistant Plasmodium vivax parasites. Evidence of resistance to ACT has been reported in Cambodia, and without new and effective anti-malarial agents, malaria burden and mortality will rise. METHODS:The used MolPrint 2D fingerprints and the Tanimoto similarity index were used to perform a structural similarity search within the Malaria Box collection to select diverse molecular scaffolds that are different from artesunate. Next, the inhibitory potency against the P. falciparum 3D7 strain (SYBR Green I inhibition assay) and the cytotoxicity against HepG2 cells (MTT and neutral red assays) were evaluated. Then, the speed of action, the combination profile of selected inhibitors with artesunate, and the P. berghei in vivo activity of the best compounds were assessed. RESULTS:A set of 11 structurally diverse compounds from the Malaria Box with a similarity threshold of less than 0.05 was selected and compared with artesunate. The in vitro inhibitory activity of each compound confirmed the reported potencies (IC50 values ranging from 0.005 to 1 µM). The cytotoxicity of each selected compound was evaluated and used to calculate the selectivity index (SI values ranging from 15.1 to 6100). Next, both the speed of action and the combination profile of each compound with artesunate was assessed. Acridine, thiazolopyrimidine, quinoxaline, benzimidazole, thiophene, benzodiazepine, isoxazole and pyrimidoindole derivatives showed fast in vitro inhibitory activity of parasite growth, whereas hydrazinobenzimidazole, indenopyridazinone and naphthalenone derivatives were slow-acting in vitro inhibitors. Combinatory profile evaluation indicated that thiazolopyrimidinone and benzodiazepine derivatives have an additive profile, suggesting that the combination of these inhibitors with artesunate is favourable for in vitro inhibitory activity. The remaining compounds showed an antagonistic combinatory profile with artesunate. The collected data indicated that the indenopyridazinone derivative, a bc1 complex inhibitor, had a similar association profile in combination with proguanil when compared to atovaquone combined with proguanil, thereby corroborating the correlation between the molecular target and the combination profile. Lastly, the in vivo activity of the thiazolopyrimidinone and benzodiazepine derivatives were assessed. Both compounds showed oral efficacy at 50 mg/kg in a mouse model of Plasmodium berghei malaria (64% and 40% reduction in parasitaemia on day 5 post-infection, respectively). CONCLUSIONS:The findings in this paper shed light on the relationship among the speed of action, molecular target and combinatory profile and identified new hits with in vivo activity as candidates for anti-malarial combination therapy.
| S-EPMC6938011 | biostudies-literature
Project description:Glycopeptides are considered antibiotics of last resort for the treatment of life-threatening infections caused by relevant Gram-positive human pathogens, such as Staphylococcus aureus, Enterococcus spp. and Clostridium difficile. The emergence of glycopeptide-resistant clinical isolates, first among enterococci and then in staphylococci, has prompted research for second generation glycopeptides and a flurry of activity aimed at understanding resistance mechanisms and their evolution. Glycopeptides are glycosylated non-ribosomal peptides produced by a diverse group of soil actinomycetes. They target Gram-positive bacteria by binding to the acyl-D-alanyl-D-alanine (D-Ala-D-Ala) terminus of the growing peptidoglycan on the outer surface of the cytoplasmatic membrane. Glycopeptide-resistant organisms avoid such a fate by replacing the D-Ala-D-Ala terminus with D-alanyl-D-lactate (D-Ala-D-Lac) or D-alanyl-D-serine (D-Ala-D-Ser), thus markedly reducing antibiotic affinity for the cellular target. Resistance has manifested itself in enterococci and staphylococci largely through the expression of genes (named van) encoding proteins that reprogram cell wall biosynthesis and, thus, evade the action of the antibiotic. These resistance mechanisms were most likely co-opted from the glycopeptide producing actinomycetes, which use them to avoid suicide during antibiotic production, rather than being orchestrated by pathogen bacteria upon continued treatment. van-like gene clusters, similar to those described in enterococci, were in fact identified in many glycopeptide-producing actinomycetes, such as Actinoplanes teichomyceticus, which produces teicoplanin, and Streptomyces toyocaensis, which produces the A47934 glycopeptide. In this paper, we describe the natural and semi-synthetic glycopeptide antibiotics currently used as last resort drugs for Gram-positive infections and compare the van gene-based strategies of glycopeptide resistance among the pathogens and the producing actinomycetes. Particular attention is given to the strategy of immunity recently described in Nonomuraea sp. ATCC 39727. Nonomuraea sp. ATCC 39727 is the producer of A40926, which is the natural precursor of the second generation semi-synthetic glycopeptide dalbavancin, very recently approved for acute bacterial skin and skin structure infections. A thorough understanding of glycopeptide immunity in this producing microorganism may be particularly relevant to predict and eventually control the evolution of resistance that might arise following introduction of dalbavancin and other second generation glycopeptides into clinics.
| S-EPMC4790382 | biostudies-literature
Project description:ObjectiveThere is increasing pre-clinical and clinical evidence that metformin, a commonly used diabetes medication, has a protective effect in cancer. The aim of this review is to discuss metformin's anti-cancer molecular mechanisms of action and to summarize the current literature demonstrating metformin's potential in gynecologic cancer prevention and treatment.MethodsA PubMed search was conducted combining the keywords "metformin" with "neoplasm", "uterine neoplasms", "ovarian neoplasms", and "uterine cervical neoplasms". Studies published in English between 1994 and 2014 were included.ResultsPre-clinical studies in endometrial, ovarian, and cervical cancer suggest that metformin inhibits the growth of cancer cells. The primary molecular mechanism mediating this effect appears to be the activation of AMP-activated protein kinase (AMPK) and the subsequent inhibition of mammalian targets of rapamycin (mTOR). The pre-clinical findings are augmented by clinical studies indicating that metformin use is associated with a reduced risk of cancer and improved survival in diabetic women with ovarian and endometrial cancers. No clinical analyses have evaluated metformin use and cervical cancer. Overall, the data showing a favorable effect of metformin is strongest for endometrial and ovarian cancer and prospective clinical testing is ongoing in these two malignancies.ConclusionsNumerous clinical studies have reported an association between metformin use by diabetic patients and improved outcomes in gynecologic cancers. In addition, pre-clinical reports have identified plausible biological mechanisms to explain the molecular mechanism of action of metformin in cancer. However, the most important question remains unanswered: Will metformin be effective against cancer in patients without diabetes? Until this question is answered with prospective clinical testing, the role of metformin in the treatment or prevention of gynecologic malignancies remains theoretical and the clinical use of metformin as a cancer therapeutic is experimental.
| S-EPMC4268303 | biostudies-literature
Project description:Drug discovery, development and registration is an expensive and time-consuming process associated with a high failure rate [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013), Woodcock and Woosley (Annu Rev Med 59:1-12, 2008)]. Drug 'repurposing' is the identification of new therapeutic purposes for already approved drugs and is more affordable and achievable than novel drug discovery [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013)]. Auranofin is a drug that is approved for the treatment of rheumatoid arthritis but is being investigated for potential therapeutic application in a number of other diseases including cancer, neurodegenerative disorders, HIV/AIDS, parasitic infections and bacterial infections [Tejman-Yarden et al. (Antimicrob Agents Chemother 57:2029-2035, 2013)]. The main mechanism of action of auranofin is through the inhibition of reduction/oxidation (redox) enzymes that are essential for maintaining intracellular levels of reactive oxygen species. Inhibition of these enzymes leads to cellular oxidative stress and intrinsic apoptosis [Pessetto et al. (Mol Cancer Ther 12:1299-1309, 2013), Fan et al. (Cell Death Dis 5:e1191, 2014), Fiskus et al. (Cancer Res 74:2520-2532, 2014), Marzano et al. (Free Radic Biol Med 42:872-881, 2007)]. Drugs such as auranofin that have already been approved for human use [Tejman-Yarden et al. (Antimicrob Agents Chemother 57:2029-2035, 2013)] can be brought into clinical use for other diseases relatively quickly and for a fraction of the cost of new drugs.
| S-EPMC4359176 | biostudies-literature
Project description:ObjectiveObservational medical databases, such as electronic health records and insurance claims, track the healthcare trajectory of millions of individuals. These databases provide real-world longitudinal information on large cohorts of patients and their medication prescription history. We present an easy-to-customize framework that systematically analyzes such databases to identify new indications for on-market prescription drugs.Materials and methodsOur framework provides an interface for defining study design parameters and extracting patient cohorts, disease-related outcomes, and potential confounders in observational databases. It then applies causal inference methodology to emulate hundreds of randomized controlled trials (RCTs) for prescribed drugs, while adjusting for confounding and selection biases. After correcting for multiple testing, it outputs the estimated effects and their statistical significance in each database.ResultsWe demonstrate the utility of the framework in a case study of Parkinson's disease (PD) and evaluate the effect of 259 drugs on various PD progression measures in two observational medical databases, covering more than 150 million patients. The results of these emulated trials reveal remarkable agreement between the two databases for the most promising candidates.DiscussionEstimating drug effects from observational data is challenging due to data biases and noise. To tackle this challenge, we integrate causal inference methodology with domain knowledge and compare the estimated effects in two separate databases.ConclusionOur framework enables systematic search for drug repurposing candidates by emulating RCTs using observational data. The high level of agreement between separate databases strongly supports the identified effects.
| S-EPMC7886555 | biostudies-literature
Project description:Artemisinin is an anti-inflammatory phytomedicine with broad-spectrum antiviral activity. Artemisinin and its antimalarial properties were discovered by the Chinese scientist Tu Youyu, who became one of the laureates of the 2015 Nobel Prize in Physiology or Medicine for this breakthrough in tropical medicine. It is a commonly used anti-malaria drug. Artemisinin has recently been repurposed as a potential COVID-19 drug. Its documented anti-SARS-CoV-2 activity has been attributed to its ability to inhibit spike-protein mediated and TGF-β-dependent early steps in the infection process as well as its ability to disrupt the post-entry intracellular events of the SARS-CoV-2 infection cycle required for viral replication. In addition, Artemisinin has anti-inflammatory activity and reduces the systemic levels of inflammatory cytokines that contribute to cytokine storm and inflammatory organ injury in high-risk COVID-19 patients. We postulate that Artemisinin may prevent the worsening of the health condition of patients with mild-moderate COVID-19 when administered early in the course of their disease.
| S-EPMC8017220 | biostudies-literature
Project description: Not available
| S-EPMC4371131 | biostudies-literature
Project description:The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
| S-EPMC8126852 | biostudies-literature
Project description:The current pandemic has highlighted the need for methodologies that can quickly and reliably prioritize clinically approved compounds for their potential effectiveness for SARS-CoV-2 infections. In the past decade, network medicine has developed and validated multiple predictive algorithms for drug repurposing, exploiting the sub-cellular network-based relationship between a drug's targets and disease genes. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs that had been experimentally screened in VeroE6 cells, and the list of drugs under clinical trial, that capture the medical community's assessment of drugs with potential COVID-19 efficacy. We find that while most algorithms offer predictive power for these ground truth data, no single method offers consistently reliable outcomes across all datasets and metrics. This prompted us to develop a multimodal approach that fuses the predictions of all algorithms, showing that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We find that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these drugs rely on network-based actions that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development.
| S-EPMC7280907 | biostudies-literature