Explore the vast range of titles available.

Effective treatments for COVID-19 are urgently needed. However, discovering single-agent therapies with activity against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been challenging. Combination therapies play an important role in antiviral therapies, due to their improved efficacy and reduced toxicity. Recent approaches have applied deep learning to identify synergistic drug combinations for diseases with vast preexisting datasets, but these are not applicable to new diseases with limited combination data, such as COVID-19. Given that drug synergy often occurs through inhibition of discrete biological targets, here we propose a neural network architecture that jointly learns drug—target interaction and drug−drug synergy. The model consists of two parts: a drug—target interaction module and a target—disease association module. This design enables the model to utilize drug—target interaction data and single-agent antiviral activity data, in addition to available drug—drug combination datasets, which may be small in nature. By incorporating additional biological information, our model performs significantly better in synergy prediction accuracy than previous methods with limited drug combination training data. We empirically validated our model predictions and discovered two drug combinations, remdesivir and reserpine as well as remdesivir and IQ-1S, which display strong antiviral SARS-CoV-2 synergy in vitro. Our approach, which was applied here to address the urgent threat of COVID-19, can be readily extended to other diseases for which a dearth of chemical—chemical combination data exists.

Key Points Malaria has devastating consequences: it strikes over 250 million people worldwide and kills approximately 1 million people each year, many of whom are children under 5 years of age. Malaria can be prevented by interventions focused on breaking the cycle of transmission, either by eliminating the mosquito (through the use of insecticides) or preventing bites (through the use of insecticide-treated bed nets). It can also be treated through the use of antimalarial drugs. Drug resistance, however, remains the biggest threat to current drug efficacy. The former mainstays of antimalarial chemotherapy, chloroquine and sulfadoxine–pyrimethamine, have been rendered ineffective for the treatment of Plasmodium falciparum malaria by the emergence and spread of drug-resistant parasites. Almost all malaria-endemic regions have switched to artemisinin (ART)-based combination therapies (ACTs) for the first-line treatment of P. falciparum malaria. ACTs combine an ART semisynthetic derivative, which has a short half-life, with a longer-lasting partner drug. This results in sustained antimalarial pressure after the plasma concentrations of the ART derivatives have fallen below therapeutic levels ACTs are discussed in terms of their modes of action and pharmacokinetic properties and the proposed mechanisms of resistance to them. We summarize several therapeutic strategies that might decrease the emergence of drug resistance and present a perspective on the current ACT-based efforts to reduce the burden of malaria. The emergence of drug-resistant Plasmodium parasites has made the treatment of malaria difficult in some areas. One of the last drugs to which there is no full resistance is artemisinin. Fidock and Eastman describe artemisinin-based combination therapies that aim to decrease the occurrence of drug resistance and that have raised the possibility of malaria eradication. Plasmodium falciparum resistance to chloroquine and sulphadoxine–pyrimethamine has led to the recent adoption of artemisinin-based combination therapies (ACTs) as the first line of treatment against malaria. ACTs comprise semisynthetic artemisinin derivatives paired with distinct chemical classes of longer acting drugs. These artemisinins are exceptionally potent against the pathogenic asexual blood stages of Plasmodium parasites and also act on the transmissible sexual stages. These combinations increase the rates of clinical and parasitological cures and decrease the selection pressure for the emergence of antimalarial resistance. This Review article discusses our current knowledge about the mode of action of ACTs, their pharmacological properties and the proposed mechanisms of drug resistance.

Clinical studies and mathematical models predict that, to achieve malaria elimination, combination therapies will need to incorporate drugs that block the transmission of Plasmodium falciparum sexual stage parasites to mosquito vectors. Efforts to measure the activity of existing antimalarials on intraerythrocytic sexual stage gametocytes and identify transmission-blocking agents have, until now, been hindered by a lack of quantitative assays. Here, we report an experimental system using P. falciparum lines that stably express gametocyte-specific GFP-luciferase reporters, which enable the assessment of dose- and time-dependent drug action on gametocyte maturation and transmission. These studies reveal activity of the first-line antimalarial dihydroartemisinin and the partner drugs lumefantrine and pyronaridine against early gametocyte stages, along with moderate inhibition of mature gametocyte transmission to Anopheles mosquitoes. The other partner agents monodesethyl-amodiaquine and piperaquine showed activity only against immature gametocytes. Our data also identify methylene blue as a potent inhibitor of gametocyte development across all stages. This thiazine dye almost fully abolishes P. falciparum transmission to mosquitoes at concentrations readily achievable in humans, highlighting the potential of this chemical class to reduce the spread of malaria.

Drug repurposing is a rapid approach to identify therapeutics for the treatment of emerging infectious diseases such as COVID-19. To address the urgent need for treatment options, we carried out a quantitative high-throughput screen using a SARS-CoV-2 cytopathic assay with a compound collection of 8,810 approved and investigational drugs, mechanism-based bioactive compounds, and natural products. Three hundred and nineteen compounds with anti-SARS-CoV-2 activities were identified and confirmed, including 91 approved drugs and 49 investigational drugs. The anti-SARS-CoV-2 activities of 230 of these confirmed compounds, of which 38 are approved drugs, have not been previously reported. Chlorprothixene, methotrimeprazine, and piperacetazine were the three most potent FDA-approved drugs with anti-SARS-CoV-2 activities. These three compounds have not been previously reported to have anti-SARS-CoV-2 activities, although their antiviral activities against SARS-CoV and Ebola virus have been reported. These results demonstrate that this comprehensive data set is a useful resource for drug repurposing efforts, including design of new drug combinations for clinical trials for SARS-CoV-2.

The global health emergency posed by the outbreak of Zika virus (ZIKV), an arthropod-borne flavivirus causing severe neonatal neurological conditions, has subsided, but there continues to be transmission of ZIKV in endemic regions. As such, there is still a medical need for discovering and developing therapeutical interventions against ZIKV. To identify small-molecule compounds that inhibit ZIKV disease and transmission, we screened multiple small-molecule collections, mostly derived from natural products, for their ability to inhibit wild-type ZIKV. As a primary high-throughput screen, we used a viral cytopathic effect (CPE) inhibition assay conducted in Vero cells that was optimized and miniaturized to a 1536-well format. Suitably active compounds identified from the primary screen were tested in a panel of orthogonal assays using recombinant Zika viruses, including a ZIKV Renilla luciferase reporter assay and a ZIKV mCherry reporter system. Compounds that were active in the wild-type ZIKV inhibition and ZIKV reporter assays were further evaluated for their inhibitory effects against other flaviviruses. Lastly, we demonstrated that wild-type ZIKV is able to infect a 3D-bioprinted outer-blood-retina barrier tissue model and disrupt its barrier function, as measured by electrical resistance. One of the identified compounds (3-Acetyl-13-deoxyphomenone, NCGC00380955) was able to prevent the pathological effects of the viral infection on this clinically relevant ZIKV infection model.

Background Nearly half of the world’s population (3.2 billion people) were at risk of malaria in 2015, and resistance to current therapies is a major concern. While the standard of care includes drug combinations, there is a pressing need to identify new combinations that can bypass current resistance mechanisms. In the work presented here, a combined transcriptional drug repositioning/discovery and machine learning approach is proposed. Methods The integrated approach utilizes gene expression data from patient-derived samples, in combination with large-scale anti-malarial combination screening data, to predict synergistic compound combinations for three Plasmodium falciparum strains (3D7, DD2 and HB3). Both single compounds and combinations predicted to be active were prospectively tested in experiment. Results One of the predicted single agents, apicidin, was active with the AC50 values of 74.9, 84.1 and 74.9 nM in 3D7, DD2 and HB3 P. falciparum strains while its maximal safe plasma concentration in human is 547.6 ± 136.6 nM. Apicidin at the safe dose of 500 nM kills on average 97% of the parasite. The synergy prediction algorithm exhibited overall precision and recall of 83.5 and 65.1% for mild-to-strong, 48.8 and 75.5% for moderate-to-strong and 12.0 and 62.7% for strong synergies. Some of the prospectively predicted combinations, such as tacrolimus-hydroxyzine and raloxifene-thioridazine, exhibited significant synergy across the three P. falciparum strains included in the study. Conclusions Systematic approaches can play an important role in accelerating discovering novel combinational therapies for malaria as it enables selecting novel synergistic compound pairs in a more informed and cost-effective manner.

Drug resistance in Plasmodium parasites is a constant threat. Novel therapeutics, especially new drug combinations, must be identified at a faster rate. In response to the urgent need for new antimalarial drug combinations we screened a large collection of approved and investigational drugs, tested 13,910 drug pairs and identified many promising antimalarial drug combinations. The activity of known antimalarial drug regimens was confirmed and a myriad of new classes of positively interacting drug pairings were discovered. Network and clustering analyses reinforced established mechanistic relationships for known drug combinations and identified several novel mechanistic hypotheses. From eleven screens comprising >4,600 combinations per parasite strain (including duplicates) we further investigated interactions between approved antimalarials, calcium homeostasis modulators and inhibitors of phosphatidylinositide 3-kinases (PI3K) and the mammalian target of rapamycin (mTOR). These studies highlight important targets and pathways and provide promising leads for clinically actionable antimalarial therapy.

The parasite is the most lethal species of Plasmodium to cause serious malaria infection in humans, and with resistance developing rapidly novel treatment modalities are currently being sought, one of which being combinations of existing compounds. The discovery of combinations of antimalarial drugs that act synergistically with one another is hence of great importance; however an exhaustive experimental screen of large drug space in a pairwise manner is not an option. In this study we apply our machine learning approach, Combination Synergy Estimation (CoSynE), which can predict novel synergistic drug interactions using only prior experimental combination screening data and knowledge of compound molecular structures, to a dataset of 1,540 antimalarial drug combinations in which 22.2% were synergistic. Cross validation of our model showed that synergistic CoSynE predictions are enriched 2.74 × compared to random selection when both compounds in a predicted combination are known from other combinations among the training data, 2.36 × when only one compound is known from the training data, and 1.5 × for entirely novel combinations. We prospectively validated our model by making predictions for 185 combinations of 23 entirely novel compounds. CoSynE predicted 20 combinations to be synergistic, which was experimentally validated for nine of them (45%), corresponding to an enrichment of 1.70 × compared to random selection from this prospective data set. Such enrichment corresponds to a 41% reduction in experimental effort. Interestingly, we found that pairwise screening of the compounds CoSynE individually predicted to be synergistic would result in an enrichment of 1.36 × compared to random selection, indicating that synergy among compound combinations is not a random event. The nine novel and correctly predicted synergistic compound combinations mainly (where sufficient bioactivity information is available) consist of efflux or transporter inhibitors (such as hydroxyzine), combined with compounds exhibiting antimalarial activity alone (such as sorafenib, apicidin, or dihydroergotamine). However, not all compound synergies could be rationalized easily in this way. Overall, this study highlights the potential for predictive modeling to expedite the discovery of novel drug combinations in fight against antimalarial resistance, while the underlying approach is also generally applicable.

Malaria is a fatal disease that displays a spectrum of symptoms and severity, which are determined by complex host-parasite interactions. It has been difficult to study the effects of parasite strains on disease severity in human infections, but the mechanisms leading to specific disease phenotypes can be investigated using strains of rodent malaria parasites that cause different disease symptoms in inbred mice. Using a unique mouse malaria model, here we investigated the mechanisms of splenic cell death and their relationship to control of parasitemia and host mortality. C57BL/6 mice infected with Plasmodium yoelii nigeriensis N67C display high levels of pro-inflammatory cytokines and chemokines (IL-6, IFN-γ, TNF-α, CXCL1, and CCL2) and extensive splenic damage with dramatic reduction of splenic cell populations. These disease phenotypes were rescued in RAG2 −/− , IFN-γ −/− , or T cell depleted mice, suggesting IFN-γ and T cell mediated disease mechanisms. Additionally, apoptosis was one of the major pathways involved in splenic cell death, which coincides with the peaks of pro-inflammatory cytokines. Our results demonstrate the critical roles of T cells and IFN-γ in mediating splenic cell apoptosis, parasitemia control, and host lethality and thus may provide important insights for preventing/reducing morbidity associated with severe malaria in humans.

Infection of mice with strains of Plasmodium yoelii parasites can result in different pathology, but molecular mechanisms to explain this variation are unclear. Here we show that a P . yoelii gene encoding a HECT-like E3 ubiquitin ligase ( Pyheul ) influences parasitemia and host mortality. We genetically cross two lethal parasites with distinct disease phenotypes, and identify 43 genetically diverse progeny by typing with microsatellites and 9230 single-nucleotide polymorphisms. A genome-wide quantitative trait loci scan links parasite growth and host mortality to two major loci on chromosomes 1 and 7 with LOD (logarithm of the odds) scores = 6.1 and 8.1, respectively. Allelic exchange of partial sequences of Pyheul in the chromosome 7 locus and modification of the gene expression alter parasite growth and host mortality. This study identifies a gene that may have a function in parasite growth, virulence, and host–parasite interaction, and therefore could be a target for drug or vaccine development. Many strains of Plasmodium differ in virulence, but factors that control these distinctions are not known. Here the authors comparatively map virulence loci using the offspring from a P . yoelii YM and N67 genetic cross, and identify a putative HECT E3 ubiquitin ligase that may explain the variance.