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Tytuł pozycji:

Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies

Tytuł:
Quantitative systems pharmacology in neuroscience: Novel methodologies and technologies
Autorzy:
Peter Bloomingdale
Tatiana Karelina
Murat Cirit
Sarah F. Muldoon
Justin Baker
William J. McCarty
Hugo Geerts
Sreeraj Macha
Temat:
Therapeutics. Pharmacology
RM1-950
Źródło:
CPT: Pharmacometrics & Systems Pharmacology, Vol 10, Iss 5, Pp 412-419 (2021)
Wydawca:
Wiley, 2021.
Rok publikacji:
2021
Kolekcja:
LCC:Therapeutics. Pharmacology
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
2163-8306
Relacje:
https://doaj.org/toc/2163-8306
DOI:
10.1002/psp4.12607
Dostęp URL:
https://doaj.org/article/1187c50447ed4c2fbd1de79fb4364c53  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.1187c50447ed4c2fbd1de79fb4364c53
Czasopismo naukowe
Abstract The development and application of quantitative systems pharmacology models in neuroscience have been modest relative to other fields, such as oncology and immunology, which may reflect the complexity of the brain. Technological and methodological advancements have enhanced the quantitative understanding of brain physiology and pathophysiology and the effects of pharmacological interventions. To maximize the knowledge gained from these novel data types, pharmacometrics modelers may need to expand their toolbox to include additional mathematical and statistical frameworks. A session was held at the 10th annual American Conference on Pharmacometrics (ACoP10) to highlight several recent advancements in quantitative and systems neuroscience. In this mini‐review, we provide a brief overview of technological and methodological advancements in the neuroscience therapeutic area that were discussed during the session and how these can be leveraged with quantitative systems pharmacology modeling to enhance our understanding of neurological diseases. Microphysiological systems using human induced pluripotent stem cells (IPSCs), digital biomarkers, and large‐scale imaging offer more clinically relevant experimental datasets, enhanced granularity, and a plethora of data to potentially improve the preclinical‐to‐clinical translation of therapeutics. Network neuroscience methodologies combined with quantitative systems models of neurodegenerative disease could help bridge the gap between cellular and molecular alterations and clinical end points through the integration of information on neural connectomics. Additional topics, such as the neuroimmune system, microbiome, single‐cell transcriptomic technologies, and digital device biomarkers, are discussed in brief.
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