Perspectives of data science in preclinical safety assessment.
Data Science
Predictive Toxicology
QSAR
Real World Data
Reverse Translation
in silico
Journal
Drug discovery today
ISSN: 1878-5832
Titre abrégé: Drug Discov Today
Pays: England
ID NLM: 9604391
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
15
03
2023
revised:
12
05
2023
accepted:
22
05
2023
medline:
24
7
2023
pubmed:
28
5
2023
entrez:
27
5
2023
Statut:
ppublish
Résumé
The data landscape in preclinical safety assessment is fundamentally changing because of not only emerging new data types, such as human systems biology, or real-world data (RWD) from clinical trials, but also technological advancements in data-processing software and analytical tools based on deep learning approaches. The recent developments of data science are illustrated with use cases for the three factors: predictive safety (new in silico tools), insight generation (new data for outstanding questions); and reverse translation (extrapolating from clinical experience to resolve preclinical questions). Further advances in this field can be expected if companies focus on overcoming identified challenges related to a lack of platforms and data silos and assuring appropriate training of data scientists within the preclinical safety teams.
Identifiants
pubmed: 37244565
pii: S1359-6446(23)00158-7
doi: 10.1016/j.drudis.2023.103642
pii:
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
103642Informations de copyright
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