Explore the vast range of titles available.

As the coronavirus disease 19 (COVID-19) global pandemic rages across the globe, the race to prevent and treat this deadly disease has led to the “off-label” repurposing of drugs such as hydroxychloroquine and lopinavir/ritonavir, which have the potential for unwanted QT-interval prolongation and a risk of drug-induced sudden cardiac death. With the possibility that a considerable proportion of the world’s population soon could receive COVID-19 pharmacotherapies with torsadogenic potential for therapy or postexposure prophylaxis, this document serves to help health care professionals mitigate the risk of drug-induced ventricular arrhythmias while minimizing risk of COVID-19 exposure to personnel and conserving the limited supply of personal protective equipment.

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.

Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases, which are broadly expected to lead to the identification of new drug targets and opportunities for treatment. Drug development, however, remains hampered by the time taken and costs expended to achieve regulatory approval, leading many clinicians and researchers to consider alternative paths to more immediate clinical outcomes. In this Review, we explore approaches that leverage common variant genetics to identify opportunities for repurposing existing drugs, also known as drug repositioning. These approaches include the identification of compounds by linking individual loci to genes and pathways that can be pharmacologically modulated, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization, and polygenic scoring.Genome-wide association studies (GWAS) have revealed important biological insights into complex diseases. The authors review approaches that leverage GWAS to identify opportunities for repurposing existing drugs, including single-loci mapping to drug targets, transcriptome-wide association studies, gene-set association, causal inference by Mendelian randomization and polygenic scoring.

Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating public data, BANDIT benchmarked a ~90% accuracy on 2000+ small molecules. Applied to 14,000+ compounds without known targets, BANDIT generated ~4,000 previously unknown molecule-target predictions. From this set we validate 14 novel microtubule inhibitors, including 3 with activity on resistant cancer cells. We applied BANDIT to ONC201—an anti-cancer compound in clinical development whose target had remained elusive. We identified and validated DRD2 as ONC201’s target, and this information is now being used for precise clinical trial design. Finally, BANDIT identifies connections between different drug classes, elucidating previously unexplained clinical observations and suggesting new drug repositioning opportunities. Overall, BANDIT represents an efficient and accurate platform to accelerate drug discovery and direct clinical application. Drug target identification is a crucial step in drug development. Here, the authors introduce a Bayesian machine learning framework that integrates multiple data types to predict the targets of small molecules, enabling identification of a new set of microtubule inhibitors and the target of the anti-cancer molecule ONC201.

The ability to computationally predict whether a compound treats a disease would improve the economy and success rate of drug approval. This study describes Project Rephetio to systematically model drug efficacy based on 755 existing treatments. First, we constructed Hetionet (neo4j.het.io), an integrative network encoding knowledge from millions of biomedical studies. Hetionet v1.0 consists of 47,031 nodes of 11 types and 2,250,197 relationships of 24 types. Data were integrated from 29 public resources to connect compounds, diseases, genes, anatomies, pathways, biological processes, molecular functions, cellular components, pharmacologic classes, side effects, and symptoms. Next, we identified network patterns that distinguish treatments from non-treatments. Then, we predicted the probability of treatment for 209,168 compound–disease pairs (het.io/repurpose). Our predictions validated on two external sets of treatment and provided pharmacological insights on epilepsy, suggesting they will help prioritize drug repurposing candidates. This study was entirely open and received realtime feedback from 40 community members. Of all the data in the world today, 90% was created in the last two years. However, taking advantage of this data in order to advance our knowledge is restricted by how quickly we can access it and analyze it in a proper context. In biomedical research, data is largely fragmented and stored in databases that typically do not “talk” to each other, thus hampering progress. One particular problem in medicine today is that the process of making a new therapeutic drug from scratch is incredibly expensive and inefficient, making it a risky business. Given the low success rate in drug discovery, there is an economic incentive in trying to repurpose an existing drug that has already been shown to be safe and effective towards a new disease or condition. Himmelstein et al. used a computational approach to analyze 50,000 data points – including drugs, diseases, genes and symptoms – from 19 different public databases. This approach made it possible to create more than two million relationships among the data points, which could be used to develop models that predict which drugs currently in use by doctors might be best suited to treat any of 136 common diseases. For example, Himmelstein et al. identified specific drugs currently used to treat depression and alcoholism that could be repurposed to treat smoking addition and epilepsy. These findings provide a new and powerful way to study drug repurposing. While this work was exclusively performed with public data, an expanded and potentially stronger set of predictions could be obtained if data owned by pharmaceutical companies were incorporated. Additional studies will be needed to test the predictions made by the models.

Key Points Concern over antibiotic resistance is growing. Resistance of up to 50% has been reported in some regions, including resistance to carbapenems, our current last line of defence. New classes of antibiotics are needed, particularly against Gram-negative bacteria. However, even if the scientific hurdles can be overcome, it could take decades before sufficient numbers of such antibiotics become available. As an interim solution, antibiotic resistance could be 'broken' by co-administering appropriate non-antibiotic drugs with failing antibiotics. Several marketed drugs that do not currently have antibacterial indications can directly kill bacteria, reduce the antibiotic minimum inhibitory concentration when used in combination with existing antibiotics, modulate host defence through effects on host innate immunity, particularly inflammation and autophagy, or a combination of these three. This article discusses how such 'antibiotic resistance breakers' (ARBs) could contribute to reducing the antibiotic resistance problem, and analyses a priority list of candidates for further investigation. Drug resistance is threatening to sideline the currently available antibiotics, and new antibiotics are unlikely to become available before the current arsenal becomes ineffective. Brown proposes the use of approved drugs or neutraceuticals as antibiotic resistance breakers — compounds that could be administered alongside current antibiotics to prolong their useful lifespan — to bridge the gap. Concern over antibiotic resistance is growing, and new classes of antibiotics, particularly against Gram-negative bacteria, are needed. However, even if the scientific hurdles can be overcome, it could take decades for sufficient numbers of such antibiotics to become available. As an interim solution, antibiotic resistance could be 'broken' by co-administering appropriate non-antibiotic drugs with failing antibiotics. Several marketed drugs that do not currently have antibacterial indications can either directly kill bacteria, reduce the antibiotic minimum inhibitory concentration when used in combination with existing antibiotics and/or modulate host defence through effects on host innate immunity, in particular by altering inflammation and autophagy. This article discusses how such 'antibiotic resistance breakers' could contribute to reducing the antibiotic resistance problem, and analyses a priority list of candidates for further investigation.

The major impact produced by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) focused many researchers attention to find treatments that can suppress transmission or ameliorate the disease. Despite the very fast and large flow of scientific data on possible treatment solutions, none have yet demonstrated unequivocal clinical utility against coronavirus disease 2019 (COVID-19). This work represents an exhaustive and critical review of all available data on potential treatments for COVID-19, highlighting their mechanistic characteristics and the strategy development rationale. Drug repurposing, also known as drug repositioning, and target based methods are the most used strategies to advance therapeutic solutions into clinical practice. Current in silico, in vitro and in vivo evidence regarding proposed treatments are summarized providing strong support for future research efforts.

The chemical diversity and known safety profiles of drugs previously tested in humans make them a valuable set of compounds to explore potential therapeutic utility in indications outside those originally targeted, especially neglected tropical diseases. This practice of “drug repurposing” has become commonplace in academic and other nonprofit drug-discovery efforts, with the appeal that significantly less time and resources are required to advance a candidate into the clinic. Here, we report a comprehensive open-access, drug repositioning screening set of 12,000 compounds (termed ReFRAME; Repurposing, Focused Rescue, and Accelerated Medchem) that was assembled by combining three widely used commercial drug competitive intelligence databases (Clarivate Integrity, GVK Excelra GoStar, and Citeline Pharmaprojects), together with extensive patent mining of small molecules that have been dosed in humans. To date, 12,000 compounds (∼80% of compounds identified from data mining) have been purchased or synthesized and subsequently plated for screening. To exemplify its utility, this collection was screened against Cryptosporidium spp., a major cause of childhood diarrhea in the developing world, and two active compounds previously tested in humans for other therapeutic indications were identified. Both compounds, VB-201 and a structurally related analog of ASP-7962, were subsequently shown to be efficacious in animal models of Cryptosporidium infection at clinically relevant doses, based on available human doses. In addition, an open-access data portal (https://reframedb.org) has been developed to share ReFRAME screen hits to encourage additional follow-up and maximize the impact of the ReFRAME screening collection.

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug–target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug–target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug–target interactions and repurpose existing drugs. Network-based data integration for drug–target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.

As competitive HMG-CoA reductase (HMGCR) inhibitors, statins not only reduce cholesterol and improve cardiovascular risk, but also exhibit pleiotropic effects that are independent of their lipid-lowering effects. Among them, the anti-cancer properties of statins have attracted much attention and indicated the potential of statins as repurposed drugs for the treatment of cancer. A large number of clinical and epidemiological studies have described the anticancer properties of statins, but the evidence for anticancer effectiveness of statins is inconsistent. It may be that certain molecular subtypes of cancer are more vulnerable to statin therapy than others. Whether statins have clinical anticancer effects is still an active area of research. Statins appear to enhance the efficacy and address the shortcomings associated with conventional cancer treatments, suggesting that statins should be considered in the context of combined therapies for cancer. Here, we present a comprehensive review of the potential of statins in anti-cancer treatments. We discuss the current understanding of the mechanisms underlying the anti-cancer properties of statins and their effects on different malignancies. We also provide recommendations for the design of future well-designed clinical trials of the anti-cancer efficacy of statins.