Journal
METABOLITES
Volume 12, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/metabo12060528
Keywords
liver; non-alcoholic fatty liver disease; NAFLD; metabolic-associated fatty liver disease; MAFLD; microphysiology systems; MPS; drug discovery; quantitative systems pharmacology; QSP; connectivity map; CMap; drug repurposing; network proximity; non-alcoholic steatohepatitis; NASH; fibrosis; lobular inflammation; steatosis; targeting disease states; drug combinations
Categories
Funding
- National Institutes of Health [1UG3TR003289-01-NIH/NCATS, 4UH3DK119973-03-NIH/NIDDK, S10OD12269-NIH, U24TR002632-NIH/NCATS, 5U01 TR00238303-NIH/NCATS, R01 DK097160-NIH/NIDDK, R01 DK088231-NIH/NIDDK, P41 GM103712 NIH/NIGMS, PO1 DK096990 NIH/NIDDK, 1P30DK120531-01]
- Gilead Sciences
- Endra Sciences
- Pfizer
- Intercept
- Enanta
- Galmed
- Genentech
- Galectin
- Celgene
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This study describes an integrated quantitative systems pharmacology (QSP) platform that comprehensively defines disease states that promote non-alcoholic fatty liver disease (NAFLD) progression and predicts drugs to normalize these states. Experimental validation demonstrated several drugs and drug combinations that can mitigate NAFLD pathology. Additionally, the study identified the involvement of the pregnane X receptor in the transcriptional dysregulation of NAFLD.
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.
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