Project description:Drug discovery must be guided not only by medical need and commercial potential, but also by the areas in which new science is creating therapeutic opportunities, such as target identification and the understanding of disease mechanisms. To systematically identify such areas of high scientific activity, we use bibliometrics and related data-mining methods to analyse over half a terabyte of data, including PubMed abstracts, literature citation data and patent filings. These analyses reveal trends in scientific activity related to disease studied at varying levels, down to individual genes and pathways, and provide methods to monitor areas in which scientific advances are likely to create new therapeutic opportunities.
Project description:BackgroundNeglected tropical diseases (NTDs) are closely related to poverty and affect over a billion people in developing countries. The unmet treatment needs cause high mortality and disability thereby imposing a huge burden with severe social and economic consequences. Although coordinated by the World Health Organization, various philanthropic organizations, national governments and the pharmaceutical industry have been making efforts in improving the situation, the control of NTDs is still inadequate and extremely difficult today. The lack of safe, effective and affordable medicines is a key contributing factor. This paper reviews the recent advances and some of the challenges that we are facing in the fight against NTDs.Main bodyIn recent years, a number of innovations have demonstrated propensity to promote drug discovery and development for NTDs. Implementation of multilateral collaborations leads to continued efforts and plays a crucial role in drug discovery. Proactive approaches and advanced technologies are urgently needed in drug innovation for NTDs. However, the control and elimination of NTDs remain a formidable task as it requires persistent international cooperation to make sustainable progresses for a long period of time. Some currently employed strategies were proposed and verified to be successful, which involve both mechanisms of 'Push' which aims at cutting the cost of research and development for industry and 'Pull' which aims at increasing market attractiveness. Coupled to this effort should be the exercise of shared responsibility globally to reduce risks, overcome obstacles and maximize benefits. Since NTDs are closely associated with poverty, it is absolutely essential that the stakeholders take concerted and long-term measures to meet multifaceted challenges by alleviating extreme poverty, strengthening social intervention, adapting climate changes, providing effective monitoring and ensuring timely delivery.ConclusionsThe ongoing endeavor at the global scale will ultimately benefit the patients, the countries they are living and, hopefully, the manufacturers who provide new preventive, diagnostic and therapeutic products.
Project description:Analyses of publicly available structural data reveal interesting insights into the impact of the three-dimensional (3D) structures of protein targets important for discovery of new drugs (e.g., G-protein-coupled receptors, voltage-gated ion channels, ligand-gated ion channels, transporters, and E3 ubiquitin ligases). The Protein Data Bank (PDB) archive currently holds > 155,000 atomic-level 3D structures of biomolecules experimentally determined using crystallography, nuclear magnetic resonance spectroscopy, and electron microscopy. The PDB was established in 1971 as the first open-access, digital-data resource in biology, and is now managed by the Worldwide PDB partnership (wwPDB; wwPDB.org). US PDB operations are the responsibility of the Research Collaboratory for Structural Bioinformatics PDB (RCSB PDB). The RCSB PDB serves millions of RCSB.org users worldwide by delivering PDB data integrated with ∼40 external biodata resources, providing rich structural views of fundamental biology, biomedicine, and energy sciences. Recently published work showed that the PDB archival holdings facilitated discovery of ∼90% of the 210 new drugs approved by the US Food and Drug Administration 2010-2016. We review user-driven development of RCSB PDB services, examine growth of the PDB archive in terms of size and complexity, and present examples and opportunities for structure-guided drug discovery for challenging targets (e.g., integral membrane proteins).
Project description:The past decade has seen significant growth in the use of 'crowdsourcing' and open innovation approaches to engage 'citizen scientists' to perform novel scientific research. Here, we quantify and summarize the current state of adoption of open innovation by major pharmaceutical companies. We also highlight recent crowdsourcing and open innovation research contributions to the field of drug discovery, and interesting future directions.
Project description:The Hedgehog (Hh) signaling pathway governs complex developmental processes, including proliferation and patterning within diverse tissues. These activities rely on a tightly regulated transduction system that converts graded Hh input signals into specific levels of pathway activity. Uncontrolled activation of Hh signaling drives tumor initiation and maintenance. However, recent entry of pathway-specific inhibitors into the clinic reveals mixed patient responses and thus prompts further exploration of pathway activation and inhibition. In this review, we share emerging insights into regulated and oncogenic Hh signaling, supplemented with updates on the development and use of Hh pathway-targeted therapies.
Project description:Political momentum and funding for combatting antimicrobial resistance (AMR) continues to build. Numerous major international and national initiatives aimed at financially incentivising the research and development (R&D) of antibiotics have been implemented. However, it remains unclear how to effectively strengthen the current set of incentive programmes to further accelerate antibiotic innovation. Based on a literature review and expert input, this study first identifies and assesses the major international, European Union, US and UK antibiotic R&D funding programmes. These programmes are then evaluated across market and public health criteria necessary for comprehensively improving the antibiotic market. The current set of incentive programmes are an important initial step to improving the economic feasibility of antibiotic development. However, there appears to be a lack of global coordination across all initiatives, which risks duplicating efforts, leaving funding gaps in the value chain and overlooking important AMR goals. This study finds that incentive programmes are overly committed to early-stage push funding of basic science and preclinical research, while there is limited late-stage push funding of clinical development. Moreover, there are almost no pull incentives to facilitate transition of antibiotic products from early clinical phases to commercialisation, focus developer concentration on the highest priority antibiotics and attract large pharmaceutical companies to invest in the market. Finally, it seems that antibiotic sustainability and patient access requirements are poorly integrated into the array of incentive mechanisms.
Project description:The generation of large-scale biomedical data is creating unprecedented opportunities for basic and translational science. Typically, the data producers perform initial analyses, but it is very likely that the most informative methods may reside with other groups. Crowdsourcing the analysis of complex and massive data has emerged as a framework to find robust methodologies. When the crowdsourcing is done in the form of collaborative scientific competitions, known as Challenges, the validation of the methods is inherently addressed. Challenges also encourage open innovation, create collaborative communities to solve diverse and important biomedical problems, and foster the creation and dissemination of well-curated data repositories.
Project description:BACKGROUND:The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE:The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. METHODS:We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. RESULTS:The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. CONCLUSIONS:To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.
Project description:Link prediction in artificial intelligence is used to identify missing links or derive future relationships that can occur in complex networks. A link prediction model was developed using the complex heterogeneous biomedical knowledge graph, SemNet, to predict missing links in biomedical literature for drug discovery. A web application visualized knowledge graph embeddings and link prediction results using TransE, CompleX, and RotatE based methods. The link prediction model achieved up to 0.44 hits@10 on the entity prediction tasks. The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), also known as COVID-19, served as a case study to demonstrate the efficacy of link prediction modeling for drug discovery. The link prediction algorithm guided identification and ranking of repurposed drug candidates for SARS-CoV-2 primarily by text mining biomedical literature from previous coronaviruses, including SARS and middle east respiratory syndrome (MERS). Repurposed drugs included potential primary SARS-CoV-2 treatment, adjunctive therapies, or therapeutics to treat side effects. The link prediction accuracy for nodes ranked highly for SARS coronavirus was 0.875 as calculated by human in the loop validation on existing COVID-19 specific data sets. Drug classes predicted as highly ranked include anti-inflammatory, nucleoside analogs, protease inhibitors, antimalarials, envelope proteins, and glycoproteins. Examples of highly ranked predicted links to SARS-CoV-2: human leukocyte interferon, recombinant interferon-gamma, cyclosporine, antiviral therapy, zidovudine, chloroquine, vaccination, methotrexate, artemisinin, alkaloids, glycyrrhizic acid, quinine, flavonoids, amprenavir, suramin, complement system proteins, fluoroquinolones, bone marrow transplantation, albuterol, ciprofloxacin, quinolone antibacterial agents, and hydroxymethylglutaryl-CoA reductase inhibitors. Approximately 40% of identified drugs were not previously connected to SARS, such as edetic acid or biotin. In summary, link prediction can effectively suggest repurposed drugs for emergent diseases.
Project description:Drug discovery in the ovarian cancer arena continues to launch important new clinical trials. Many biologic agents are being studied in phase II and phase III clinical trials for recurrent disease. These agents include compounds that disrupt angiogenesis through a variety of mechanisms. Other oncogenic pathways are also specifically targeted such as PARP, MEK, and topoisomerase inhibitors which are currently being studied in phase III trials. Various cytotoxic agents, as well as therapeutic vaccines, are also under investigation, and continue to demonstrate promising new data. The relevant agents in the treatment of ovarian cancer which have demonstrated positive phase II activity will be discussed.