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1.
Drug Discov Today ; 29(4): 103942, 2024 Apr.
Article in English | MEDLINE | ID: mdl-38447929

ABSTRACT

Despite successes with new drug approvals over the past two decades through conventional drug development approaches, many human diseases remain intractable to current therapeutic interventions. Possible barriers may be that the complexity of the target, and disease biology, are impervious to such conventional drug development approaches. The US National Institutes of Health hosted a workshop with the goal of identifying challenges and opportunities with alternative modalities for developing treatments across diseases associated with historically undruggable targets. This report highlights key issues discussed during the workshop that, if addressed, could expand the pool of therapeutic approaches for treating various diseases.


Subject(s)
Drug Development , National Institutes of Health (U.S.) , United States , Education
2.
Drug Discov Today ; 29(3): 103887, 2024 Mar.
Article in English | MEDLINE | ID: mdl-38278477

Subject(s)
Genome , Genomics
3.
Drug Discov Today ; 29(3): 103805, 2024 Mar.
Article in English | MEDLINE | ID: mdl-37890715

ABSTRACT

There are ∼4500 genes within the 'druggable genome', the subset of the human genome that expresses proteins able to bind drug-like molecules, yet existing drugs only target a few hundred. A substantial subset of druggable proteins are largely uncharacterized or understudied, with many falling within G protein-coupled receptor (GPCR), ion channel, and kinase protein families. To improve scientific understanding of these three understudied protein families, the US National Institutes of Health launched the Illuminating the Druggable Genome Program. Now, as the program draws to a close, this review will lay out resources developed by the program that are intended to equip the scientific community with the tools necessary to explore previously understudied biology with the potential to rapidly impact human health.


Subject(s)
Genome, Human , Receptors, G-Protein-Coupled , Humans , Receptors, G-Protein-Coupled/genetics , Receptors, G-Protein-Coupled/metabolism
4.
J Clin Transl Sci ; 7(1): e33, 2023.
Article in English | MEDLINE | ID: mdl-36845315

ABSTRACT

The National Center for Advancing Translational Science (NCATS) seeks to improve upon the translational process to advance research and treatment across all diseases and conditions and bring these interventions to all who need them. Addressing the racial/ethnic health disparities and health inequities that persist in screening, diagnosis, treatment, and health outcomes (e.g., morbidity, mortality) is central to NCATS' mission to deliver more interventions to all people more quickly. Working toward this goal will require enhancing diversity, equity, inclusion, and accessibility (DEIA) in the translational workforce and in research conducted across the translational continuum, to support health equity. This paper discusses how aspects of DEIA are integral to the mission of translational science (TS). It describes recent NIH and NCATS efforts to advance DEIA in the TS workforce and in the research we support. Additionally, NCATS is developing approaches to apply a lens of DEIA in its activities and research - with relevance to the activities of the TS community - and will elucidate these approaches through related examples of NCATS-led, partnered, and supported activities, working toward the Center's goal of bringing more treatments to all people more quickly.

5.
Nucleic Acids Res ; 51(D1): D1405-D1416, 2023 01 06.
Article in English | MEDLINE | ID: mdl-36624666

ABSTRACT

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.


Subject(s)
Databases, Factual , Molecular Targeted Therapy , Proteome , Humans , Biological Products , Drug Discovery , Internet , Proteome/drug effects
6.
NPJ Digit Med ; 3: 47, 2020.
Article in English | MEDLINE | ID: mdl-32258429

ABSTRACT

Machine Intelligence (MI) is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics/devices, and precision medicine. Such tools can uncover new possibilities for researchers, physicians, and patients, allowing them to make more informed decisions and achieve better outcomes. When deployed in healthcare settings, these approaches have the potential to enhance efficiency and effectiveness of the health research and care ecosystem, and ultimately improve quality of patient care. In response to the increased use of MI in healthcare, and issues associated when applying such approaches to clinical care settings, the National Institutes of Health (NIH) and National Center for Advancing Translational Sciences (NCATS) co-hosted a Machine Intelligence in Healthcare workshop with the National Cancer Institute (NCI) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) on 12 July 2019. Speakers and attendees included researchers, clinicians and patients/ patient advocates, with representation from industry, academia, and federal agencies. A number of issues were addressed, including: data quality and quantity; access and use of electronic health records (EHRs); transparency and explainability of the system in contrast to the entire clinical workflow; and the impact of bias on system outputs, among other topics. This whitepaper reports on key issues associated with MI specific to applications in the healthcare field, identifies areas of improvement for MI systems in the context of healthcare, and proposes avenues and solutions for these issues, with the aim of surfacing key areas that, if appropriately addressed, could accelerate progress in the field effectively, transparently, and ethically.

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