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  1. Article ; Online: Approaches for Prioritizing High-Quality Chemical Matter in Chemical Probe and Drug Discovery.

    Dahlin, Jayme L

    SLAS discovery : advancing life sciences R & D

    2021  Volume 26, Issue 7, Page(s) 833–834

    MeSH term(s) Drug Discovery/methods ; Humans ; Small Molecule Libraries/standards
    Chemical Substances Small Molecule Libraries
    Language English
    Publishing date 2021-04-15
    Publishing country United States
    Document type Editorial ; Introductory Journal Article
    ZDB-ID 2885123-7
    ISSN 2472-5560 ; 2472-5552
    ISSN (online) 2472-5560
    ISSN 2472-5552
    DOI 10.1177/24725552211027215
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4911: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
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  2. Article ; Online: Corrigendum to "Instability of 7-aminoclonazepam in frozen storage conditions" [Clin. Mass Spectrom. 9 (2018) 23-24].

    Dahlin, Jayme L / Petrides, Athena K

    Journal of mass spectrometry and advances in the clinical lab

    2022  Volume 26, Page(s) 34

    Abstract: This corrects the article DOI: 10.1016/j.clinms.2018.07.002.]. ...

    Abstract [This corrects the article DOI: 10.1016/j.clinms.2018.07.002.].
    Language English
    Publishing date 2022-09-24
    Publishing country Netherlands
    Document type Published Erratum
    ISSN 2667-145X
    ISSN (online) 2667-145X
    DOI 10.1016/j.jmsacl.2022.09.006
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Assay Guidance Manual

    Markossian, Sarine / Coussens, Nathan P / Dahlin, Jayme L / Sitta Sittampalam, G

    SLAS technology

    2021  Volume 26, Issue 6, Page(s) 553–554

    MeSH term(s) Biological Assay ; Drug Discovery ; Technology
    Language English
    Publishing date 2021-11-23
    Publishing country United States
    Document type Editorial ; Introductory Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2900310-6
    ISSN 2472-6311 ; 2472-6303
    ISSN (online) 2472-6311
    ISSN 2472-6303
    DOI 10.1177/24726303211056338
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Assay Guidance Manual

    Markossian, Sarine / Coussens, Nathan P / Dahlin, Jayme L / Sittampalam, G Sitta

    SLAS discovery : advancing life sciences R & D

    2021  Volume 26, Issue 10, Page(s) 1241–1242

    MeSH term(s) Biological Assay/methods ; Drug Discovery/methods ; Humans
    Language English
    Publishing date 2021-11-23
    Publishing country United States
    Document type Editorial ; Introductory Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 2885123-7
    ISSN 2472-5560 ; 2472-5552
    ISSN (online) 2472-5560
    ISSN 2472-5552
    DOI 10.1177/24725552211054044
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4911: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  5. Article ; Online: Addressing Compound Reactivity and Aggregation Assay Interferences: Case Studies of Biochemical High-Throughput Screening Campaigns Benefiting from the National Institutes of Health

    Coussens, Nathan P / Auld, Douglas S / Thielman, Jonathan R / Wagner, Bridget K / Dahlin, Jayme L

    SLAS discovery : advancing life sciences R & D

    2021  Volume 26, Issue 10, Page(s) 1280–1290

    Abstract: Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS) ...

    Abstract Compound-dependent assay interferences represent a continued burden in drug and chemical probe discovery. The open-source National Institutes of Health/National Center for Advancing Translational Sciences (NIH/NCATS)
    MeSH term(s) Biological Assay/methods ; Drug Discovery/methods ; High-Throughput Screening Assays/methods ; Humans ; National Institutes of Health (U.S.) ; Small Molecule Libraries/chemistry ; Translational Science, Biomedical/methods ; United States
    Chemical Substances Small Molecule Libraries
    Language English
    Publishing date 2021-07-03
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural
    ZDB-ID 2885123-7
    ISSN 2472-5560 ; 2472-5552
    ISSN (online) 2472-5560
    ISSN 2472-5552
    DOI 10.1177/24725552211026239
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4911: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: Risk Management in Early Discovery Medicinal Chemistry.

    Singh, Gurpreet / Dahlin, Jayme L / Walters, Michael A

    Methods in enzymology

    2018  Volume 610, Page(s) 1–25

    Abstract: Drug discovery is inherently very risky. The management of these risks can enable the effective use of limited human and monetary resources. A careful attention to risk management in early discovery is especially important given that what happens in the ... ...

    Abstract Drug discovery is inherently very risky. The management of these risks can enable the effective use of limited human and monetary resources. A careful attention to risk management in early discovery is especially important given that what happens in the early phases of a project may dictate the course of a research program for months or years. Risk management in early discovery starts with high-level managerial concerns: careful project selection, sufficient staffing and funding, and access to the appropriate instrumentation and tools. Herein we describe the operational elements of risk management that range from the very broad to the extremely specific. These elements have as their base an embedded culture of risk management that extends down to the experiment level, and project ownership in which all researchers anticipate risk, but are not paralyzed by it. In our model, on this base of culture stand the four pillars of early discovery risk management: right libraries, right assays, right series, and right structure-activity relationships. Appropriate attention to these considerations can decrease the risks inherent in early discovery medicinal chemistry, thereby potentially increasing the return on the investment of necessarily finite resources.
    MeSH term(s) Animals ; Chemistry, Pharmaceutical/methods ; Drug Discovery/methods ; Drug Evaluation, Preclinical/methods ; High-Throughput Screening Assays/methods ; Humans ; Research Design ; Risk Management ; Structure-Activity Relationship
    Language English
    Publishing date 2018-10-19
    Publishing country United States
    Document type Journal Article
    ISSN 1557-7988 ; 0076-6879
    ISSN (online) 1557-7988
    ISSN 0076-6879
    DOI 10.1016/bs.mie.2018.09.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: How to Triage PAINS-Full Research.

    Dahlin, Jayme L / Walters, Michael A

    Assay and drug development technologies

    2015  Volume 14, Issue 3, Page(s) 168–174

    Abstract: Nonspecific bioactivity and assay artifacts have gained increasing attention in recent years. This focus has arisen primarily from the publication of a set of chemical substructures, termed pan assay interference compounds (PAINS), which are associated ... ...

    Abstract Nonspecific bioactivity and assay artifacts have gained increasing attention in recent years. This focus has arisen primarily from the publication of a set of chemical substructures, termed pan assay interference compounds (PAINS), which are associated with promiscuous bioactivity and assay interference in real and virtual high-throughput screening (HTS) campaigns. Despite an increasing awareness in the HTS and medicinal chemistry communities about the liabilities of these compounds, articles featuring PAINS and PAINS-like compounds are still being published. In this perspective, we describe some of the factors we believe are driving this resource-sapping trend. We also provide what we hope are helpful insights that may lead to the earlier recognition of these generally nontranslatable compounds, thus preventing the propagation of PAINS-full costly research.
    MeSH term(s) Artifacts ; Chemistry, Pharmaceutical ; Drug Discovery/methods ; High-Throughput Screening Assays/methods ; Humans ; Molecular Structure ; Organic Chemicals/chemistry ; Organic Chemicals/pharmacology ; Organic Chemicals/therapeutic use
    Chemical Substances Organic Chemicals
    Language English
    Publishing date 2015-10-23
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 1557-8127
    ISSN (online) 1557-8127
    DOI 10.1089/adt.2015.674
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    2005 A 2339: Show issues

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  8. Article ; Online: The essential roles of chemistry in high-throughput screening triage.

    Dahlin, Jayme L / Walters, Michael A

    Future medicinal chemistry

    2014  Volume 6, Issue 11, Page(s) 1265–1290

    Abstract: It is increasingly clear that academic high-throughput screening (HTS) and virtual HTS triage suffers from a lack of scientists trained in the art and science of early drug discovery chemistry. Many recent publications report the discovery of compounds ... ...

    Abstract It is increasingly clear that academic high-throughput screening (HTS) and virtual HTS triage suffers from a lack of scientists trained in the art and science of early drug discovery chemistry. Many recent publications report the discovery of compounds by screening that are most likely artifacts or promiscuous bioactive compounds, and these results are not placed into the context of previous studies. For HTS to be most successful, it is our contention that there must exist an early partnership between biologists and medicinal chemists. Their combined skill sets are necessary to design robust assays and efficient workflows that will weed out assay artifacts, false positives, promiscuous bioactive compounds and intractable screening hits, efforts that ultimately give projects a better chance at identifying truly useful chemical matter. Expertise in medicinal chemistry, cheminformatics and purification sciences (analytical chemistry) can enhance the post-HTS triage process by quickly removing these problematic chemotypes from consideration, while simultaneously prioritizing the more promising chemical matter for follow-up testing. It is only when biologists and chemists collaborate effectively that HTS can manifest its full promise.
    MeSH term(s) Animals ; Chemistry, Pharmaceutical/trends ; Drug Discovery ; High-Throughput Screening Assays/methods ; Humans ; Informatics
    Keywords covid19
    Language English
    Publishing date 2014-08-27
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Review
    ISSN 1756-8927
    ISSN (online) 1756-8927
    DOI 10.4155/fmc.14.60
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: 79-Year-Old Woman With Jaundice and Anemia.

    Tsang, Mazie / Dahlin, Jayme L / Sundsted, Karna K

    Mayo Clinic proceedings

    2017  Volume 93, Issue 3, Page(s) 381–385

    MeSH term(s) Aged ; Anemia, Hemolytic, Autoimmune/diagnosis ; Anemia, Hemolytic, Autoimmune/therapy ; Blood Cell Count/methods ; Coombs Test/methods ; Diagnosis, Differential ; Female ; Glucocorticoids/therapeutic use ; Humans ; Immunologic Factors/therapeutic use ; Jaundice/etiology
    Chemical Substances Glucocorticoids ; Immunologic Factors
    Language English
    Publishing date 2017-12-16
    Publishing country England
    Document type Case Reports ; Journal Article
    ZDB-ID 124027-4
    ISSN 1942-5546 ; 0025-6196
    ISSN (online) 1942-5546
    ISSN 0025-6196
    DOI 10.1016/j.mayocp.2017.03.025
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Ua VI Zs.183: Show issues Location:
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  10. Article ; Online: Modeling Small-Molecule Reactivity Identifies Promiscuous Bioactive Compounds.

    Matlock, Matthew K / Hughes, Tyler B / Dahlin, Jayme L / Swamidass, S Joshua

    Journal of chemical information and modeling

    2018  Volume 58, Issue 8, Page(s) 1483–1500

    Abstract: Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that ... ...

    Abstract Scientists rely on high-throughput screening tools to identify promising small-molecule compounds for the development of biochemical probes and drugs. This study focuses on the identification of promiscuous bioactive compounds, which are compounds that appear active in many high-throughput screening experiments against diverse targets but are often false-positives which may not be easily developed into successful probes. These compounds can exhibit bioactivity due to nonspecific, intractable mechanisms of action and/or by interference with specific assay technology readouts. Such "frequent hitters" are now commonly identified using substructure filters, including pan assay interference compounds (PAINS). Herein, we show that mechanistic modeling of small-molecule reactivity using deep learning can improve upon PAINS filters when modeling promiscuous bioactivity in PubChem assays. Without training on high-throughput screening data, a deep learning model of small-molecule reactivity achieves a sensitivity and specificity of 18.5% and 95.5%, respectively, in identifying promiscuous bioactive compounds. This performance is similar to PAINS filters, which achieve a sensitivity of 20.3% at the same specificity. Importantly, such reactivity modeling is complementary to PAINS filters. When PAINS filters and reactivity models are combined, the resulting model outperforms either method alone, achieving a sensitivity of 24% at the same specificity. However, as a probabilistic model, the sensitivity and specificity of the deep learning model can be tuned by adjusting the threshold. Moreover, for a subset of PAINS filters, this reactivity model can help discriminate between promiscuous and nonpromiscuous bioactive compounds even among compounds matching those filters. Critically, the reactivity model provides mechanistic hypotheses for assay interference by predicting the precise atoms involved in compound reactivity. Overall, our analysis suggests that deep learning approaches to modeling promiscuous compound bioactivity may provide a complementary approach to current methods for identifying promiscuous compounds.
    MeSH term(s) Animals ; Computer Simulation ; Databases, Factual ; Drug Discovery/methods ; Enzyme Inhibitors/chemistry ; Enzyme Inhibitors/pharmacology ; High-Throughput Screening Assays/methods ; Histone Acetyltransferases/antagonists & inhibitors ; Histone Acetyltransferases/metabolism ; Humans ; Models, Biological ; Neural Networks, Computer ; Small Molecule Libraries/chemistry ; Small Molecule Libraries/pharmacology
    Chemical Substances Enzyme Inhibitors ; Small Molecule Libraries ; Histone Acetyltransferases (EC 2.3.1.48)
    Language English
    Publishing date 2018-07-23
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 190019-5
    ISSN 1549-960X ; 0095-2338
    ISSN (online) 1549-960X
    ISSN 0095-2338
    DOI 10.1021/acs.jcim.8b00104
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 1230: Show issues Location:
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