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A Quantitative Systems Pharmacology Platform Reveals NAFLD Pathophysiological States and Targeting Strategies.

Authors :
Lefever, Daniel E.
Miedel, Mark T.
Pei, Fen
DiStefano, Johanna K.
Debiasio, Richard
Shun, Tong Ying
Saydmohammed, Manush
Chikina, Maria
Vernetti, Lawrence A.
Soto-Gutierrez, Alejandro
Monga, Satdarshan P.
Bataller, Ramon
Behari, Jaideep
Yechoor, Vijay K.
Bahar, Ivet
Gough, Albert
Stern, Andrew M.
Taylor, D. Lansing
Source :
Metabolites (2218-1989); Jun2022, Vol. 12 Issue 6, p528-N.PAG, 31p
Publication Year :
2022

Abstract

Non-alcoholic fatty liver disease (NAFLD) has a high global prevalence with a heterogeneous and complex pathophysiology that presents barriers to traditional targeted therapeutic approaches. We describe an integrated quantitative systems pharmacology (QSP) platform that comprehensively and unbiasedly defines disease states, in contrast to just individual genes or pathways, that promote NAFLD progression. The QSP platform can be used to predict drugs that normalize these disease states and experimentally test predictions in a human liver acinus microphysiology system (LAMPS) that recapitulates key aspects of NAFLD. Analysis of a 182 patient-derived hepatic RNA-sequencing dataset generated 12 gene signatures mirroring these states. Screening against the LINCS L1000 database led to the identification of drugs predicted to revert these signatures and corresponding disease states. A proof-of-concept study in LAMPS demonstrated mitigation of steatosis, inflammation, and fibrosis, especially with drug combinations. Mechanistically, several structurally diverse drugs were predicted to interact with a subnetwork of nuclear receptors, including pregnane X receptor (PXR; NR1I2), that has evolved to respond to both xenobiotic and endogenous ligands and is intrinsic to NAFLD-associated transcription dysregulation. In conjunction with iPSC-derived cells, this platform has the potential for developing personalized NAFLD therapeutic strategies, informing disease mechanisms, and defining optimal cohorts of patients for clinical trials. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
22181989
Volume :
12
Issue :
6
Database :
Complementary Index
Journal :
Metabolites (2218-1989)
Publication Type :
Academic Journal
Accession number :
157793589
Full Text :
https://doi.org/10.3390/metabo12060528