AI & experimental-based discovery and preclinical IND-enabling studies of selective BMX inhibitors for development of cancer therapeutics.
Academia-industry bridging
Cancer therapeutics development
Discovery pharmaceutics
IND-enabling
Novel targets
Predictive AI-based platform
Translational pharmaceutics
Journal
International journal of pharmaceutics
ISSN: 1873-3476
Titre abrégé: Int J Pharm
Pays: Netherlands
ID NLM: 7804127
Informations de publication
Date de publication:
15 Oct 2023
15 Oct 2023
Historique:
received:
13
06
2023
revised:
14
08
2023
accepted:
04
09
2023
pubmed:
8
9
2023
medline:
8
9
2023
entrez:
7
9
2023
Statut:
ppublish
Résumé
The current work aims to design and provide a preliminary IND-enabling study of selective BMX inhibitors for cancer therapeutics development. BMX is an emerging target, more notably in oncological and immunological diseases. In this work, we have employed a predictive AI-based platform to design the selective inhibitors considering the novelty, IP prior protection, and drug-likeness properties. Furthermore, selected top candidates from the initial iteration of the design were synthesized and chemically characterized utilizing
Identifiants
pubmed: 37678472
pii: S0378-5173(23)00805-0
doi: 10.1016/j.ijpharm.2023.123384
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
123384Informations de copyright
Copyright © 2023 Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.